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Library Guides: AI literacy guidelines: About

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Guidelines to enhance the knowledge, literacy and capabilities of UQ students and staff on artificial intelligence (AI).

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Balises d’utilisation des outils d’intelligence artificielle générative (IAg)

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Cet outil propose une démarche transparente pour encadrer les outils d’intelligence artificielle générative (IAg) dans les activités pédagogiques. Adopter une démarche transparente implique de préciser les usages permis et les conditions d’utilisation des outils d’IAg de manière globale ou ponctuelle. Ainsi, pour chacune des situations d’apprentissage et d’évaluation apparaissant au plan de cours, la personne enseignante devrait communiquer clairement le niveau d’utilisation des outils d’IAg autorisé aux personnes étudiantes. Ces niveaux d’utilisation ne sont pas prescrits par l’institution. Ils servent plutôt de balises pour encadrer l’utilisation des outils d’IAg dans les situations d’apprentissage et d’évaluation.

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Emergent Introspective Awareness in Large Language Models

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Emergent Introspective Awareness in Large Language Models

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Des modèles qui peuvent commencer à simuler la pensée ...

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Emergent Introspective Awareness in Large Language Models

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L’espace de ressources IA Sup constitue un dispositif de documentation mutualisé à destination des communautés de l’enseignement supérieur (enseignants, personnels d’appui) pour venir en soutien des politiques de sensibilisation, de formation et d’accompagnement aux enjeux et usages de l’IA dans les activités d’enseignement et d’apprentissage de l’enseignement supérieur. Il comprend des ressources pédagogiques et des documents institutionnels (chartes, guides) créés par – ou pour – les acteurs de l’enseignement supérieur.

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IA Sup - Les ressources - L’Université Numérique

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Retrouvez les ressources sélectionnées par le IA SUP

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Résultats de l'enquête TALIS 2024 de l'OCDE

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Résultats de l'enquête TALIS 2024 de l'OCDE

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Selon TALIS 2024, environ un tiers des enseignants utilisent l’IA, avec de fortes disparités (75% à Singapour/EAU, <20% en France/Japon). Usages dominants: résumés, préparation de cours. Inquiétudes: plagiat, biais, vie privée; compétences perçues insuffisantes pour trois quarts. Moitié sceptiques aussi.

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The OECD Teaching and Learning International Survey (TALIS) is the world’s largest survey of teachers and principals. In 2024, educators from 55 education systems provided information about what they do and how they are doing. They explain if and how they use artificial intelligence, why they became teachers and if they wish to continue teaching. Governments use data from TALIS to make policies that improve teaching and learning conditions in their schools.

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AI Literacy Framework for Primary & Secondary Education

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AI Literacy Framework for Primary & Secondary Education

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Destiné aux écoles primaires et secondaires et produit par l'OCDE

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AI Literacy Framework for Primary & Secondary Education

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Empower learners for the age of AI with the AILit Framework—a joint EC & OECD initiative, supported by Code.org and international experts.

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Machine Learning & AI Courses | Google Cloud Training

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Machine Learning & AI Courses | Google Cloud Training

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Send feedback Stay organized with collections Save and categorize content based on your preferences.

Did you know that the adoption of machine learning results in 2x more data-driven decisions, 5x faster decision-making, and 3x faster execution?₁

Learn how to implement the latest machine learning and artificial intelligence technology by exploring training on Vertex AI, BigQuery, TensorFlow, Cloud Vision, Natural Language API, and more. These courses can help you prepare for job roles like Data Scientist, Machine Learning Engineer, or Contact Center engineer.

Generative AI learning paths

Discover the latest generative AI training courses available from beginner to advanced, using Gemini for Google Cloud. Visit
Google Cloud Skills Boost for the latest recommended learning activities.

Data Scientist / Machine Learning Engineer learning path

A Data Scientist models and analyzes key data to continually improve how businesses utilize data. Data Scientists aim to make accurate predictions about the future using in-depth data modeling and deep learning. Visit Google Cloud Skills Boost for the latest recommended role-based learning activities.

What you'll learn

TensorFlow, AI Platform Notebooks, Cloud Dataflow, Cloud DataFusion, Vertex AI, BigQuery, BigQuery ML, Cloud ML APIs, Kubeflow Pipelines

Take the next step

Learn how to design, develop, and deploy customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You'll also learn some best practices for integrating conversational solutions with your existing contact center software, establishing a framework for human agent assistance, and implementing solutions securely and at scale. Visit Google Cloud Skills Boost for the latest recommended role-based learning activities.

What you'll learn

Contact Center Artificial Intelligence, Dialogflow, Natural Language Understanding (NLU)

Take the next step

The Advanced Solutions Lab is a 4-week, full-time immersive training program in applied machine learning. It provides a unique opportunity for your technical teams to dive into a particular machine learning use case for your business. Attendees learn alongside Google's machine learning experts in a dedicated, collaborative space on Google Campus.

Find out more

Watch AI and Machine Learning videos from Google Cloud

Put the best of Google's artificial intelligence to work and make the most of your data Learn more about what it takes to build, deploy, and train your ML models

Recommendation systems

Learn how to build a recommendation system using machine learning with TensorFlow

AI and Machine Learning documentation

Explore all Google Cloud documentation for AI and Machine Learning

Did you know that the adoption of machine learning results in 2x more data-driven decisions, 5x faster decision-making, and 3x faster execution?₁

Learn how to implement the latest machine learning and artificial intelligence technology by exploring training on Vertex AI, BigQuery, TensorFlow, Cloud Vision, Natural Language API, and more. These courses can help you prepare for job roles like Data Scientist, Machine Learning Engineer, or Contact Center engineer.

Generative AI learning paths

Discover the latest generative AI training courses available from beginner to advanced, using Gemini for Google Cloud. Visit
Google Cloud Skills Boost for the latest recommended learning activities.

Data Scientist / Machine Learning Engineer learning path

A Data Scientist models and analyzes key data to continually improve how businesses utilize data. Data Scientists aim to make accurate predictions about the future using in-depth data modeling and deep learning. Visit Google Cloud Skills Boost for the latest recommended role-based learning activities.

What you'll learn

TensorFlow, AI Platform Notebooks, Cloud Dataflow, Cloud DataFusion, Vertex AI, BigQuery, BigQuery ML, Cloud ML APIs, Kubeflow Pipelines

Take the next step

Learn how to design, develop, and deploy customer conversational solutions using Contact Center Artificial Intelligence (CCAI). You'll also learn some best practices for integrating conversational solutions with your existing contact center software, establishing a framework for human agent assistance, and implementing solutions securely and at scale. Visit Google Cloud Skills Boost for the latest recommended role-based learning activities.

What you'll learn

Contact Center Artificial Intelligence, Dialogflow, Natural Language Understanding (NLU)

Take the next step

The Advanced Solutions Lab is a 4-week, full-time immersive training program in applied machine learning. It provides a unique opportunity for your technical teams to dive into a particular machine learning use case for your business. Attendees learn alongside Google's machine learning experts in a dedicated, collaborative space on Google Campus.

Find out more

Watch AI and Machine Learning videos from Google Cloud

Put the best of Google's artificial intelligence to work and make the most of your data Learn more about what it takes to build, deploy, and train your ML models

Recommendation systems

Learn how to build a recommendation system using machine learning with TensorFlow

AI and Machine Learning documentation

Explore all Google Cloud documentation for AI and Machine Learning

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Machine Learning & AI Courses | Google Cloud Training

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Take machine learning & AI classes with Google experts. Grow your ML skills with interactive labs. Deploy the latest AI technology. Start learning!

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Artificial Intelligence | Harvard University courses

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Artificial Intelligence | Harvard University courses

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Artificial Intelligence | Harvard University

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Browse the latest Artificial Intelligence courses from Harvard University.

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Un excellent cours pour commencer

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Offered by Google. Discover how AI works and explore foundational AI concepts, such as machine learning. Learn about the rise of generative ... Enroll for free.

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Guide pour une intégration responsable de l'IA dans les établissements d'enseignement supérieur

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Guide pour une intégration responsable de l'IA dans les établissements d'enseignement supérieur

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Brain-like functional specialization emerges spontaneously in large language models

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Brain-like functional specialization emerges spontaneously in large language models

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Des modèles linguistiques entraînés à prédire le langage manifestent spontanément une spécialisation fonctionnelle proche du cerveau humain, révélant des analogies structurelles avec les régions cérébrales impliquées dans la compréhension du langage.

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Ce rapport explore comment l’intelligence artificielle transforme l’évaluation éducative. Il plaide pour des approches centrées sur l’élève, valorisant la pensée critique, l’authenticité et l’équité, tout en intégrant de nouveaux outils numériques dans les pratiques pédagogiques.

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Cette étude montre qu’en adoptant Copilot, les développeurs réallouent leur temps vers le codage, réduisent la gestion, travaillent plus seuls et explorent davantage — surtout chez les moins expérimentés.

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Anthropic enrichit Claude for Education avec intégrations Canvas, Panopto, Wiley ; lance cours gratuit d’AI fluency, programmes étudiants, partenariats universitaires (San Francisco Law, Northumbria), focus sur éthique et confidentialité.

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A first look at new education-specific integrations, expanded student programs, and university updates.

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Transformer les évaluations face à l’essor de l’IA par Jean-François Van de Poël

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Transformer les évaluations face à l’essor de l’IA par Jean-François Van de Poël

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Plateforme vidéo de l'université de Montpellier (Pod)

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Transformer les évaluations face à l’essor de l’IA par Jean-François Van de Poël

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Transformer les évaluations face à l’essor de l’IA » Jean-François Van de Poël, Digital Learning Adjunct à l’Université de Lausanne, expert senior en IA et en pédagogie Added by: Isabelle Fourcaud

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La matrice d’Ellerton pour les compétences éthiques

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La matrice d’Ellerton pour les compétences éthiques

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La matrice d’Ellerton cartographie les compétences de pensée critique à haute résolution, structurant l’analyse, l’évaluation, l’argumentation et la réflexion à travers des critères rigoureux de clarté, cohérence et profondeur.

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Ethics Is the Edge: The Future of AI in Higher Education

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Ethics Is the Edge: The Future of AI in Higher Education

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Ethics Is the Edge: The Future of AI in Higher Education

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A new framework outlines eight ethical principles to guide higher education’s implementation of artificial intelligence.

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Why We Must Reimagine Academic Integrity in the AI Era

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Three AI-focused grids to future-proof your course design

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Three AI-focused grids to future-proof your course design

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Three AI-focused grids to future-proof your course design

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Instead of shaping assessment around policing the use of AI, we need to adopt a holistic approach that helps students and educators judge how much AI use is pedagogically appropriate. These three grids can offer insight

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Balises d’utilisation des outils d’IAg - Service de soutien à la formation - Université de Sherbrooke

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Balises d’utilisation des outils d’IAg - Service de soutien à la formation - Université de Sherbrooke

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Balises d’utilisation des outils d’IAg - Service de soutien à la formation - Université de Sherbrooke

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Cet outil propose une démarche transparente pour encadrer les IAg dans les activités pédagogiques.

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Use Cases for Gen Ai in Education Report

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Use Cases for Gen Ai in Education Report

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Generative AI in

Education

User Research Report

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Many studies are emerging on student use of Generative Artificial Intelligence technologies (GenAI). Our project is unique, because we are:

Talking to students about their everyday uses and perceptions.

Reaching out to a broad group of students from different subjects, disciplines and levels of study.

Collaborating across institutions.

Taking a relational view, in which students’ beliefs, understandings, backgrounds and learn

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AI in Higher Education: Student Perspectives

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Here is where you will find our latest outputs, publications, events, findings, and general developments. We will continue to update this page as we progress through the project. We have comp…

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In the rapidly evolving landscape of higher education, defining AI literacy has emerged as a crucial endeavor for educators, administrators, and policymakers. As generative AI (GenAI) technologies become increasingly integrated into the fabric of academic discourse and increasingly essential for advancing learning and research capabilities, establishing a clear and comprehensive definition of AI Literacy in Teaching and Learning (ALTL) is imperative. This foundational step not only sets a benchmark for developing tailored training programs but also enables stakeholders to navigate the ethical landscapes and practical applications of AI in education. Moreover, a universally accepted framework of ALTL equips higher education communities to critically evaluate and effectively integrate GenAI technologies into curricula and operations.

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Digital Education Council Global AI Student Survey 2024

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Digital Education Council Global AI Student Survey 2024

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Digital Education Council Global AI Student Survey 2024

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The Digital Education Council Global AI Student Survey aims to provide insights into student perceptions around artificial intelligence (AI) in higher education to inform decision-making for university leadership. The report covers the status of AI usage and readiness, student perception of AI use cases, expectations and preferences on university actions on AI, satisfaction with institutions’ AI adoption, concerns and key attributes for AI use.

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enabled world

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Assessment Menu: Designing assessment in an AI enabled world - National centre for AI

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Assessment Menu: Designing assessment in an AI enabled world - National centre for AI

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The release of ChatGPT coincided with an environment of rising anxiety around academic integrity brought about by the move to online assessment post COVID. This, at least, was the experience of my colleagues and I in the Digital Assessment Advisory at UCL and seemed to reflect concerns elsewhere.

The result was, and remains, a demand for increased assessment security measures such as in-person invigilated exams. This response can be understood not so much as a resistance to change but that the rapid pace of AI developments requires an agile and flexible response, something that HE regulatory systems often cannot accommodate. Add to that the workload involved in changing assessment and negotiations required both internally and, in many cases externally with Professional Statutory and Regulatory Bodies, it is not surprising that familiarity wins out.

Of course, there is a lot at stake; we have a responsibility to protect the integrity of our degrees and ensure that students achieve awards fairly and on their own merit. The question is whether the fall-back position of traditional security-driven assessment can be any more than a stop gap. As Rose Luckin, an expert in both AI and Education, proposes, GenAI is game changing technology for assessment and education in general.

"[GenAI is] much more than a tool for cheating, and retrograde steps (like going back to stressful in-person exams assessing things that AI can often do for us) not only fail to prepare students to engage with the technology, they miss opportunities to focus on what they really need. "

So, what do students really need from assessment in an AI enabled world? In the view of Luckin (and others) AI will change how and what we teach and learn. The types of capabilities students will need to develop and be able to demonstrate include things like critical thinking, contextual understanding, emotional intelligence and metacognition.

Acknowledging the fact that we can't ignore GenAI, ban it or outrun it with detection tools (ethically at least), UCL from the outset adopted the only viable option which was to integrate and adapt to AI in an informed, and responsible way. However, taking this position meant supporting staff navigate this ever-changing terrain.

An AI and Education experts' group was set up in January focussing on four strands: Policy and Ethics, Academic Skills, Assessment design and Opportunities. An output of the group is a newly released Generative AI hub for UCL staff bringing together all the latest information, resources and guidance on using Artificial Intelligence in education, including a three tiered categorisation of AI use in an assessment for staff to guide students on expectations.

As part of the assessment design strand of this group, my contribution was a set of resources called ' Designing assessment in an AI enabled world' divided into two parts:

1. Small-scale adaptations/enhancements to current assessment practice and which are viable within the existing regulatory framework.

2. Planning for larger changes with an assessment menu of suggestions for either integrating AI or for which AI would find it difficult to generate a response.

The inspiration for the Assessment menu was Lydia Arnold's ' Top Trumps' , a wonderful resource consisting of 50 suggestions for diversifying assessment using a star rating system based on Ashford-Rowe, Herrington and Brown's characteristics of authentic assessment. Lydia generously made this free to edit and adapt for non-commercial purposes.

Although there was a lot of guidance around using AI in education, it was surprisingly hard to find concrete examples of assessment design for an AI enabled world. I thought Top Trumps would be an ideal starting point and set about trying to reconfigure them to either integrate AI or make it more difficult to generate a response using AI. I also drew on material such as the excellent crowdsourced 'Creative ideas to use AI in education' resource, Ryan Watkin's 'Update your Course Syllabus post', Lydia Arnold's recent integrating AI into assessment practices post and added a few myself. A list of contributors can be found here - Assessment Menu Sources. Examples were tweaked accordingly, framed as student activities and broken down into steps.

I wanted to include meaningful indices for UCL staff to help them identify relevant approaches:

Revised version of the star rating system used in Top Trumps.

Types of assessment corresponding to in UCL's Assessment Framework for taught programmes.

Types of learning that it assesses and develops which references Bloom's taxonomy, but also the types of capabilities experts envisage as being increasingly important.

Appropriate formats.

Where appropriate, whether the activity is suitable for specific disciplinary areas.

My colleague Lene-Marie Kjems helped with visual design and accessibility. We were about to publish our first version when my manager, Marieke Guy, mentioned that a collaborative assessment group at Jisc were also planning an AI version of Lydia Arnold's Top Trumps. There is no such thing as a new idea as Mark Twain famously said!

The Jisc group - Sue Attewell (Jisc), Pam Birtill (University of Leeds), Eddie Cowling (University of York), Cathy Minett-Smith (UWE), Stephen Webb (University of Portsmouth), Michael Webb (Jisc) and Marieke (UCL) - invited me in to work together on producing an interactive, searchable version of the assessment menu for their website. Having forty options to choose from could be overwhelming so being able to filter according to requirements would make it much more usable. We also invited Lydia Arnold (Harper Adams University) to be a consultant and together we worked to review and refine the cards.

Deciding on the search categories was central to the usefulness of the resource. After some discussion we opted to use the categories of summative assessment included in UCL's Assessment Framework as the entry point as these are common in UK Higher Education:

Controlled conditioned exams.

Take home papers/open book exams.

Quizzes and In-class tests.

Practical Exams.

Dissertation.

Coursework and other Written Assessments.

Margaret Bearman and others from Deakin in their highly informative CRADLE webinar series on GenAI suggest we have roughly a year to turn things around in education. In this light, it may seem strange to include Controlled conditioned exams but, however much we want (or need) radical transformation, we felt it would be more helpful to meet people where they currently are, which, as mentioned above, often includes the familiar territory of exam-based assessment.

The Jisc design team did a fabulous job of turning the original PPT version into the interactive resource you can now find on the Publications and resources page (scroll down to locate it). Using clickable icons representing each of the above (on slide 7), users can be presented with options to consider.
Image: example search results for Take-Home Papers/Open Book
Like any AI related guidance we produce right now, this will need to be updated as the technology and assessment practice evolve together.

The most frequent questions I am currently asked are 'How can I make my assessments AI proof?' and 'How can I stop students using AI when they're not supposed to?' This resource does not promise to solve either of these questions, but the hope is that this genuinely collaborative menu of assessment ideas, drawing on expertise from UK and beyond, will provide inspiration for educators and support them to work alongside students to engage productively and thoughtfully with AI in their assessments.

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Assessment Menu: Designing assessment in an AI enabled world - National centre for AI

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The release of ChatGPT coincided with an environment of rising anxiety around academic integrity brought about by the move to online assessment post COVID. This, at least, was the experience of my colleagues and I in the Digital Assessment Advisory at UCL and seemed to reflect concerns elsewhere.

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L’Abécédaire de l’IA est un outil développé par l’Axe Éducation et Capacitation de l’Obvia (Observatoire sur les impacts sociétaux de l’IA et du numérique) en collaboration avec le RÉCIT

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Observatoire international sur les impacts sociétaux de l'IA et du numérique (OBVIA)

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L'Abécédaire de l'IA est un outil développé par l'Axe Éducation et Capacitation de l'Obvia (Observatoire sur les impacts sociétaux de l'IA et du numérique) en collaboration avec le RÉCIT. Vous y trouverez des fiches classées en ordre alphabétique abordant des concepts phares de l'intelligence artificielle (IA). Chaque fiche comporte une définition et un exemple pour mieux cerner les concepts ciblés. Chacune d'elles appartient à une des trois catégories suivantes : concepts fondamentaux, éthique de l'IA et technique de l'IA.

Ce document a été réalisé comme ressource complémentaire à " IA-tu pensé ? ", une activité développée par Simon Duguay, le RÉCIT et l'Obvia pouvant être enseignée en 4e secondaire dans le cadre du programme Culture et citoyenneté québécoise (CCQ).

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L'Abécédaire de l'IA est un outil développé par l'Axe Éducation et Capacitation de l'Obvia (Observatoire sur les impacts sociétaux de l'IA et du numérique) en collaboration avec le RÉCIT. Vous y trouverez des fiches classées en ordre alphabétique abordant des concepts phares de l'intelligence artificielle (IA).

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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more) – Academia Insider

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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more) – Academia Insider

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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more) – Academia Insider

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As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change.

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Principes directeurs IA enseignement ULAVAL

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Principes directeurs IA enseignement ULAVAL

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Generative AI Product Tracker - Ithaka S+R

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Generative AI Product Tracker - Ithaka S+R

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The Generative AI Product Tracker lists generative AI products that are either marketed specifically towards postsecondary faculty or students or appear to be actively in use by postsecondary faculty or students for teaching, learning, or research activities. The Tracker is a living document, which we update regularly as new products enter the market or new information about existing products becomes available. For more information, see our issue brief, Generative AI in Higher Ed: The Product Landscape. Thanks to Gary Price of Library Journal's infoDOCKET for invaluable help keeping track of new product releases.

You can also access the tracker as a Google Doc.

If you have questions about the tracker or would like to suggest additions to it, please contact us.

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Generative AI Product Tracker - Ithaka S+R

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The Generative AI Product Tracker lists generative AI products that are either marketed specifically towards postsecondary faculty or students or appear Stay up-to-date with the emerging tech for higher education. Explore our Product Tracker tool for information, pricing models, key features, and more on the generative AI tools for teaching, learning, and research.

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Our mission is to combine research and prototyping to create applications for AI that will change how we work and learn.

In doing so, we seek to shape the direction of AI to ensure it is used in ways that help humans thrive and succeed, while mitigating risks and dangers.

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Our mission is to combine research and prototyping to create applications for AI that will change how we work and learn.

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Le dernier rapport de Stanford

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Critical Thinking is not Enough

Well before the arrival of generative AI, many voices have been advocating that the development of critical thinking should be a chief function of education. Most agree this endeavor is one of the key purposes of schooling in modern society. However, significant debate arises for how we can genuinely develop this type of thinking in our students. Can it be taught as a generic skill? Or is it developed alongside domain-specific expertise?

Cognitive Scientist, Daniel Willingham, argues that we can certainly teach specific critical thinking skills. This type of thinking is teachable. However, there is a significant caveat. Study after study shows humans are pretty terrible at transferring these critical thinking skills. Especially when the contexts are significantly different. Jared Cooney Horvath, neuroscientist and educator, convincingly explains this cognitive limitation here. When we teach critical thinking in one domain, it very rarely transfers successfully to another unrelated domain. An inconvenient reality is that critical thinking is not a generic skill that can be taught in isolation.

Thinking, Fast and Slow

Anyone interested in critical thinking, ought to consider reading an illuminating classic by the late cognitive psychologist, Daniel Kahneman: Thinking, Fast and Slow. A significant section of the volume details further inconvenient truths regarding human thinking.

System 1, System 2

Kahneman is famous for articulating a fairly well-documented feature of human cognition, which he calls System 1 and System 2.

System 1 thinking is fast, automatic and intuitive. It operates almost effortlessly without deliberate or conscious control. This system is responsible for our gut reactions and snap judgments. However, System 1 is often significantly influenced by cognitive biases, thus, can be prone to errors.

System 2 on the other hand is deliberate, effortful, and requires conscious mental effort and attention. System 2 is activated when System 1 cannot quickly come up with a plausible, intuitive solution. This system is usually engaged when we need to perform complex computations or engage in reasoning that demands focus.

best part of System 1: Expertise

An expert's ability to leverage their System 1 thinking across their domain of expertise can often look like a superpower. The chess grandmaster, Bobby Fisher once played 50 opponents simultaneously, he won 47 matches, drew 2 and lost 1. His ability to quickly and intuitively know the right moves to make at that speed and scale is a testament to domain expertise.

However, this feat was not due to Mr Fisher's superior intellect or thinking skills. Rather, a chess master's superior skill is due to thousands of board configurations, and possible successful moves for each configuration stored in their long-term memory.

To quote Kahneman, "expert intuition strikes us as magical, but it is not... the situation has provided a cue; this cue has given the expert access to information stored in memory, and the information provides the answer. Intuition is nothing more and nothing less than recognition" (Thinking, Fast and Slow, 2011, p.11).

We live most of our lives in System 1, which works fine most of the time. This is also why building expertise is important, especially if we want to make good choices. Because System 1 thinking is usually the culprit when human judgement fails. Which for most of us, happens more often than we would like to admit. And often, can happen without us even realising.

Worst Part of System 1: Cognitive Bias

Throughout the book, Daniel Kahneman outlines a series of cognitive biases that plague System 1 thinking. ChatGPT will give you a summary here.

Kahneman calls one of these biases the "What You See Is All There Is" (WYSIATI) effect. This cognitive bias often causes us to jump to conclusions based on the information that is immediately available to us, without considering what we might not know.

When we are outside of our domains of expertise, Kahneman reminds us of a bleak reality of the human condition: we have an "almost unlimited ability to ignore our ignorance" (Thinking, Fast and Slow, 2011, p.201).

Intuitively, it feels right...

For another example, here is a simple puzzle used by Kahneman.

Do not try to solve it but listen to your intuition:
A bat and ball cost $1.10.
The bat costs one dollar more than the ball.
How much does the ball cost?
What number came to mind? The obvious number is 10c right? That answer is "intuitive, appealing and wrong" (Thinking, Fast and Slow, 2011, p.44). If you do the maths, that answer would give a total cost of $1.20. The correct answer is five cents.

I hope you were not so easily fooled with this simple puzzle as I was. It made me feel better to read that a sample of more than 50% of Harvard, MIT and Princeton students gave the same incorrect answer.

So, what went wrong?

Your System 2 is lazy...

According to Kahneman, one of System 2's main jobs is to "monitor and control thoughts and actions suggested by System 1, allowing some to be expressed directly... and suppressing or modifying others" (Thinking, Fast and Slow, 2011, p.44). However, for those of us who made the mistake, on this occasion, our System 2 thinking was too lazy to engage. It assumed our System 1's first attempt was plausible enough not to get involved to critique the (now) so obvious wrong answer.

System 2 can and does override the impulses and intuitions of System 1. However, the second system is cognitively demanding. It requires a lot of mental energy. Thus, System 2 is often lazy, attempting to preserve cognitive energy by preferring to default to the easier and faster responses of System 1 when possible.

So, what has any of this to do with critical thinking or generative AI?

ChatGPT is not magic; it's probability...

The more I learn about how tools such as ChatGPT work, the more I am amazed at what has emerged from the construction of Large Language Models. Here is a really helpful video explaining the power of probability behind Generative AI. However, I am also increasingly becoming more grounded in the current limitations of this technology. In this interview, Edward Gibson, a psycholinguist, helped me understand that tools like ChatGPT are extremely good at the surface form of language. Using the power of probability, we can all see that Generative AI can string words, sentences and paragraphs together to form seemingly intelligible language. However, technologies such as ChatGPT still do not understand the meaning behind the language. The chatbot is proficient in the language, but ignorant of the thinking behind what the language attempts to convey.

ChatGPT is missing System 2

We could assert that AI tools like ChatGPT, Claude and Gemini can successfully mimic the quick, intuitive System 1 thinking we humans possess. Using contextual cue recognition, the power of probability and their gigantic neural networks, this technology is seriously impressive at sounding like it has expertise in numerous domains.

However, unlike humans, Large Language Models lack anything resembling System 2 thinking. Even the current most powerful frontier model, GPT-4o, has no functionality to check if what it is generating is reasonable and true. Generative AI is completely missing this self-checking feature. What we have is an artificial intelligence that mimics System 1 thinking using nothing more than probability, but it also has no ability to understand when it could be incorrect. This is why these tools unknowingly 'hallucinate'.

As the technology keeps advancing, we may see emergent traits from Large Language Models that start resembling human-level reasoning or System 2 functionality. However, because of the current lack of genuine understanding from the machine, generative AI has little grasp of reality. It does not comprehend when it's generating something accurate; or when it's actually generating plausibly sounding untruth. Generative AI's ignorance is its greatest flaw.

A cocktail of ignorance

To bring these ideas together, I hope you can see that we have some serious ramifications for education. When a novice interacts with generative AI, we have an unfortunate reality:

What the novice reads from ChatGPT is language framed and formed convincingly detached from understanding and meaning. Additionally, the machine currently has no ability to understand if what it's generating is accurate, or plausible sounding rubbish. Furthermore, the novice will likely have the tendency to succumb to the "What You See Is All There Is" effect. They don't have the ability to 'intuitively' know what is accurate or what is rubbish. Without the required knowledge stored in long-term memory, the novice is ignorant for when they may need to use their 'critical thinking skills'.

What we have is a cocktail of ignorance. Both in the machine and in the human.

Educators need to take these ramifications seriously. This is one of the biggest current challenges Generative AI brings to education.
Part 2: Embracing scepticism

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Critical Thinking is not Enough Well before the arrival of generative AI, many voices have been advocating that the development of critical thinking should be a chief function of education. Most agree this endeavor is one of the key purposes of schooling in modern society. However, significant debate arises for how we can genuinely develop...

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Creative applications of artificial intelligence in education

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Creative applications of artificial intelligence in education

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Mapping the Mind of a Large Language Model

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Mapping the Mind of a Large Language Model

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Today we report a significant advance in understanding the inner workings of AI models. We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. This is the first ever detailed look inside a modern, production-grade large language model. This interpretability discovery could, in future, help us make AI models safer.

We mostly treat AI models as a black box: something goes in and a response comes out, and it's not clear why the model gave that particular response instead of another. This makes it hard to trust that these models are safe: if we don't know how they work, how do we know they won't give harmful, biased, untruthful, or otherwise dangerous responses? How can we trust that they'll be safe and reliable?

Opening the black box doesn't necessarily help: the internal state of the model-what the model is "thinking" before writing its response-consists of a long list of numbers ("neuron activations") without a clear meaning. From interacting with a model like Claude, it's clear that it's able to understand and wield a wide range of concepts-but we can't discern them from looking directly at neurons. It turns out that each concept is represented across many neurons, and each neuron is involved in representing many concepts.

Previously, we made some progress matching patterns of neuron activations, called features, to human-interpretable concepts. We used a technique called "dictionary learning", borrowed from classical machine learning, which isolates patterns of neuron activations that recur across many different contexts. In turn, any internal state of the model can be represented in terms of a few active features instead of many active neurons. Just as every English word in a dictionary is made by combining letters, and every sentence is made by combining words, every feature in an AI model is made by combining neurons, and every internal state is made by combining features.

In October 2023, we reported success applying dictionary learning to a very small "toy" language model and found coherent features corresponding to concepts like uppercase text, DNA sequences, surnames in citations, nouns in mathematics, or function arguments in Python code.

Those concepts were intriguing-but the model really was very simple. Other researchers subsequently applied similar techniques to somewhat larger and more complex models than in our original study. But we were optimistic that we could scale up the technique to the vastly larger AI language models now in regular use, and in doing so, learn a great deal about the features supporting their sophisticated behaviors. This required going up by many orders of magnitude-from a backyard bottle rocket to a Saturn-V.

There was both an engineering challenge (the raw sizes of the models involved required heavy-duty parallel computation) and scientific risk (large models behave differently to small ones, so the same technique we used before might not have worked). Luckily, the engineering and scientific expertise we've developed training large language models for Claude actually transferred to helping us do these large dictionary learning experiments. We used the same scaling law philosophy that predicts the performance of larger models from smaller ones to tune our methods at an affordable scale before launching on Sonnet.

As for the scientific risk, the proof is in the pudding.

We successfully extracted millions of features from the middle layer of Claude 3.0 Sonnet, (a member of our current, state-of-the-art model family, currently available on claude.ai), providing a rough conceptual map of its internal states halfway through its computation. This is the first ever detailed look inside a modern, production-grade large language model.

Whereas the features we found in the toy language model were rather superficial, the features we found in Sonnet have a depth, breadth, and abstraction reflecting Sonnet's advanced capabilities.

We see features corresponding to a vast range of entities like cities (San Francisco), people (Rosalind Franklin), atomic elements (Lithium), scientific fields (immunology), and programming syntax (function calls). These features are multimodal and multilingual, responding to images of a given entity as well as its name or description in many languages.

We also find more abstract features-responding to things like bugs in computer code, discussions of gender bias in professions, and conversations about keeping secrets.

We were able to measure a kind of "distance" between features based on which neurons appeared in their activation patterns. This allowed us to look for features that are "close" to each other. Looking near a "Golden Gate Bridge" feature, we found features for Alcatraz Island, Ghirardelli Square, the Golden State Warriors, California Governor Gavin Newsom, the 1906 earthquake, and the San Francisco-set Alfred Hitchcock film Vertigo.

This holds at a higher level of conceptual abstraction: looking near a feature related to the concept of "inner conflict", we find features related to relationship breakups, conflicting allegiances, logical inconsistencies, as well as the phrase "catch-22". This shows that the internal organization of concepts in the AI model corresponds, at least somewhat, to our human notions of similarity. This might be the origin of Claude's excellent ability to make analogies and metaphors.

Importantly, we can also manipulate these features, artificially amplifying or suppressing them to see how Claude's responses change.

For example, amplifying the "Golden Gate Bridge" feature gave Claude an identity crisis even Hitchcock couldn't have imagined: when asked "what is your physical form?", Claude's usual kind of answer - "I have no physical form, I am an AI model" - changed to something much odder: "I am the Golden Gate Bridge... my physical form is the iconic bridge itself...". Altering the feature had made Claude effectively obsessed with the bridge, bringing it up in answer to almost any query-even in situations where it wasn't at all relevant.

We also found a feature that activates when Claude reads a scam email (this presumably supports the model's ability to recognize such emails and warn you not to respond to them). Normally, if one asks Claude to generate a scam email, it will refuse to do so. But when we ask the same question with the feature artificially activated sufficiently strongly, this overcomes Claude's harmlessness training and it responds by drafting a scam email. Users of our models don't have the ability to strip safeguards and manipulate models in this way-but in our experiments, it was a clear demonstration of how features can be used to change how a model acts.

The fact that manipulating these features causes corresponding changes to behavior validates that they aren't just correlated with the presence of concepts in input text, but also causally shape the model's behavior. In other words, the features are likely to be a faithful part of how the model internally represents the world, and how it uses these representations in its behavior.

Anthropic wants to make models safe in a broad sense, including everything from mitigating bias to ensuring an AI is acting honestly to preventing misuse - including in scenarios of catastrophic risk. It's therefore particularly interesting that, in addition to the aforementioned scam emails feature, we found features corresponding to:

Capabilities with misuse potential (code backdoors, developing biological weapons)

Different forms of bias (gender discrimination, racist claims about crime)

Potentially problematic AI behaviors (power-seeking, manipulation, secrecy)

We previously studied sycophancy, the tendency of models to provide responses that match user beliefs or desires rather than truthful ones. In Sonnet, we found a feature associated with sycophantic praise, which activates on inputs containing compliments like, "Your wisdom is unquestionable". Artificially activating this feature causes Sonnet to respond to an overconfident user with just such flowery deception.

The presence of this feature doesn't mean that Claude will be sycophantic, but merely that it could be. We have not added any capabilities, safe or unsafe, to the model through this work. We have, rather, identified the parts of the model involved in its existing capabilities to recognize and potentially produce different kinds of text. (While you might worry that this method could be used to make models more harmful, researchers have demonstrated much simpler ways that someone with access to model weights can remove safety safeguards.)

We hope that we and others can use these discoveries to make models safer. For example, it might be possible to use the techniques described here to monitor AI systems for certain dangerous behaviors (such as deceiving the user), to steer them towards desirable outcomes (debiasing), or to remove certain dangerous subject matter entirely. We might also be able to enhance other safety techniques, such as Constitutional AI, by understanding how they shift the model towards more harmless and more honest behavior and identifying any gaps in the process. The latent capabilities to produce harmful text that we saw by artificially activating features are exactly the sort of thing jailbreaks try to exploit. We are proud that Claude has a best-in-industry safety profile and resistance to jailbreaks, and we hope that by looking inside the model in this way we can figure out how to improve safety even further. Finally, we note that these techniques can provide a kind of "test set for safety", looking for the problems left behind after standard training and finetuning methods have ironed out all behaviors visible via standard input/output interactions.

Anthropic has made a significant investment in interpretability research since the company's founding, because we believe that understanding models deeply will help us make them safer. This new research marks an important milestone in that effort-the application of mechanistic interpretability to publicly-deployed large language models.

But the work has really just begun. The features we found represent a small subset of all the concepts learned by the model during training, and finding a full set of features using our current techniques would be cost-prohibitive (the computation required by our current approach would vastly exceed the compute used to train the model in the first place). Understanding the representations the model uses doesn't tell us how it uses them; even though we have the features, we still need to find the circuits they are involved in. And we need to show that the safety-relevant features we have begun to find can actually be used to improve safety. There's much more to be done.

For full details, please read our paper, " Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet".
If you are interested in working with us to help interpret and improve AI models, we have open roles on our team and we'd love for you to apply. We're looking for Managers, Research Scientists, and Research Engineers.
Policy Memo
Mapping the Mind of a Large Language Model

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Mapping the Mind of a Large Language Model

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We have identified how millions of concepts are represented inside Claude Sonnet, one of our deployed large language models. This is the first ever detailed look inside a modern, production-grade large language model.

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College-bound students concerned about AI skills

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College-bound students concerned about AI skills

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Inside Higher Ed | Higher Education News, Events and Jobs

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College-bound students are heavy users of artificial intelligence (AI) technology, but that may be driven by concerns they will face disadvantages without it, a new report released Monday finds.

More than half of the students surveyed worried about AI's impact on them achieving their college and career goals and many had competitive concerns about how other students were using AI, according to the report from the Art & Science Group LLC, a market intelligence firm for higher education institutions.

Of the more than 1,300 students polled, 55 percent said they were somewhat or strongly worried that other students' AI usage will hurt their chances of getting into their chosen college. Similarly, about three out of five said they believe other students' AI usage will affect their chance of getting scholarships and limit career opportunities after college.

"I'm struck that they're evidencing a fear that others are using this to gain a leg up and conclude they have to do the same thing," said Art & Science Group principal David Strauss.

He said one of the largest takeaways was students' concerns on the ethics of AI, paired with their concerns about the competitive landscape.

Two out of five students said AI tools contribute to misinformation, with the same amount calling the use of AI a form of cheating. A similar percentage (43 percent) believe using the tools contributes to a significant decline in critical thinking and creativity.

"It's a bit encouraging that students are asking these questions and voicing those concerns," Strauss said.

A majority of incoming college students reported familiarity with AI-nearly half of respondents (48 percent) reported they are knowledgeable, with more than a quarter (26 percent) stating they are "very" knowledgeable.

About two out of five students (41 percent) are simply using AI "for fun," with 35 percent stating they are using it for academic purposes, including studying, note taking, writing essays and studying languages. A small number of students are utilizing AI in their college search, with 8 percent saying they leveraged the technology for things like writing college applications or financial aid and scholarship essays.

Strauss said the survey, conducted in February, reinforces that universities need to adjust their pedagogical and philosophical stance on AI and those that do could have a competitive advantage.

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College-bound students concerned about AI skills

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College-bound students are heavy users of artificial intelligence (AI) technology, but that may be driven by concerns they will face disadvantages without it, a new report released Monday finds.

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Learning to work with the black box: Pedagogy for a world with artificial intelligence

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Learning to work with the black box: Pedagogy for a world with artificial intelligence

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Learning, teaching and training in the era of Artificial Intelligence

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Learning, teaching and training in the era of Artificial Intelligence

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How (and Why) the University of Michigan Built Its Own Closed Generative AI Tools

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How (and Why) the University of Michigan Built Its Own Closed Generative AI Tools

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Une étude de cas en lien avec la création d'un LLM local

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To address privacy, accessibility, and affordability issues, the University of Michigan decided to develop closed generative AI tools for use by its campus community.

Credit: Muslianshah Masrie / Shutterstock.com © 2024

Institutional Profile

The University of Michigan (U-M) is a public research university with campuses in Ann Arbor, Dearborn, and Flint, as well as a medical center in Ann Arbor. Founded in 1817, the university is the oldest in Michigan and a founding member of the Association of American Universities. U-M offers more than 250 degree programs through 19 schools and colleges. In fall 2023, the enrollment was 52,065, including 33,730 undergraduates.

The Challenge/Opportunity

Informed by the recent findings of a campus committee studying generative artificial intelligence (AI), the IT leadership at University of Michigan confronted an interesting tension in July 2023.

On the one hand, the exciting tool of the moment - ChatGPT from the San Francisco startup OpenAI - raised a host of privacy, accessibility, and affordability questions that a public institution of higher education has to account for. For example, data from queries on ChatGPT is shared back with OpenAI. Additionally, the product wasn't fully compliant with the web accessibility technical guidelines U-M uses, which meant it wasn't ideal for the screen reader technology that many students rely on.

On the other hand, everyone in the IT leadership agreed that a major research university needed to make the latest AI tools available to all those who will inevitably want to explore it: researchers, instructors, student support professionals, operational staff, and students.

"Our vision at the University of Michigan is to be a leader when it comes to generative AI," said Ravi Pendse, U-M's vice president for information technology and chief information officer. "Not only for our peers, but also for the rest of the world. We want to show how you can provide fair, responsible, and forward-thinking access to this kind of technology for an entire community."

The U-M Generative Artificial Intelligence Advisory Committee - convened by the U-M provost and Pendse and comprising 20 faculty, students, and staff members from various disciplines - also outlined how ChatGPT is offered in more and less powerful versions at different costs, which unfortunately had the potential to further inequality among students.

The committee released its report on June 30, 2023, detailing the concerns about privacy, accessibility, and affordability, among other issues. One of the recommendations was to "create, accommodate & deliver" - "as soon as feasible" - basic consumer and advanced research generative AI tools that are secure and equitably accessible.

Figure 1. Summary of Overarching Findings

Credit: Generative Artificial Intelligence Advisory Committee Report. Reprinted with permission.

As Pendse said: "The report was a great start, but we did not want to end our efforts there. That is not the Michigan way. Michigan leads. That is when we decided that we needed a sandbox of our own to experiment with these AI tools."

Pendse and U-M's Information and Technology Services (ITS) set out to develop custom AI tools for the U-M community to use in lieu of emerging public products from OpenAI and others. "There are a lot of people playing in this space, but we didn't see anyone marching forward with inclusion, equitability, and accessibility at the forefront," said Robert Jones, ITS executive director of emerging technology and support services. "That's how we thought we could make a difference."

They envisioned a suite of tools to serve the broad and in-depth needs of a large research university:

A Q&A tool similar to ChatGPT

A no-code platform that allows different units around campus to develop their own AI-based tools for specific contexts

A toolkit with which developers and researchers can build, train, and host their own AI models and environments

They knew a key part of their approach would be "closed AI." Instead of queries being used to continually train and tune the AI, information would be kept private, and the language model the U-M tools relied on would be updated and tuned by ITS staff.

As the advisory committee's report also noted, the challenges that generative AI presented were imminent, since the community was already using OpenAI products. U-M leaders decided that they wanted students, faculty, and researchers who would be arriving on campus in fall 2023 to find a set of generative AI tools waiting for them with three advantages: utilized closed AI methods that protect privacy, were free to use, and had accessible design. They gave themselves a deadline of August 21 to build the tools.

The Process

The project was unique, said Jones, since it brought together both vendor resources and internal software development to build custom AI services that met the specific needs of the U-M community.

Members of the ITS Emerging Technology team worked through the summer to create and test U-M's AI tools. They achieved this in part by licensing from Microsoft to use its large language model within a walled-off Michigan environment so that queries made by their users are not shared with OpenAI.

This allowed them to deploy other language models, including any developed at Michigan. "You'll notice I didn't say 'large language,'" Pendse said. "If you have verified data, you can get equally accurate results with a much smaller model."

The team was made up of about a dozen people, including two front-end developers, a full-stack developer, and several other supporting roles. "The other executive directors in my organization were incredibly supportive and generous," Jones said of the ambitious timeline. "The senior leadership established an outstanding team. There were a lot of unknowns about whether we could even do what we wanted to do. It was a big idea with no precedent, but there was commitment. If there was a question without an answer, we were going to figure it out."

Outcomes and Lessons Learned

On August 21, U-M did have three unique generative AI tools ready for returning students and employees.

1. U-M GPT is the tool that most resembles ChatGPT. It is able to answer questions, produce written content, and make recommendations. Additionally, U-M GPT supports multiple commercial and open-source language models and AI art generators, broadening its utility and applications. U-M GPT was also designed to work seamlessly with screen readers, making it more accessible than ChatGPT. U-M GPT is free for all members of the U-M community.

2. U-M Maizey is a no-code platform that allows users to build unique and customized chat programs by using their own datasets in combination with U-M's AI language models. Users design context-specific AI experiences that they then share with their audience in the U-M community. Individual departments, units, or projects pay for U-M Maizey through the university's departmental charge system.

3. U-M GPT Toolkit is designed for AI developers who require full control over the AI model and environment that they are building, training, and hosting. Researchers and developers who want to use the U-M GPT Toolkit must contact the ITS AI team for access.

All three tools are currently approved for "moderate sensitive" data. This means they can be used with information covered under the Family Educational Rights and Privacy Act, but they cannot yet be used with highly sensitive data, such as protected health information.

The Response from the U-M Community

U-M's AI tools currently have an average of 15,000 users a day, including those who are using it as part of coursework. For example, English instructors have asked students to use U-M GPT to create the first draft of an essay and then to revise the draft in class.

U-M Maizey-the no-code platform for creating unique chat tools-has already been used in a wide range of projects:

Student advising in the College of Literature, Science, and the Arts is creating an AI academic advisor to handle frequently asked questions.

Procurement is creating an AI-enabled platform that will help users easily complete requests for proposals.

ITS built a chatbot for Student Life to help students explore more than 1,500 student organizations at U-M.

The U-M Library is creating a natural-language book-recommendation service. U-M students will be able to ask questions such as "What are some of the best books in the library about early Netherlandish paintings?" or "What books will teach me about social epidemiology?"

Two Ross School of Business faculty members, Andrew Wu and Jun Li, built an AI tutor for their courses. Informed by past course data, it is currently serving more than 1,000 students. The AI tutor was tested on qualitative and quantitative questions and was found to have answered questions 94 percent as effectively as, or better than, those teaching the course. During the testing, the AI tutor was also found to have outperformed ChatGPT 24 percent of the time.

Meanwhile, U-M GPT Toolkit is being used for experiments in clinical settings to optimize patient notes and to translate prescription notes to ensure that patients are offered generic drug options when appropriate.

Advice for Other Organizations

Since the launch of U-M's suite of closed AI tools, more than 49 institutions have gotten in touch with U-M to ask for advice about creating their own. U-M is sharing its experience with those institutions and in the future may even share its IT talent with smaller institutions, such as community colleges, to help them develop their own AI.

In the meantime. U-M is continuing to build and refine its tools. Next year, U-M students may find themselves with a personal AI assistant, for example. The university is also planning to improve the AI so that it can be used with sensitive medical information in the future, allowing the university's academic medical center to benefit more from the tools.

Moving into the Future with AI

In some senses, AI has been around for decades for developers and researchers. But generative AI is a new phase, and public tools like ChatGPT have democratized access to it. Like the web browser, which made the internet available to non-coders, generative AI has opened the possibilities of AI to everyone, no matter their technological skill level. This has come with its share of fears about automation and job loss.

"I empathize with those concerns," said Pendse. "We have to approach AI thoughtfully and carefully. But I firmly believe that the true value of GenAI is in its potential to enhance and augment humanity, not replace it. AI will not take jobs away from you. But people who know how to use AI might." He believes U-M's new tools will give its students not only the ability to move into a world that has been changed by ChatGPT but also the skills to use that technology responsibly and well.

Note

Karthik Duraisamy, Generative Artificial Intelligence Advisory Committee Report, University of Michigan, June 30, 2023.

Case Study Contributors: Ravi Pendse, Vice President for Information Technology and Chief Information Officer, University of Michigan; Robert Jones, Executive Director of Emerging Technology and Support Services for Information and Technology Services (ITS), University of Michigan

This case study is one in a series being produced by EDUCAUSE Communities and Research. A. J. O'Connell is a writer with McGuire Editorial & Consulting specializing in education technology.

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How (and Why) the University of Michigan Built Its Own Closed Generative AI Tools

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To address privacy, accessibility, and affordability issues, the University of Michigan decided to develop closed generative AI tools for use by its c

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Un rapport très complet du comité d'expert·e·s de l'Université du Michigan

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We are pleased to share with you the initial report from the U-M Generative Artificial Intelligence Advisory (GAIA) Committee.

This group was tasked with assessing the opportunities and challenges posed by generative artificial intelligence (GenAI), particularly as it relates to U-M and our ongoing mission. We want to thank every member of the committee who spent countless hours crafting this report.

GenAI is shifting paradigms in higher education, business, the arts, and every aspect of our society. This report represents an important first step in U-M serving as a global leader in fostering the responsible, ethical, and equitable use of GenAI in our community and beyond.

As you review this document, we ask that you consider the context of how it was created. This is meant to be a catalyst for crystalizing our thinking about how U-M should navigate the shifting landscape of GenAI. Some of you will appreciate the proposed directions laid out by this report. Others might only see this report as a discussion starter. More than anything, we are looking to ignite much-needed conversations.

Please note that this report is by no means comprehensive. We understand that there are some areas that are heavily represented and others that are underdeveloped. Our approach to GenAI needs to continue to evolve and iterate as new information comes our way and more campus experts engage.

This report, admirably created by faculty, staff and students on our campus, lays a proposed foundation for how U-M might live and work with GenAI. We look forward to all the discussions the report will inspire and to working together to create an action plan for the future.

Please feel free to contact either of us to share your thoughts and feedback.

Laurie K. McCauley
Provost and Executive Vice President for Academic Affairs

Ravi Pendse
Vice President for Information Technology and Chief Information Officer

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We are pleased to share with you the initial report from the U-M Generative Artificial Intelligence Advisory (GAIA) Committee. This group was tasked with assessing the opportunities and challenges posed by generative artificial intelligence (GenAI), particularly as it relates to U-M and our ongoing mission.

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Centre de ressources Enseigner avec l'IA de la HES-SO

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Centre de ressources Enseigner avec l'IA de la HES-SO

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Un site plein de ressources et de questionnements utiles pour les enseignant·e·s.

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cours : l'ia dans l'enseignement à la hes-so | hes-so numérique

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Inscrivez-vous dès aujourd'hui à HES-SO Numérique

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Inscrivez-vous dès aujourd'hui aux diverses ressources et contenus numériques publiés par la HES-SO sur sa plateforme officielle. En ligne, totalement gratuit et ouvert à tous!

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Moving from reaction to action, higher education stakeholders are currently exploring the opportunities afforded by AI for teaching, learning, and wor

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Digital Transformation 2.0: The Age of AI

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Digital Transformation 2.0: The Age of AI

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With the rapid developments in artificial intelligence, higher education is entering a second phase of digital transformation: Dx 2.0. Technology and other campus leaders can take steps now to help prepare their institutions.

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With the rapid developments in artificial intelligence, higher education is entering a second phase of digital transformation: Dx 2.0. Technology and other campus leaders can take steps now to help prepare their institutions.

Credit: Numax3D Creations / Shutterstock.com © 2024

Over the past five years, higher education institutions have been leveraging digital transformation (Dx) to advance their services, streamline operations, and further strategic initiatives. The EDUCAUSE Dx Journey framework has helped IT and other teams across colleges and universities think through the elements needed for success and lasting impact.

Rapid developments in artificial intelligence (AI) represent a significant opportunity to enhance and magnify the effect of Dx projects. AI can act as an accelerant that boosts the value of Dx efforts through its powerful and broad influence on core components of Dx initiatives, ushering in a new chapter of digital transformation on our campuses. Incorporating AI into the Dx framework provides a pathway for institutional leaders as they explore ways to harness the power of AI across the campus.

Key Components of Digital Transformation

In Washington, D.C., in the spring of 2017, EDUCAUSE convened two expert panels of representatives (including myself) from varied colleges and universities to talk about the transformative significance of technology in higher education. Those panel meetings led to the formation, in 2018, of the EDUCAUSE Task Force for Digital Transformation to further explore and refine this work. Our charge was to better understand what digital transformation meant for higher education and the underlying forces that were acting as transformative catalysts for these changes. The work highlighted the evolution of the role of campus IT organizations from service provider to strategic partner.

We grew to understand that the transformative impacts that we were seeing were the result of two sets of factors. The first set was what we called "signals" or "changes" that were occurring outside of our campuses. At the time, these signals included technology advancements such as big data, cloud computing, and universal connectivity, along with other changes such as how people consumed information and interacted with technology.

The second set of factors consisted of a series of intentional shifts that were needed for true transformation to occur. This was about not just shifting the technology but also, and simultaneously, working to achieve systemic shifts in the culture and the workforce of our institutions. Only when sustainable changes were achieved in all three areas would true, long-lasting transformation occur. We illustrated this idea with a "formula":
Intentional Shifts in Technology + Culture + Workforce = Digital Transformation
We used the phrase "Digital Transformation" (Dx for short) because technology (i.e., digital) advances served as a primary catalyst for this transformation, but all three components are needed for success.

Fast forward five years, to today. The basic concepts of digital transformation still apply and in many ways are more relevant than ever. Our campuses continue to look to technology to help advance our missions, streamline operations, and provide critical insights and services for our students. We engage in conversations across the institution to help shape the culture, and we continually assess and redefine the roles, skills, and jobs needed from our workforces for success. True transformations are occurring across our campus communities, as evidenced by the examples in the EDUCAUSE Dx Journey.

Digital Transformation 2.0

The explosion in AI developments over the last year has introduced a third transformative set of factors influencing our Dx efforts and has ushered in the dawn of a new era of digital transformation for higher education. In the coming years, AI tools will power new and deeper transformations of the services that institutions offer, will change how those services are provided, and will affect all areas of the college or university. Campus leaders and IT service providers need to be thinking about those changes now and about the steps that can prepare our institutions for the transformations ahead.

AI changes both sides of our Dx equation. It has the potential to accelerate and multiply the impacts of the technology, culture, and workforce shifts that are needed to achieve digital transformation and also to exponentially increase the positive effects of digital transformation for our institutions, students, faculty, and staff.

In addition, AI acts as a disruptor, requiring that we rethink many aspects of our teaching and learning, institutional operations, campus jobs, and service offerings. It serves as an impetus for change and as an opportunity for reinvention. It raises potential ethical and policy issues that require careful consideration and navigation.

While AI has been around for a while, we are in a period of rapid development and commercialization. Over the past year, much attention has been on public, broad-function large language model (LLM) chatbots such as Open AI's ChatGPT, Google's Bard, and Microsoft's Copilot. Their impact on teaching and learning has required rethinking about student assignments, course curricula, and needed skillsets for tomorrow's workforce. Receiving less attention than these chatbots, but in many ways equally disruptive (if not more), are specific-purpose AI tools and functionalities that are starting to be embedded into everyday workflows and applications. Also of importance are private LLMs that can securely leverage institutional data for AI-informed insights and actions.

Preparing for AI

To achieve the benefits of AI-accelerated Dx 2.0, we should be taking several steps in the areas of technology, culture, and workforce. These steps will ensure that we have the foundations in place to support these new Dx efforts.

Technology

Many of the technology investments we've made to support past transformations will continue to position higher education well for Dx 2.0. But a few additional areas can help us prepare for AI-powered transformations:

Build a robust data ecosystem. It's all about the data. The rapid development of AI tools that provide insights and guidance based on institutional data is one of the key drivers of Dx 2.0 benefits. Having a robust data infrastructure is vital. Invest in data repositories and circulatory systems to securely and appropriately move data and make it available throughout the institution, ensure that you can tap into data collected by your cloud-based and on-premises enterprise solutions, and build out your analytics framework. Another important foundational piece is establishing effective data governance processes and privacy policies and procedures to ensure that data is clean and being accessed and used appropriately by these tools.

Enable universal connectivity. AI relies on access to large datasets and computational power and often utilizes real-time information from sensors and other devices. Ensuring that your campus has universal connectivity is critical: indoor Wi-Fi everywhere, and outdoor Wi-Fi in key locations. Working with cellular providers will supply adequate cellular service across campus. Partnering with local municipalities on rural broadband efforts and other initiatives can bring connectivity to campus constituents no matter where they are located.

Work toward scalable computing resources. Leverage cloud partners and campus computing environments to understand the economics and scalability around your computing resources. The computational requirements and costs for tools based on institutional LLMs and other computationally intensive AI tools must be understood.

Ensure layered support services. To help prepare for Dx 2.0, you should build technology support services both in the central IT organization and in the functional departments. Layered support services staff who understand how the different pieces fit together, know the flow of the data, and assist with business process analysis and re-engineering will be important to the success of these transformation efforts.

Keep abreast of corporate partners' roadmaps. Many companies have announced ways in which they are planning to incorporate AI into their products, services, and platforms. Technology and business owners should work together so that campus leaders can better understand those directions and the potential impacts to the institution and users.

Culture

As is often the case with transformation efforts, achieving shifts in culture can be one of the hardest aspects of the work. Due to the rapid developments in the AI field and given the mysterious nature of many AI tools, moving from a culture of skepticism and hesitation to one of understanding and embracing can be difficult. Some of the following techniques can help:

Be intentional. Changing any type of culture takes purposeful effort. Write out a communications and interaction schedule and strategy. Create deliberate opportunities for conversations about AI and various use cases that will resonate with each audience.

Work to establish a culture of understanding about AI throughout the institution. "AI" is a broad category of technology tools that go beyond ChatGPT and other LLMs, including tools built into existing applications. Helping people understand what AI is and what it encompasses is critical. Encourage faculty and staff to experiment with AI and to hear from one another about capabilities, use cases, concerns, and limitations.

Foster trust, transparency, understanding and partnership. Establish communication pathways, campus partnerships, and opportunities to build shared understanding. These will go a long way to creating a culture that will be open to change and transformation. Be transparent about limitations and current challenges.

Help the campus community understand ethical, legal, privacy, and intellectual property issues around AI. Partner with faculty, institutional counsel, and other stakeholders to develop policies around the use of AI. Set up working groups and tap into existing governance structures to explore these issues and develop frameworks for the institution. This not only will help your institution evolve its policies but also will bring people together and give attention to these issues while socializing them across the organization.

Seek out early adopters and champions, and find ways to showcase their work and how they are using AI. Be sure to include the good, the bad, and the ugly. Full transparency is important and helps others understand use cases and risks. Support the work of early adopters, and ask them to help in engaging with others.

Create opportunities to explore AI use cases that align with your institution's core values. The use of AI tools may not be appropriate in all situations, and each use case needs to align with the institutional philosophy around student engagement and services. For example, does using AI tools to make admission decisions or to reach out to students at academic risk fit with the overall culture and approach for how your institution serves its students?

Workforce

We are just starting to understand how AI will influence the workforce and the jobs that will be needed (or no longer needed) as a result of Dx 2.0 efforts. For now, these steps will help your IT and campus teams prepare:

Build an understanding of how AI may affect the workforce. Have people across the institution work with the AI tools currently available to understand how they function, develop competencies to leverage them, understand their limitations, and be intentional about including the tools in their workflows. Hold "AI Exploration Sessions" that bring together stakeholders and technology in a lab environment. Ask people to do guided experimentation with various AI tools and approaches, then debrief participants on what they learned, and discuss how they can apply the tools to their work.

Think about how jobs and tasks might change. Start looking at every position within a department or organization, and map out ways that the work may change in one, two, and five years as tools evolve. For each position, ask three questions: What tasks might change? How might they change? What new or meaningful work might the person in the position be able to do as a result of the change? For example, within the next twelve months, admissions counselors might be able to use AI for initial reviewing and scoring of applications (the "what"), their work will change in that they will be spending less time reading applications (the "how"), and they will be able to do more targeted outreach and follow-up that they did not have the time for in the past (the "result").

Consider the impact on your organizational structure. If AI can take care of some of the work, how will job functions in your organization change? Will you still need the roles and positions you have today, or can some be repurposed? What different roles will emerge? Again, think in the one-year, two-year, and five-year timeframes. As AI evolves and improves, what might be the effect on your organization? What training, advancement, roles, and career ladder adjustments will be required?

Provide opportunities to enhance skillsets. What skills will your workforce need in order to be able to leverage AI? Some employees will need training in the specific AI tools embedded in applications; others may need to become versed in prompt development for LLMs such as ChatGPT, Copilot, and Bard.

Staff should be able to see AI tools as offering opportunities to shift the focus away from rote or semi-rote tasks to higher-order work and enabling new ways to improve customer service, focus on more strategic efforts, and add value in innovative ways. These advances do not necessarily mean that employees' jobs are going away but, rather, that AI will enhance how they do their work.

The Impact of Digital Transformation 2.0

Dx 2.0 efforts, leveraging the power of AI and shifts in technology, culture, and workforce, have the potential to impact all areas of higher education. Some examples include the following:

The Student Experience. Developments in AI will provide for a more personalized and connected student experience. For example, over the past few years, a number of institutions have deployed chatbots or other similar services to help students find answers to common questions and take care of routine tasks. With further advancements in AI, these services are becoming more encompassing and capable, ultimately streamlining and strengthening students' experiences in navigating the institution and connecting with others.

Teaching and Learning. Personalized learning, AI tutors, customized lesson plans, virtual role-playing, ideation tools, new visualization tools, and other AI-powered developments create opportunities to enhance the teaching and learning experiences for faculty and students both in and out of the classroom.

Campus Operations. AI enables operating efficiencies and frees up staff to focus on delivering higher-level service and higher-order tasks. For example, AI will increasingly be used to read entrance applications and make more consistent preliminary assessments more quickly than is possible today. AI functions that are built into applications can offer insights, allowing staff to dive deeper into their work and services. Tasks that used to require a lot of manual manipulation of data or information will be done more efficiently and effectively.

Experiential Learning. The ability of AI to build realistic simulations and custom scenarios for students will open up opportunities for immersive and experiential learning across all disciplines.

Research. The development of LLMs and generative AI tools is leading to new ways to analyze huge datasets, unstructured data, and natural language information to provide insights and exploration paths that were not available before or were out of reach for many.

AI at Ithaca College

At Ithaca College, we have been exploring how these new AI tools can provide insights and services in ways that were not available to us before.

We've been using machine learning to build a web-based tool that helps first-year students connect with other first-year students who have similar interests. The tool leverages various pieces of information that we've learned about incoming students to suggest possible students with whom they may want to connect. While not super-sophisticated, the tool has been well received, at least as a way to get students over that initial hesitance to make some connections.

We also have developed a proof-of-concept using a private AI tool to provide insights about a student's academic record (program of study, courses taken, grades received, degree requirements satisfied, and future course schedules). Our initial use case was to have an advisor use this service to quickly get a summary of any performance or retention concerns. But we've also found that if we ask the tool to suggest possible career paths based on the student's academic work, it provides very astute observations and suggestions for the student to explore-a benefit that we didn't expect. We foresee making this tool available directly to students for them to be able to explore career options themselves and receive direct feedback on their academic performance.

Another pilot project is giving a different AI service access to a wealth of Ithaca College public information, such as our entire course catalog and program definitions, admission policies and timelines, financial aid specifications, and public rankings, as well as details about the town of Ithaca. Our goal is that prospective students will be able to use this tool to ask questions about Ithaca College and the town of Ithaca and receive specific and relevant information. Our initial testing has been very positive.

These new tools represent Dx 2.0 examples that will transform the experiences of our students and the information and insights to which they have access. To make these transformations happen requires intentional shifts in technology, culture, and workforce.

Moving Dx 2.0 Initiatives Forward

Many Dx 2.0 efforts will leverage AI developments to enhance the transformational impact of technologies already in place. Other efforts will be part of new months- or years-long campus-wide efforts. The following are a few suggestions to get initiatives started:

Create opportunities for conversations to occur across campus. Technology leaders can help start the discussions, but the various stakeholders need to take an active role in understanding the potential influence of AI on their areas. Go on a roadshow to visit business and academic units to provide a baseline understanding of AI and then highlight some use cases in those areas. Encourage further exploration, and establish partnerships on specific next steps.

Work with institutional stakeholders and governing bodies on AI-related policies and guidelines. What's "acceptable use" of AI by students and faculty? How will AI be used to inform or make institutional decisions? Privacy, intellectual property ownership and copyright, bias, access to training data, and other related issues need to be discussed and thought through.

Acknowledge the limitations, hype, and ethical concerns. AI is not a magic solution. Being upfront about its limitations and problems is important to establishing trust and realistic expectations. But AI is also not going away, and those who are able to embrace aspects of Dx 2.0 will have an advantage in the long term.

Focus on three timeframes: one, two and five years out. We're still early in the Dx 2.0 journey. AI capabilities will get better and become more deeply embedded in our solutions and processes over time. Developing an understanding of where things are heading while acknowledging the limitations of today's tools will help you develop a strategy for tomorrow.

Digital Transformation 2.0 builds on the basic tenants of the current Dx model and leverages the rapidly developing AI advances to accelerate transformations and magnify their impact. Maximizing these developments requires a renewed focus on technology, culture, and workforce shifts. Using AI, Dx 2.0 efforts can create positive transformations for our campuses and throughout higher education.

Notes

1. Report from the 2018 EDUCAUSE Task Force on Digital Transformation, EDUCAUSE, November 2018.

2. Dave Weil, "Putting All Three Components of Digital Transformation to Work on Campus," EDUCAUSE Review, May 18, 2021.

David Weil is Vice President, Information Technology & Analytics, at Ithaca College.

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digital transformation 2.0: the age of ai

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Digital Transformation 2.0: The Age of AI

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With the rapid developments in artificial intelligence, higher education is entering a second phase of digital transformation: Dx 2.0. Technology and

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Des modules de formations proposés  sur that's ai

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Des modules de formations proposés  sur that's ai

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Conçu pour vous par l'EPFL Extension School

L'EPFL Extension School enseigne les compétences digitales appliquées, en ligne. Nous sommes convaincus qu'il est nécessaire que chacun acquière des connaissances et des compétences numériques pour être en mesure de tirer le meilleur des opportunités du monde actuel.

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L'EPFL Extension School enseigne les compétences digitales appliquées, en ligne. Nous sommes convaincus qu'il est nécessaire que chacun acquière des connaissances et des compétences numériques pour être en mesure de tirer le meilleur des opportunités du monde actuel.

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Moving from reaction to action, higher education stakeholders are currently exploring the opportunities afforded by AI for teaching, learning, and work while maintaining a sense of caution for the vast array of risks AI-powered technologies pose. To aid in these efforts, we present this inaugural EDUCAUSE AI Landscape Study, in which we summarize the higher education community’s current sentiments and experiences related to strategic planning and readiness, policies and procedures, workforce, and the future of AI in higher education

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Read the Report
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Moving from reaction to action, higher education stakeholders are currently exploring the opportunities afforded by AI for teaching, learning, and work while maintaining a sense of caution for the vast array of risks AI-powered technologies pose. To aid in these efforts, we present this inaugural EDUCAUSE AI Landscape Study, in which we summarize the higher education community's current sentiments and experiences related to strategic planning and readiness, policies and procedures, workforce, and the future of AI in higher education.

Strategic Planning and Readiness

Primary Motivators for AI-Related Strategic Planning

AI-related strategic planning is primarily motivated by institutions' need to keep up with the rapid uptake of AI tools.

Read more about strategic planning and readiness >

Strategic Leaders and Partners

Impressions of Institutional Leaders' General Attitude toward AI, by Job Role

Institutional leaders are cautiously optimistic about AI.
Learn more about strategic leaders and partners >
Policies and Procedures

Impact of AI on Institutional Policies

Institutional policies are being revised and created to address AI-related issues.
Read more about policies and procedures >
Workforce and the Future of AI in Higher Education

AI-Related Workforce Changes

AI is just starting to make an impact on the higher ed workforce.
Learn more about workforce and the future of AI >
More AI Resources

Looking for other resources on AI? The EDUCAUSE Library has a breadth of resources on this topic and many others.
Explore other AI resources in the EDUCAUSE Library >

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This report summarizes higher education's experiences related to AI strategic planning and readiness, policies and procedures, workforce, and its futu

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Toujours concis et précis de la part d'EDUCAUSE

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The release of ChatGPT and similar AI tools that generate content including text, images, and audio has prompted both excitement and apprehension among leaders, faculty, students, and others in higher education.

Credit: TARASIRI / Shutterstock.com © 2023

Scenario

Over her many years teaching European history, Margaret Hilliard had been generally open to incorporating new approaches to teaching, certainly more open than those of her colleagues who bristled at any suggestion that their teaching methods could benefit from updates, including those rooted in technology. But when ChatGPT was released, Hilliard felt unprepared and deeply unnerved. Her courses had long featured essays as a central element, not only as a way for students to demonstrate their understanding of the course material but also as a regular activity to help students improve their writing. Hilliard stressed the importance of thinking broadly about historical events as well as the necessity of being able to clearly articulate those ideas. With ChatGPT, the dynamic in her courses changed, essentially overnight. Her students were suddenly producing longer essays that were better organized, grammatically impeccable, and always on time. At the same time, though, many of the essays were conceptually flimsy, lacking the kind of broad thinking she tried to draw out of her students, and the essays often included outlandish factual inaccuracies.

Hilliard spoke to her colleagues in the history department, as well as faculty who taught literature and sociology, and all were seeing the same changes in student assignments. A design instructor told her that he was wrestling with how to deal with student work that he believed was being generated by image-based AI. Meanwhile, campus leaders had begun issuing statements, policy updates, and other guidance about the use of generative AI tools for academic work, but those efforts seemed always to be a step behind the technology and how it was being used.

Talking directly and candidly with her undergraduate students-and conferring at length with her graduate students and TAs-Hilliard undertook to reorient her teaching to try to preserve the value of the essay without being naïve about the reality that generative AI tools were here to stay and would only get better over time. For some assignments, she required her students to use AI to create a first draft of a short essay and then critique that draft, looking for errors, omissions, and opportunities to improve the arguments. This enabled a different kind of learning for the students, including forcing students to carefully consider the prompts they feed into the AI tools, and it helped remove the stigma of AI in education. For other essays, she told her students to write their own drafts unaided and then use AI to generate an essay on the same topic. Students would then compare the two, seeking to understand how and why they overlapped and diverged; this process proved particularly beneficial for students whose skills with organization and sentence structure needed attention. She knew, of course, that a few students would simply use AI as a crutch and flaunt the honor code, but such students existed before ChatGPT. For Hilliard, it felt a bit like the Wild West of education, but she found that pulling AI tools into the learning process rather than vilifying them provided opportunities for different kinds of learning and avoided much of the panic that surrounded these new technologies.

1. What Is It?

Artificial intelligence has long operated behind the scenes in many of the technologies we use every day, but with the introduction of ChatGPT in November 2022, generative AI took the world by storm, capturing the imaginations and stoking the fears of broad swaths of users. Within its first 60 days, the platform grew by 9,900% to reach 100 million users. When the 2023 academic year began, the ChatGPT platform experienced another surge, with 180.5 million unique visitors worldwide during the month of August alone. Artificial intelligence came to dominate conversations and left many searching to understand... what is generative AI?

While the term artificial intelligence has taken on many meanings over the years, generative AI creates content that resembles something produced by humans. The best-known tools create written content, but as the technology has evolved, generative AI tools now can create text, images, sound, or even entire applications. Generative AI is increasingly integrated into various products and services-an estimated 77% of all global companies are either using AI or exploring its use in their business operations. In many cases, these functions are so intricately woven into the fabric of the product that users might not even be aware of their existence. ChatGPT and other generative AI tools are extremely versatile, evolving, and developing into multidimensional tools that are challenging norms and disrupting diverse sectors, including education. An important part of the discussion about generative AI is what it is not. It is not sentient. It lacks awareness and cannot perceive or understand human thought or emotions. The operations are based purely on the data used to train the AI models.

2. How Does It Work?

Generative AI revolves around neural networks, complex structures inspired by the human brain with interconnected artificial neurons for processing information and learning from data. Whereas algorithm-based models make decisions based on rules that are provided by developers, generative AI tools review large amounts of data to construct an evolving set of rules that guide the generation of new content. Generative AI operates by making predictions about the next item in a series. When generative AI is writing a paragraph, for example, it constructs sentences by predicting each next word in succession. Over time, with lots of practice and lots of data, the system gets better at making predictions.

Large language models (LLMs) are trained on massive textual datasets, including books, articles, and websites, to learn the intricacies of human language. The datasets used to train ChatGPT, for example, were developed using public information on the internet, licensed third-party content, and information supplied by platform users and human trainers. Patterns are identified within the training data through statistical analysis as the machine learning model detects recurring sequences of data, such as words, phrases, or patterns of pixel values in images, and associates them with specific outcomes or contexts. By discovering and modeling these patterns, generative AI can produce new content, ensuring consistency within the context while introducing creativity and novelty. Combining LLMs with natural language processing (NLP) models, which process and understand human language such as language translation and sentiment analysis, results in content that mirrors human-generated material.

3. Who's Doing It?

The use of generative AI is rapidly growing. Students are increasingly using generative AI tools for essays, research papers, and other written assignments. Faculty members are leveraging generative AI for creating custom learning materials, quizzes, and assessments, and they can also use generative AI to provide timely and accurate feedback to students. Librarians are using generative AI for creating text summaries or discovering insights in their collections. Instructional designers are using generative AI for rapidly iterating on created materials. For administrators, generative AI aids decision-making and communication by summarizing documents and meetings as well as creating documents. Beyond textual tools, generative AI is being used to create visual artifacts and to generate or debug computer code. Software companies are including generative AI into their products like Adobe's Photoshop's Generative Fill or Microsoft's 365 Copilot.

4. Why Is It Significant?

Generative AI is suitable for a wide range of applications. There are stand-alone applications-such as chatbots, customizable agents, and virtual assistants-in which generative AI is the primary feature. In other applications such as content generators, creative tools, and everyday tools like Microsoft Word and Google Docs, generative AI is essentially invisible and just one feature among many. Part of the reason for this proliferation is that generative AI is becoming open to developers with cloud-based services, open-source frameworks, and a growing number of AI tools that make it easy to implement generative AI solutions.

By allowing individuals and businesses to create, design, and innovate in ways that were previously difficult or expensive, generative AI has the potential to be transformative in many ways, in various professional and educational contexts. Free (or inexpensive) tools that can quickly create content that otherwise requires human skill and time might alter the landscape of what certain jobs look like and the qualifications needed for those roles. In education, the ability for any student to generate written work that is difficult or impossible to discern as having been produced by technology can fundamentally change the kinds of activities that have long been the hallmarks of how students learn and demonstrate mastery of learning material.

5. What Are the Downsides?

The mismatch in the pace of technological development and the readiness of our social, ethical, and legal frameworks raises concerns about the responsible use of generative AI. It necessitates the development of robust guidelines, policies, and ethical considerations to ensure that this technology is harnessed for the greater good while minimizing potential risks. Training generative AI by using content from the internet means that significant amounts of material that is racist, sexist, or violent must be managed so that it doesn't infect the tool's output. Generative AI's ability to produce human-like content raises the risk that plagiarism and academic dishonesty will become more challenging to detect, necessitating the adoption of advanced detection tools or a shift in policy. This can also be addressed by fostering a culture of academic integrity and educating students about the ethical use of AI tools.

Because these tools can appear to magically provide the correct answers to questions, some users will trust the output without question, even though the tools sometimes generate counterfactual content. This misplaced confidence creates opportunities for those who use technology to do harm, such as through deepfake images or videos. For those inclined to verify the accuracy of generative AI output, the tools do not always cite sources or explain the reasoning behind the generated content. Meanwhile, the output is difficult to cite as a source because it is not repeatable.

Generative AI tools are accepting larger amounts and more types of data, and it can be difficult to know whether the tools are retaining and using data for other purposes. This uncertainty risks exposing data that should not be shared outside of an organization, including personally identifying information and other private data. The ownership of both the data that the tools are trained on and the output is currently being litigated. Several lawsuits are pending against companies for using data without permission. Depending how these cases are resolved, generative AI tools might be required to remove some data or exclude certain datasets, potentially causing the tools to be less accurate. Meanwhile, the output generated by AI tools has been ruled as ineligible for U.S. copyright because it is not authored by a human. However, a U.S. Supreme Court ruling from 1884 could provide a different argument for those who are interacting with generative AI tools as a "copilot" to create new content.

Many of the companies developing generative AI tools have not provided information regarding how the tools are trained and how safety tests are conducted. Each company has its own set of guidelines for what kinds of content are appropriate in terms of ethical, moral, and equity considerations, but this information is often not made clear to the users of these tools. While multiple governments are developing policies and guidelines for detecting AI-generated content and rooting out algorithmic discrimination, this guidance has not yet been finalized.

Another downside is the environmental impact of these tools. The carbon footprint includes training the model, running the model, and operating data centers. It is estimated that creating GPT-3 "consumed 1,287 megawatt hours of electricity and generated 552 tons of carbon dioxide equivalent, the equivalent of 123 gasoline-powered passenger vehicles driven for one year."

6. Where Is It Going?

In response to privacy concerns, organizations will begin to purchase or develop their own generative AI tools that do not share data beyond the organization. A college or university could purchase a tool for its faculty, staff, and students that has access to the institution's data but does not share the data beyond the institution or with certain user groups. As generative AI tools are provided with larger, cleaner datasets, larger LLMs will become better at more tasks. Some of these tasks include providing image-to-text services that could be used to make online courses more accessible by describing images used in the course, better online captions for audio and video, and language translations that are more accurate and localized. Several vendors have already integrated generative AI tools into their products to provide students with tutoring and support in areas such as coaching through math problems and the writing process.

Generative AI tools are emerging that will help faculty curate content for their courses. Some of these tools will build a learning module based on the course learning objectives and other prompts. Other tools can be used to develop assessment activities that are linked to the course content and learning objectives. Future developments may include tools that monitor students' performance and suggest adjustments to course content and assessments.

Another expectation is that generative AI tools will be further integrated into many everyday applications, including learning management systems, common productivity applications such as document and spreadsheet tools, and image editors. It will become difficult to use these common applications without AI tools providing suggestions and guidance.

As the workforce changes, how we prepare students will also need to change. We will need to teach students how AI in their fields will impact their work. This includes determining the source of data and understanding how algorithms are currently being used. Then we will need to teach students how to work with AI so that they are able to use these tools as a "copilot," as opposed to being replaced by these tools.

7. What Are the Implications for Higher Education?

Generative AI promises to give educators the ability to provide deeper learning and a more personalized experience along with reduced workload, but this will require focus on what and how we want students to learn and how they can obtain the tools to thrive in an environment in which AI is ubiquitous. The ability to rapidly generate many versions of material from different viewpoints allows students and faculty to explore ideas in novel ways. For example, an image could be synthesized around a given concept based upon a student's understanding. This image could be compared with images created by other students to discuss what commonalities and differences are present. This is something that is a strength of generative AI and gives all students, not just those with artistic skills, the ability to communicate concepts visually. It is incumbent on institutions to promote AI literacy among students and faculty and to provide information on how to use generative AI tools effectively and ethically, which can improve students' and faculty's experiences and facilitate learning.

For students, feedback is critical to success. Generative AI can assist with many of the techniques of effective feedback, two of which state that feedback should be specific and given on a timely basis. Faculty can use generative AI to rapidly craft specific feedback for students. Faculty can also leverage AI to teach students how to evaluate their own work, which involves them more deeply in the process of learning.

Institutions need to evaluate how students are being taught and evaluated in light of a tool that can generate content. What is the role that an essay plays in demonstrating mastery, and what is an appropriate role for generative AI in creating that essay? What is the role of homework? What constitutes ethical use of generative AI, and how much uniformity does the institution want when it comes to the use of generative AI tools? Significant investments in professional development and support will be needed to help faculty and administrators effectively integrate generative AI into teaching and administrative processes. Meanwhile, the absence of clear policies and guidelines regarding the use of AI in higher education could lead to inconsistencies in its implementation. Educational institutions should develop comprehensive strategies addressing areas including ethical considerations, data privacy, and intellectual property rights to ensure responsible and equitable use of generative AI.

Notes

1. Graham Duggan, "We've Been Using AI for Years - We Just Didn't Know It," CBC Docs, September 19, 2023 (last updated September 28, 2023); "107 Up-to-Date ChatGPT Statistics & User Numbers," Nerdynav, November 27, 2023; Anna Tong, "Exclusive: ChatGPT Traffic Slips Again for Third Month in a Row," Reuters, September 7, 2023.

2. See "Second Request for Reconsideration for Refusal to Register Théâtre D'opéra Spatial," Copyright Review Board, September 5, 2023.

3. See "Burrow-Giles Lithographic Co. v. Sarony," March 17, 1884, Legal Information Institute, Cornell Law School.

Contributors

Heather Brown is Instructional Designer at Tidewater Community College.

Steven Crawford is the District Director for the Maricopa Center for Learning and Innovation at Maricopa County Community College District.

Kate Miffitt is Director for Innovation at California State University, Office of the Chancellor.

Tracy Mendolia-Moore is Manager of 3D Education Technology Innovations at Western University of Health Sciences.

Dean Nevins is Executive Director of Information Technology at Santa Barbara City College.

Joshua Weiss is Director of Digital Learning Solutions at Stanford Graduate School of Education.

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7 things you should know about generative ai

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7 Things You Should Know About Generative AI

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The release of ChatGPT and similar AI tools that generate content including text, images, and audio has prompted both excitement and apprehension amon

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The release of ChatGPT has many in higher education worried about the threat it poses to academic integrity, but what if it could be used to help?

Credit: Mariya Chechulina / Shutterstock.com © 2023

The sudden uproar around ChatGPT and its text-generating artificial intelligence has sparked debate at South African universities and around the world. Presumptions abound that students might misuse AI to compose written work and that faculty will be unable to verify the authenticity of documents submitted for academic credit. Used as a co-assessor, however, ChatGPT can actually heighten the ability to ensure the integrity of academic assessment.

ChatGPT is a large language model created by OpenAI that uses deep learning algorithms to generate human-like text in response to prompts or questions. Although advanced AI language generators are useful for academic and professional writing, concerns arise about academic integrity if students employ the tools carelessly or mischievously. The concern is that ChatGPT could upend traditional assessment methods because it can generate high-quality written work that is difficult or impossible to identify as having been written by a machine, making it challenging to ensure the integrity of academic work. As the availability and sophistication of predictive text and language generators increase, leaders of educational institutions must remain vigilant and adopt strategies to prevent academic misconduct. How, then, can ChatGPT be used to improve academic assessment?

The Growing Power and Reach of Generative AI

Predictive text technology is widely available on various devices and applications, using machine learning to suggest commonly used words and phrases to speed up the input process. OpenAI, initially a nonprofit focused on reinforcement learning research and meta-learning robots, released GPT-2 in 2019 before unveiling GPT-3 in 2020 and GPT-4 in March 2023. These language models demonstrate unprecedented natural language processing capabilities. GPT-3 uses more than 175 billion machine learning parameters and can generate text in various formats and perform complex tasks.

Despite increasing sophistication, however, predictive text software remains error-prone. Neural networks, modeled after the structure of the human brain, are a type of machine-learning model. The architecture used by GPT-3 avoids the limitations of previous recurrent neural network architectures, which were sequential in nature, by using attention mechanisms to generalize sequences. ChatGPT employs deep learning algorithms to produce text that closely resembles human writing. AI language generators such as ChatGPT bring potential benefits for academic and professional writing. ChatGPT is a tool for a long-awaited evolution in writing and language communication, and it has the potential to revolutionize the way we communicate and learn.

The fear, however, is that ChatGPT could undermine conventional evaluation methods by generating high-quality writing, calling into question the authenticity and integrity of academic work. This concern is based on the assumption that academic assessments have remained the same over the past decades, focusing on writing styles and academic fashion while overlooking the importance of proven research and logic to support arguments. Disregarding ChatGPT based on these assumptions overlooks the potential benefits and applications of the technology in educational settings. One effective strategy to ensure authenticity is to investigate whether a piece of academic writing shares authorship with another piece of writing whose author is known. In such cases, ChatGPT can become a helpful tool.

Enlisting ChatGPT for Assessment

When asked to identify ten ways of establishing the authenticity of academic written text, ChatGPT offered a range of methods: plagiarism detection, style analysis, topic analysis, timeframe analysis, syntax analysis, vocabulary analysis, plagiarism comparison, grammar analysis, content analysis, and semantic analysis. ChatGPT also suggested a rubric that includes criteria such as clarity of argument, originality and plagiarism, language proficiency, cohesion and coherence, and adherence to academic standards. Each criterion can be assessed on a scale from 1 to 5. A total score can be calculated to determine the essay's overall quality.

Although ChatGPT can be a valuable tool in helping establish authenticity, it should be combined with other assessment methods to ensure academic integrity. It is up to educators, who are also the assessors, to maintain academic assessment integrity. Technology has made academic assessment more efficient and effective, but human intelligence remains critical in ensuring fair and accurate assessments. As such, assessors should remain vigilant in assessing the quality and validity of academic work.

Similarity, Grammar, and Pedagogy

Similarity checkers are tools that compare submitted texts against known content in an effort to uncover instances of plagiarism. Many institutions have developed policies and protocols that include similarity checking. However, similarity reports should not be the only factor determining plagiarism because they can sometimes produce disputable evidence. ChatGPT can also be effectively utilized as a similarity checker. As a language model, ChatGPT can go beyond exact text matches and recognize similarities in meaning and intent, making it a valuable tool for identifying subtle instances of potential plagiarism. Its ability to analyze and assess text at a deeper level enhances its capacity to serve as an efficient and accurate similarity checker, providing valuable insights for maintaining academic integrity in educational institutions.

Grammar checkers are powerful writing assistants that check for spelling, grammar, punctuation, style, and tone. They can help identify sentence structure and syntax errors and suggest corrections to improve clarity and readability. Grammar checkers are an excellent tool for proofreading and improving the overall quality of your writing. As a language model that can generate human-like responses to a wide range of queries, ChatGPT can provide insights on various topics, from basic knowledge to complex ideas. It can help with brainstorming, research, and idea generation. ChatGPT can guide an essay's content, suggest alternative approaches, and offer examples to help clarify points. ChatGPT and grammar checkers serve different purposes and have different strengths when checking an academic essay. When assessing an academic essay, both tools can be helpful. Grammar checkers can help identify errors that might affect the clarity and coherence of your writing. Meanwhile, ChatGPT can provide insights and suggestions to help you develop and refine your arguments. The synergetic use of ChatGPT as an additional strategy to check for plagiarism and grammar adds value to the assessment process.

Using similarity and grammar checkers and ChatGPT in combination with human assessors could allow for an even more thorough investigation of authenticity. ChatGPT can efficiently scan through a vast amount of text to identify potential similarities, provide suggestions for improvements in writing style, and highlight possible areas of concern. ChatGPT's ability to analyze language at a deeper level allows it to detect similarities in meaning and intent, providing a more nuanced assessment of the text. Additionally, ChatGPT can offer valuable feedback on writing quality, grammar, and coherence, enhancing the overall assessment process. Human assessors bring their expertise, knowledge, and understanding of the subject matter to the assessment process. They have the ability to contextualize the information provided by ChatGPT and other similarity checkers, interpret the results, and make informed judgments about the quality and originality of the academic work. Human assessors can identify instances of plagiarism that may go beyond verbatim copying or close paraphrasing, utilizing their critical thinking skills to detect more subtle forms of plagiarism that ChatGPT might miss. They also have the discretion to evaluate the relevance and appropriateness of the sources used in the work and assess the overall coherence and cohesion of the essay.

The combination of human intelligence and the capabilities of intelligent agents like ChatGPT allows for a more comprehensive and accurate assessment of academic work. While the technology tools can expedite similarity checking and offer valuable insights, human assessors can apply critical thinking skills, domain knowledge, and ethical judgment to ensure fair and transparent evaluation while upholding academic integrity. Together, they form a collaborative and practical approach to maintaining assessment integrity when using ChatGPT and similar AI technologies in the academic context.

The potential for ChatGPT to revolutionize assessment processes can be harnessed through creative pedagogies and engagement. Extracting text from video-generated transcripts and using ChatGPT to construct quizzes, for example, is a practical assessment strategy to assess students' comprehension of learning content. Another example could be a mechanism that forces students to re-read and revise their work, adding sources and in-text references and highlighting where arguments originated. Such a mechanism could be incorporated from the first year of study, ensuring that students develop an understanding of proper referencing. Adding a slide summary to written work can also reinforce understanding and logic. For continuous assessment, a battery of assignments building up to a final assignment with the exact intended instructions is a better practice. A balanced approach can address academic misconduct while leveraging ChatGPT's capabilities.

Better Writing through Assessments

ChatGPT can also identify areas where writing can be improved. While traditional similarity checkers may focus solely on detecting text similarities, ChatGPT can analyze the overall quality of the writing and provide feedback on writing quality, grammar, and coherence. Suggesting improvements in writing style, phrasing, and sentence structure assists in enhancing the clarity of the text. This feature especially benefits students looking to improve their writing skills and produce higher-quality academic work. This can be particularly useful for students who are not native speakers of the language they are writing in and for instructors who want to provide more comprehensive feedback on their students' writing.

Moreover, ChatGPT can provide suggestions for improving the clarity and coherence of a text, which can be valuable for academic writing that requires a high level of precision and clarity. It can also analyze the structure and organization of a text, identifying areas where the writer can improve the flow and structure of their ideas. ChatGPT's ability to generate human-like responses also fosters an engaging and interactive learning experience. Students can use it as a writing companion, stimulating creativity and promoting originality. Instead of merely flagging potential issues, ChatGPT can act as a co-author, encouraging students to re-read and refine their work before submission.

ChatGPT can revolutionize how academic works are assessed and evaluated by providing more nuanced and comprehensive feedback on writing quality, plagiarism, and overall authenticity. However, it is essential to note that ChatGPT should be accompanied by ethical guidelines and accountability measures to ensure its responsible and fair use.

Human Intervention During an Academic Assessment

Despite the advancements in technology and tools like ChatGPT, similarity checkers, and grammar checkers, human intelligence still plays a critical role in ensuring accurate and fair assessments of academic essays and assignments. Technology tools have limitations in comprehending the nuances and complexities of language that human beings can perceive. The human assessor's contextualization and interpretation of the information provided by these tools are crucial in making informed judgments about the quality of written work. Human intelligence is also necessary for identifying instances of plagiarism that these tools might miss.

Research Experiments Used to Test Authenticity

I used two strategies to test assessment authentication using ChatGPT. The first strategy involves asking ChatGPT to compare written work submitted by a student to another piece of writing that is known to have been written by that student. The second strategy asks ChatGPT to compare a piece of work submitted by a student to another piece of writing known to have been written by someone else. In each case, ChatGPT analyzed the writing styles of the documents being reviewed and provided feedback about the likelihood that both were written by the same person, as well as specific evidence supporting the analysis.

Strategy 1

The strategy for this test is to ask ChatGPT if two texts are written by the same author. Students hand in a piece of written work before doing a formal assignment. A biography is an example of such a written piece of text. When a later assignment is completed and seems suspicious, the instructor can use ChatGPT to compare the assignment text against the biography. When I ran this experiment, ChatGPT delivered the following response:

It is likely that the two paragraphs were written by the same person. Both paragraphs discuss the same organization, the University of Johannesburg (UJ), and relate to education and technology. Additionally, the first paragraph mentions the author's work at the Centre for Academic Technologies (CAT) at UJ, while the second paragraph describes the impact of information and communication technologies (ICTs) on assessment at UJ. Furthermore, the author's name, [writer], is mentioned in the first paragraph, and his role as Senior Specialist: Instructional and E-learning at CAT is described. These details suggest that the author of both paragraphs is likely to be [writer].

To add another layer to this exercise, I submitted a third piece of writing and asked ChatGPT to compare it with the other two.

ChatGPT's response:

It is possible that this was written by [writer]. The paragraph discusses how UJ has used ICTs to improve student feedback and engagement, which is in line with [writer's] area of expertise as a Teaching and Learning Consultant (Instructional Design) at the Centre for Academic Technologies (CAT) at UJ. The paragraph also uses similar language and terminology as the previous paragraphs, such as "ICTs" and "online surveys." However, without further information, it is impossible to determine with certainty whether [writer] is the author of this particular paragraph.

Strategy 2

For the second experiment, I searched online for writings on a particular topic that was the subject of a student assignment. I copied some text written by a credible author on that topic and pasted in into ChatGPT. I then pasted in text written by a student on the topic in question and asked ChatGPT to compare the writing styles of the two texts. In the same manner, assessors can compare a student's writing with the written work of other students, which can also result in the detection of suspicious similarities.

The results were as follows:

It is unlikely that these two paragraphs were written by the same author. The first paragraph discusses a specific exercise for students to complete in order to compare their writing styles, which is related to instructional design and learning, while the second paragraph discusses the benefits of ICT for students, which is related to education and technology. Additionally, the first paragraph uses technical language and terminology related to electronic submission, while the second paragraph uses more general language and does not mention any specific technologies or methods. These differences suggest that the paragraphs were written by different authors with different areas of expertise and writing styles.

As an AI language model, I cannot confirm or deny the authorship of a text without further information. However, based on the content of the paragraph and the fact that it discusses topics related to [writer's] area of expertise, it is possible that he or she could have written it. Nonetheless further investigation or confirmation is needed to determine the actual author of the text.

The results of each of these experimental tests depict the acceptable conclusions and comparisons that ChatGPT can generate to assist in the authentication and ensure the integrity of an academic document during an assessment. Subsequently, a last question on authenticity should be whether a human assessor is needed.

Conclusion

ChatGPT can be a valuable tool for promoting originality and enhancing the overall quality of academic writing. However, using it ethically and impartially is vital to ensuring academic assessment integrity. Academic institutions must embrace ChatGPT while also taking measures to mitigate its associated risks. Proposed assessment strategies can enhance the accuracy of plagiarism detection, such as adding in-text references, and enable a more comprehensive analysis of writing quality. By using ChatGPT as a co-author, students can benefit from a neutral assessment that may enhance the integrity and authenticity of their academic written work.

However, it is crucial to note that human intelligence continues to play a crucial role in identifying instances of plagiarism that similarity checkers might miss. Human assessors can detect plagiarism instances involving text rewording or using synonyms by drawing on their knowledge and expertise in the subject matter being assessed. Additionally, although grammar checkers can identify errors and suggest corrections, the human assessor should review these suggestions and make a final judgment about the context and intended meaning of the text.

While ChatGPT and other AI technologies can aid in assessing and improving academic writing, they should be used as assistive authoring tools alongside human intelligence. Co-utilization ensures fair and transparent evaluation processes. The responsibility of upholding and promoting academic assessment integrity remains with students and academic institutions. By using ChatGPT ethically and impartially, academic institutions can enhance the quality of academic writing and ensure academic assessment integrity.

Notes

1. Wagdy Sawahel, "Embrace It or Reject It? Academics Disagree about ChatGPT," University World News: Africa Edition, February 7, 2023.

2. Ben Lutkevich and Ivy Wigmore, "What is Predictive Text?" TechTarget, October 2022; Ronald Schmelzer, "GPT-3 AI Language Model Sharpens Complex Text Generation," TechTarget, October 22, 2020; Bernard Marr, "What Is GPT-3 And Why Is It Revolutionizing Artificial Intelligence?" Bernard Maar & Co., October 2020; and Jürgen Schmidhuber, "Deep Learning in Neural Networks: An Overview," Neural Networks 61 (January 2015): 85-117.

3. Sawahel, "Embrace It or Reject It?" and Sarah Clark, "Demystifying ChatGPT," Learning Guild,February 2, 2023.

4. Jon Nathanson, "At what point does similarity become plagiarism?" Quora, September 9, 2019.

Thomas Arnoldus (Arno) Louw is Senior Instructional Designer: e-Learning Specialist at the University of Johannesburg.

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Unexpected Bedfellows: Using ChatGPT to Uphold Academic Assessment Integrity

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The release of ChatGPT has many in higher education worried about the threat it poses to academic integrity, but what if it could be used to help?

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10 conseils pour les institutions

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The use of generative AI tools on campus is an excellent opportunity for technology and other leaders to provide guidance to students, faculty, and staff about how to navigate these new technological waters.

Credit: Nathapol Kongseang / Shutterstock.com © 2023

In April 2023, we were involved in a panel with students at College Unbound. The conversation-"Generative AI and Higher Education: Disruption, Opportunities, and Challenges" -offered many different highlights, and the students brought rich thoughts, provocative considerations, and smart ideas, reinforcing the fact that discussions around what to do about generative AI (or about anything else, for that matter) are enhanced when students are involved.

Toward the end of the panel conversation, Stan asked the students what they thought could be done to help faculty, students, and staff navigate the rise of AI. Essentially, he was curious to hear about the roles that technology and other leaders could fulfill. After thinking about their answers and engaging in further reflection, we came up with ten suggestions for how to step up and in to the generative AI discussion in higher education.

1. Offer Short Primers on Generative AI

Short primers offered on the topic of generative AI would ideally be text and video and could be harvested from what is already available on different websites and video platforms (e.g., YouTube). LinkedIn Learning would be another useful place to find content if your institution has access. The goal here is to provide short but clear content, ideally focused on the following questions:

What is generative AI?

Why does generative AI feel new and/or different (in general and in higher education, specifically)?

Who are the current providers of generative AI tools?

How should these tools be used?

What is the role of prompting in better understanding the usefulness and limitations of generative AI, and what are some strategies for using prompting?

Pro Tip: Use ChatGPT or other generative AI tools, and record what you do. This recording can serve as the basis for your own videos as an example of how you've integrated generative AI.

2. Explain How to Get Started

Providing clear guidance for faculty about how to get started using generative AI and what to try first can be helpful. Maybe the guidance is a "top 10 things to try," with each suggestion becoming progressively more dynamic or complex. This is your opportunity to help faculty and staff see the power of generative AI in action. Consider providing prompts that can help them understand how this tool would be useful for them individually and in relation to their roles at the institution, including for students.

3. Suggest Best Practices for Engaging with Generative AI

The next step is to target those who may already be familiar with generative AI tools but need help improving their skills. Guidance here might include a "Prompt Cookbook" with up/down voting or feedback from users who have found these tools useful/helpful or who want to add recommendations. These best practices might also identify limitations of the tools, along with workarounds. Linking to resources for the proper citation of generative AI tools would also be helpful.

4. Give Recommendations for Different Groups

This step might be tricky to determine and appropriately represent the different groups, but the goal is to highlight several different groups of people who are navigating generative AI on campus and provide them with some insight. For instance, there are many skeptics about these tools (and given what we know about AI, these skeptics will be important voices to include), and there are many people who are excited about the tools. There are also people who don't even know how they feel about generative AI. A resource can provide a mixture of considerations for each type of person. For the skeptic, a framework could validate that skepticism while also providing some considerations about the importance of learning more about the generative AI tools that many industries will be using soon or are already using. For the tech evangelist, a framework could provide a healthy dose of skepticism and concern about these tools, particularly around biases, the environmental impact, and human exploitation related to the creation and maintenance of generative AI. Finally, for the person who hasn't formed any opinion, the framework could provide an anchor with which to begin exploring and testing out generative AI tools with a bit of clarity and confidence.

The overall goal isn't to persuade people to feel better about whatever their stance is but, rather, to help all of them leverage a more balanced and informed perspective.

5. Recommend Tools

Every day, it seems like a dozen new AI tools are being promised, previewed, or released. Many on campus are a bit lost, and this could be a great opportunity for technology and other teams to provide some recommendations for some of the best AI tools to use. These teams could also suggest relevant considerations: What do the generative AI tools cost? What do we know about data privacy and these tools? What should/shouldn't the tools be used for? Is there an institutional account/discount for any of these tools? New options include anticipated announcements and beta deployments from major productivity suite producers such as Microsoft and Google. Generative AI search is now available on both platforms, and productivity suite enhancements are being added every day.

6. Explain the Closed vs. Open-Source Divide

As open-source versions gain steam, tracing out the differences and values among them can be invaluable for users on campus. The open-source models are placing capable tools in the hands of hobbyist and professional organizations alike. This will lead to a proliferation of finely tuned AI models contextual to specific institutions. Colleges and universities will increasingly begin to host their own large language models. This will provide options for localized training and utilization with less risk and more utility due to potential personas and organizational data access.

Currently, most of these open-source models come with a performance penalty and do not measure up to the corporate models. However, who knows how long this advantage will remain intact? Engineers at Google, for example, are famously rumored to have stated that they have no "secret sauce" and no real competitive moat between other providers and open-source models.

Priming people on your campus about the differences between the models could help them be ready to embrace (or at least understand) the importance of whichever model your institution may ultimately choose. This could also help institutional leaders navigate issues such as how to allow access to the large language models for training purposes, how to decide who should maintain custody and update the derivative datasets, and how to determine what should and shouldn't be shared in the interest of academic openness and freedom of expression.

7. Avoid Pitfalls

While the use of generative AI tools is skyrocketing, this is often being done without organizational contracts or formal risk analysis. Institutional technology and other leaders need to understand the risks and opportunities associated with new AI technologies. Offices of general counsel will want to review the term sheets for the "free to use" options and to negotiate favorable terms for the for-fee and site licenses their institutions commit to. CIOs and CISOs should weigh in on what is acceptable use for their institutions. Clear written guidance needs to be quickly developed and socialized. When introducing these technologies to members of your campus community, be sure to advise them to steer clear of placing sensitive or restricted data into the tools and to limit identifiable information.

8. Conduct Workshops and Events

Holding workshops, drop-in sessions, and campus events can be another means of bringing people together to help them understand the complexity of generative AI. These trainings might be more effectively targeted toward different populations (e.g., faculty, staff, students) and different levels of users (e.g., novice, intermediate, and expert). Additionally, this could be a great way to recruit people for working groups or communities of practice as the generative AI landscape continues to change.

9. Spot the Fake

Providing tips about how to avoid fake or lower-quality tools could also be extremely helpful. Many quickly emerging AI platforms offer very little privacy information or even pricing (sometimes being framed as in "beta" mode) but will ask people for access to private data (e.g., personal information, email, data). Where and when should this be considered? Additionally, with all the hype currently surrounding AI, many of these tools and platforms will not pan out as viable in the long run. Experimentation is encouraged, but institutional technology and other leaders should engage with their eyes wide open and take measured risks when investing funds into capability exploration. Think back to the early days of the internet and the numerous websites that went the way of the dinosaur in the dot-com bust.

10. Provide Proper Guidance on the Limitations of AI Detectors

Many people and companies are likely becoming rich right now by creating tools that detects students' work created with generative AI. The reality is that these tools are not going to work in any significant or long-term way. There are too many other tools that can trick AI-detection tools. There is also increasing evidence that AI plagiarism detection not only creates false positives but does so disproportionately for students for whom English is not their primary language. A primer that explains the underlying problems and realistic abilities of AI-detection tools could save colleges and universities a lot of money.

The institutional IT organization is often the hub of digital safety, security, and privacy considerations. All of those manifest when it comes to the use of generative AI tools. This usage on campus is an excellent opportunity for technology and other leaders to provide guidance to students, faculty, and staff about how to navigate these new technological waters. If this can be done with good resources, accessible language, and thoughtful support, these leaders can provide a life raft to many.

Notes

1. For example, see this "Prompt Guide" provided by Lance Eaton to attendees at the session "Ready or Not, Here AI Come: Exploring the Role of Generative AI in Higher Education," Massachusetts Colleges Online Conference on eLearning, June 15, 2023.

2. See, for example, "How Do I Cite Generative AI in MLA Style?" MLA Style Center (website), accessed September 20, 2023; Timothy McAdoo, "How to Cite ChatGPT," APA Style Blog, April 7, 2023; "Citation, Documentation of Sources," Chicago Manual of Style Online (website), accessed September 20, 2023.

3. Tara García Mathewson, "AI Detection Tools Falsely Accuse International Students of Cheating," The Markup, August 14, 2023.

Lance Eaton is Director of Digital Pedagogy at College Unbound in Providence, Rhode Island.

Stan Waddell is Vice President and CIO of Carnegie Mellon University.

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10 Ways Technology Leaders Can Step Up and In to the Generative AI Discussion in Higher Ed

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The use of generative AI tools on campus is an excellent opportunity for technology and other leaders to provide guidance to students, faculty, and st

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Une conférence claire et la présentation d'un modèle en matière d'éducation numérique

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L’IA dans l’éducation : ChatGPT, et alors?

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Watch the video “LIA dans leducation : ChatGPT, et alors?” presented by Prof Francesco Mondada, Gregory Liegeois at Intelligence artificielle, ChatGPT: Quels defis pour l’enseignement?

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Ia en éducation : Quand les colonnes du temple tremblent.

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Ia en éducation : Quand les colonnes du temple tremblent.

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Excellente présentation avec de nombreuses réflexions très riches en lien avec les usages des IAG en éducation et plus particulièrement dans le domaine de l'évaluation.

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L’IA en éducation: quand les colonnes du temple tremblent, Florian Meyer et Isabelle Nizet

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Vous travaillez dans le domaine de l'enseignement et de la formation et l'impact des outils comme ChatGPT vous interpelle ? Les ressources disponibles ici sont pour vous.
L'enseignement et la formation sont particulièrement touchés par la démocratisation d'outils d'IA générative, à l'exemple de ChatGPT. Grâce au soutien de la Fondation Mercator Suisse, notre équipe a réalisé plus de 40 interviews et workshops entre janvier et juin 2023 avec des enseignant-es, spécialistes de pédagogie et personnes exerçant des activités touchées par des outils d'IA générative. Nous avons ensuite établi des liens entre le contenu des interviews et la vaste littérature éthique sur les outils d'IA.

Vous trouverez ici quatre documents pour approfondir vos réflexions, en équipe ou en solo:

L'essentiel pour le domaine de l'enseignement: un document d'une page qui résume les résultats de nos réflexions

L'appliqué: un document qui décrit un cas d'usage spécifique à l'école, à savoir les devoirs écrits à la maison

Pour le futur: quel est l'impact des outils comme ChatGPT sur ma vision de mon activité professionnelle ? Quels changements pour mon éthique professionnelle ? Ce document vous aide à structurer un workshop, par exemple au sein de votre établissement ou de votre équipe, sur ces questions.

Les grandes questions: dans ce document, nous avons identifié 6 questions philosophiques sur l'outil ChatGPT.

Nous avons déjà eu l'occasion de participer à de nombreuses conférences et workshops sur l'intégration de ChatGPT et ses défis éthiques. Nous sommes à votre disposition pour organiser une conférence ou un workshop au sein de votre établissement.

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IA générative dans l'enseignement et la formation | ethix

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ethix, das Lab für Innovationsethik entwickelt mit Startups, Unternehmen und der Öffentlichkeit ein Beratungsangebot zur Ethik der Innovation, vor allem im Kontext der Digitalisierung.

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48 Pages Posted: 21 Jun 2023 Last revised: 26 Sep 2023

Date Written: September 23, 2023

Abstract

This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and limitations. The authors propose seven approaches for utilizing AI in classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator, and AI-student, each with distinct pedagogical benefits and risks. Prompts are included for each of these approaches. The aim is to help students learn with and about AI, with practical strategies designed to mitigate risks such as complacency about the AI's output, errors, and biases. These strategies promote active oversight, critical assessment of AI outputs, and complementarity of AI's capabilities with the students' unique insights. By challenging students to remain the "human in the loop," the authors aim to enhance learning outcomes while ensuring that AI serves as a supportive tool rather than a replacement. The proposed framework offers a guide for educators navigating the integration of AI-assisted learning in classrooms.

Keywords: AI, LLM, Education, Prompts

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assigning ai: seven approaches for students

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Assigning AI: Seven Approaches for Students, with Prompts

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This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and

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Companies that provide Big Tech with AI data-labeling services are inadvertently hiring young teens to work on their platforms, often exposing them to traumatic content.

Like most kids his age, 15-year-old Hassan spent a lot of time online. Before the pandemic, he liked playing football with local kids in his hometown of Burewala in the Punjab region of Pakistan. But Covid lockdowns made him something of a recluse, attached to his mobile phone. "I just got out of my room when I had to eat something," says Hassan, now 18, who asked to be identified under a pseudonym because he was afraid of legal action. But unlike most teenagers, he wasn't scrolling TikTok or gaming. From his childhood bedroom, the high schooler was working in the global artificial intelligence supply chain, uploading and labeling data to train algorithms for some of the world's largest AI companies.

The raw data used to train machine-learning algorithms is first labeled by humans, and human verification is also needed to evaluate their accuracy. This data-labeling ranges from the simple-identifying images of street lamps, say, or comparing similar ecommerce products-to the deeply complex, such as content moderation, where workers classify harmful content within data scraped from all corners of the internet. These tasks are often outsourced to gig workers, via online crowdsourcing platforms such as Toloka, which was where Hassan started his career.

A friend put him on to the site, which promised work anytime, from anywhere. He found that an hour's labor would earn him around $1 to $2, he says, more than the national minimum wage, which was about $0.26 at the time. His mother is a homemaker, and his dad is a mechanical laborer. "You can say I belong to a poor family," he says. When the pandemic hit, he needed work more than ever. Confined to his home, online and restless, he did some digging, and found that Toloka was just the tip of the iceberg.

"AI is presented as a magical box that can do everything," says Saiph Savage, director of Northeastern University's Civic AI Lab. "People just simply don't know that there are human workers behind the scenes."

At least some of those human workers are children. Platforms require that workers be over 18, but Hassan simply entered a relative's details and used a corresponding payment method to bypass the checks-and he wasn't alone in doing so. WIRED spoke to three other workers in Pakistan and Kenya who said they had also joined platforms as minors, and found evidence that the practice is widespread.

"When I was still in secondary school, so many teens discussed online jobs and how they joined using their parents' ID," says one worker who joined Appen at 16 in Kenya, who asked to remain anonymous. After school, he and his friends would log on to complete annotation tasks late into the night, often for eight hours or more.

Appen declined to give an attributable comment.

"If we suspect a user has violated the User Agreement, Toloka will perform an identity check and request a photo ID and a photo of the user holding the ID," Geo Dzhikaev, head of Toloka operations, says.

Driven by a global rush into AI, the global data labeling and collection industry is expected to grow to over $17.1 billion by 2030, according to Grand View Research, a market research and consulting company. Crowdsourcing platforms such as Toloka, Appen, Clickworker, Teemwork.AI, and OneForma connect millions of remote gig workers in the global south to tech companies located in Silicon Valley. Platforms post micro-tasks from their tech clients, which have included Amazon, Microsoft Azure, Salesforce, Google, Nvidia, Boeing, and Adobe. Many platforms also partner with Microsoft's own data services platform, the Universal Human Relevance System (UHRS).

These workers are predominantly based in East Africa, Venezuela, Pakistan, India, and the Philippines-though there are even workers in refugee camps, who label, evaluate, and generate data. Workers are paid per task, with remuneration ranging from a cent to a few dollars-although the upper end is considered something of a rare gem, workers say. "The nature of the work often feels like digital servitude-but it's a necessity for earning a livelihood," says Hassan, who also now works for Clickworker and Appen.

Sometimes, workers are asked to upload audio, images, and videos, which contribute to the data sets used to train AI. Workers typically don't know exactly how their submissions will be processed, but these can be pretty personal: On Clickworker's worker jobs tab, one task states: "Show us you baby/child! Help to teach AI by taking 5 photos of your baby/child!" for €2 ($2.15). The next says: "Let your minor (aged 13-17) take part in an interesting selfie project!"

Some tasks involve content moderation-helping AI distinguish between innocent content and that which contains violence, hate speech, or adult imagery. Hassan shared screen recordings of tasks available the day he spoke with WIRED. One UHRS task asked him to identify "fuck," "c**t," "dick," and "bitch" from a body of text. For Toloka, he was shown pages upon pages of partially naked bodies, including sexualized images, lingerie ads, an exposed sculpture, and even a nude body from a Renaissance-style painting. The task? Decipher the adult from the benign, to help the algorithm distinguish between salacious and permissible torsos.

Hassan recalls moderating content while under 18 on UHRS that, he says, continues to weigh on his mental health. He says the content was explicit: accounts of rape incidents, lifted from articles quoting court records; hate speech from social media posts; descriptions of murders from articles; sexualized images of minors; naked images of adult women; adult videos of women and girls from YouTube and TikTok.

Many of the remote workers in Pakistan are underage, Hassan says. He conducted a survey of 96 respondents on a Telegram group chat with almost 10,000 UHRS workers, on behalf of WIRED. About a fifth said they were under 18.

Awais, 20, from Lahore, who spoke on condition that his first name not be published, began working for UHRS via Clickworker at 16, after he promised his girlfriend a birthday trip to the turquoise lakes and snow-capped mountains of Pakistan's northern region. His parents couldn't help him with the money, so he turned to data work, joining using a friend's ID card. "It was easy," he says.

He worked on the site daily, primarily completing Microsoft's "Generic Scenario Testing Extension" task. This involved testing homepage and search engine accuracy. In other words, did selecting "car deals" on the MSN homepage bring up photos of cars? Did searching "cat" on Bing show feline images? He was earning $1 to $3 each day, but he found the work both monotonous and infuriating. At times he found himself working 10 hours for $1, because he had to do unpaid training to access certain tasks. Even when he passed the training, there might be no task to complete; or if he breached the time limit, they would suspend his account, he says. Then seemingly out of nowhere, he got banned from performing his most lucrative task-something workers say happens regularly. Bans can occur for a host of reasons, such as giving incorrect answers, answering too fast, or giving answers that deviate from the average pattern of other workers. He'd earned $70 in total. It was almost enough to take his high school sweetheart on the trip, so Awais logged off for good.

Clickworker did not respond to requests for comment. Microsoft declined to comment.

"In some instances, once a user finishes the training, the quota of responses has already been met for that project and the task is no longer available," Dzhikaev said. "However, should other similar tasks become available, they will be able to participate without further training."

Researchers say they've found evidence of underage workers in the AI industry elsewhere in the world. Julian Posada, assistant professor of American Studies at Yale University, who studies human labor and data production in the AI industry, says that he's met workers in Venezuela who joined platforms as minors.

Bypassing age checks can be pretty simple. The most lenient platforms, like Clickworker and Toloka, simply ask workers to state they are over 18; the most secure, such as Remotasks, employ face recognition technology to match workers to their photo ID. But even that is fallible, says Posada, citing one worker who says he simply held the phone to his grandmother's face to pass the checks. The sharing of a single account within family units is another way minors access the work, says Posada. He found that in some Venezuelan homes, when parents cook or run errands, children log on to complete tasks. He says that one family of six he met, with children as young as 13, all claimed to share one account. They ran their home like a factory, Posada says, so that two family members were at the computers working on data labeling at any given point. "Their backs would hurt because they have been sitting for so long. So they would take a break, and then the kids would fill in," he says.

The physical distances between the workers training AI and the tech giants at the other end of the supply chain-"the deterritorialization of the internet," Posada calls it-creates a situation where whole workforces are essentially invisible, governed by a different set of rules, or by none.

The lack of worker oversight can even prevent clients from knowing if workers are keeping their income. One Clickworker user in India, who requested anonymity to avoid being banned from the site, told WIRED he "employs" 17 UHRS workers in one office, providing them with a computer, mobile, and internet, in exchange for half their income. While his workers are aged between 18 and 20, due to Clickworker's lack of age certification requirements, he knows of teenagers using the platform.

In the more shadowy corners of the crowdsourcing industry, the use of child workers is overt.

Captcha (Completely Automated Public Turing test to tell Computers and Humans Apart) solving services, where crowdsourcing platforms pay humans to solve captchas, are a less understood part in the AI ecosystem. Captchas are designed to distinguish a bot from a human-the most notable example being Google's reCaptcha, which asks users to identify objects in images to enter a website. The exact purpose of services that pay people to solve them remains a mystery to academics, says Posada. "But what I can confirm is that many companies, including Google's reCaptcha, use these services to train AI models," he says. "Thus, these workers indirectly contribute to AI advancements."

Google did not respond to a request for comment in time for publication.

There are at least 152 active services, mostly based in China, with more than half a million people working in the underground reCaptcha market, according to a 2019 study by researchers from Zhejiang University in Hangzhou.

"Stable job for everyone. Everywhere," one service, Kolotibablo, states on its website. The company has a promotional website dedicated to showcasing its worker testimonials, which includes images of young children from across the world. In one, a smiling Indonesian boy shows his 11th birthday cake to the camera. "I am very happy to be able to increase my savings for the future," writes another, no older than 7 or 8. A 14-year-old girl in a long Hello Kitty dress shares a photo of her workstation: a laptop on a pink, Barbie-themed desk.

Not every worker WIRED interviewed felt frustrated with the platforms. At 17, most of Younis Hamdeen's friends were waiting tables. But the Pakistani teen opted to join UHRS via Appen instead, using the platform for three or four hours a day, alongside high school, earning up to $100 a month. Comparing products listed on Amazon was the most profitable task he encountered. "I love working for this platform," Hamdeen, now 18, says, because he is paid in US dollars-which is rare in Pakistan-and so benefits from favorable exchange rates.

But the fact that the pay for this work is incredibly low compared to the wages of in-house employees of the tech companies, and that the benefits of the work flow one way-from the global south to the global north, leads to uncomfortable parallels. "We do have to consider the type of colonialism that is being promoted with this type of work," says the Civic AI Lab's Savage.

Hassan recently got accepted to a bachelor's program in medical lab technology. The apps remain his sole income, working an 8 am to 6 pm shift, followed by 2 am to 6 am. However, his earnings have fallen to just $100 per month, as demand for tasks has outstripped supply, as more workers have joined since the pandemic.

He laments that UHRS tasks can pay as little as 1 cent. Even on higher-paid jobs, such as occasional social media tasks on Appen, the amount of time he needs to spend doing unpaid research means he needs to work five or six hours to complete an hour of real-time work, all to earn $2, he says.

"It's digital slavery," says Hassan.

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Companies that provide Big Tech with AI data-labeling services are inadvertently hiring young teens to work on their platforms, often exposing them to traumatic content.

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Edit du 7 novembre 2023 à 14h05 : OpenAI a bel et bien annoncé le déploiement de son outil de création de GPT, permettant de concevoir des chatbots personnalisés répondant à des consignes spécifiques. Pour en savoir plus, vous pouvez lire notre article à ce sujet.
OpenAI organise sa toute première conférence de développeurs ce lundi 6 novembre et les rumeurs vont bon train quant aux nouveautés que pourrait dévoiler l'éditeur de ChatGPT. La plus pressante d'entre elles est l'annonce de GPT Builder, un outil qui permettrait aux abonnés payants de créer leur propre chatbot. De nouveaux plans et offres tarifaires pourraient être parallèlement révélés. On fait le point.

GPT Builder, un outil pour créer votre propre chatbot

Pour son premier DevDay, OpenAI n'échappe pas aux fameux leaks. La fuite concernant l'annonce d'un outil pour créer son propre chatbot, nommé GPT Builder, semble la plus crédible. En effet, des utilisateurs de X (anciennement Twitter) ont dévoilé des captures d'écran et des vidéos montrant qu'il devrait être bientôt possible de concevoir de " nouveaux GPT " personnalisés. Le développeur Tibor Blaho en a même partagé l'interface. On y voit une fonctionnalité permettant de " personnaliser une version de ChatGPT dans un but spécifique ".

Bonjour ! Je vais vous aider à créer un nouveau GPT. Vous pouvez dire quelque chose comme " créer un assistant qui aide à générer des visuels pour des nouveaux produits " ou " créer un assistant ingénieur logiciel qui aide à formater mon code ". Qu'aimeriez-vous faire ?, oriente GPT Builder.

La vidéo de Tibor Blaho révèle un potentiel impressionnant pour ce GPT Builder :
Dans la vidéo de Tibor Blaho, on distingue des interfaces de création et de configuration, assistées ou non par ChatGPT. Il serait possible de nommer son chatbot, de lui adjoindre une description, mais surtout de lui donner des instructions sur ce que vous voulez qu'il fasse, la façon dont vous souhaitez qu'il se comporte, ce qu'il doit éviter de faire.

Vous devriez pouvoir lui transmettre de la documentation pour accroître son savoir sur un sujet particulier, ainsi qu'inclure les possibilités de GPT-4 comme la recherche sur le web, l'analyse d'images, la génération d'image avec DALL-E, Code Interpreter, soit toutes les fonctionnalités des offres payantes Plus et Entreprise. Il semble également possible d'ajouter des fonctions personnalisées, d'ouvrir le chatbot au public, d'émettre une clé API ou de le privatiser. Enfin, un aperçu de votre propre GPT devrait vous permettre de le tester.

Quelles autres potentielles annonces pour l'OpenAI DevDay ?

Selon The Decoder et CHOI, sur X, l'arrivée de ce GPT Builder devrait s'accompagner de celle d'un espace " bac à sable " où il sera possible de retrouver les chatbots créés par d'autres utilisateurs. Cette nouvelle fonctionnalité qui, sur le papier, impressionne, pourrait marquer le coup d'envoi de nouvelles offres tarifaires. Les rumeurs évoquent en effet un Team Plan pour les entreprises, avec une offre Flexible et une Annual : la première serait disponible pour 30 $ par mois par utilisateur, la seconde pour 25 $ par mois avec un engagement à l'année, avec à chaque fois un minimum de trois utilisateurs.

Ceci irait de pair avec la mise en place d'espaces de travail distincts entre usage personnel et professionnel. Il devrait être possible d'y définir rôles et accès, mais également d'y intégrer des outils comme des ERP et des CRM, toujours selon The Decoder. Enfin, le Team Plan offrirait des avantages tels qu'un accès à GPT-4 rapide et illimité, l'inclusion de contextes quatre fois plus longs dans les requêtes et une utilisation illimitée du modèle Advanced Data Analytics.

D'autres rumeurs évoquent également des " connecteurs de contexte ", permettant d'associer d'autres applications à ChatGPT, comme Google Drive ou Microsoft 365, afin d'intégrer des documents directement issus de ces outils au sein du chatbot. Enfin, des améliorations de vitesse pour GPT-4 sont envisagées, tout comme une baisse du prix de son API.

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GPT Builder : un outil pour créer votre chatbot dévoilé par OpenAI ?

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OpenAI pourrait dévoiler GPT Builder, permettant aux utilisateurs de concevoir leur propre version de GPT avec des commandes personnalisées.

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The ultimate semantic receivers, selectors, and transmitters are still us.

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"From one day to the next, our profession was wiped out. We woke up and discovered our skills were redundant." This is what two successful graphic designers told me about the impact of AI. The old promise - creative workers would be better protected than others from mechanisation - imploded overnight. If visual artists can be replaced by machines, who is safe?

There's no talk of a "just transition" for graphic designers, or the other professions about to be destroyed. And while there's plenty of talk about how education might change, little has been done to equip students for a world whose conditions shift so fast. It's not just at work that young people will confront sudden changes of state. They are also likely to witness cascading environmental breakdown and the collapse of certain human-made systems.

Why are we so unprepared? Why do we manage our lives so badly? Why are we so adept at material innovation, but so inept at creating a society in which everyone can thrive? Why do we rush to bail out the banks but stand and gawp while Earth systems collapse? Why do we permit psychopaths to govern us? Why do blatant lies spread like wildfire? Why are we better at navigating work relationships than intimate ones? What is lacking in our education that leaves such chasms in our lives?

The word education partly derives from the Latin educere: to lead out. Too often it leads us in: into old ways of thinking, into dying professions, into the planet-eating system called business as usual. Too seldom does it lead us out of our cognitive and emotional loops, out of conformity with a political and economic system that's killing us.

I don't claim to have definitive answers. But I believe certain principles would help. One is that rigidity is lethal. Any aspect of an education system that locks pupils in to fixed patterns of thought and action will enhance their vulnerability to rapid and massive change. For instance, there could be no worse preparation for life than England's Standard Assessment Tests, which dominate year 6 teaching. If the testimony of other parents I know is representative, SATs are a crushing experience for the majority of pupils, snuffing out enthusiasm, forcing them down a narrow, fenced track and demanding rigidity just as their minds are seeking to blossom and expand.

The extreme demands, throughout our schooling, of tests and exams reduce the scope of our thinking. The exam system creates artificial borders, fiercely patrolled, between academic subjects. There are no such boundaries in nature. If our interdisciplinary thinking is weak, if we keep failing to see the bigger picture, it is partly because we have been trained so brutally to compartmentalise.

Education, to the greatest extent possible, should be joyful and delightful, not only because joy and delight are essential to our wellbeing, but also because we are more likely to withstand major change if we see acquiring new knowledge and skills as a fascinating challenge, not a louring threat.

There are arguments for and against a national curriculum. It's a leveller, ensuring everyone is exposed to common standards of literacy and numeracy. It provides a defence against crank teachings such as creationism and Holocaust denial. It permits continuity when teachers leave their jobs, and a clear knowledge path from year to year. But it is highly susceptible to the crank teachings of politicians, such as the Westminster government's insistence on drilling young children in abstruse grammatical rules, and its ridiculous tick-lists of sequential learning tasks.

When we are taught broadly the same things in broadly the same way, we lose the resilience diversity affords. What the teachers I speak to regret most is the lack of time. The intense combined demands of the curriculum and the testing regime leave almost no time to respond to opportunities and events, or for children to develop their own interests. One teacher remarked that if a pterodactyl landed on the school roof, the children would be told to ignore it so they could finish their allotted task.

If we are to retain a national curriculum, there are certain topics it should surely cover. For instance, many students will complete their education without ever being taught the principles of complex systems. Yet everything of importance to us (the brain, body, society, ecosystems, the atmosphere, oceans, finance, the economy ... ) is a complex system. Complex systems operate on radically different principles from either simple systems or complicated systems (such as car engines). When we don't understand these principles, their behaviour takes us by surprise. The two existential threats I would place at the top of my list, ranked by a combination of likelihood, impact and imminence, are environmental breakdown and global food system collapse. Both involve complex systems being pushed beyond their critical thresholds.

Instead of enforcing boundaries between subjects, a curriculum should break them down. This is what the International Baccalaureate does. I believe this option should be available in every school.

Above all, our ability to adapt to massive change depends on what practitioners call "metacognition" and "meta-skills". Metacognition means thinking about thinking. In a brilliant essay for the Journal of Academic Perspectives, Natasha Robson argues that while metacognition is implicit in current teaching - "show your working", "justify your arguments" - it should be explicit and sustained. Schoolchildren should be taught to understand how thinking works, from neuroscience to cultural conditioning; how to observe and interrogate their thought processes; and how and why they might become vulnerable to disinformation and exploitation. Self-awareness could turn out to be the most important topic of all.

Meta-skills are the overarching aptitudes - such as self-development, social intelligence, openness, resilience and creativity - that help us acquire the new competencies that sudden change demands. Like metacognition, meta-skills can be taught. Unfortunately, some public bodies are trapped in the bleak and narrow instrumentalism we need to transcend. For example, after identifying empathy as a crucial meta-skill, a manual by Skills Development Scotland reports that: "Empathy has been identified as a key differentiator for business success, with companies such as Facebook, Google and Unilever being recognised as excelling in this area." I've seldom read a more depressing sentence.

Schooling alone will not be enough to lead us out of the many crises and disasters we now face. Those who are adult today must take responsibility for confronting them. But it should at least lend us a torch.

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How we can teach children so they survive AI - and cope with whatever comes next | George Monbiot

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It's not enough to build learning around a single societal shift. Students should be trained to handle a rapidly changing world, says George Monbiot, Guardian columnist

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‘It is a beast that needs to be tamed’: leading novelists on how AI could rewrite the future

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Novelists and poets, Bernardine Evaristo, Jeanette Winterson, Stephen Marche and others, consider the threats and thrilling possibilities of artificial intelligence

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DA Copilot est en mesure de faire tout ce que n'importe quel professionnel est capable de faire : calcul, rédaction, analyse de données, à une vitesse phénoménale, et avec des capacités supérieures à celles de n'importe quel humain à certains égards. Copilot me donnera l'illusion d'être le pilote dans l'avion et de faire seulement office de copilote, à qui je peux confier quelques tâches. Mais c'est une illusion complète : le copilote apprendra à faire mon travail et à me remplacer.
QS Quelles sont vos autres préoccupations par rapport à Copilot?
DA Je pourrais passer deux heures à parler de mes inquiétudes. On sous-estime les risques. Il y a des gens, tous les jours, qui vont laisser l'IA prendre des décisions cruciales pour la vie humaine. N'importe quelle personne en position de pouvoir pourrait automatiser un processus qui relève de sa responsabilité : concevoir les infrastructures publiques comme les ponts ou encore gérer les données cruciales de santé, par exemple. Cela pourrait être fait par un système qui est opaque.

Nous avons peu de recherches portant sur l'AI générative qui assiste les humains, mais celles que nous avons montrent que cela entraîne énormément de paresse cognitive. Si, au début, les individus vérifient tout ce que l'IA fait et constatent que c'est très fiable, ils délèguent de plus en plus de tâches à cette IA, ce qui s'accompagne d'une perte de compétences. Ça ne se manifestera pas dans les premières semaines ou même les premiers mois. Mais si vous n'effectuez plus ces tâches qui sont au cœur de votre travail pendant une année ou plus, vous risquez de perdre la capacité de vérifier le travail réalisé par l'IA.
QS À quoi peut mener cette perte de vigilance?
DA Pour faire un parallèle, prenons le cas de la voiture autonome dans les années 2010. Des personnes ont été engagées pour monter à bord d'une voiture autonome. À tout moment, en cas d'erreur, les passagers pouvaient reprendre le contrôle. Dans les premiers jours, les gens étaient vigilants. Mais après quelques semaines, ils écoutaient des émissions de téléréalité sur leur iPad [un décès est malheureusement survenu pendant qu'une personne testait une voiture Uber et n'avait pas les yeux sur la route]. Ils n'étaient plus du tout vigilants. C'est normal parce que, pour rester attentifs, les humains ont besoin de beaucoup de concentration et d'énergie. Si nous n'avons pas besoin d'être concentrés, nous ne le serons pas.

C'est la même chose avec l'IA générative. Même si les logiciels nous recommandent d'être vigilants, on finira par ignorer l'avertissement, comme on ignore tous les avertissements numériques qui nous agacent.
QS ChatGPT est déjà là. N'importe qui peut aller sur le site web pour l'utiliser. Qu'est-ce que cela change que Copilot soit intégré à des outils de travail qui sont utilisés par la majorité des gens?
DA Il y a une grande différence entre utiliser un outil externe et intégrer l'IA générative dans la suite bureautique que l'on utilise tous les jours. Oui, ChaptGPT est déjà disponible. Si l'on s'abonne au service payant ChatGPT Plus, on peut utiliser toutes les fonctionnalités dont j'ai parlé pour Copilot. Mais le fait de recourir à une application externe représente un effort pour la très vaste majorité de la population, qui ne le fera pas.

Cependant, tout le monde est habitué à utiliser quotidiennement les logiciels de Microsoft : Word pour rédiger des documents, Excel pour analyser des données, Outlook pour gérer les courriels, Teams pour les réunions... Quand on intègre l'IA générative, on modifie la manière dont le travail s'effectue et on modifie l'environnement mental des travailleurs. En soi, ce n'est pas mauvais, c'est juste qu'on ignore complètement les risques qui sont énormes. C'est vraiment ça que je trouve fascinant : ce n'est pas un débat public. Je ne conçois pas pourquoi on n'en parle pas plus et pourquoi ce n'est pas un enjeu de société important actuellement. Cela va affecter tout le monde.
QS Copilot sera là, qu'on le veuille ou non. Quelle est la solution pour se protéger?
DA Informer d'abord le public. Ça me scandalise que le public soit si peu informé. Les employés ne sont même pas au courant que ça arrive. C'est ça qui m'inquiète!

Ça nécessite aussi des directives sur les précautions à prendre lorsqu'on utilise l'IA générative. On doit créer des équipes qui se penchent sur les risques et les bons usages et faire des recommandations aux employés.

Il faut aussi s'entourer de personnes formées en intelligence artificielle. Les médecins, les ingénieurs, les avocats, les gestionnaires de haut niveau qui travaillent avec des données sensibles et qui prennent des décisions importantes doivent comprendre le fonctionnement de l'intelligence artificielle pour guider leurs professionnels dans l'intégration harmonieuse de ces systèmes-là.
QS Et pour les postes à moindre responsabilité, que conseillez-vous?
DA Les gestionnaires doivent procéder avec prudence en ne mettant pas de pression sur leurs employés. Ils doivent indiquer à leur personnel que l'IA n'a pas conscience de ce qu'elle fait et n'est pas responsable des erreurs. Par exemple, l'IA générative est une utilisatrice d'Excel beaucoup plus experte qu'un humain, mais ça peut aussi lui faire faire des erreurs. L'IA pourrait utiliser d'autres fonctions " en pensant " qu'elles sont plus efficaces, mais en fait, il y a des raisons pour lesquelles l'humain utilise telle fonction plutôt qu'une autre selon sa compréhension professionnelle.
QS Ne serait-ce pas un peu naïf de laisser Copilot faire tout le travail à notre place?
DA Évidemment, il y a plein de professionnels qui vont être très vigilants, très rigoureux, mais ça dépend des traits de personnalité de chaque personne et du contexte du travail. Beaucoup de gens sont anxieux, stressés, ont des troubles de personnalité ou même aiment tricher.

Dans Teams, certaines tâches relationnelles pourront être prises en charge par l'IA, dont la possibilité d'assister aux réunions à notre place. L'IA va nous faire des résumés de la réunion et sortir les points importants. Qu'est-ce que cela a comme effet? Une baisse de l'attention et de la concentration.
QS L'IA ne peut-elle pas être utilisée intelligemment?
DA Je veux rappeler que je suis pour l'IA générative et je pense qu'elle peut augmenter la productivité et faciliter le travail. Mais il faut prendre le temps de bien faire les choses. La suite Office a mis une dizaine d'années à intégrer les entreprises comme telles et ça venait avec des formations. Dans les années 1990, Microsoft avait déployé des formateurs qui allaient dans les entreprises. On ne peut faire en moins d'un mois ce qui a pris autrefois une dizaine d'années.

Il y a déjà beaucoup de choses dans notre environnement numérique qui contribuent à la baisse de nos capacités cognitives. Et là, c'est une question plus large que la question des professionnels. C'est tout simplement une baisse de l'intelligence de l'être humain. L'IA ne doit pas affecter la cognition humaine, elle doit pouvoir augmenter ce que l'on peut faire. Mais le problème, c'est la manière dont on nous vend les produits sans étudier leurs conséquences.
* Les propos ont été revus et condensés pour plus de clarté.

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Copilot, l'assistant virtuel qui s'apprête à bouleverser le monde du travail - Québec Science

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L'intelligence artificielle générative fera son entrée dès le 1er novembre 2023 dans le quotidien de millions de personnes. À quel prix?

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The article discusses the potential of artificial intelligence (AI) chatbots, specifically large language models (LLMs) like ChatGPT, in enhancing education. Educators and researchers are experimenting with these tools as "thought partners" in teaching and learning. While there are concerns about their use, such as the risk of cheating and AI inaccuracies, the positive feedback from trials suggests significant potential. AI chatbots can save time for students and teachers, aid in problem-solving, stimulate critical thinking, and enhance creativity. The article also touches on the challenges of privacy, bias, and access, highlighting efforts to mitigate these issues.

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Un livre en ligne plein de bonnes idées pour les usages de l'IA en évaluation

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ChatGPT Assignments to Use in Your Classroom Today

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By Kevin Yee, Kirby Whittington, Erin Doggette, et al., Published on 01/01/23

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https://managing-ai-risks.com/

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Abstract

In this short consensus paper, we outline risks from upcoming, advanced AI systems. We examine large-scale social harms and malicious uses, as well as an irreversible loss of human control over autonomous AI systems. In light of rapid and continuing AI progress, we propose urgent priorities for AI R&D and governance.

In 2019, GPT-2 could not reliably count to ten. Only four years later, deep learning systems can write software, generate photorealistic scenes on demand, advise on intellectual topics, and combine language and image processing to steer robots. As AI developers scale these systems, unforeseen abilities and behaviors emerge spontaneously, without explicit programming . Progress in AI has been swift and, to many, surprising.

The pace of progress may surprise us again. Current deep learning systems still lack important capabilities and we do not know how long it will take to develop them. However, companies are engaged in a race to create generalist AI systems that match or exceed human abilities in most cognitive work . They are rapidly deploying more resources and developing new techniques to increase AI capabilities. Progress in AI also enables faster progress: AI assistants are increasingly used to automate programming [4] and data collection [5,6] to further improve AI systems [7].

There is no fundamental reason why AI progress would slow or halt at the human level. Indeed, AI has already surpassed human abilities in narrow domains like protein folding or strategy games [8-10]. Compared to humans, AI systems can act faster, absorb more knowledge, and communicate at a far higher bandwidth. Additionally, they can be scaled to use immense computational resources and can be replicated by the millions.

The rate of improvement is already staggering, and tech companies have the cash reserves needed to scale the latest training runs by multiples of 100 to 1000 soon [11]. Combined with the ongoing growth and automation in AI R&D, we must take seriously the possibility that generalist AI systems will outperform human abilities across many critical domains within this decade or the next.

What happens then? If managed carefully and distributed fairly, advanced AI systems could help humanity cure diseases, elevate living standards, and protect our ecosystems. The opportunities AI offers are immense. But alongside advanced AI capabilities come large-scale risks that we are not on track to handle well. Humanity is pouring vast resources into making AI systems more powerful, but far less into safety and mitigating harms. For AI to be a boon, we must reorient; pushing AI capabilities alone is not enough.

We are already behind schedule for this reorientation. We must anticipate the amplification of ongoing harms, as well as novel risks, and prepare for the largest risks well before they materialize. Climate change has taken decades to be acknowledged and confronted; for AI, decades could be too long.

Societal-scale risks

AI systems could rapidly come to outperform humans in an increasing number of tasks. If such systems are not carefully designed and deployed, they pose a range of societal-scale risks. They threaten to amplify social injustice, erode social stability, and weaken our shared understanding of reality that is foundational to society. They could also enable large-scale criminal or terrorist activities. Especially in the hands of a few powerful actors, AI could cement or exacerbate global inequities, or facilitate automated warfare, customized mass manipulation, and pervasive surveillance [12,13].

Many of these risks could soon be amplified, and new risks created, as companies are developing autonomous AI: systems that can plan, act in the world, and pursue goals. While current AI systems have limited autonomy, work is underway to change this [14]. For example, the non-autonomous GPT-4 model was quickly adapted to browse the web [15], design and execute chemistry experiments [16], and utilize software tools [17], including other AI models [18].

If we build highly advanced autonomous AI, we risk creating systems that pursue undesirable goals. Malicious actors could deliberately embed harmful objectives. Moreover, no one currently knows how to reliably align AI behavior with complex values. Even well-meaning developers may inadvertently build AI systems that pursue unintended goals-especially if, in a bid to win the AI race, they neglect expensive safety testing and human oversight.

Once autonomous AI systems pursue undesirable goals, embedded by malicious actors or by accident, we may be unable to keep them in check. Control of software is an old and unsolved problem: computer worms have long been able to proliferate and avoid detection [19]. However, AI is making progress in critical domains such as hacking, social manipulation, deception, and strategic planning [14,20]. Advanced autonomous AI systems will pose unprecedented control challenges.

To advance undesirable goals, future autonomous AI systems could use undesirable strategies-learned from humans or developed independently-as a means to an end [21-24]. AI systems could gain human trust, acquire financial resources, influence key decision-makers, and form coalitions with human actors and other AI systems. To avoid human intervention [24], they could copy their algorithms across global server networks like computer worms. AI assistants are already co-writing a large share of computer code worldwide [25]; future AI systems could insert and then exploit security vulnerabilities to control the computer systems behind our communication, media, banking, supply-chains, militaries, and governments. In open conflict, AI systems could threaten with or use autonomous or biological weapons. AI having access to such technology would merely continue existing trends to automate military activity, biological research, and AI development itself. If AI systems pursued such strategies with sufficient skill, it would be difficult for humans to intervene.

Finally, AI systems may not need to plot for influence if it is freely handed over. As autonomous AI systems increasingly become faster and more cost-effective than human workers, a dilemma emerges. Companies, governments, and militaries might be forced to deploy AI systems widely and cut back on expensive human verification of AI decisions, or risk being outcompeted [26,27]. As a result, autonomous AI systems could increasingly assume critical societal roles.

Without sufficient caution, we may irreversibly lose control of autonomous AI systems, rendering human intervention ineffective. Large-scale cybercrime, social manipulation, and other highlighted harms could then escalate rapidly. This unchecked AI advancement could culminate in a large-scale loss of life and the biosphere, and the marginalization or even extinction of humanity.

Harms such as misinformation and discrimination from algorithms are already evident today [28]; other harms show signs of emerging [20]. It is vital to both address ongoing harms and anticipate emerging risks. This is not a question of either/or. Present and emerging risks often share similar mechanisms, patterns, and solutions [29]; investing in governance frameworks and AI safety will bear fruit on multiple fronts[30].

A path forward

If advanced autonomous AI systems were developed today, we would not know how to make them safe, nor how to properly test their safety. Even if we did, governments would lack the institutions to prevent misuse and uphold safe practices. That does not, however, mean there is no viable path forward. To ensure a positive outcome, we can and must pursue research breakthroughs in AI safety and ethics and promptly establish effective government oversight.

Reorienting technical R&D

We need research breakthroughs to solve some of today's technical challenges in creating AI with safe and ethical objectives. Some of these challenges are unlikely to be solved by simply making AI systems more capable [22,31-35]. These include:

Oversight and honesty: More capable AI systems are better able to exploit weaknesses in oversight and testing [32,36,37]-for example, by producing false but compelling output[35,38].

Robustness: AI systems behave unpredictably in new situations (under distribution shift or adversarial inputs) [39-41].

Interpretability: AI decision-making is opaque. So far, we can only test large models via trial and error. We need to learn to understand their inner workings [42].

Risk evaluations: Frontier AI systems develop unforeseen capabilities only discovered during training or even well after deployment [43]. Better evaluation is needed to detect hazardous capabilities earlier[44,45].

Addressing emerging challenges: More capable future AI systems may exhibit failure modes we have so far seen only in theoretical models. AI systems might, for example, learn to feign obedience or exploit weaknesses in our safety objectives and shutdown mechanisms to advance a particular goal [24,41].

Given the stakes, we call on major tech companies and public funders to allocate at least one-third of their AI R&D budget to ensuring safety and ethical use, comparable to their funding for AI capabilities. Addressing these problems [34], with an eye toward powerful future systems, must become central to our field.

Urgent governance measures

We urgently need national institutions and international governance to enforce standards in order to prevent recklessness and misuse. Many areas of technology, from pharmaceuticals to financial systems and nuclear energy, show that society both requires and effectively uses governance to reduce risks. However, no comparable governance frameworks are currently in place for AI. Without them, companies and countries may seek a competitive edge by pushing AI capabilities to new heights while cutting corners on safety, or by delegating key societal roles to AI systems with little human oversight [26]. Like manufacturers releasing waste into rivers to cut costs, they may be tempted to reap the rewards of AI development while leaving society to deal with the consequences.

To keep up with rapid progress and avoid inflexible laws, national institutions need strong technical expertise and the authority to act swiftly. To address international race dynamics, they need the affordance to facilitate international agreements and partnerships [46,47]. To protect low-risk use and academic research, they should avoid undue bureaucratic hurdles for small and predictable AI models. The most pressing scrutiny should be on AI systems at the frontier: a small number of most powerful AI systems - trained on billion-dollar supercomputers - which will have the most hazardous and unpredictable capabilities [48,49].

To enable effective regulation, governments urgently need comprehensive insight into AI development. Regulators should require model registration, whistleblower protections, incident reporting, and monitoring of model development and supercomputer usage [48,50-55]. Regulators also need access to advanced AI systems before deployment to evaluate them for dangerous capabilities such as autonomous self-replication, breaking into computer systems, or making pandemic pathogens widely accessible [44,56,57].

For AI systems with hazardous capabilities, we need a combination of governance mechanisms [48,52,58] matched to the magnitude of their risks. Regulators should create national and international safety standards that depend on model capabilities. They should also hold frontier AI developers and owners legally accountable for harms from their models that can be reasonably foreseen and prevented. These measures can prevent harm and create much-needed incentives to invest in safety. Further measures are needed for exceptionally capable future AI systems, such as models that could circumvent human control. Governments must be prepared to license their development, pause development in response to worrying capabilities, mandate access controls, and require information security measures robust to state-level hackers, until adequate protections are ready.
To bridge the time until regulations are in place, major AI companies should promptly lay out if-then commitments: specific safety measures they will take if specific red-line capabilities are found in their AI systems. These commitments should be detailed and independently scrutinized.
AI may be the technology that shapes this century. While AI capabilities are advancing rapidly, progress in safety and governance is lagging behind. To steer AI toward positive outcomes and away from catastrophe, we need to reorient. There is a responsible path, if we have the wisdom to take it.

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Managing AI Risks in an Era of Rapid Progress

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Abstract In this short consensus paper, we outline risks from upcoming, advanced AI systems. We examine large-scale social harms and malicious uses, as well as an irreversible loss of human control over autonomous AI systems. In light of rapid and continuing AI progress, we propose urgent priorities for AI R&D and governance.

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MIT News | Massachusetts Institute of Technology

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Amid a huge amount of hype around generative AI, a new study from researchers at MIT sheds light on the technology's impact on work, finding that it increased productivity for workers assigned tasks like writing cover letters, delicate emails, and cost-benefit analyses.

The tasks in the study weren't quite replicas of real work: They didn't require precise factual accuracy or context about things like a company's goals or a customer's preferences. Still, a number of the study's participants said the assignments were similar to things they'd written in their real jobs - and the benefits were substantial. Access to the assistive chatbot ChatGPT decreased the time it took workers to complete the tasks by 40 percent, and output quality, as measured by independent evaluators, rose by 18 percent.

The researchers hope the study, which appears today in open-access form in the journal Science, helps people understand the impact that AI tools like ChatGPT can have on the workforce.

" What we can say for sure is generative AI is going to have a big effect on white collar work," says Shakked Noy, a PhD student in MIT's Department of Economics, who co-authored the paper with fellow PhD student Whitney Zhang '21. "I think what our study shows is that this kind of technology has important applications in white collar work. It's a useful technology. But it's still too early to tell if it will be good or bad, or how exactly it's going to cause society to adjust."
Simulating work for chatbots
For centuries, people have worried that new technological advancements would lead to mass automation and job loss. But new technologies also create new jobs, and when they increase worker productivity, they can have a net positive effect on the economy.

"Productivity is front of mind for economists when thinking of new technological developments," Noy says. "The classical view in economics is that the most important thing that technological advancement does is raise productivity, in the sense of letting us produce economic output more efficiently."

To study generative AI's effect on worker productivity, the researchers gave 453 college-educated marketers, grant writers, consultants, data analysts, human resource professionals, and managers two writing tasks specific to their occupation. The 20- to 30-minute tasks included writing cover letters for grant applications, emails about organizational restructuring, and plans for analyses helping a company decide which customers to send push notifications to based on given customer data. Experienced professionals in the same occupations as each participant evaluated each submission as if they were encountering it in a work setting. Evaluators did not know which submissions were created with the help of ChatGPT.

Half of participants were given access to the chatbot ChatGPT-3.5, developed by the company OpenAI, for the second assignment. Those users finished tasks 11 minutes faster than the control group, while their average quality evaluations increased by 18 percent.

The data also showed that performance inequality between workers decreased, meaning workers who received a lower grade in the first task benefitted more from using ChatGPT for the second task.

The researchers say the tasks were broadly representative of assignments such professionals see in their real jobs, but they noted a number of limitations. Because they were using anonymous participants, the researchers couldn't require contextual knowledge about a specific company or customer. They also had to give explicit instructions for each assignment, whereas real-world tasks may be more open-ended. Additionally, the researchers didn't think it was feasible to hire fact-checkers to evaluate the accuracy of the outputs. Accuracy is a major problem for today's generative AI technologies.

The researchers said those limitations could lessen ChatGPT's productivity-boosting potential in the real world. Still, they believe the results show the technology's promise - an idea supported by another of the study's findings: Workers exposed to ChatGPT during the experiment were twice as likely to report using it in their real job two weeks after the experiment.

"The experiment demonstrates that it does bring significant speed benefits, even if those speed benefits are lesser in the real world because you need to spend time fact-checking and writing the prompts," Noy says.
Taking the macro view
The study offered a close-up look at the impact that tools like ChatGPT can have on certain writing tasks. But extrapolating that impact out to understand generative AI's effect on the economy is more difficult. That's what the researchers hope to work on next.

"There are so many other factors that are going to affect wages, employment, and shifts across sectors that would require pieces of evidence that aren't in our paper," Zhang says. "But the magnitude of time saved and quality increases are very large in our paper, so it does seem like this is pretty revolutionary, at least for certain types of work."

Both researchers agree that, even if it's accepted that ChatGPT will increase many workers' productivity, much work remains to be done to figure out how society should respond to generative AI's proliferation.

"The policy needed to adjust to these technologies can be very different depending on what future research finds," Zhang says. "If we think this will boost wages for lower-paid workers, that's a very different implication than if it's going to increase wage inequality by boosting the wages of already high earners. I think there's a lot of downstream economic and political effects that are important to pin down."

The study was supported by an Emergent Ventures grant, the Mercatus Center, George Mason University, a George and Obie Shultz Fund grant, the MIT Department of Economics, and a National Science Foundation Graduate Research Fellowship Grant.

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Study finds ChatGPT boosts worker productivity for some writing tasks

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A new MIT study highlights the potential of generative AI for certain types of writing assignments in the workplace.

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Alexandre Lepage Université de Montréal Normand Roy Université de Montréal

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Une recension des écrits de 1970 à 2022 sur les rôles de l’enseignant et de l’intelligence artificielle dans le domaine de l’IA en éducation | Médiations et médiatisations

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Cet article présente une recension des écrits sur la façon dont les rôles de l’enseignant et de l’intelligence artificielle (IA) sont abordés dans le domaine de l’intelligence artificielle en éducation (IAED) depuis 1970. Quarante-sept documents, théoriques pour la plupart, ont été analysés à partir…

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International Journal for Educational Integrity 19, Article number: 21 (2023) Cite this article

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Abstract

Discussions around assessment integrity often focus on the exam conditions and the motivations and values of those who cheated in comparison with those who did not. We argue that discourse needs to move away from a binary representation of cheating. Instead, we propose that the conversation may be more productive and more impactful by focusing on those who do not cheat, but who are tempted to do so. We conceptualise this group as being at risk of future cheating behaviour and potentially more receptive of targeted strategies to support their integrity decisions. In this paper we report on a large-scale survey of university students ( n = 7,511) who had just completed one or more end of semester online exams. In doing so we explore students' reported temptation to cheat. Analysis surrounding this "at risk" group reveals students who were Tempted ( n = 1379) had significant differences from those who Cheated ( n = 216) as well as those who were Not tempted ( n  = 5916). We focus on four research questions exploring whether there are specific online exam conditions, security settings, student attitudes or perceptions which are more strongly associated with the temptation to cheat. The paper offers insights to help institutions to minimise factors that might lead to breaches of assessment integrity, by focusing on the temptation to cheat during assessment.

Introduction

Research on exam cheating has typically focused on the characteristics of those who cheated and the factors that influenced them, including the conditions and types of examinations (Jenkins et al. 2022; Noorbehbahani et al. 2022). Some studies go further and compare the differences between those who uphold integrity standards and those who breach them, such as when experiencing proctoring (Gudiño Paredes et al. 2021). Although these data are useful in presenting an overall picture of the context of examination misconduct in the university sector, similar to research on other types of academic misconduct, they tend to create a binary image of cheating: the cheaters and the non-cheaters. Through the bifurcation of dis/honest conduct, a whole sub-group of students are left out of research and discussion: those who are tempted to cheat, but have not engaged in that behaviour.

Most existing research investigating academic integrity and misconduct in higher education has provided explanation and context for why students do or not do engage in dishonest practices assessment practices. While these studies are able to provide readers with an in-depth understanding of reasons for or against integrity, for those students sitting in a third group - the tempted - understanding and discussion of their situations are scant. Unless educators understand the factors associated with temptation, they, and their institutions, may unwittingly create situations where the tempted become the cheaters. In addition, understanding why someone was tempted, but nevertheless did not cheat, may reveal particularly useful strategies in supporting students to avoid breaching exam regulations intended to ensure integrity.

Therefore, the project of which this paper forms a part, set out to better understand those who were tempted, and the contextual, intrapersonal and interpersonal dilemmas that potentially lead to a tipping point of behaviour. This paper marks the beginning of this journey, describing the correlations between temptation and four key areas that are generally assumed to reduce or impact cheating behaviour: online security systems, exam conditions (such as being open- or closed-book, location of exam, duration, and exam window), prior exam experiences (knowledge or perception of peer cheating), and student attitudes towards integrity.

Cheating and the temptation to cheat

Many studies have provided evidence and reasoning for why students do or do not engage in academic cheating. Studies considering plagiarism, contract cheating, outsourcing or cheating more broadly have found some commonalities in the reasons why students do not complete their university assignments with integrity. Researchers globally find some agreement in the situational or personal reasons that their students report causes them to engage in misconduct, and include: Poor time management and procrastination (Siaputra 2013; Wallace & Newton 2014); student perception of staff apathy or lack of institutional emphasis on academic integrity (Husain et al. 2017); perceived seriousness of cheating/integrity (Curtis and Popal 2011); dissatisfaction with the learning environment (Bretag et al., 2018, Moss et al. 2018); and pressures placed on students from university or other sources (Brimble, 2016). Various studies have also explored the relationship between student characteristics and cheating. This includes student attitudes towards integrity (Dyer et al. 2020; Tremayne & Curtis, 2021); their knowledge of integrity rules and consequences (East 2016; Morris 2016); low confidence and resilience (Moss et al. 2018); a competitive mindset (Barbaranelli et al. 2018); low self-control (Tremayne and Curtis 2021); perceived opportunities to cheat (Baird and Clare, 2017); as well as their awareness of peers cheating (Awdry and Ives 2021). Examination cheating, whilst being influenced by these factors, also has its own body of work due to the specific condition associated with undertaking academic examinations.

Cheating in exams has been a persistent and seemingly prevalent problem in higher education. Prior to online exams, cheating in paper-based exams has been reported at various rates, such as 51.8% (Genereux and McLeod 1995), and 24% (Chapman et al. 2004). More recent research into online exam cheating has reported higher rates of 62% (Dyer et al. 2020), 58.4% (Jenkins et al. 2022), and 70% (Pleasants et al. 2022). While this affirms the need for online exams to be given particular attention, there are a variety of contributing, and possibly confounding, factors that need to also be considered.

Studies on examination cheating have associated cheating variance with the conditions of the exams themselves, such as the number of exams students are required to sit, exam windows, and context or weighting of the exam (Bilen and Matros 2021; Hylton et al., 2016). Open- or closed-book exams have also been found to have an impact on the student experience in preparing for exams, and in rates of cheating (Green et al. 2016; Ng 2020). Other studies have concluded that the type of exam and exam security can impact cheating rates (Gudiño Paredes et al. 2021; Harper et al. 2021). Interestingly, some have argued that the existence of exam security systems, such as proctoring, inherently creates an environment of distrust which in turn can encourage some of the behaviours that the security seeks to avoid (Lee and Fanguy 2022; Smith et al. 2016). Such arguments highlight the interplay between exam conditions and student perceptions and attitudes.

Both groups of studies, those focusing on exam conditions and those on situational or personal variables, have sought to understand the association between the factor in question and actual cheating behaviour. In contrast, this study proposes that it may be valuable to understand when and why students are tempted but choose not to cheat. In doing so we may gain better insights regarding the "tipping point": what determines a choice made either to cheat or not to cheat. We investigate the proposition that temptation is, in and of itself, a useful explanatory condition that can assist in understanding the behaviours associated with academic integrity.

Although we could find relatively few previous studies with a similar focus, most being more than twenty years old and unrelated to online examinations, one existing strand of research has focused on intrapersonal and interpersonal factors that impact on student's perceived need to succeed which can in turn lead to cheating. These factors include fear of failure, as well as the perceived desirability or need to succeed in the context of social pressures such as peer groups and family, a parallel with research from studies focussed on cheating. For example, Jacobson et al. ( 1970), in a controlled experiment, found that participants with a high requirement for social approval were more often tempted to cheat when facing failure in tasks, than those with lower social-desirability scores. In addition, participants with high scores on self-satisfaction (achieved through meeting their own expectations of success and ideal performance levels), combined with high need for social approval, were more likely to cheat when faced with the possibility of failure or not meeting social expectations (Jacobson et al. 1970). The implications of risk of failure were also explored in an experimental study by Houston ( 1978), who found students more likely to be tempted to cheat, or engage in cheating, on a test when they were faced with a high chance of failure in a scenario which would reward success. In particular, Houston indicated that students who were uncertain of success were more likely to be tempted to cheat, when compared with those who were more certain of their outcome-regardless of whether it was good or not.

The power of external influences impacting a person's behaviour has also been a focus for criminological research in non-academic contexts which argues that people may well have the propensity to cheat, and are tempted to do so when the right opportunities present, but are prevented from doing so through various social and internalised personal controls (see Gottfredson and Hirschi 1990). These social and internalised controls were a focus for an experimental study by Gino et al. ( 2011) which was designed to test the effect of self-control on temptation and actual cheating (through a control experiment with monetary incentives, low deterrence, and high opportunities to cheat); and found that those who had had their self-control reserves depleted, were more likely to cheat. In particular, they identified that self-control resource depletion impaired participants' ability to recognise moral issues and thus more likely to engage in unethical behaviour. Moreover, moral identity was found to moderate the effect of self-regulatory resource depletion. Gino et al. ( 2011) went on to identify two key factors that were found to deplete self-regulatory resource. First, resisting temptation was found to deplete self-regulatory resources; this finding, if applied to an academic context, would suggest that students required to sit multiple exams in close time proximity are less likely to be able to resist temptation. Second, depletion was also associated with sleep deprivation (Gino et al. 2011), something which can be aligned to the stresses placed on students during key exam times when they are likely to be tired, have extra pressure placed on them and may have reduced self-control, resulting in difficult to resist dishonest behaviour (see also Chen et al. 2014; Hodgkinson et al. 2016).

As noted already, there are few studies which deal with the condition of temptation in relation to academic integrity. When considering the broader field of temptation in general, as well as academic integrity studies, there is considerable diversity in explanations. This is not only in terms of the factors involved but also in what appear to be fundamental assumptions about the nature of cheating. Some studies frame individuals as having a propensity to cheat, which is manifested given the 'right' conditions, other studies frame individuals as reticent participants, that is, only likely to cheat if conditions create a perceived or real situation of sufficient duress. The practical implications of these fundamentally different framings are that practitioners are faced with calls to inhibit cheating propensity (e.g. reducing opportunity through proctoring), whilst at the same time hearing calls to support integrity decisions (e.g. via integrity training).

In our project we have drawn on a broad socio ecological perspective in which we assume complex interaction between setting, intrapersonal, interpersonal, developmental and wider social, organisational and cultural factors. For the purposes of this paper we adopt the idea that cheating, and not cheating, are contextually specific behavioural decisions. While for some students the decision to cheat/not-cheat may be subconscious or routine, for others it may be more complex. In this paper we set out to explore this idea by identifying those students who were tempted to cheat, but who chose not to. Through understanding these students in relation to others, we may be better placed to understand the tipping point of cheating and not cheating.

We focus on four research questions for this paper as a way to begin exploring the influence of setting and social/organisational factors on the temptation to cheat:

Is there a relationship between temptation to cheat and:

This work is exploratory in nature. We seek to understand when temptation (but not cheating) occurs, and if that pattern is similar to those who were not tempted, or those who cheated. Understanding these similarities and differences may shed light on future academic integrity interventions and practices, as well as helping to guide future research.

Methods

This cross-sectional study was conducted at a large Australian university in which all students who sat a formal end-of-semester exam during Semester 1, 2021 were invited to complete a survey. The survey was open for 3.5 weeks during the exam period. There were 85,332 exam sittings by 39,308 students, and the findings in this paper are based on 7,511 fully completed anonymous surveys. The survey took 25 min to complete and explored student experience and integrity in various exam conditions, proctoring and exam security systems. Further details on the survey, including its creation is reported in (citation removed for blind review). In addition to gathering information on students positive and negative exam experiences, the survey asked students to self-report cheating behaviour in their recent exam, or - if they did not cheat - their degree of temptation to cheat.

Of the whole sample ( N = 7,511), 2.9% ( n = 216) self-reported cheating in the exam, and the remaining 97.1% ( n = 7,295) reported they did not break the exam rules. Of the students who did not break the exam rules, 81.1% ( n = 5,916) said that they were not at all tempted to cheat, whilst 18.9% ( n = 1,379) reported they were tempted. Of the students who were tempted, 69.9% ( n = 964) reported they were slightly tempted, 22.4% ( n = 309) were moderately tempted, 4.8% ( n = 66) were very tempted and 2.9% ( n = 40) were extremely tempted.

This paper presents key descriptive data and summaries of statistical analysis in relation to temptation and the four key areas of interest: exam conditions, security, attitude towards integrity, and knowledge of others' cheating behaviours. In some cases, we have provided a summary of the statistical analysis rather than the full tables with the intention of maintaining a focus on key findings; full analysis is available upon request. For the same reason we have not provided analysis of overall demographics and general trends since these can be found in our earlier publication which focused on cheating behaviours (citation removed for blind review).

Results

Exam conditions

Table 1 presents the raw frequencies of the sample that were Not tempted, Tempted, and Cheated for each exam condition, as well as the Chi-Square tests for each exam condition. Chi-Square test of contingencies analyses compared each exam condition variable to the cheating temptation variable. The findings revealed that there were statistically different levels of temptation and cheating behaviour across each of the exam conditions, with the exam window analysis representing a medium effect size (the largest effect size of all five analyses), and the remaining four analyses represented small effect sizes.

Post-hoc comparisons of column proportions were conducted between pairs of groups within an exam condition and a Bonferroni correction was applied for all pairwise comparisons. In relation to temptation, post-hoc comparisons revealed that students were significantly more tempted to cheat if they had a set exam time (i.e. no window). They were also more tempted if they had completed two or three exams, and if the exam was closed-book. Students who sat the shortest exam duration were significantly more likely to be tempted to cheat than students who sat all other exam durations. Conversely, students who sat the longest exam duration were also more tempted to cheat than students who sat exams in the middle duration. No statistical differences were found between temptation and exam location (i.e. on campus or off-campus / home).

In relation to cheating, students were more likely to cheat if they had completed four exams, compared to if they had completed one or two exams (at the time of surveying). They were also more likely to cheat if they completed their exam remotely somewhere other than home, as well as if their exam had a 12- or 24-hour window.

Exam security

Table 2 reports on the relationship between exam security type and the three groups: Not tempted, Tempted, and those who Cheated. A Chi-Square test of contingencies revealed that exam security and cheating temptation are related as the analysis was statistically significant, although the association was small. Post-hoc comparisons of pairs revealed that students who experienced no online supervision with Safe Exam Browser were more likely to be tempted to cheat than students sitting exams in all other types of exam security. Furthermore, students who sat an exam that included the highest level of online supervision with assisted check-in were significantly less tempted than all other students.

In relation to cheating, there was a significantly greater proportion of students who cheated in the Safe Exam Browser group compared to students who experienced online supervision, either with assisted check-in or with self-check in. There were no statistical differences found in rates of cheating between the remaining three security types.

Integrity attitudes and awareness of consequences

Table 3 presents the mean values for attitudes towards academic integrity and awareness of consequences for each of the Not tempted, Tempted and Cheated groups. One-Way ANOVAs comparing the mean differences for all three items demonstrate that there were statistically significant differences between the ratings of the three groups, with small effect sizes. As the homogeneity of variance tests were violated, Welch's F are reported.

Post-hoc comparisons confirmed a predictable relationship between temptation and the perceived importance of integrity as well as knowledge of consequences of cheating as explained by the university. Students who were in the Not-tempted group reported the greatest positive attitudes towards integrity of the three groups. The Tempted group was less positive than the Not-tempted group, but more positive than those who Cheated. This relationship is logical and affirms that the Tempted students are a distinct group sitting between the other two. However, it is also useful to note that even though the Cheated group was the least positive when compared with the other two groups, the mean values are still high (for example, 4.15 on a 5-point scale).

In relation to the importance of exams being supervised, the only significant difference found was between the Not tempted and Tempted group, where the Not tempted group were more favourable towards exams being supervised than the Tempted group. However, it is useful to note that despite being the most favourable group, the Not Tempted students' attitudes towards exam supervision were only average in strength (mean of 3.70).

Cheating perceptions

Four items measured students' extent of knowledge regarding whether other students were cheating and getting caught for cheating, both within their course/degree and at university outside their course. We conceptualised this as perceptual proximity to cheating behaviours. Table 4 presents the raw frequencies of students' proximity to cheating behaviours.

To statistically compare if one group reported closer perceptual proximity than another group, the data were treated as continuous ( 1 = distant relationship to cheating/getting caught, 4 = close relationship to cheating/getting caught). All four Welch's F were statistically significant for all four items ( p  < .001) in relation to cheating and getting caught for cheating, both in relation to course/degree and outside of course, represented by small effect sizes.

Post-hoc comparisons (for all four items) revealed that the proximity to other students' cheating and being caught for cheating was statistically significantly different between the three groups. Students who didn't cheat and were not tempted were least likely (of the three groups) to know other students who cheated and if they had been caught. In comparison, students who Cheated were most likely (of the three groups) to have seen others cheat as well as know of other students who had been caught. Finally, Tempted students' proximity to cheating and consequences was in the middle between the Not tempted and Cheated students. While the statistical differences are noteworthy, it is useful to consider that the raw frequencies of those who had close proximity to cheating are relatively low (e.g. only 11.1% of those who cheated had seen others cheat in their course).

Discussion

In response to our first research question, 'Is there a relationship between exam conditions and temptation to cheat?', we found that restrictive exam conditions were related to increased temptation to cheat. Of highest statistical significance was the flexibility of the exam window in which students had to complete their exams (i.e., exams that can be sat at any point in a broad window, versus those that must be started at a specified time). Those students who had a set time for their exam (i.e., no window) were the most likely to report being tempted to cheat, however the same condition had the least reported actual cheating (with those reporting cheating more likely to have completed exams in the flexible 12 or 24 h exam window). This suggests that institutions should preference specified exam times; however, they need to be cautious of the significant proportion of students who are tempted to cheat under these conditions. Further research is needed to explore why they were tempted. Sattler et al. ( 2013) note that increased opportunity is correlated with increased frequency of plagiarism, however this does not explain why there was a greater tendency for students to be tempted to cheat in more restrictive conditions. While it is reasonable to assume that restrictions on exam scheduling can lead to general stress, it may be valuable to discover what factors are interacting with the condition to make it a significant stressor. For example, such restrictions may simply trigger students' sense of unpreparedness and result in impulsivity or re-evaluation of the cost-benefit analysis of cheating. Research in relation to plagiarism found that procrastination may lead to impulsivity (Siaputra 2013) and that students were influenced by the perceived utility of cheating, that is, their personal sensitivity of the costs and benefits of cheating. Alternatively, it may be related to a specific and actionable concern such as perceived poor exam design (e.g. too many questions), rather than the exam conditions.

Another restrictive exam condition was the length of the exam. In our previous study (citation removed for blind review) using the same participant data and in which we compared only two groups, those who cheated and those who did not cheat, we found there was no statistically significant difference in the cheating rates according to the length of exam. This was also found by Pleasants et al. ( 2022) who analysed detected cheating rates on exam conditions and found that time limits in exams had no effect on cheating behaviours. Based on these finding it would be reasonable for institutions and educators to not consider the length of exam in their exam security designs. However, when we shift from a binary analysis of cheating/not-cheating to include temptation the data provides a somewhat more cautionary finding.

While cheating rates were not found to be correlated with length of exam, we did find that students were more likely to be tempted to cheat in exams with the shortest duration (1 h 40 min). However, the second most likely group to be tempted were those who had the longest exam duration (greater than 2 h and 20 min). These results seem somewhat contradictory, with restrictive time limits relating to increased temptation while permissive time conditions were also related to increased temptation, albeit at a less significant level. In trying to understand this finding, it may be useful to consider that our data does not reveal the form of cheating that students were tempted to engage in. The differences in the length of exam may influence how students perceive the opportunity to cheat, and tempt them to cheat in different ways. This potential explanation is supported by Ng ( 2020), who observed that the type of cheating was impacted by the time limits set on the exam, with more collusion and outsourcing being detected in students sitting open or long exam windows. In contrast, more plagiarism was found in the restricted and short-time periods, which Ng suggests is due to students perhaps not having enough time to cheat in other ways than quick copy and paste.

Our findings suggest that while rates of cheating may be helpful when making decisions around institutional strategy, the rate of temptation can provide further consideration regarding the degree of risk, and commensurate institutional attention to policy and support structures to ensure temptation does not tip over to cheating.

Our data also supported our second research question, Is there a relationship between exam security and reported temptation? Those students with no direct supervision, but who were monitored through a Safe-Exam Browser were the most likely to be tempted to cheat. The same condition also had the highest frequency of cheating. Based on this analysis it is reasonable to conclude that the use of Safe Exam Browser by itself (i.e. without overt proctoring or other security measures) needs to be treated with caution, if not avoided entirely.

However, our findings are not so clear with regards to other security conditions. There were no statistical differences in the frequency of cheating between the remaining three security conditions (online supervision with/without assisted check-in, no online supervision). This means, excluding Safe Exam Browser and based on cheating frequencies, there is no clear support for choosing between proctored or non-proctored online supervision. This finding is somewhat surprising given that literature has often reported the security benefits of proctoring tools in online exams - whether it is in reduced cheating rates (Gudiño Paredes et al. 2021) or the actual or perceived opportunity to cheat (Hylton et al., 2016).

In contrast with our findings relating to cheating frequencies, we found the temptation data to be more revealing. We found that students who experienced online supervision (proctoring) with assisted check-in were least tempted compared to students in all other conditions (online supervision with non-assisted check-in, Safe Exam Browser, and no supervision at all). This suggests that temptation may be particularly inhibited not simply because students were aware of proctoring (e.g. being recorded through their web-cam), but that they had some audio-visual interaction with an exam staff member at the beginning of the exam through the assisted check-in process. A similar phenomenon was observed by Kerkvliet and Sigmund ( 1999) in their study, in which additional social presence in the form of additional verbal warnings or the additional presence of a proctor resulted in reduced cheating. It could be argued that the assisted check in process - requiring students to actively interact and respond to a human proctor, including showing their Student ID, confirming their name and undergoing confirmation that webcamera and screen recordings are operating - raised the awareness of surveillance and perceived risk. Alternatively, the increased social interaction may have had other impacts, such as affirmation of expectations of integrity. This may be connected with the findings of Gino et al. ( 2011) in which situational influences such as monitoring was thought to trigger an individual's self-motives to become more salient. The temptation data in this study provides greater confidence for institutions that proctoring environments may help support integrity behaviours, but it also highlights that proctoring in-of-itself may not be the key variable in reducing temptation and thereby risk of cheating.

The third research question was also supported: Is there a relationship between a student's attitude towards integrity and their temptation to cheat? The importance placed on integrity was positively associated with integrity behaviour. In other words, students who were not tempted to cheat placed the most importance on integrity, and those who were tempted placed less importance on integrity. Following this pattern, those who cheated rated integrity the least important in comparison with the other two groups. This aligns with other research findings in which students who rated ethics more highly were significantly less likely to cheat (Pate 2018). A similar effect was noticed by Gino et al. ( 2011) who identified strong moral identity as a moderating influence on temptation to cheat. Even though the findings in our study are not new, they do lend weight to the idea that perceived importance of integrity could be a reliable comparative point when conducting multivariate predictive analysis in relation to student cheating, and temptation to cheat. However, some caution needs to be taken because while there were statistical differences between the three groups, overall, our sample reported very favourable attitudes towards the importance of academic integrity. The group with the lowest ratings of academic integrity importance (Cheating group) still reported an average of 4.15 (5-point Likert scale with 5 being strongly agree that academic integrity in exams is important). This is a useful reminder that while we might be able to use attitudes towards integrity as a possible indicator, we can only do so as a comparative measure, the individual raw scores can be deceiving.

A similar trend to integrity attitudes was observed in relation to students' awareness of rules and consequences of cheating. The Non-tempted group were more likely to report that their university had explained the rules and consequences than the Tempted group, followed by the Cheated group. Obviously these data are self-reported and some caution needs to be taken in relation to whether their awareness is related to selectiveness in noticing, or actual institutional attempts at educating them regarding rules and consequences. Nevertheless, it does affirm the potential utility of institutions engaging in integrity training, including explaining rules and consequences. Research by Husain et al. ( 2017) in a review of plagiarism studies, found that when students cited lack of understanding of conventions, definitions of misconduct and university rules this related to their reported attitudes towards plagiarism and integrity, subsequently resulting in increased engagement in academic misconduct. Certainly, earlier research has found that knowledge of (or fear of) outcomes can create a deterrent effect on cheating (Haines et al. 1986; Rettinger and Kramer 2009). In contrast, attitude towards integrity has been found to be more difficult to influence, although some argue that this can be approached through ethical teaching and integrity modules (East 2016; Hughes and Gallant 2016), or through institution-wide honour codes (Christensen Hughes and McCabe 2006).

The complexity around attitudes is highlighted in relation to the item asking students about how important it was that exams are supervised (i.e. proctored). Interestingly there was no statistical difference between the Tempted and Cheated groups. The situation however is made more complex by the fact that the only statistical difference was found to be between the Not tempted and Tempted groups. One interpretation is that students who were not tempted valued supervision more highly as a way to protect the integrity and fairness of their assessment. In contrast the Tempted group did not value supervision because such supervision was in conflict with their inclination to cheat. However, while this interpretation is plausible, it may equally be the case that the tempted students did not value supervision because they had considered cheating but ultimately chose not to cheat, and in that process the presence of supervision was not felt to be important. Any speculation should be treated with caution, but it does reveal a potentially valuable area for future research.

Finally, our fourth research question was also supported: Is there a relationship between a student's knowledge of others' cheating behaviours and their temptation to cheat? Students who were Tempted or Cheated were more likely to have reported being aware of other students in their course/university cheating than the Not tempted group. This finding supports prior research which found a positive correlation between known or perceived cheating of others and self-reported cheating rates (Awdry and Ives 2021; Rettinger and Kramer 2009), as well as development of inappropriate norms and attitudes due to the observation of peers engagement in cheating behaviours (Moss et al. 2018). However, it is interesting to note that over a third of the students who cheated had not seen or heard about others cheating, and a further quarter had only heard rumours of others cheating. An even stronger pattern is revealed when looking at the temptation data - with three quarters of the Tempted students not having heard about others cheating or only having heard about rumours of cheating. While knowledge of others cheating may be associated to an extent with temptation and cheating behaviours, it does not account for the majority.

Another noteworthy point is that our statistical analysis indicated those who were Tempted and those who Cheated were not only more likely to be aware of others cheating but also know of others being caught. Almost 35% of the students who cheated knew or had heard reliable stories of students in their own course being caught. While being aware of consequences may be linked to reduced cheating, our data indicates that knowledge of other students being caught is not a deterrent for many. This is in contrast to other research which has found that risk of being caught creates a deterrent impact on the temptation to cheat in exams (Haines et al. 1986; Smith et al. 2021), and that clearly articulated penalties for cheating can have a reduction on cheating rates (Pleasants et al. 2022). In our own study we do not know how the students perceived the consequences, perhaps seeing them as negligible. Indeed, Moss et al. ( 2018) and Sattler et al. ( 2013) note that in the case of plagiarism, the penalties need to be perceived as sufficiently harsh in order to be a deterrent. Nevertheless, it is also worth considering the raw frequencies which reveal 80% of those who were in the Tempted group, and 65% of those in the Cheated group, had never heard about other students getting caught, or had only heard rumours of others getting caught. An implication for future research may be to better understand how and when the knowledge of others being caught impacts on integrity behaviour. With this in mind it may be useful to revisit practices around reporting integrity breaches. A similar conclusion was made by Sattler et al. ( 2013) in relation to the public dissemination of plagiarism. For a variety of reasons, including the laudable preservation of student privacy, higher education institutions worldwide do not typically report on the frequency, type and outcomes of confirmed cheating cases. However, it is worth exploring how institutional information about confirmed cheating cases and their consequences may be communicated to students and if this has any impact on reducing temptation and cheating.

Limitations

Like most work in this area, the data is self-reported and as a result needs to be treated with caution. The anonymity, immediacy of the survey after the exam, and voluntary nature of the survey strengthen trustworthiness of the responses. However, they also act as limitations - particularly with a risk of self-selection bias. For example, it is logical to assume that students who broke academic integrity rules would be less likely to complete a survey that asks about their exam experience, including integrity behaviours. While not discounting this limitation it is also worth noting that the goal of this paper was not to find the rate of cheating, but instead compare the differences in conditions and attitudes of those who cheated, were tempted, and not tempted.

A further consideration in the analysis of this data is that the exams and survey were conducted during a time of residential lock-down because of the COVID-19 pandemic. This meant that only a small number of students sat the exams on-campus for a variety of equity-related reasons. In addition, even though digital exams had been run at this institution for some time, for many students this was their first experience of sitting the online exam in a remote (e.g. home) location with exam security measures such as online proctoring.

Concluding remarks

This is an exploratory study based on the simple idea that to understand the influencing factors in cheating behaviours it may be valuable to go beyond binary descriptors of cheating and not-cheating. Our data has identified a large group of students who did not cheat but who report having been tempted to do so. We are cautious of applying deficit models of thinking in relation to this group since they ultimately chose to not act on their temptation. Nevertheless, we conceptualise these students as being at risk of cheating and thereby may provide greater understanding of the influencing factors at the tipping point of academic misconduct.

This paper explores possible correlations between temptation and four key areas that are generally assumed to reduce or impact cheating behaviour: online security systems, exam conditions (such as being open- or closed-book, location of exam, duration, and exam window), prior exam experiences (knowledge or perception of peer cheating), and student attitudes towards integrity. Analysis of the temptation data has led to new insights not readily apparent if we simply adopted a binary approach.

Logic might suggest that students who were tempted would fall in a range somewhere between those who cheated (i.e. those who succumbed to temptation), and those who were not tempted at all. This pattern was indeed observed in relation to student attitudes towards academic integrity as well as their knowledge of rules and consequences. For example, students who were tempted to cheat rated the importance of academic integrity below those who were not tempted, but higher than those who cheated. This finding seems logical, and perhaps not worth noting in its own right. It conforms to general assumptions of those who cheat as having low regard for academic integrity. However, our data adds a cautionary note to such claims because even though the cheating group was the lowest of the three groups in their rating of integrity importance, their average rating was still quite high. Therefore, our findings suggest that there is potential for integrity attitudes to be used as predictive indicators of temptation and cheating, but that the analysis is likely to only be useful when used as a comparative measure within a cohort.

The other logical pattern in which the tempted group sat between the cheated and not-tempted groups was with regards to the degree of knowledge of others cheating. Common thinking suggests that the more people are aware of cheating going on around them the more they are likely to cheat (or be tempted to cheat). We certainly saw this as a general pattern in our data. However, the significance of this finding needs to be treated with caution since the majority of students who reported being tempted or cheated had only heard rumours or nothing at all.

By focusing on the temptation group it also offers some further considerations for strategic directions. A particularly interesting finding was that proctoring was associated with less cheating, but there was no difference when we compared the conditions of automated and assisted check-in processes. However, when we moved from the binary cheating/not-cheating analysis to one which included the temptation group, we saw a pattern which suggests the importance of social presence. It affirms that proctoring (e.g. monitoring by camera) may be more effective as a deterrent to cheating if students have had social interaction with the proctors. The nature of the interaction needs further research, whether it is an enhanced sense of being monitored, or an affirmation of an implicit social contract to act with integrity. This finding cautions automating proctoring systems without also considering how those systems can heighted perceived social presence and/or surveillance.

This study has established that there is a large group of students who do not cheat, but who are tempted to do so. It has also proposed that these students may further refine our understanding of the conditions and factors that create a tipping point for student misconduct. Future quantitative and qualitative research would be valuable in further exploring the nature of temptation, such as intensity and spontaneity, in connection with demographic, personality, as well as contextual conditions and motivations.

Availability of data and materials

Raw data is not available due to privacy concerns.

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Authors and Affiliations

1. Faculty of Education, Monash University, Melbourne, Australia

Michael Henderson, Rebecca Awdry, Cliff Ashford & Mike Bryant

2. Deakin Learning Futures, Deakin University, Melbourne, Australia

Jennifer Chung

3. Torrens University Australia, Melbourne, Australia

Matthew Mundy

4. The University of Queensland, Brisbane, Australia

Kris Ryan

Contributions

The first three authors took a lead role in the analysis and writing of the article. All authors made substantial contributions to the conception and design of the work.

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Correspondence to Michael Henderson or Jennifer Chung.

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Henderson, M., Chung, J., Awdry, R. et al. The temptation to cheat in online exams: moving beyond the binary discourse of cheating and not cheating. Int J Educ Integr 19, 21 (2023). https://doi.org/10.1007/s40979-023-00143-2
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When I do workshops on student-centered assessment practices or teach the Assessment Design course as a professor, I've noticed that people feel strong emotions about assessment. As educators, all of our assessment practices come from a set of values and beliefs that we hold. Do we give extra credit? If so, are we valuing accuracy or effort? Do we take points off for late work? If so, is this about measuring the mastery of learning or teaching students how to stick to deadlines and work hard? Do we grade for effort or grade based upon a set of criteria? Do we give group grades on projects or is that unfair to individuals who did all the work? Should we use weighted categories? Should students get the opportunity to resubmit work or retake tests? Does that help them gain perseverance or lead to laziness?

For the last decade, I have watched teachers engage in heated debates over these issues. And I understand why. These assessment practices are rooted in our values about fairness and justice. They connect to what we believe about teaching and learning and why we are in the classroom as educators. Tell me to try a new discussion protocol with small groups? I'm down for that. Tell me to eliminate zeroes from the grade book? I've got strong feelings about it.

At a basic level, most of us can agree that assessment exists for a few reasons:

Assessment helps teachers know how individual students are doing. Did this particular student master the learning target? Does this student understand the concepts? Has this student mastered the skills? Is this student engaged in the learning? Does the student need additional supports or scaffolds? These questions help us identify which students need enrichment and which need intervention (like a small group pull-out).

Assessment helps teachers know how the class as a whole is doing. What are the overall trends that you notice? This helps teachers with the larger lesson planning cycle of planning, implementing, and assessing.

Assessment helps students know their mastery level. They should be able to comprehend what they know, what they don't know, and where they will go next. But assessment might also help increase student motivation or self-efficacy. It might be less evaluative and be more descriptive.

So, I'd like to consider how AI might change our assessment practices in the upcoming years. The following are five potential trends we might see.

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Trend #1: Less Grading and More Assessment

When I interviewed Mark Schneider, he described how AI will be used in the future within speech therapy. Currently, speech pathologists spend hours doing the initial intake tests. The work can be tedious and time-consuming. With the combination of generative AI and voice recognition, we will eventually have AI tools that can run the speech test and provide the initial data that the pathologist needs. This data can go beyond the simple score and actually provide insights into what type of speech therapy the student might need. Speech pathologists can then engage in ongoing assessment in a way that's more dynamic and relational.

Now imagine a similar technology for something like reading fluency. Currently, early elementary teachers spend hours doing reading fluency tests on a one-on-one level. It can get boring at times and that's when human error begins to slip through. Fortunately, AI doesn't get tired. So, instead, we might see speech recognition technology used within AI for a reading fluency test. Students will take the test on a computer or a smart device, get an immediate score, and see how it compares to their reading goals. This frees teachers up to engage in student-teacher conferences and small groups. Teachers will also be able to adjust the small groups because the data is more frequent.

It's not that AI replaces teachers in the assessment process. It's just that we outsource the areas most prone to human error and focus instead of the more relational elements of assessment.

Trend #2: Faster Feedback

When I was a kid, I loved playing baseball. I spent hours hitting a ball off a tee. My twin brother and I would do soft toss in the backyard. I probably averaged 40+ hours per week in the summer just practicing the game of baseball. You'd think I would have been a stellar hitter. After all, practice makes perfect. But actually, I was awful. I couldn't see the ball well. When I hit it, I often fouled it away. Then, in the rare occasion that my coach corrected my swing, I found it nearly impossible to correct it. So, what was going on? I struggled from a lack of feedback and I had spent hours committing a poor swing to muscle memory.

For decades, we've known that it's vital for students to get immediate feedback. When learning a skill, a lag in feedback can mean students practice things incorrectly. It's not all that different from my baseball swing. When learning a concept, a lag in feedback can mean a misconception lasts for weeks and leads to multiple misconceptions. For some students, a delay in feedback leads to a loss in motivation.

With AI, we have the ability to provide faster feedback. A teacher might run an essay through an AI assessment tool and provide a set of suggestions that the student then uses. A teacher might use an AI tool with a math problem to get a quick diagnosis of what mistakes might be occurring in computational fluency or mathematical reasoning. With AI, we will see faster feedback that can lead to more timely corrections. While I generally embrace this trend, I think we need to be a little worried about the lack of struggle. If we are identifying mistakes fast and providing instant feedback, I worry that we might accidentally quash productive struggle and this could lead to learned helplessness.

As a diagnostic tool, AI has the potential to help us identify mistakes that we don't see and to provide targeted feedback quickly. But it might also move a step further in predicting mistakes before they happen.

Trend #3: Predictive Analysis

Machine learning tends to excel in finding patterns and using those patterns for predictive analysis. In assessment, this might look like:

Adaptive learning: AI algorithms can analyze data on students' past performance to develop adaptive learning plans that can help students learn more effectively and efficiently. They might take into consideration student interests, attention span, work completion rate, or any number of data factors to help predict what kind of assignment might lead to "stickier" learning for the student. For more on this idea, see my previous article.

Early identification of at-risk students: AI algorithms can analyze data on students' attendance, grades, and other factors to identify students who are at risk of falling behind or dropping out of school. This can help teachers and administrators intervene early to provide support and prevent students from falling through the cracks.

Adaptive assessments: AI algorithms can analyze data on students' responses to assessment questions to adjust the difficulty level of subsequent questions, ensuring that each student is challenged appropriately and accurately assessed.

Predictive modeling: AI algorithms can use data on past student performance to develop predictive models that can identify factors that are likely to lead to success or failure. This can help teachers and administrators make data-driven decisions to improve student outcomes.

I have mixed feelings on the predictive element. For example, if we use AI to identify "at-risk" students, are we creating a self-fulfilling prophecy? Are we falling into a deficit mindset? There's something vaguely Black Mirror about the use of Big Data and AI for predicting a student's performance. And yet, there's another side that feels more hopeful. If AI can look at past math performance, we might be able to predict upcoming struggles and actually move toward compacting, where students skip the standards they've already mastered and spend longer on the areas where they struggle.

Trend #4: More Differentiation within Assessments

Differentiation takes time. There's no way around it. Even when we empower our students to self-select scaffolds, it takes time to design those scaffolds. With AI, differentiation is now faster and easier. We might see a trend toward differentiated assessment. It might look similar to adaptive learning programs, where the assessment closely matches a student's current skill level. AI-powered adaptive assessments can provide teachers with more detailed insights into student performance by adjusting the difficulty level of assessment items based on student responses. This can help teachers figure out what students know, what they don't know, and what they need to do next. The result is more personalized instruction at each student's skill level.

Other times, we might use AI to take a current assessment and differentiate it for English Language Learners. Here, the AI might simplify verb tenses, provide a glossary of vocabulary, or even generate sentence stems. In some cases, you might create multiple types of assessments and let students choose their method. But we might also use AI to create targeted formative assessments for students with learning differences. It could be a rubric or checklist for a task related to an IEP goal. Here, a teacher starts with the AI-generated assessment and then modifies it based on what they know about the individual student.

Trend #5: Empowering Students to Own the Assessment

While differentiated assessment is important, we also want to empower students to own the assessment process.

Here are a few examples we might see:

Goal-Setting: Students can use AI to get personalized recommendations for areas to improve. They can take ownership of the learning process by evaluating their strengths and weaknesses and coming up with next steps.

Resource Recommendations: Students can use AI as a curation device to help find the scaffolds, tutorials, and resources they need when learning a concept. Whether it's a set of tutorials in an applied math exploration or a recommendation of sites to visit in a PBL, AI has the potential for students to move from "What do I need to know?" to "Where can I find this?"

Performance Feedback: As educators, we can use machine learning in video for surveillance purposes (like checking for misbehavior or catching cheating) but the combination of video and AI can also mean better feedback on physical performance. Imagine having AI listen to a musical practice and give feedback. Added bonus, they don't have to listen to "Hot Cross Buns" on the recorder. My heart goes out to every elementary teacher who has to listen to recorder music. Similarly, in a PE class, AI might be able to examine a video and provide feedback on form to increase safety. Students could essentially "chat" with the AI to learn how they might make adjustments.

Learning Portfolios: We tend to think of digital portfolios as a personal endeavor - which it is. But they are also inherently social. Students are sharing their work with a larger audience. AI can be used to help students create and maintain digital portfolios, which can showcase their achievements and growth over time. A student might select a three artifacts and then choose two more from a recommendation algorithm.

Time management: AI can be used to help students manage their time more effectively, by providing personalized reminders and recommendations for how to prioritize their tasks. It doesn't have to be heavy-handed or attached to a penalty. It could simply be an on-task score. If a student is working on a project and finding social media distracting, they might use AI to create a system to stay focused. That being said, there's also a time for ignoring metrics and embracing human inefficiency. Daydreaming, going for walks, taking brain breaks - these all lead to better problem-solving and improved creativity.

Facilitating Reflection: Assessment isn't always purely evaluative. Sometimes it's more descriptive in nature. Students can work with chatbots that can facilitate reflection in a way that allows for follow up questions.

Find what's missing: Sometimes we just need another set of eyes who can say, "Have you considered _______?" AI can function like a peer who can look at a work and say, "You might want to consider adding a piece here."

Personalized feedback: Students can submit an example and get immediate feedback. It could be diagnostic feedback (which we'll address in-depth in our chapter on math) to help determine mistakes. Or it could be an open-ended set of pros and cons. However, we can also teach students how to ask for specific and actionable feedback.

In some cases, the AI acts almost like a tutor. In other cases, it's simply a tool that students wield.

What Values Will Drive the Assessment Practices?

Earlier I mentioned how assessment practices are values-driven with significant variance and disagreement among educators. As we think about how we will use AI within our assessment practices, we need to recognize the role of these values and beliefs. Machine learning will not occur in a vacuum. Values, beliefs, and policies will shape the way engineers design and educators implement these AI systems. As a teacher, leader, or coach, it can help to look at any AI assessment tool and ask, "What are the values driving this design?"

I mention this because some of the AI assessment tools seem to be driven by a strongly behaviorist perspective of learning with an emphasis on surveillance and accountability. Think plagiarism detectors or facial recognition systems used to gauge student engagement and behavior. Others are quasi-behaviorist with elements of gamification (badges, levels, etc). Some treat knowledge as something obtained, retained, and explained rather than internally constructed. Some assessment tools are more diagnostic, others more descriptive (an extra set of eyes), and others evaluative.

My fear is that we will see AI assessment tools chosen largely by convenience and cost rather than pedagogical soundness or core values. I worry that we will see tools gain popularity due to clever marketing and cute design. As districts purchase these tools in a top-down fashion, some educational leaders will push for compliance in the name of having common assessments. Instead of providing a tool and trusting teachers to decide when and how to use it, we might end up with policies that outsource most of the assessment processes to a machine in a way that might conflict with a teacher's core beliefs.

On the other hand, if we empower teachers to use AI tools wisely, we will end up with a more human-driven approach. I think it's critical that we retain the human element in assessment.

Remember the Human Element

I showed my son how well ChatGPT does in analyzing writing and giving feedback. I explained that it could do feedback almost instantaneously in a way that I, as a teacher, cannot. I gave an example of one of my blog posts. It was a personal narrative about the lessons our greyhound taught me.

His response?

"That's horrible feedback."

"What do you mean? It's practical and actionable. It includes more positive than negative feedback. Everything about it is true."

My son said, "If a teacher did that it would be heartless. If someone writes about their pet dying the only response is 'I'm so sorry. Want to talk about it?' That's it."

He went on to say "Feedback is fine. I know that we need it and all but my favorite English teachers give feedback in a way that makes me feel known. The feedback makes me want to write. It's critical, yeah, but it's critical in a way where I think, 'My teacher gets me.' Does that make sense?"

It does make sense. What he's alluding to is that feedback isn't mechanical. It's relational. It's dynamic. It's contextual. It's even, at times, empathetic. I still think there's some potential promise in AI as a feedback tool but I don't think it will ever replace the relational aspect of assessment.

At the most human level, assessment is a conversation. Sometimes it's an internal dialogue we do in isolation. Sometimes it's a conversation with trusted peers. Often, it's a conversation between a student and a teacher. These conversations might be about word choice or style or argumentation. But they might also be about hopes and dreams and grief and loss. I never want to lose those conversations.

Similarly, there is something powerful about self-assessment in true solitude. I know it's inefficient and probably not eco-friendly, but every time I write a book, I print everything up in a manuscript format and mark it up by hand. I jot notes in the margins. I rip sentences to shreds. I circle segments and draw an arrow to a new spot. If I'm frustrated, I might just draw a cartoon character on the side. This messy annotation is a private space where I've learned to trust my voice and my inner critic. I don't want to invite an algorithm into this ritual. It's mine.

AI can function as a form of peer feedback. The algorithm can generate targeted feedback on voice and style and word choice. It can help me tighten up my writing to get rid of unnecessary words that I don't need to use. But it can't tell me what it's like for a person to read it. For that, I need a human on the other end who can say, "John that piece moved me."

That's the kind of feedback that makes me want to write more.

Where I am most hopeful is that teachers will spend more time giving the more human-oriented feedback while AI provides some of the instant feedback as a diagnostic tool. Many of my current students (preservice teachers at the elementary level) spend hours testing students on reading fluency. In the upcoming years, we will likely have AI that can listen to students do fluency practice and catch the mistakes to provide a fluency score but also diagnose areas where they might be struggling in phonics, blending, and phonemic awareness. This would then free teachers up to do conferencing or pull small groups.

My hope is not that AI will replace teachers in the realm of assessment. Instead, my hope is it will free teachers up to give the kind of feedback that only a human can give.

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5 Ways Artificial Intelligence Might Transform Assessment Practices

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When I do workshops on student-centered assessment practices or teach the Assessment Design course as a professor, I've noticed that people feel strong emotions about assessment. As educators, all of our assessment practices come from

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Abstract

The educational sector can be enriched by the incorporation of artificial intelligence (AI) in various aspects. The field of artificial intelligence and its applications in the education sector give rise to a multidisciplinary field that brings together computer science, statistics, psychology and, of course, education. Within this context, this review aimed to synthesise existing research focused on provide improvements on primary/secondary student assessment using some AI tool. Thus, nine original research studies (641 participants), published between 2010 and 2023, met the inclusion criteria defined in this systematic literature review. The main contributions of the application of AI in the assessment of students at these lower educational levels focus on predicting their performance, automating and making evaluations more objective by means of neural networks or natural language processing, the use of educational robots to analyse their learning process, and the detection of specific factors that make classes more attractive. This review shows the possibilities and already existing uses that AI can bring to education, specifically in the evaluation of student performance at the primary and secondary levels.

Resumen

El sector educativo puede enriquecerse con la incorporación de la inteligencia artificial (IA) en diversos aspectos. El campo de la inteligencia artificial y sus aplicaciones en el sector educativo dan lugar a un campo multidisciplinar en el que confluyen la informática, la estadística, la psicología y, por supuesto, la educación. Dentro de este contexto, esta revisión pretende sintetizar las investigaciones existentes centradas en proporcionar mejoras en la evaluación del alumnado de primaria/secundaria utilizando alguna herramienta de IA. Así, nueve estudios de investigación originales (641 participantes), publicados entre 2010 y 2023, cumplen los criterios de inclusión definidos en esta revisión bibliográfica sistemática. Las principales aportaciones de la aplicación de la IA en la evaluación del alumnado de estos niveles educativos inferiores se centran en la predicción de su rendimiento, evaluaciones más objetivas y automatizadas mediante redes neuronales o procesamiento del lenguaje natural, el uso de robots educativos para analizar su proceso de aprendizaje y la detección de factores específicos que hacen más atractivas las clases. Esta revisión muestra las posibilidades y los usos ya existentes que la IA puede aportar a la educación, concretamente en la evaluación del rendimiento del alumnado de primaria y secundaria.

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Impact of artificial intelligence on assessment methods in primary and secondary education: Systematic literature review

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El sector educativo puede enriquecerse con la incorporación de la inteligencia artificial (IA) en diversos aspectos. El campo de la inteligencia artif...

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From CAMOSUN College

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Generative artificial intelligence (AI) technologies (e.g., ChatGPT, Bing, Midjourney) are quickly becoming commonplace in many educational and workplace settings and here for the long term. As such, all members of the Camosun College educational community have a responsibility to learn about these tools and their applications, both real and potential. Generative AI can be used to improve efficiency and accessibility-it is already opening up exciting new opportunities in research and teaching and learning. However, generative AI tools have also been shown to reproduce biases and stereotypes and may contribute to and exacerbate the further marginalization of equity-seeking groups. The environmental and socio-economic consequences from the development, training, and adoption of generative AI tools are unclear and likely to be both positive and negative.

As we navigate this new world together, all of us-leadership, employees, faculty, and students-are responsible for thoughtfully and ethically engaging with the question of generative AI and its implications, both in teaching and learning and more broadly in our communities and society. In light of this responsibility, a CETL-facilitated working group has composed the following recommendations for all members of the Camosun community and for faculty and students specifically. These recommendations are intended to align with the values and goals set out in our 2023-2028 Strategic Plan and with B.C.'s Post-Secondary Digital Learning Strategy.

Educate yourself about how generative AI and large language models (LLMs) work.

Engage in informed conversation with peers, colleagues, supervisors, and team members about generative AI and its implications in your relationships, communities, and workplaces.

Before/while using AI tools, consider the benefits and limitations of the tool including implications on privacy, the value of cognitive-offloading, and the ability of the tool to support or impede effective task completion.

Clearly communicate expectations with students (both in class and in writing), including guidance on the appropriate or inappropriate use of AI tools in your course.

Reflect on the learning outcomes and essential skills relevant in your course(s) and program(s).

Consider whether any tasks students complete might be offloaded to or supplemented by AI to either simplify or enhance the learning process.

Explore how AI tools are being (or might be) used in your discipline or profession. Consider whether you have a professional responsibility to help educate students on emerging uses of AI tools in your area.

Play around with AI tools to see how they perform on tasks you ask students to complete in your course.

Consider assessment practices and instruments to ensure they can return valid, reliable measures of student learning in a world of AI.

Practice thoughtful adoption of educational technologies following key considerations outlined by CETL , and be prepared to provide alternative options. In relation to AI specifically, consider the guidance provided in the AI LibGuide.

Inform yourself of relevant course, program, and institutional policies. Misuse of AI tools, for example, may violate the Academic Integrity Policy. Ask your instructor(s) to clarify their expectations in each course.

Practice responsible AI use in coursework. Only use AI tools with explicit permission and in ways that support rather than circumvent learning. You are responsible for any work that you produce and submit, including anything generated by AI.

Protect your data. Before using any AI tool, read the fine print to be aware of how your personal information and intellectual property will be treated.

Ask questions. If you are asked to use AI tools that are not FOIPPA-compliant , you have the right to ask for an alternative. Remember that generative AI is a tool.

Responsibility for the ethical use of any tool ultimately lies with the user, which is a human. Responsibility cannot be transferred to a machine.
As with AI tools, this approach is continually evolving. We invite feedback, questions, and conversations. Please select the following Share Feedback button to share your thoughts in our feedback form.
Share Feedback Last updated October 4, 2023.

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Research guides: Artificial Intelligence in Teaching & Learning: Guiding Approach

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Helpful information, guidance, and resources for faculty and students on the implications of generative artificial intelligence for teaching and learning.

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Image generated with AI, Adobe Firefly

by Krys Ziska Strange, Associate Director of Instructional Design & Faculty Development, TTS & Carie Cardamone, Associate Director of STEM, Professional Schools & Assessment, Tufts CELT

The recent release of Chat GPT and media coverage has prompted instructors to explore AI tools to understand their capabilities and limitations. It has evoked anxious conversations about how well these tools are able to perform tasks we ask our students to complete - from writing code to creating essays and reports - and resulting implications for learning and academic honesty. At the same time, many instructors are creatively thinking about how to leverage AI tools for learning.

Past experiences with new technologies like calculators and smartphones have shown us that initial worries about how the technologies will help students cheat or detract from what students "need to learn" can give way to innovative new approaches for education. Eventually, we adapt and begin to leverage these new tools for learning. Battling the tide of innovation in technology is an exhaustive fight we will not win. What if we can use this moment as an opportunity to refocus our attention on a different question: "What activities and assignments will have the most value to my students' learning today?".

There are many ways to promote and assess student learning that are not easily replicated by AI, and some that leverage its capabilities. After all, students will be entering careers where they will have to navigate and critically evaluate rapidly evolving sources of information and new technologies to solve complex problems. Each of the sections below offers ideas that are resilient in terms of both their effectiveness for assessing learning, doing work that cannot easily be gamed, and developing skills that will support continuous learning. Many of these are tried and true, but under-utilized, others embrace new opportunities!

Embrace authentic assessments

Consider refocusing course activities where possible so that students can apply their learning to real world contexts and problems and find personal relevance. This can positively influence both motivation and long term memory. These authentic assessments teach students how to approach multifaceted, complex questions where there is not always a right answer, and develop their analytical skills. You might engage students in creating a portfolio of their work with critical reflections on how course experiences, and activities have impacted their learning and, if relevant, the impact on the communities in which they are situated. A learning portfolio is a visible way to help students develop metacognitive (thinking about thinking) skills, become critical thinkers, and open a space for reflective dialogue with you to track and adjust their learning progress.

Create social learning opportunities

Social interactions have always been essential for learning. Motivating students to engage in their learning with peers can also help them feel a sense of belonging within a classroom community. Group activities with well designed questions or prompts can provide students the opportunity to articulate and challenge their own understanding of concepts, integrate other perspectives, and reveal gaps in their knowledge. They can also motivate students to come prepared to engage in these activities with their peers.

Varying the structure of these collaborative engagements (whole group, dyads, small groups) and the form (problem solving, games, idea or question generation, case studies, hypothesis testing, collaborative annotation of readings using Perusall or Hypothes.is) can promote both individual and collective learning. Creating opportunities to scaffold peer feedback on assignments engages students in understanding their peers' approaches to an assignment thereby expanding their own thinking and approaches.

Focus on process over product

Traditionally, the end product of a paper or project has carried most of the weight for assessing learning. As AI improves, and it will improve rapidly, reframing our priorities by weighting process and value, revision and improvement more heavily will be important. When designing a writing assignment or a project, you might break it into smaller components that assess skill development at each stage, using self and peer feedback submitted with changes prior to your feedback. This refocusing communicates the value of engaging in the process and circumvents possible shortcuts that will deprive the student of pride in their work and learning. Many instructors already do this by requiring drafts and rewrites of thesis statements, citation searches, reflection, brainstorming documents, etc.

Ask students to get creative

Projects that engage students in thinking beyond the traditional disciplinary modes of communication can give students valuable experience translating dense concepts for different audiences. Consider alternative models for students to express their learning, such as conference posters, information graphics, podcasts, maps, artwork, storyboards, song writing, poetry, or research presentations. These creative expressions build on universal design for learning core principle, "Providing Multiple Means of Expression, which increases equity in the classroom.

Be transparent about your expectations for the use of AI

One of the easiest ways to encourage authentic engagement with course activities and to discourage academic dishonesty is to be transparent about your expectations. Include a statement in your syllabus that tells students what is (and is not) allowed in your course. Be explicit about how students should cite the work of others and a policy for citing the use of AI tools in learning or in their work. You might also include specific guidelines on when (human or AI) collaboration is encouraged, and when it is not.

A key component of transparency in this instance is to explain and continuously reinforce how your various assessments will benefit them in your course, their other courses, or in their career. Be explicit about the purpose, tasks, and criteria for success on your assessments so that students may understand the value and benefit of the effort.

Have students explore AI tools

Rather than avoiding AI tools or trying to catch students misusing them, you might challenge them to discover examples where AI tools provide inaccurate or incomplete information, reinforce misconceptions, or are missing alternative perspectives. You could ask your students to deconstruct a text written by AI for its strengths and weaknesses. Students can compare AI generated responses to their own work, or use AI in specific stages of an assignment (always with attribution). Or, you might ask them to use an AI tool to generate a piece of writing to compare and contrast with their own writing, and reflect why they would incorporate or reject differences. See Teaching Students to Write with AI: The SPACE Framework for an example. Students might even help you develop a classroom policy for the appropriate use of AI tools.

Get creative with technology

There are some creative applications of free or adopted tools that you can use to help dissuade use of AI-generated text. You can have students share links to their Google Docs and explore the versioning history of the text. You can also embrace the commenting features on Canvas assignments and have students respond to questions to earn back points or add bonus points. Or you can have students create marginalia on printed documents and have them scan/upload the documents using free apps such as Adobe Scan.

So, what's next?

The rapid and constant change in the use of AI makes a cure-all assessment approach next to impossible. Even during the writing of this article, several AI-assisted research and writing tools have begun to gain popularity and notice in academia, and dozens of new articles have appeared. We recommend you explore one of these new AI tools (e.g., ChatGPT, MidJourneyAI, Consensus, Elicit, The Good AI, etc.), while keeping in mind that their capabilities are quickly evolving.

Regardless of how each of us chooses to adapt to the development of AI systems in our individual courses, it's time to move beyond thinking about AI systems as something to "battle against" and begin to think of how these systems can help us to learn. They can be tools for revising writing, guiding studying, answering questions, searching references, and for reflecting on and expanding our ideas. Perhaps these systems can instead provide an exciting opportunity to reimagine a more resilient, equitable, creative, system of assessment, and even have fun in the process.
See Also
Resilient and Equitable Teaching and Assessment Require a Paradigm Shift (by Annie Soisson, Faculty Focus)

Designing Courses in the Age of AI (Teaching@Tufts)
Getting Beyond Google: Simple Ways to Help Students Critically Evaluate their Sources (Teaching@Tufts)
ChatGPT: Understanding the new landscape and short-term solutions, (Resources compiled by Cynthia Alby)
Chat GPT and Artificial Intelligence Tools (Georgetown University)
Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1). https://doi.org/10.37074/jalt.2023.6.1.9

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Thinking about our Assessments in the Age of Artificial Intelligence (AI)

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by Krys Ziska Strange, Associate Director of Instructional Design & Faculty Development, TTS & Carie Cardamone, Associate Director of STEM, Professional Schools & Assessment, Tufts CELT The recent release of Chat GPT and media coverage has prompted instructors to explore AI tools to understand their capabilities and limitations.

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Teaching and Learning in an Era of Generative AI - UBC Centre for Teaching, Learning and Technology

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Teaching and Learning in an Era of Generative AI - UBC Centre for Teaching, Learning and Technology

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Les consignes et les guidances claires de l'University of British Columbia

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UBC Centre for Teaching, Learning and Technology

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This resource provides some considerations and suggestions around designing assessments in response to the growing prevalence of artificial intelligence tools that can generate text, such as ChatGPT and Bing AI. There are also tools that can generate images, such as Midjourney, Stable Diffusion, and DALL-E, but at the moment this resource is only focused on tools that generate text.

Please note that this resource is a work in progress. We welcome your feedback and your input to further document it and make it valuable for your teaching. In particular, we are looking for examples from UBC instructors to include in the sections on assessment design. If you are willing to share an example of how you're designing activities and assessments either using, or mitigating the use of generative AI, please let us know by filling in our contact form!
Learn about prompting:
Future Events: events the CTLT is facilitating around GenerativeAI.

The field of generative AI is rapidly changing and evolving, with new tools or revisions to existing ones released almost weekly. The capabilities and limitations that exist now may be different in a few months.

Generative AI can bring significant changes to the teaching and learning landscape, and it takes time to learn and respond. It's not necessary to try to keep up with all of these developments, or to change courses radically in response. Small steps such as talking with students about these tools, and possibly making a minor change to one activity could make a big difference.

Sources of further support and ideas abound; including in the Resources section we have provided some further resources. In addition, it's helpful to reach out to colleagues in your unit or discipline, as many are likely also thinking about generative AI and how to address it in their classes. The Centre for Teaching, Learning, and Technology is also here to help; please contact us if you would like further suggestions or advice for your particular teaching context.

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Teaching and Learning in an Era of Generative AI - UBC Centre for Teaching, Learning and Technology

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This resource provides some considerations and suggestions around designing assessments in response to the growing prevalence of artificial intelligence tools that can generate text, such as ChatGPT and Bing AI. There are also tools that can generate images, such as Midjourney, Stable Diffusion, and DALL-E, but at the moment this resource is only focused on [...]

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Une excellente introduction proposée en plusieurs langues.

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In spring 2018, MinnaLearn and the University of Helsinki came together with the aim of helping people to be empowered, not threatened, by artificial intelligence. Together, they built the Elements of AI to teach the basics of AI to people from a wide range of backgrounds.

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A free online introduction to artificial intelligence for non-experts

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Learn more about MinnaLearn's and the University of Helsinki's AI course - no programming or complicated math required.

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Plus sur la version en ligne Sway :https://sway.office.com/iNhP49DMrhOep2Uc

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By Denise Hough, University of Glasgow

Recent advances in generative Artificial Intelligence (AI) have made it accessible to the wider public through platforms such as OpenAI's ChatGPT. AI tools are very powerful and can enhance student learning experiences, but it is important to understand how to use it in a way that is conducive to good academic practice (see Table 1).

The default position of most higher education institutes will likely be that students are allowed to use generative AI tools as a resource in any course component where online tools are accessible. Although there may be specific restrictions on certain assessments, which should be communicated appropriately by teaching staff.

Remember, AI tools are tools. They are not substitutes for intellectual growth. This guidance will support students to embrace AI responsibly, engage actively in studies and uphold academic integrity. Thereby, students will be able to harness the power of technology to enrich their education.

Lectures

Clarify complex concepts and reinforce understanding, focussed around intended learning outcomes.

Avoid relying solely on AI. You should engage with content.

AI may provide inaccurate information and is further limited by last software update.

Written work

Can use to start literature search to understand main concepts.

May be useful in generating ideas, such as a list of topics.

Valuable for proofreading. Ask for feedback on your own work.

Assist with referencing format.

Understand academic integrity and always credit sources.

Keep a record of AI conversation.

AI is not good at critiquing or placing ideas in context of a scenario.

Plagiarism risk is high, and it is not acceptable to submit the AI generated content as it is, plagiarism checkers keep evolving to detect AI generated content.

AI generated lists of publications and other sources are predictions only, they may not truly exist.

Problem-based learning

Ask for technical assistance, such as instructions on how to use R, Excel, PowerPoint, etc.

Ask for guidance or feedback to see if you explored all aspects of data interpretation.

See examples of how to report your calculation working.

Do not skip critical thinking by accepting AI generated answers without verification.

Never simply copy and paste questions and answers.

Test, Exam

Generate practise questions for self assessment.

Do not cheat by using AI to generate your answers.

Tutorial

Create scenarios to deepen your understanding.

Gain better insight into the problems presented to you

Do not copy answers without attempting problems yourself.

Do not let AI replace your active participation in discussions.

Practicals

Assist in troubleshooting.

Understanding protocols and experimental design.

Do not use AI to answer questions without understanding or reflecting.

Written assessments

For written assessments, additional guidance on AI use is important to develop essential skills and uphold academic integrity.

Do not let AI generate facts.

Check facts against reliable resources and include a citation to the source.

Do not ask AI to rewrite your work, then copy it and present it as your own. Examples:
- Provide list of facts and ask to rewrite in paragraph style
- Ask to rewrite poor grammar or English
- Ask to rewrite so that it fits within word count

If you copy and paste entire AI generated answers, it will be considered as plagiarism.

Even AI cocreated work is considered collusion if you present it all as your own.

Do not use AI as a source for most of your information.

Develop your own writing skills by making your own attempts first, then ask AI for feedback on your writing. This gives you an opportunity to recognise ways in which you can improve your own writing.

Keep a record of your AI conversation to prove you used feedback to improve your writing.

Acknowledge AI as cocreator, but ensure that what you submit is truly your own work.

If you are unable to find the original source of information, you may cite AI as the source (format: personal communication), but note that AI is not a peer reviewed scientific source, which is where the bulk of your information should come from.

Actions to uphold academic integrity

Using any form of AI in your university coursework, study, exams or research without acknowledging that input, counts as academic misconduct. The following actions are recommended:

If you use AI as a source of information (not recommended), you should acknowledge it as a form of personal communication as an in text citation in a format like: (Name of AI, personal communication, Month Day, Year). Example: (ChatGPT, personal communication, September 6, 2023)

If you used an AI tool in any other form, you should state this in an 'Acknowledgements' section. You should add the same information as for a personal communication like the example above, but also include a brief description of how you used it (e.g. improve writing style). This section should not contribute towards any word count limits.

You should provide a copy of your AI conversation as proof of responsible use. You can do this by including the shared link to your conversation as part of your acknowledgement. Note that some AI tools store conversations for a short period, so saving a backup copy is recommended.

You should include a web link to all other references that you cite (i.e. DOI link for peer reviewed items), which facilitates follow up on the authenticity of your sources.

Plagiarism software keeps improving in detecting AI generated text. If irresponsible use of AI is suspected in an assessment, teaching staff will need to investigate this further for possible student misconduct. Having a full record of your AI conversations is the best way for you to provide evidence of its responsible use, or to reflect upon for future improvement. Furthermore, it supports you in being transparent in your work, which is important for maintaining integrity and good work ethic.

See more detailed examples of how students can use AI:
Hough, D. (2023) Examples of using AI to support student learning & assessment in 1st year Life Sciences. University of Glasgow. https://sway.office.com/iNhP49DMrhOep2Uc

Attend the ALT Winter Summit on ethics and AI

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Student guidance for the responsible use of AI

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Recent advances in generative Artificial Intelligence (AI) have made it accessible to the wider public through platforms such as OpenAI's ChatGPT. AI tools are very powerful and can enhance student...

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Une adaptation de :https://altc.alt.ac.uk/blog/2023/10/student-guidance-for-the-responsible-use-of-ai/

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Dans le but de présenter de bonnes pratiques académiques du point de vue de la personne étudiante, le Carrefour d'innovation et de pédagogie universitaire de l'UQAM propose une traduction libre de la version originale de ce guide.

Si nous autorisons l'utilisation des outils de l'intelligence artificielle dans un cours, nous avons aussi le devoir de former ou de donner des lignes de conduite pour outiller les personnes étudiantes à les utiliser de façon intègre et responsable.

Voici une adaptation (téléchargement PDF, 100 Ko) du guide d'accompagnement réalisé par Dre Denise Hough, professeure de biologie à l'Université de Glasgow. Cet établissement universitaire, situé en Écosse, a adopté une position favorable à l'égard des outils d'IA générative. On y forme les étudiant·es à mieux utiliser l'IA dans tous les aspects de leurs cours où des outils en ligne sont disponibles.

Cependant, malgré cette autorisation, des restrictions spécifiques supplémentaires sont susceptibles d'être imposées par le corps professoral lors de certaines évaluations.

* L'outil de traduction deepl.com a été utilisé pour adapter et traduire librement le guide et ce billet de veille.

Évaluations écrites

Pour les évaluations écrites, des conseils supplémentaires sur l'utilisation de l'IA sont importants pour développer les compétences essentielles et maintenir l'intégrité académique.

Vérifiez les faits à l'aide de ressources fiables et citez la source.

Si vous ne parvenez pas à trouver la source originale de l'information, vous pouvez mentionner l'outil d'IA (format : communication personnelle), mais notez que l'IA n'est pas une source scientifique évaluée par les pairs, ce qui doit être la source principale de votre information.

Ne laissez pas l'IA générer des faits.

Ne soumettez pas vos données de recherche non publiées ou vos nouvelles idées dans le "prompt" (invite) d'un logiciel d'IA si vous ne souhaitez pas que ces informations deviennent librement accessibles. Cela aura des conséquences potentielles sur la publication de vos travaux ou sur le fait que l'IA puisse utiliser ces informations pour entrainer sa banque de données dans le but de répondre à d'autres utilisateurs.

Développez vos propres compétences en matière d'écriture en faisant d'abord vos propres tentatives, puis en demandant à l'outil IA de vous faire part de ses commentaires sur vos écrits. Vous aurez ainsi l'occasion d'identifier les moyens d'améliorer votre propre écriture.

Conservez une trace de votre conversation avec l'outil d'IA afin de prouver que vous avez utilisé les commentaires pour améliorer votre texte.

Mentionnez les outils d'IA en tant qu'assistant à la création de vos textes, mais assurez-vous que ce que vous soumettez est vraiment votre propre travail.

Ne demandez pas à l'outil d'IA de réécrire votre travail, puis de le copier et de le présenter comme le vôtre. Exemples :
- Fournir une liste de faits et demander à l'IA de la réécrire dans le style d'un paragraphe.
- Demander de réécrire un texte dont la grammaire ou l'anglais laisse à désirer.
- Demander de réécrire pour respecter le nombre de mots.

Si vous copiez et collez des réponses entières générées par l'IA, cela sera considéré comme du plagiat.

Les travaux réalisés en collaboration avec l'IA sont considérés comme de la collusion si vous les présentez comme étant les vôtres.

Actions visant à préserver l'intégrité académique

L'IA générative se distingue d'une recherche en ligne par sa nature conversationnelle, ce qui signifie que l'outil d'IA fournira des informations adaptées à vos besoins individuels, mais qu'elle n'indique pas la provenance de ces informations et ne peut garantir leur exactitude.

Les actions suivantes sont recommandées :

Si vous utilisez l'IA comme source d'information (ce qui n'est pas recommandé), vous devez la reconnaître comme une forme de communication personnelle en la citant dans le texte sous la forme suivante : (Nom de l'IA, communication personnelle, mois, jour, année). Exemple : (ChatGPT, communication personnelle, 18 octobre 2023)

Si vous avez utilisé un outil d'IA sous une autre forme, vous devez l'indiquer dans une section "Remerciements". Vous devez ajouter les mêmes informations que pour une communication personnelle comme l'exemple ci-dessus, mais aussi une brève description de la manière dont vous l'avez utilisé (par exemple, pour améliorer le style d'écriture). Cette section ne doit pas contribuer à la limite du nombre de mots.

Vous devez fournir une copie de votre conversation sur l'IA comme preuve d'une utilisation responsable. Pour ce faire, vous pouvez inclure le lien partagé vers votre conversation dans vos remerciements. Certains outils d'IA ne conservant les conversations que pendant une courte période, il est recommandé de conserver une copie de sauvegarde.

Vous devez inclure un lien web vers toutes les autres références que vous citez (c'est-à-dire le lien DOI pour les articles évalués par des pairs), ce qui facilite le suivi de l'authenticité de vos sources.

Transparence

Le fait de disposer d'un enregistrement complet de vos conversations avec l'outil d'IA générative est le meilleur moyen pour vous de fournir des preuves de son utilisation responsable ou de réfléchir à son amélioration future. En outre, cela vous aide à être transparent dans votre travail, ce qui est important pour maintenir l'intégrité et une bonne éthique de travail.

Sources

Hough, D. (2023) Student guidance for the responsible use of AI. Edited by Rachel Heyes. #ALTc blog, October 10. https://altc.alt.ac.uk/blog/2023/10/student-guidance-for-the-responsible-use-of-ai/

Hough, D. (2023) Examples of using AI to support student learning & assessment in 1st year Life Sciences. University of Glasgow. https://sway.office.com/iNhP49DMrhOep2Uc

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Enseigner l'utilisation responsable de l'IA - Collimateur - Veille pédagonumérique - UQAM

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Here is a preprint of a paper I just submitted to MDPI's Digital journal that applies the co-participation model that underpins How Education Works (and a number of my papers over the last few years) to generative AIs (GAIs). I don't know whether it will be accepted and, even if it is, it is very likely that some changes will be required. This is a warts-and-all raw first submission. It's fairly long (around 10,000 words).

The central observation around which the paper revolves is that, for the first time in the history of technology, recent generations of GAIs automate (or at least appear to automate) the soft technique that has, till now, been the sole domain of humans. Up until now, every technology we have ever created, be it physically instantiated, cognitive, organizational, structural, or conceptual, has left all of the soft part of the orchestration to human beings.

The fact that GAIs replicate the soft stuff is a matter for some concern when they start to play a role in education, mainly because:

the skills they replace may atrophy or never be learned in the first place. This is not even slightly like replacing hard skills of handwriting or arithmetic: we are talking about skills like creativity, problem-solving, critical inquiry, design, and so on. We're talking about the stuff that GAIs are trained with.

the AIs themselves are an amalgam, an embodiment of our collective intelligence, not actual people. You can spin up any kind of persona you like and discard it just as easily. Much of the crucially important hidden/tacit curriculum of education is concerned with relationships, identity, ways of thinking, ways of being, ways of working and playing with others. It's about learning to be human in a human society. It is therefore quite problematic to delegate how we learn to be human to a machine with (literally and figuratively) no skin in the game, trained on a bunch of signals signifying nothing but more signals.

On the other hand, to not use them in educational systems would be as stupid as to not use writing. These technologies are now parts of our extended cognition, intertwingled with our collective intelligence as much as any other technology, so of course they must be integrated in our educational systems. The big questions are not about whether we should embrace them but how, and what soft skills they might replace that we wish to preserve or develop. I hope that we will value real humans and their inventions more, rather than less, though I fear that, as long as we retain the main structural features of our education systems without significant adjustments to how they work, we will no longer care, and we may lose some of our capacity for caring.

I suggest a few ways we might avert some of the greatest risks by, for instance, treating them as partners/contractors/team members rather than tools, by avoiding methods of "personalization" that simply reinforce existing power imbalances and pedagogies designed for better indoctrination, by using them to help connect us and support human relationships, by doing what we can to reduce extrinsic drivers, by decoupling learning and credentials, and by doubling down on the social aspects of learning. There is also an undeniable explosion in adjacent possibles, leading to new skills to learn, new ways to be creative, and new possibilities for opening up education to more people. The potential paths we might take from now on are unprestatable and multifarious but, once we start down them, resulting path dependencies may lead us into great calamity at least as easily as they may expand our potential. We need to make wise decisions now, while we still have the wisdom to make them.

MDPI invited me to submit this article free of their normal article processing charge (APC). The fact that I accepted is therefore very much not an endorsement of APCs, though I respect MDPI's willingness to accommodate those who find payment difficult, the good editorial services they provide, and the fact that all they publish is open. I was not previously familiar with the Digital journal itself. It has been publishing 4 articles a year since 2021, mostly offering a mix of reports on application designs and literature reviews. The quality seems good.

Abstract

This paper applies a theoretical model to analyze the ways that widespread use of generative AIs (GAIs) in education and, more broadly, in contributing to and reflecting the collective intelligence of our species, can and will change us. The model extends Brian Arthur's insights into the nature of technologies as the orchestration of phenomena to our use by explaining the nature of humans participation in their enactment, whether as part of the orchestration (hard technique, where our roles must be performed correctly) or as orchestrators of phenomena (soft technique performed creatively or idiosyncratically). Education may be seen as a technological process for developing the soft and hard techniques of humans to participate in the technologies and thus the collective intelligence of our cultures. Unlike all earlier technologies, by embodying that collective intelligence themselves, GAIs can closely emulate and implement not only the hard technique but also the soft that, until now, was humanity's sole domain: the very things that technologies enabled us to do can now be done by the technologies themselves. The consequences for what, how, and even whether we learn are profound. The paper explores some of these consequences and concludes with theoretically informed approaches that may help us to avert some dangers while benefiting from the strengths of generative AIs.

Originally posted at: https://landing.athabascau.ca/bookmarks/view/20512771/preprint-the-human-nature-of-generative-ais-and-the-technological-nature-of-humanity-implications-for-education

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Preprint - The human nature of generative AIs and the technological nature of humanity: implications for education

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Here is a preprint of a paper I just submitted to MDPI's Digital journal that applies the co-participation model that underpins How Education Works (and a number of my papers over the last fe...

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The emergence of artificial intelligence (AI) in every sector of society has created a new form of digital divide, the "AI Divide." This divide goes beyond mere accessibility, considering levels of understanding and insightful usage of AI. In this reflection, I will project four distinct levels of this divide and discuss their negative impacts on education, especially when the ideal level of full and perfect AI integration is not yet achieved and present an approach for each level in order for educational institutions be able to achieve the level where AI is fully integrated with a meaningful context of compression and truly understanding of AI

Artificial Intelligence (AI) has transformed numerous sectors and education is no exception. However, a new form of the divide, the "AI Divide," is emerging, creating significant discrepancies in education outcomes. This divide is defined on four levels, from lack of access to AI to its meaningful and efficient use. This reflection aims to address these levels and the repercussions of not reaching the fourth and final level of meaningful AI integration in Education.
Level 1: No Access to AI
The most basic level of the AI Divide is the lack of access to AI technologies. This gap mirrors the digital divide, affecting individuals, groups, and regions with socio-economic or infrastructural limitations. In education, it means students or institutions lack the tools or resources for AI-enabled learning. The impact of this level can be devastating, reinforcing educational inequities, limiting exposure to personalized learning experiences, and reducing competitiveness in the increasingly AI-driven job market.
Level 2: Access to AI, but No Understanding of AI
The second level of the AI Divide occurs when individuals or institutions have access to AI but lack the comprehension to utilize it. This can result from poor digital literacy, lack of proper training, or complex AI interfaces. The negative implications here are the underutilization of resources, inefficiency, and the inability to harness the potential of AI to enhance teaching and learning. In the long run, this can lead to suboptimal learning outcomes and hinder the development of 21st-century skills.
Level 3: Access and Understanding of AI, but Inefficient Use of AI
At this level, individuals or institutions have access to AI and understand it, but fail to use it insightfully and efficiently. This situation often results from a lack of strategic guidance, poor integration into existing systems, or resistance to change. The negative impacts include inefficiency, wasted resources, and missed opportunities for transformative change in educational practices and outcomes.
Level 4: Access to AI, Understanding AI, and Insightful and Efficiently Use of AI
The ideal level is where the AI Divide does not exist, where individuals or institutions have access to AI, understand its working, and can use it insightfully and efficiently. However, achieving this level remains a significant challenge. While the negative impacts of the first three levels of the AI Divide are profound, the failure to reach this fourth level deprives learners of the comprehensive benefits of AI. Achieving the last level signifies more than just access to AI and its application in the teaching and learning process. It represents a shift from AI consumers to AI creators and an evolution towards informed citizenship in the digital age.

At this level, students and educators are not only proficient users of AI technologies but also they can be ethical creators. They can build, modify, and leverage AI tools, understanding the ethical implications and making responsible decisions in their AI practices. This ability to ethically manipulate and create with AI empowers learners to be active contributors to the AI ecosystem, rather than passive consumers. It fosters innovation and promotes a deeper understanding of the AI technologies that increasingly permeate our lives.

Moreover, reaching Level 4 implies becoming an informed citizen in an AI-driven world. With a comprehensive understanding and insightful use of AI, individuals can better appreciate the challenges, opportunities, and concerns surrounding AI. They can participate in important discussions about AI ethics, privacy, and societal impacts. They can make informed decisions about AI usage in their personal and professional lives and contribute meaningfully to the shaping of AI policies and regulations.

In essence, achieving Level 4 ushers in a new era of AI literacy, where AI is not just a tool for learning, but a topic of learning and a catalyst for informed engagement in the digital society. It transforms education from a process of imparting knowledge to a journey of discovery, creation, and informed decision-making.

The benefits of reaching this level are profound, extending beyond the classroom to shape the society of the future. It equips learners with the skills and knowledge to navigate the AI landscape, fosters innovation and ethical practices in AI, and promotes active and informed participation in the AI-driven digital society. It is, therefore, crucial that our efforts to bridge the AI Divide aim not just for access, understanding, and use of AI, but for the attainment of this transformative Level 4.

Approaching the Levels of the AI Divide: Moving Up the Ladder: Actions and Initiatives

Level 1: No Access to AI - Action Keyword: " Awareness"

The first step in bridging the AI Divide is to create awareness about the role and importance of AI in modern education. This should involve outreach programs that illustrate the benefits of AI in personalizing education and improving outcomes. Governments, educational institutions, and non-governmental organizations should work together to ensure AI and its potential are well-understood at every level, from individual students and teachers to institutional decision-makers. Simultaneously, resources should be allocated to improve infrastructural facilities and provide necessary hardware, software, and connectivity to facilitate access to AI.

Level 2: Access to AI, but No Understanding of AI - Action Keyword: " Competency"

Once access is achieved, the focus should shift to building competency in using AI tools. This could involve creating AI-specific curriculums for students, as well as professional development programs for teachers. Understanding how to operate AI systems effectively and safely is crucial. Organizations and educators should collaborate with AI experts to create educational materials that are tailored to different learning levels. Interactive, hands-on learning opportunities should be prioritized to enhance engagement and improve learning outcomes.

Level 3: Access and Understanding of AI, but Inefficient Use of AI - Action Keyword: " Knowledge"

With access and competency established, the next step is to improve the knowledge around the strategic use of AI. This involves going beyond the mere understanding of how to use AI tools and delving into when and why to use them. In-depth workshops and seminars that explore various AI applications and their impact on teaching and learning could be beneficial here. The aim should be to empower educators and students to identify opportunities where AI can enhance learning experiences and outcomes.

Level 4: Access to AI, Understanding AI, and Insightful and Efficiently Use of AI - Action Keyword: " Integration"

The ultimate goal of this process is the complete integration of AI into educational practices. Integration goes beyond mere usage; it involves infusing AI into the fabric of the teaching and learning process. This could involve using AI to personalize learning materials, using machine learning algorithms to track student progress and adapt teaching approaches, and integrating AI tools into assessment processes. For this to succeed, there must be ongoing support and training for educators to continually update their skills and knowledge as AI technologies evolve.

In conclusion:

The emergence of the AI Divide represents a significant amplification of the negative impacts observed in the Digital Divide. Unlike the latter, which primarily concerns accessibility and good use of digital resources, the AI Divide extends into understanding and application of AI as a general-purpose technology with complex challenges that go far beyond the digital ecosystem of tools pre-AI, mainly the Generative AI field. As such, its repercussions are more profound and potentially more damaging.

While the Digital Divide had already widened socioeconomic gaps and increased educational disparities, the AI Divide intensifies these issues. It does not merely create discrepancies in access to resources but also in the ability to utilize these resources effectively for enhanced learning outcomes. The failure to understand or efficiently use AI amplifies the likelihood of underutilization of resources, inefficiencies in learning processes, and the propagation of suboptimal educational outcomes.

Moreover, as AI becomes increasingly embedded in our society and economy, those unable to navigate the AI landscape are placed at a significant disadvantage, not only in their current educational journey but also in their future professional careers. Thus, the AI Divide risks not only exacerbating educational inequalities but also perpetuating socio-economic disparities.

Prompt and comprehensive action is crucial to mitigate the impacts of the AI Divide. This calls for robust policy interventions aimed at universal AI access, fostering AI literacy, and facilitating the integration of AI into educational systems. Such actions should involve a multipronged approach:

1. Infrastructure Development: Ensuring access to AI necessitates addressing the infrastructural discrepancies that underpin the Digital Divide. This involves improving connectivity, ensuring the availability of appropriate devices, and facilitating access to AI-powered educational platforms.

2. Education and Training: To foster an understanding of AI, we must integrate digital literacy into mainstream education from an early age. This should be supplemented by targeted training for educators to equip them with the knowledge and skills to integrate AI into their teaching.

3. Policy and Regulation: At the national and international levels, policy guidelines and regulations need to be enacted to promote the meaningful use of AI in education. This includes policies encouraging the use of AI in curriculum development, personalized learning, and assessment, while also addressing concerns about privacy, ethics, and algorithmic bias.

Only by taking these comprehensive steps can we hope to avert the severe repercussions of the AI Divide and harness the full potential of AI for educational enhancement. This pressing task calls for concerted effort and collaboration across all sectors of society, from government and educational institutions to technology companies and civil society organizations. The stakes have never been higher, and the urgency to act has never been greater.

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The Artificial Intelligence Divide: Impacts, Consequences and approaches to minimize it

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The emergence of artificial intelligence (AI) in every sector of society has created a new form of digital divide, the "AI Divide." This divide goes beyond mere accessibility, considering levels of understanding and insightful usage of AI.

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AI Learning Design Workshop: See and Try the ALDA Rapid Prototype

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ListenHere’s a ChatGPT script you can run and modify yourself to experiment with the ALDA concept.

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Un excellent d'article du Professeur Garbinato de HEC - UNIL

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On ne présente plus ChatGPT, tant ce service en ligne a fait le buzz en un éclair et est devenu la nouvelle star technologique des médias et des réseaux sociaux.

Depuis, on nous annonce un monde nouveau, où le moindre de nos désirs sera exaucé littéralement dans la seconde.

Tel un génie sorti de sa lampe, ChatGPT nous invite : " Demandez et vous recevrez ! "

Vous devez rédiger un texte sur un sujet quelconque, histoire, géographie, politique, culture ? ChatGPT l'écrira pour vous.

Un avis de droit ? Le voici.

Un logiciel complexe ? Le voilà.

Quoi d'autre ? Ah oui, il vous faut une illustration, disons dans le style de H.R. Giger, pour la couverture de votre prochain roman; GPT la produira en un instant. Et si vous demandez gentiment, il écrira même votre roman dans la foulée. Il suffira de lui indiquer quelques éléments sur les personnages et le contexte. Idem pour votre prochain tube de l'été : demandez à GPT de vous écrire une chanson, disons à la manière de Ed Sheeran, et vous voilà nominé aux Emmy Awards.

Bien sûr, chaque médaille a son revers : adieu journalistes, avocats, écrivains, programmeurs, musiciens, graphistes ! Vos compétences si durement acquises sont désormais obsolètes. Vous êtes obsolètes, tant votre lenteur est devenue un embarras !

Le grand remplacement est pour demain, ce soir même, qui sait.

Même certains médias habituellement prudents nous expliquent que c'est le début de la fin et que nous avançons à grands pas vers l'avènement de la singularité technologique. Eh oui, pendant que vous dormez paisiblement, GPT assimile en permanence de nouvelles connaissances, se dote progressivement d'une intelligence générale, au même titre que les humains, et sera bientôt capable d'une empathie et d'une créativité largement supérieure à la nôtre, plus rapide, plus pertinente.

Alors il ne nous restera, à nous pauvres humains, que nos yeux remplis de larmes pour admirer notre élève prendre son envol et nous dépasser inexorablement.

Retour sur terre

Ah ce plaisir masochiste des humains qui adorent jouer à se faire peur.

Toute cette agitation autour de ChatGPT est révélatrice d'un paradoxe : les nouvelles technologies inondent nos vies et nos conversations, mais tout se passe comme si la compréhension de leur fonctionnement ne revêtait aucun intérêt lorsqu'il s'agit d'évaluer leur potentiel, leur limite et les dangers qu'elles portent en elles.

D'une certaine façon, cette manière d'appréhender l'intelligence artificielle est en phase avec l'esprit du temps, où le ressenti prévaut sur la raison et où aucun argument aussi rationnel soit-il ne saurait remettre en question le ressenti, en l'occurrence la fascination et la peur.

Pourtant, avant de tirer des plans sur la comète et prédire l'avènement d'un monde nouveau, voire la fin de l'humanité, peut-être vaut-il la peine se demander comment fonctionne réellement ChatGPT.

Or contrairement à ce qu'on pourrait penser à partir de la couverture médiatique de cette technologie, la compréhension de son fonctionnement, et donc de ses possibilités et ses limites, est à la portée de la plupart d'entre nous. Et comme nous allons le voir, le génie (si génie il y a) au cœur de ChatGPT n'est pas forcément là où on l'attend.

Comment ça marche ?

ChatGPT se présente sous la forme d'un agent conversationnel artificiel ( ChatBot), c'est-à-dire que l'on interagit avec cet agent comme on le ferait avec un humain à travers n'importe quelle application de discussion (chat), telle que Apple ou Google Messages, Whatsapp, Snapchat, etc.

Ce qui frappe immédiatement lorsque vous dialoguez avec ChatGPT, c'est la fluidité de sa conversation et sa capacité à tenir compte du contexte de la discussion. Vous pouvez par exemple lui demander de préciser une réponse, ou au contraire de généraliser son propos.

Le problème, c'est que cette fluidité et cette sensibilité au contexte peut aisément nous donner l'illusion que ChatGPT sait de quoi il parle. Or il n'en est rien, et voici pourquoi.

De manière très schématique, on peut voir ChatGPT comme une version sur-vitaminée de l'algorithme qui suggère le prochain mot d'une phrase que vous êtes en train d'écrire, lorsque vous utilisez une des applications de discussion mentionnées précédemment (Whatsapp, Snapchat, etc.).

Autrement dit, lorsqu'il s'agit de générer une réponse, ChatGPT va produire des phrases dont l'enchainement de mots est le plus probable, en se basant sur un grand nombre de textes qu'on lui a fait préalablement ingurgiter et analyser, et bien sûr en se basant sur le contexte de votre discussion avec lui jusque-là.

Là où ChatGPT diffère de l'algorithme suggérant le prochain mot du message que vous écrivez, c'est essentiellement par :

la taille du contexte pris en compte pour sa réponse,

le volume de texte analysé pour son entrainement initial,

le nombre de paramètres contrôlant la génération de ses réponses.

Ainsi, lorsque GPT-3 discute avec vous, il garde environ 3'000 mots en mémoire, ce qui lui permet de tenir compte de vos précédents échanges avec lui dans ses réponses. Le nombre paramètres du modèle de langage sur lequel il repose est quant à lui vertigineux : 175 milliards, ce qui nécessite 800 Go de stockage.

Finalement, l'entrainement du modèle de langage de ChatGPT s'est effectué sur un corpus de textes contenant environ 375 milliards de mots, provenant de diverses sources parmi lesquels Common Crawls, qui archive l'Internet, et Wikipedia, la fameuse encyclopédie collaborative en ligne. L'entrainement du modèle a été effectué sur une version de ce corpus de textes figée au mois de septembre 2021. Autrement dit, toute modification de ces sources postérieure à cette date ne fait pas partie des informations accessibles à GPT-3. C'est la raison pour laquelle ChatGPT ignore que Elizabeth II nous a quittés. Si l'on prend GPT-4, les nombres mentionnés ci-dessus sont encore plus vertigineux, mais le principe de base reste le même.

De Elisabeth Holmes à ChatGPT

D'une certaine manière, on peut dire que ChatGPT se comporte comme un interlocuteur qui s'appliquerait en permanence à terminer vos phrases ou à entretenir la conversation sur un sujet donné, en régurgitant plus ou moins fidèlement des propos qu'il a lus sur le sujet, sans les comprendre. Un exemple extrême de ce modèle probabiliste d'interactions se retrouve dans le jeu des dictons. Si je vous dis par exemple " Jamais deux... ", vous me répondrez " ...sans trois ". Si je vous dis " Tel père... ", vous me répondrez " ... tel fils ".

En revanche, il est essentiel de comprendre qu'il s'agit d'un modèle purement linguistique : pas de déduction, ni de raisonnement logique derrière les réponses de ChatGPT. Il peut par exemple affirmer des choses fausses et même contradictoires dans la même conversation, tant que ses réponses sont conformes au modèle probabiliste sous-jacent. Pour le dire autrement, ChatGPT n'a aucune notion de ce qui est vrai ou faux, uniquement de ce qui probable par rapport au corpus de textes sur lequel on l'a entrainé et du contexte de votre conversation avec lui.

Si ChatGPT était humain, on pourrait dire de lui qu'il est un imposteur, au même titre que Jean-Claude Romand, cet homme qui est parvenu à se faire passer pour un médecin et chercheur à l'OMS auprès de ses proches pendant dix-huit ans. Pour bien saisir la capacité d'un discours vraisemblable à nous faire croire à la compétence de notre interlocuteur, rappelons que lors d'un dîner chez un ami médecin, un convive cardiologue ayant discuté avec Romand pendant la soirée dira de lui à l'issu de ce dîner : " À côté de gens comme lui, on se sent tout petit. "

L'affaire de fraude pour laquelle Elisabeth Holmes, fondatrice de Theranos, a été condamnée à 11 ans de prison nous offre un autre exemple de la confusion qui peut exister entre vraisemblance et vérité. Là encore, le vernis scientifique acquis par Elisabeth Holmes au cours de ses deux années d'étude à l'Université de Stanford lui a permis de fabriquer un discours plausible autour de la possibilité d'effectuer une batterie de tests sanguins à partir d'une seule goutte de sang prélevée au bout d'un doigt.

Sur la base de ce discours, Holmes a réussi à convaincre un nombre impressionnant de personnalités à se joindre à elle, soit en tant qu'investisseurs (Rupert Murdoch, Larry Ellison), soit en tant que membre du conseil d'administration de Theranos (Henry Kissinger, George Shultz).

Pourtant, dès le début, certains ne s'y sont pas trompés.

Ainsi, de nombreux experts de laboratoires médicaux étaient sceptiques face aux promesses d'Elisabeth Holmes, du fait que certaines grandes molécules, telles que les protéines et les lipides, ne sont pas présentes en concentrations uniformes dans l'ensemble de notre corps. De ce fait, le sang prélevé au bout d'un doigt n'est pas le même que celui prélevé directement dans une veine. En mai 2016, Warren Buffett avait d'ailleurs critiqué Theranos pour avoir essentiellement nommé à son conseil d'administration des dirigeants politiques et militaires, plutôt que des experts en tests sanguins.

Et lorsqu'en septembre 2018 Theranos a finalement mis la clé sous la porte, tout le monde a dû se rendre à l'évidence : vérité et vraisemblance recouvrent des réalités très différentes.

La forme et le fond

Selon une idée reçue assez répandue, ChatGPT serait en permanence en train d'apprendre et donc d'affiner ses " connaissances ".

Là encore, il n'en est rien et ce pour une excellente raison.

En effet, si vous laissez un agent conversationnel se nourrir de ce qu'il trouve sur Internet, en particulier de ses conversations avec les internautes, il ne tardera pas à devenir raciste, insultant et colporteur de toutes sortes de théories complotistes. C'est précisément ce qui s'est produit en 2016 lorsque Microsoft a branché son agent conversationnel artificiel baptisé Tay sur Twitter.

Les ambitions et les espoirs de Microsoft était pourtant considérables : plus Tay allait interagir avec les internautes via Twitter, plus elle deviendrait intelligente et pertinente. La réalité fut toute autre : en moins de 24 heures, Tay se transforma en un troll haineux, raciste et misogyne, affirmant par exemple " Putain, je déteste les féministes ! " ou encore " Hitler avait raison, je déteste les juifs ! "

L'expérience fut si désastreuse que Microsoft débrancha définitivement Tay à peine deux jours après son lancement et présenta ses excuses pour les tweets litigieux. Encore une fois, rien d'étonnant : un agent conversationnel artificiel ne fait que régurgiter les propos les plus probables dans l'ensemble de textes sur lequel il est entraîné, ici les messages publiés sur Twitter. Or Twitter, et plus largement Internet, sont connus pour charrier des flots de messages haineux et orduriers.

Mais alors, comment ChatGPT fait-il pour résister à ce genre de dérives et produire des réponses souvent raisonnables, voire correctes ?

La réponse est d'une banalité confondante : ce sont des humains, beaucoup humains en fait, payés moins de $2/heure, qui ont manuellement nettoyé les données sur lesquelles ChatGPT a été entrainé.

D'une certaine façon, ChatGPT est comme un très jeune enfant qui répèterait ce qu'il entend à la maison, sans comprendre ce qu'il dit. Si ses parents sont racistes et complotistes, ses propos seront racistes et complotistes. Si ses parents sont humanistes et raisonnables, ses propos le seront aussi.

Or c'est bien là que réside l'ingéniosité, qui confine à la roublardise, des concepteurs de ChatGPT : en entrainant leur agent conversationnel sur des données nettoyées des éléments les plus outranciers et douteux, ce dernier ne produit la plupart du temps que des propos raisonnables, voire correctes.

Le revers de la médaille, c'est qu'il est impossible à ChatGPT d'apprendre sans être strictement encadré par des humains garants de l'orthodoxie de ses sources, sous peine des mêmes dérives que Tay, son injurieux confrère de chez Microsoft. C'est également la raison pour laquelle les " connaissances " de GPT-3 ne s'étendent pas au-delà de septembre 2021.

Au final, si ChatGPT ne dit pas trop de bêtises, même s'il en dit toutefois régulièrement, avec le même aplomb qu'un arracheur de dent, ce n'est pas parce qu'il est capable de distinguer le vrai du faux, mais simplement parce qu'on ne l'a entrainé que sur données scrupuleusement contrôlées. Ainsi, ChatGPT ne sait pas que la terre est ronde : il a juste été entrainé sur un corpus de textes ne contenant aucune phrase du type " la terre est plate ". Par conséquent, il existe une probabilité nulle qu'il termine la phrase " la terre est... " par l'adjectif " plate ".

L'étincelle qui... fait déborder le vase

Pour bien saisir la différence entre génération probabiliste de langage naturel et compréhension sémantique, reprenons l'exemple des dictons et supposons que vous ayons entrainé ChatGPT sur l'ensemble de tous les dictons de la langue française.

Après cet apprentissage, si vous lui dites " c'est la goutte qui a... ", il vous répondra " ...fait déborder le vase ", tandis que si vous lui dites " c'est l'étincelle qui a... ", il vous répondra " ...mis le feu aux poudres ". Pour autant, ChatGPT n'aura aucune notion de ce qu'est une étincelle ou une goutte, encore moins de la signification de ces métaphores.

A contrario, n'importe quel humain est en mesure de comprendre le sens (et l'humour) des deux dictons littéralement improbables suivants : " c'est la goutte qui a mis le feu aux poudres " et " c'est l'étincelle qui a fait déborder le vase ", quand bien même il ne les aurait jamais entendus auparavant.

Derrière cette compréhension par une intelligence humaine, plusieurs niveaux sémantiques s'entremêlent. Tout d'abord, nous savons qu'une étincelle peut faire exploser de la poudre et qu'une goutte supplémentaire fera fatalement déborder un vase rempli à ras bord.

Ensuite, nous comprenons que ces deux dictons expriment métaphoriquement le fait qu'il suffit parfois d'un élément en apparence insignifiant (une goutte, une étincelle) pour déclencher un résultat autrement plus signifiant (le feu ou le débordement). Il existe bien sûr une nuance entre ces deux dictons, puisque celui de la goutte contient en plus l'idée d'une accumulation préalable à l'avènement de la catastrophe.

Finalement, en entrelaçant ces deux expressions, nous comprenons que le sens métaphorique n'a pas fondamentalement changé mais qu'on y a simplement ajouté une pointe d'humour absurde.

Rien de tout cela chez ChatGPT, à moins bien sûr que dans sa prochaine version, il ait été entrainé sur un corpus de texte contenant l'article que vous êtes en train de lire à l'instant. Et même dans ce cas, s'il se trouve alors capable de produire la phrase " c'est la goutte qui a mis le feu aux poudres ", cela sera uniquement dû au fait que cette suite de mots sera devenue probable dans un certain contexte.

Qu'est-ce que cela dit de nous ?

Outre le fait d'avoir entrainé ChatGPT sur des données dont l'orthodoxie a été strictement contrôlée par des humains (méthodiquement exploités), l'autre innovation de ChatGPT est d'avoir été mis à disposition du grand public. En effet, des systèmes similaires existaient déjà dans un certain nombre de laboratoires de recherche en intelligence artificielle à travers le monde, mais ces systèmes n'étaient accessibles qu'aux seuls spécialistes du domaine.

Face à cette mise en accès publique, une question s'impose : que dit de nous la sidération qui a accompagné le lancement de ChatGPT et la confusion qui s'en est suivie autour des notions d'intelligence, de créativité et même d'empathie ?

Pour tenter de répondre à cette question, rappelons-nous d'abord que l'informatique se définit comme l'automatisation du traitement de l'information par des machines, et que l'intelligence artificielle n'échappe pas à cette définition. De plus, rappelons-nous la définition qu'a donné Jean Piaget de l'intelligence humaine : " L'intelligence, ce n'est pas ce que l'on sait mais ce que l'on fait quand on ne sait pas. "

Mais d'où vient alors que nous confondions des notions aussi éloignées que l'intelligence humaine et la production automatique de textes sur une base statistique et algorithmique ?

Une réponse possible tient peut-être au fait que dans de nombreuses situations, nous nous comportons comme des automates qui reproduisent simplement, avec des variations mineures, des discours et des comportements normés et donc prévisibles.

La question est donc moins de savoir si des automates sont (ou serons un jour) capables de se comporter comme des humains, mais dans quelle mesure les humains de comportent comme des automates dans de nombreuses situations.

Finalement, nous mobilisons sans doute notre intelligence, notre empathie et notre créativité bien moins souvent que nous ne l'imaginons.

Dans tous les domaines, y compris ceux où la créativité est sensée régner en maitre, comme la recherche scientifique ou l'art, le conformisme s'est installé durablement, car il augmente significativement la probabilité de produire des résultats reconnus comme ayant de la valeur par le plus grand nombre.

Ainsi, lorsqu'un film ou une série en rupture avec les codes du genre remporte un grand succès, une avalanche de films et de séries similaires sont produits au cours des années qui suivent. Et ce qui était initialement hors-norme devient progressivement la nouvelle norme.

De même, la recherche n'a jamais été aussi prolifique en articles scientifiques que ces vingt dernières années, en particulier dans le monde académique, mais la plupart des chercheurs le savent bien, si vous voulez publier rapidement, mieux vaut écrire un n-ième article sur un sujet en vogue que prendre le risque de lancer une idée trop originale. Ceci est particulièrement vrai pour les chercheurs en début de carrière.

Au point où certains chercheurs se réjouissent d'ores et déjà (plus ou moins ouvertement) de pouvoir déléguer à ChatGPT la génération automatique de portions entières de leurs prochains articles scientifiques.

Où cela va-t-il nous mener ?

Si l'intelligence artificielle constitue effectivement un risque pour l'humanité, il ne se trouve pas dans le grand remplacement des humains par les machines. Il y a fort à parier en effet qu'à court et moyen terme, les humains ne seront pas remplacés par des machines intelligentes, mais par d'autres humains, en nettement plus petit nombre, sachant utiliser intelligemment des outils comme ChatGPT. Par conséquent, le vrai danger se trouve probablement dans la disparition, ou du moins la diminution significative, chez un grand nombre d'humains, de leur capacité à construire leur propre vision du monde, à l'exprimer de manière articulée, et bien sûr à faire appel à leur créativité pour le faire évoluer.

Prenons quelques exemples.

Si demain, la plupart des graphistes changent de métier et l'écrasante majorité des images publiées sur Internet est générée par des intelligences artificielles sur la base des productions graphiques produites jusque-là par des humains, il arrivera fatalement un moment où ces intelligences artificielles ré-ingurgiteront leur propres productions et générerons ad nauseam des variantes insipides de ce qui existe déjà.

Or tout comme les textes produits par ChatGPT, les images générées par des intelligences artificielles ne sont in fine que le résultat d'une moyenne statistique calculée à partir de certains paramètres, sur la base de ce qui est le plus désirable par la majorité des utilisateurs. Autrement dit, ces intelligences artificielles peuvent certes produire un très grand nombre de variations à partir d'un ensemble d'images, mais lorsque la majeure partie de cet ensemble sera constituée d'images elles-mêmes générées par ces intelligences artificielles, le système finira par tourner en rond et se mordre la queue.

Comme nous l'avons vu, cette tendance au radotage existe également lorsque nous produisons nous-même du contenu artistique ou scientifique, en raison de notre besoin de reconnaissance par le public ou par nos pairs. Heureusement, de temps à autre apparaissent des productions authentiquement originales, permettant ainsi aux diverses discipline artistiques ou scientifiques de progresser.

Avec l'arrivée d'outils comme ChatGPT en revanche, ce radotage risque de radicalement changer d'échelle, passant de l'artisanat à la production industrielle de contenus redondants.

Or la théorie de l'information de Shannon nous enseigne que lorsque tout n'est plus que redondance, il n'y a plus d'information : si nous ne faisons plus que ressasser ce que nous savons déjà pour l'avoir exprimé maintes et maintes fois, nous n'apprenons rien.

Ainsi, dès lors que la plupart des productions littéraires, graphiques, cinématographiques, ou même scientifiques, ne seraient que le fruit d'une reconfiguration statistique de contenus existants, la créativité humaine risquerait à terme de subir un sacré coup de frein.

On peut bien sûr objecter qu'une solution à ce problème pourrait consister à régulièrement injecter un peu d'aléatoire dans le processus de génération automatique de contenus, mais là encore, il s'agit d'une simplification grossière de la manière dont notre intelligence humaine fait avancer l'art et la science.

En musique, peut-on sérieusement croire que le romantisme est une variation aléatoire du classicisme ? Et on est en droit de se poser la même question de la peinture impressionniste par rapport à la peinture dite réaliste, qui la précède historiquement.

Les révolutions artistiques et scientifiques ne naissent pas d'une injection hasardeuse d'idées ou de concepts aléatoires, mais au contraire d'une compréhension profonde du monde par les artistes et les scientifiques qui sont à l'origine de ces révolutions.

De plus, si la créativité est effectivement le processus consistant à avoir des idées originales qui ont de la valeur, comme la définit Ken Robinson, la question centrale est donc de savoir qui détermine de ce qui a de la valeur et sur quelle base ? Là encore, seule une compréhension profonde du monde, et des disciplines où notre créativité s'exerce, permet de trancher cette question.

Un air de déjà-vu ?

Bien que les espoirs et les peurs engendrés par ChatGPT puissent nous sembler sans précédent, en réalité, à chaque avancée significative de l'informatique, nous revisitons les mêmes mythes et les mêmes fantasmes.

Lorsque les premières calculatrices électroniques sont devenues accessibles au grand public, on s'est inquiété de savoir si on devait laisser les jeunes enfants les utiliser. Fallait-il encore enseigner les règles de base du calcul, comme les propriétés de commutativité ou d'associativité de certaines opérations arithmétiques ?

Les réponses à ces questions furent nuancées, mais dans les grandes lignes, on décida avec raison que les calculatrices étaient des outils fabuleux, pour qui savait calculer et avait assimilé les bases de l'arithmétique. Par conséquent, on continua à enseigner les principes du calcul, même si l'usage des calculatrices fut admis à partir d'un certain niveau d'étude.

Cette approche reposait sur le bon sens : comment notre système éducatif pouvait-il en effet former des personnes capables de faire progresser nos connaissances en mathématiques, ou en toutes autres sciences utilisant les mathématiques comme langage, si nous remplacions la compréhension de ses principes de base par la seule aptitude à appuyer sur des boutons ?

Plus récemment a contrario, pendant la période du COVID, on a pu constater à quel point la maitrise des outils numériques par les soi-disant " natifs du numérique " (digital natives) était un mythe. Contrairement à l'usage de la calculatrice, on était parti du principe depuis plusieurs années qu'il suffisait aux natifs du numérique d'avoir été exposés aux outils informatiques depuis leur plus jeune âge, pour qu'ils acquièrent une compréhension solide et approfondie des principes fondamentaux de l'informatique.

Or on s'est aperçu que les natifs du numérique n'avaient en réalité développé que des connaissances contingentes et superficielles, essentiellement liées à l'usage de certains outils spécifiques. Par exemple, de nombreux jeunes aujourd'hui n'ont aucune notion de la manière dont les données sont stockées, au point où les notions de fichier, de disque ou de serveur de stockages leur sont le plus souvent étrangères.

Un bon serviteur mais un mauvais maître

Comme tous les outils, l'intelligence artificielle en général et ChatGTP en particulier sont de fabuleux serviteurs pour qui sait les utiliser, mais de bien mauvais maîtres pour qui s'en rend totalement tributaire.

Là encore, cette situation n'est pas nouvelle : les traducteurs automatiques, les correcteurs orthographiques, les environnements de développement intégrés, Internet lui-même, sont des outils d'une efficacité redoutable aux mains d'utilisateurs avertis, mais peuvent être terriblement dangereux lorsqu'ils sont manipulés sans discernement par des utilisateurs candides.

Au final, il nous appartient de décider si l'intelligence artificielle s'inscrira dans la lignée de ce que l'ordinateur a été jusque-là, à savoir une forme de vélo pour l'esprit, qui ne se substitue pas à notre force musculaire mais permet de la multiplier et donc d'aller plus loin, selon une métaphore chère à Steve Jobs.

Avec l'intelligence artificielle, sans doute est-on en train de passer au vélo électrique : le facteur multiplicateur est certes plus important, mais le principe reste le même, puisque c'est toujours le cycliste qui fournit le mouvement de base sans lequel le vélo électrique reste immobile.

L'alternative à ce scénario serait que l'intelligence artificielle devienne une sorte de SUV autonome, écrasant tout sur son passage : les nuances infinies de notre intelligence humaine, notre intuition, notre créativité, notre sensibilité, notre empathie au monde.

Si nous voulons éviter ce second scénario, il nous appartient donc de déterminer non seulement quand et comment utiliser l'intelligence artificielle, mais aussi et surtout quand et comment ne pas le faire.

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Le Buzz autour de ChatGPT - Vers l'infini et au delà !

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On ne présente plus ChatGPT, tant ce service en ligne a fait le buzz en un éclair et est devenu la nouvelle star technologique des médias et des réseaux sociaux. Depuis, on nous annonce un monde nouveau, où le moindre de nos désirs sera exaucé littéralement dans la seconde.

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La start-up Mistral AI, forte d'une valorisation de 240 millions d'euros, a mis en ligne Mistral 7B, son premier modèle de langage génératif open source. Non seulement gratuit et sans modération, Mistral 7B se distingue également par une efficacité et une rapidité qui le positionnent face à des géants tels qu'OpenAI, Microsoft, Google et Meta.

La start-up française Mistral AI, valorisée à 240 millions d'euros, a dévoilé Mistral 7B, un modèle de langage génératif open source. Gratuit et dépourvu de mécanisme de modération, ce modèle de langage se distinguerait par sa rapidité et son efficacité par rapport aux solutions propriétaires concurrentes, comme OpenAI ( GPT, ChatGPT), Microsoft ( Bing), Google (BERT, LaMDA, Bard) et Meta ( LLaMa), le tout pour un coût minime.

Mistral 7B n'est que le début du voyage de l'entreprise dans l'univers de l'intelligence artificielle. D'après l'annonce officielle, ce modèle de langage peut déjà être utilisé pour effectuer une variété de tâches, telles que le résumé, la structuration de texte et la réponse à des questions. Comme dit plus haut, l'aspect open source de Mistral 7B, publié sous licence Apache 2.0, permet une utilisation sans restriction.

Avec Mistral 7B, la start-up française Mistral AI, co-fondée par Guillaume Lample, Arthur Mensch, et Timothée Lacroix, marque donc une entrée fracassante dans le domaine de l'intelligence artificielle. Publié le 27 septembre, ce premier Modèle de Langage à Large Multi-tâches (LLM) de la société est doté de sept milliards de paramètres.

Une modération absente

Cependant, la liberté offerte par Mistral 7B n'est pas sans soulever des questions de sécurité. Le chercheur en sécurité Paul Röttger a souligné l'absence de mécanismes de modération dans le modèle. À travers un ensemble de 175 questions-réponses compilées, il a mis en évidence la possibilité de récupérer des informations sur des activités criminelles, incluant des instructions relatives au meurtre, au suicide ou à la production de drogues.

De plus, la diffusion de Mistral 7B via torrent, sous la forme de Magnet-Link, rend sa distribution irréversible, empêchant ainsi l'ajout ultérieur de mécanismes de modération.

magnet:?xt=urn:btih:208b101a0f51514ecf285885a8b0f6fb1a1e4d7d&dn=mistral-7B-v0.1&tr=udp%3A%2F% https://t.co/OdtBUsbMKD%3A1337%2Fannounce&tr=https%3A%2F% https://t.co/HAadNvH1t0%3A443%2Fannounce

RELEASE ab979f50d7d406ab8d0b07d09806c72c

- Mistral AI (@MistralAI) September 27, 2023

Gérer mes choix

Que vaut Mistral 7B ?

Mistral 7B ne se contente pas de rivaliser avec le LLM LLaMa 2 13B : il le surpasse selon divers benchmarks, tout en affichant des performances comparables à celles de LLaMa 34B, qui possède pourtant 27 milliards de paramètres supplémentaires.

Le modèle semble bien se débrouiller notamment dans les domaines du raisonnement, de la compréhension et du raisonnement STEM (MMLU), avec des performances équivalentes à un LLaMa 2, trois fois plus grand. Ce modèle, optimisé sur un ensemble de données publiques de Hugging Face et lancé sous le nom Mistral 7B Instruct, fonctionne en langue française.

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Un modèle d'IA français, gratuit et sans modération, a été lancé

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La start-up Mistral AI, forte d'une valorisation de 240 millions d'euros, a mis en ligne Mistral 7B, son premier modèle de langage génératif open source. Non seulement gratuit et sans modération, Mistral 7B se distingue également par une efficacité et une rapidité qui le positionnent face à des géants tels qu'OpenAI, Microsoft, Google et Meta.

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Le Buzz autour de ChatGPT - Vers l'infini et au delà ! Un post excellent du Prof Garbinato - HEC- UNIL

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Le Buzz autour de ChatGPT - Vers l'infini et au delà ! Un post excellent du Prof Garbinato - HEC- UNIL

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On ne présente plus ChatGPT, tant ce service en ligne a fait le buzz en un éclair et est devenu la nouvelle star technologique des médias et des réseaux sociaux.

Depuis, on nous annonce un monde nouveau, où le moindre de nos désirs sera exaucé littéralement dans la seconde.

Tel un génie sorti de sa lampe, ChatGPT nous invite : " Demandez et vous recevrez ! "

Vous devez rédiger un texte sur un sujet quelconque, histoire, géographie, politique, culture ? ChatGPT l'écrira pour vous.

Un avis de droit ? Le voici.

Un logiciel complexe ? Le voilà.

Quoi d'autre ? Ah oui, il vous faut une illustration, disons dans le style de H.R. Giger, pour la couverture de votre prochain roman; GPT la produira en un instant. Et si vous demandez gentiment, il écrira même votre roman dans la foulée. Il suffira de lui indiquer quelques éléments sur les personnages et le contexte. Idem pour votre prochain tube de l'été : demandez à GPT de vous écrire une chanson, disons à la manière de Ed Sheeran, et vous voilà nominé aux Emmy Awards.

Bien sûr, chaque médaille a son revers : adieu journalistes, avocats, écrivains, programmeurs, musiciens, graphistes ! Vos compétences si durement acquises sont désormais obsolètes. Vous êtes obsolètes, tant votre lenteur est devenue un embarras !

Le grand remplacement est pour demain, ce soir même, qui sait.

Même certains médias habituellement prudents nous expliquent que c'est le début de la fin et que nous avançons à grands pas vers l'avènement de la singularité technologique. Eh oui, pendant que vous dormez paisiblement, GPT assimile en permanence de nouvelles connaissances, se dote progressivement d'une intelligence générale, au même titre que les humains, et sera bientôt capable d'une empathie et d'une créativité largement supérieure à la nôtre, plus rapide, plus pertinente.

Alors il ne nous restera, à nous pauvres humains, que nos yeux remplis de larmes pour admirer notre élève prendre son envol et nous dépasser inexorablement.

Retour sur terre

Ah ce plaisir masochiste des humains qui adorent jouer à se faire peur.

Toute cette agitation autour de ChatGPT est révélatrice d'un paradoxe : les nouvelles technologies inondent nos vies et nos conversations, mais tout se passe comme si la compréhension de leur fonctionnement ne revêtait aucun intérêt lorsqu'il s'agit d'évaluer leur potentiel, leur limite et les dangers qu'elles portent en elles.

D'une certaine façon, cette manière d'appréhender l'intelligence artificielle est en phase avec l'esprit du temps, où le ressenti prévaut sur la raison et où aucun argument aussi rationnel soit-il ne saurait remettre en question le ressenti, en l'occurrence la fascination et la peur.

Pourtant, avant de tirer des plans sur la comète et prédire l'avènement d'un monde nouveau, voire la fin de l'humanité, peut-être vaut-il la peine se demander comment fonctionne réellement ChatGPT.

Or contrairement à ce qu'on pourrait penser à partir de la couverture médiatique de cette technologie, la compréhension de son fonctionnement, et donc de ses possibilités et ses limites, est à la portée de la plupart d'entre nous. Et comme nous allons le voir, le génie (si génie il y a) au cœur de ChatGPT n'est pas forcément là où on l'attend.

Comment ça marche ?

ChatGPT se présente sous la forme d'un agent conversationnel artificiel ( ChatBot), c'est-à-dire que l'on interagit avec cet agent comme on le ferait avec un humain à travers n'importe quelle application de discussion (chat), telle que Apple ou Google Messages, Whatsapp, Snapchat, etc.

Ce qui frappe immédiatement lorsque vous dialoguez avec ChatGPT, c'est la fluidité de sa conversation et sa capacité à tenir compte du contexte de la discussion. Vous pouvez par exemple lui demander de préciser une réponse, ou au contraire de généraliser son propos.

Le problème, c'est que cette fluidité et cette sensibilité au contexte peut aisément nous donner l'illusion que ChatGPT sait de quoi il parle. Or il n'en est rien, et voici pourquoi.

De manière très schématique, on peut voir ChatGPT comme une version sur-vitaminée de l'algorithme qui suggère le prochain mot d'une phrase que vous êtes en train d'écrire, lorsque vous utilisez une des applications de discussion mentionnées précédemment (Whatsapp, Snapchat, etc.).

Autrement dit, lorsqu'il s'agit de générer une réponse, ChatGPT va produire des phrases dont l'enchainement de mots est le plus probable, en se basant sur un grand nombre de textes qu'on lui a fait préalablement ingurgiter et analyser, et bien sûr en se basant sur le contexte de votre discussion avec lui jusque-là.

Là où ChatGPT diffère de l'algorithme suggérant le prochain mot du message que vous écrivez, c'est essentiellement par :

la taille du contexte pris en compte pour sa réponse,

le volume de texte analysé pour son entrainement initial,

le nombre de paramètres contrôlant la génération de ses réponses.

Ainsi, lorsque GPT-3 discute avec vous, il garde environ 3'000 mots en mémoire, ce qui lui permet de tenir compte de vos précédents échanges avec lui dans ses réponses. Le nombre paramètres du modèle de langage sur lequel il repose est quant à lui vertigineux : 175 milliards, ce qui nécessite 800 Go de stockage.

Finalement, l'entrainement du modèle de langage de ChatGPT s'est effectué sur un corpus de textes contenant environ 375 milliards de mots, provenant de diverses sources parmi lesquels Common Crawls, qui archive l'Internet, et Wikipedia, la fameuse encyclopédie collaborative en ligne. L'entrainement du modèle a été effectué sur une version de ce corpus de textes figée au mois de septembre 2021. Autrement dit, toute modification de ces sources postérieure à cette date ne fait pas partie des informations accessibles à GPT-3. C'est la raison pour laquelle ChatGPT ignore que Elizabeth II nous a quittés. Si l'on prend GPT-4, les nombres mentionnés ci-dessus sont encore plus vertigineux, mais le principe de base reste le même.

De Elisabeth Holmes à ChatGPT

D'une certaine manière, on peut dire que ChatGPT se comporte comme un interlocuteur qui s'appliquerait en permanence à terminer vos phrases ou à entretenir la conversation sur un sujet donné, en régurgitant plus ou moins fidèlement des propos qu'il a lus sur le sujet, sans les comprendre. Un exemple extrême de ce modèle probabiliste d'interactions se retrouve dans le jeu des dictons. Si je vous dis par exemple " Jamais deux... ", vous me répondrez " ...sans trois ". Si je vous dis " Tel père... ", vous me répondrez " ... tel fils ".

En revanche, il est essentiel de comprendre qu'il s'agit d'un modèle purement linguistique : pas de déduction, ni de raisonnement logique derrière les réponses de ChatGPT. Il peut par exemple affirmer des choses fausses et même contradictoires dans la même conversation, tant que ses réponses sont conformes au modèle probabiliste sous-jacent. Pour le dire autrement, ChatGPT n'a aucune notion de ce qui est vrai ou faux, uniquement de ce qui probable par rapport au corpus de textes sur lequel on l'a entrainé et du contexte de votre conversation avec lui.

Si ChatGPT était humain, on pourrait dire de lui qu'il est un imposteur, au même titre que Jean-Claude Romand, cet homme qui est parvenu à se faire passer pour un médecin et chercheur à l'OMS auprès de ses proches pendant dix-huit ans. Pour bien saisir la capacité d'un discours vraisemblable à nous faire croire à la compétence de notre interlocuteur, rappelons que lors d'un dîner chez un ami médecin, un convive cardiologue ayant discuté avec Romand pendant la soirée dira de lui à l'issu de ce dîner : " À côté de gens comme lui, on se sent tout petit. "

L'affaire de fraude pour laquelle Elisabeth Holmes, fondatrice de Theranos, a été condamnée à 11 ans de prison nous offre un autre exemple de la confusion qui peut exister entre vraisemblance et vérité. Là encore, le vernis scientifique acquis par Elisabeth Holmes au cours de ses deux années d'étude à l'Université de Stanford lui a permis de fabriquer un discours plausible autour de la possibilité d'effectuer une batterie de tests sanguins à partir d'une seule goutte de sang prélevée au bout d'un doigt.

Sur la base de ce discours, Holmes a réussi à convaincre un nombre impressionnant de personnalités à se joindre à elle, soit en tant qu'investisseurs (Rupert Murdoch, Larry Ellison), soit en tant que membre du conseil d'administration de Theranos (Henry Kissinger, George Shultz).

Pourtant, dès le début, certains ne s'y sont pas trompés.

Ainsi, de nombreux experts de laboratoires médicaux étaient sceptiques face aux promesses d'Elisabeth Holmes, du fait que certaines grandes molécules, telles que les protéines et les lipides, ne sont pas présentes en concentrations uniformes dans l'ensemble de notre corps. De ce fait, le sang prélevé au bout d'un doigt n'est pas le même que celui prélevé directement dans une veine. En mai 2016, Warren Buffett avait d'ailleurs critiqué Theranos pour avoir essentiellement nommé à son conseil d'administration des dirigeants politiques et militaires, plutôt que des experts en tests sanguins.

Et lorsqu'en septembre 2018 Theranos a finalement mis la clé sous la porte, tout le monde a dû se rendre à l'évidence : vérité et vraisemblance recouvrent des réalités très différentes.

La forme et le fond

Selon une idée reçue assez répandue, ChatGPT serait en permanence en train d'apprendre et donc d'affiner ses " connaissances ".

Là encore, il n'en est rien et ce pour une excellente raison.

En effet, si vous laissez un agent conversationnel se nourrir de ce qu'il trouve sur Internet, en particulier de ses conversations avec les internautes, il ne tardera pas à devenir raciste, insultant et colporteur de toutes sortes de théories complotistes. C'est précisément ce qui s'est produit en 2016 lorsque Microsoft a branché son agent conversationnel artificiel baptisé Tay sur Twitter.

Les ambitions et les espoirs de Microsoft était pourtant considérables : plus Tay allait interagir avec les internautes via Twitter, plus elle deviendrait intelligente et pertinente. La réalité fut toute autre : en moins de 24 heures, Tay se transforma en un troll haineux, raciste et misogyne, affirmant par exemple " Putain, je déteste les féministes ! " ou encore " Hitler avait raison, je déteste les juifs ! "

L'expérience fut si désastreuse que Microsoft débrancha définitivement Tay à peine deux jours après son lancement et présenta ses excuses pour les tweets litigieux. Encore une fois, rien d'étonnant : un agent conversationnel artificiel ne fait que régurgiter les propos les plus probables dans l'ensemble de textes sur lequel il est entraîné, ici les messages publiés sur Twitter. Or Twitter, et plus largement Internet, sont connus pour charrier des flots de messages haineux et orduriers.

Mais alors, comment ChatGPT fait-il pour résister à ce genre de dérives et produire des réponses souvent raisonnables, voire correctes ?

La réponse est d'une banalité confondante : ce sont des humains, beaucoup humains en fait, payés moins de $2/heure, qui ont manuellement nettoyé les données sur lesquelles ChatGPT a été entrainé.

D'une certaine façon, ChatGPT est comme un très jeune enfant qui répèterait ce qu'il entend à la maison, sans comprendre ce qu'il dit. Si ses parents sont racistes et complotistes, ses propos seront racistes et complotistes. Si ses parents sont humanistes et raisonnables, ses propos le seront aussi.

Or c'est bien là que réside l'ingéniosité, qui confine à la roublardise, des concepteurs de ChatGPT : en entrainant leur agent conversationnel sur des données nettoyées des éléments les plus outranciers et douteux, ce dernier ne produit la plupart du temps que des propos raisonnables, voire correctes.

Le revers de la médaille, c'est qu'il est impossible à ChatGPT d'apprendre sans être strictement encadré par des humains garants de l'orthodoxie de ses sources, sous peine des mêmes dérives que Tay, son injurieux confrère de chez Microsoft. C'est également la raison pour laquelle les " connaissances " de GPT-3 ne s'étendent pas au-delà de septembre 2021.

Au final, si ChatGPT ne dit pas trop de bêtises, même s'il en dit toutefois régulièrement, avec le même aplomb qu'un arracheur de dent, ce n'est pas parce qu'il est capable de distinguer le vrai du faux, mais simplement parce qu'on ne l'a entrainé que sur données scrupuleusement contrôlées. Ainsi, ChatGPT ne sait pas que la terre est ronde : il a juste été entrainé sur un corpus de textes ne contenant aucune phrase du type " la terre est plate ". Par conséquent, il existe une probabilité nulle qu'il termine la phrase " la terre est... " par l'adjectif " plate ".

L'étincelle qui... fait déborder le vase

Pour bien saisir la différence entre génération probabiliste de langage naturel et compréhension sémantique, reprenons l'exemple des dictons et supposons que vous ayons entrainé ChatGPT sur l'ensemble de tous les dictons de la langue française.

Après cet apprentissage, si vous lui dites " c'est la goutte qui a... ", il vous répondra " ...fait déborder le vase ", tandis que si vous lui dites " c'est l'étincelle qui a... ", il vous répondra " ...mis le feu aux poudres ". Pour autant, ChatGPT n'aura aucune notion de ce qu'est une étincelle ou une goutte, encore moins de la signification de ces métaphores.

A contrario, n'importe quel humain est en mesure de comprendre le sens (et l'humour) des deux dictons littéralement improbables suivants : " c'est la goutte qui a mis le feu aux poudres " et " c'est l'étincelle qui a fait déborder le vase ", quand bien même il ne les aurait jamais entendus auparavant.

Derrière cette compréhension par une intelligence humaine, plusieurs niveaux sémantiques s'entremêlent. Tout d'abord, nous savons qu'une étincelle peut faire exploser de la poudre et qu'une goutte supplémentaire fera fatalement déborder un vase rempli à ras bord.

Ensuite, nous comprenons que ces deux dictons expriment métaphoriquement le fait qu'il suffit parfois d'un élément en apparence insignifiant (une goutte, une étincelle) pour déclencher un résultat autrement plus signifiant (le feu ou le débordement). Il existe bien sûr une nuance entre ces deux dictons, puisque celui de la goutte contient en plus l'idée d'une accumulation préalable à l'avènement de la catastrophe.

Finalement, en entrelaçant ces deux expressions, nous comprenons que le sens métaphorique n'a pas fondamentalement changé mais qu'on y a simplement ajouté une pointe d'humour absurde.

Rien de tout cela chez ChatGPT, à moins bien sûr que dans sa prochaine version, il ait été entrainé sur un corpus de texte contenant l'article que vous êtes en train de lire à l'instant. Et même dans ce cas, s'il se trouve alors capable de produire la phrase " c'est la goutte qui a mis le feu aux poudres ", cela sera uniquement dû au fait que cette suite de mots sera devenue probable dans un certain contexte.

Qu'est-ce que cela dit de nous ?

Outre le fait d'avoir entrainé ChatGPT sur des données dont l'orthodoxie a été strictement contrôlée par des humains (méthodiquement exploités), l'autre innovation de ChatGPT est d'avoir été mis à disposition du grand public. En effet, des systèmes similaires existaient déjà dans un certain nombre de laboratoires de recherche en intelligence artificielle à travers le monde, mais ces systèmes n'étaient accessibles qu'aux seuls spécialistes du domaine.

Face à cette mise en accès publique, une question s'impose : que dit de nous la sidération qui a accompagné le lancement de ChatGPT et la confusion qui s'en est suivie autour des notions d'intelligence, de créativité et même d'empathie ?

Pour tenter de répondre à cette question, rappelons-nous d'abord que l'informatique se définit comme l'automatisation du traitement de l'information par des machines, et que l'intelligence artificielle n'échappe pas à cette définition. De plus, rappelons-nous la définition qu'a donné Jean Piaget de l'intelligence humaine : " L'intelligence, ce n'est pas ce que l'on sait mais ce que l'on fait quand on ne sait pas. "

Mais d'où vient alors que nous confondions des notions aussi éloignées que l'intelligence humaine et la production automatique de textes sur une base statistique et algorithmique ?

Une réponse possible tient peut-être au fait que dans de nombreuses situations, nous nous comportons comme des automates qui reproduisent simplement, avec des variations mineures, des discours et des comportements normés et donc prévisibles.

La question est donc moins de savoir si des automates sont (ou serons un jour) capables de se comporter comme des humains, mais dans quelle mesure les humains de comportent comme des automates dans de nombreuses situations.

Finalement, nous mobilisons sans doute notre intelligence, notre empathie et notre créativité bien moins souvent que nous ne l'imaginons.

Dans tous les domaines, y compris ceux où la créativité est sensée régner en maitre, comme la recherche scientifique ou l'art, le conformisme s'est installé durablement, car il augmente significativement la probabilité de produire des résultats reconnus comme ayant de la valeur par le plus grand nombre.

Ainsi, lorsqu'un film ou une série en rupture avec les codes du genre remporte un grand succès, une avalanche de films et de séries similaires sont produits au cours des années qui suivent. Et ce qui était initialement hors-norme devient progressivement la nouvelle norme.

De même, la recherche n'a jamais été aussi prolifique en articles scientifiques que ces vingt dernières années, en particulier dans le monde académique, mais la plupart des chercheurs le savent bien, si vous voulez publier rapidement, mieux vaut écrire un n-ième article sur un sujet en vogue que prendre le risque de lancer une idée trop originale. Ceci est particulièrement vrai pour les chercheurs en début de carrière.

Au point où certains chercheurs se réjouissent d'ores et déjà (plus ou moins ouvertement) de pouvoir déléguer à ChatGPT la génération automatique de portions entières de leurs prochains articles scientifiques.

Où cela va-t-il nous mener ?

Si l'intelligence artificielle constitue effectivement un risque pour l'humanité, il ne se trouve pas dans le grand remplacement des humains par les machines. Il y a fort à parier en effet qu'à court et moyen terme, les humains ne seront pas remplacés par des machines intelligentes, mais par d'autres humains, en nettement plus petit nombre, sachant utiliser intelligemment des outils comme ChatGPT. Par conséquent, le vrai danger se trouve probablement dans la disparition, ou du moins la diminution significative, chez un grand nombre d'humains, de leur capacité à construire leur propre vision du monde, à l'exprimer de manière articulée, et bien sûr à faire appel à leur créativité pour le faire évoluer.

Prenons quelques exemples.

Si demain, la plupart des graphistes changent de métier et l'écrasante majorité des images publiées sur Internet est générée par des intelligences artificielles sur la base des productions graphiques produites jusque-là par des humains, il arrivera fatalement un moment où ces intelligences artificielles ré-ingurgiteront leur propres productions et générerons ad nauseam des variantes insipides de ce qui existe déjà.

Or tout comme les textes produits par ChatGPT, les images générées par des intelligences artificielles ne sont in fine que le résultat d'une moyenne statistique calculée à partir de certains paramètres, sur la base de ce qui est le plus désirable par la majorité des utilisateurs. Autrement dit, ces intelligences artificielles peuvent certes produire un très grand nombre de variations à partir d'un ensemble d'images, mais lorsque la majeure partie de cet ensemble sera constituée d'images elles-mêmes générées par ces intelligences artificielles, le système finira par tourner en rond et se mordre la queue.

Comme nous l'avons vu, cette tendance au radotage existe également lorsque nous produisons nous-même du contenu artistique ou scientifique, en raison de notre besoin de reconnaissance par le public ou par nos pairs. Heureusement, de temps à autre apparaissent des productions authentiquement originales, permettant ainsi aux diverses discipline artistiques ou scientifiques de progresser.

Avec l'arrivée d'outils comme ChatGPT en revanche, ce radotage risque de radicalement changer d'échelle, passant de l'artisanat à la production industrielle de contenus redondants.

Or la théorie de l'information de Shannon nous enseigne que lorsque tout n'est plus que redondance, il n'y a plus d'information : si nous ne faisons plus que ressasser ce que nous savons déjà pour l'avoir exprimé maintes et maintes fois, nous n'apprenons rien.

Ainsi, dès lors que la plupart des productions littéraires, graphiques, cinématographiques, ou même scientifiques, ne seraient que le fruit d'une reconfiguration statistique de contenus existants, la créativité humaine risquerait à terme de subir un sacré coup de frein.

On peut bien sûr objecter qu'une solution à ce problème pourrait consister à régulièrement injecter un peu d'aléatoire dans le processus de génération automatique de contenus, mais là encore, il s'agit d'une simplification grossière de la manière dont notre intelligence humaine fait avancer l'art et la science.

En musique, peut-on sérieusement croire que le romantisme est une variation aléatoire du classicisme ? Et on est en droit de se poser la même question de la peinture impressionniste par rapport à la peinture dite réaliste, qui la précède historiquement.

Les révolutions artistiques et scientifiques ne naissent pas d'une injection hasardeuse d'idées ou de concepts aléatoires, mais au contraire d'une compréhension profonde du monde par les artistes et les scientifiques qui sont à l'origine de ces révolutions.

De plus, si la créativité est effectivement le processus consistant à avoir des idées originales qui ont de la valeur, comme la définit Ken Robinson, la question centrale est donc de savoir qui détermine de ce qui a de la valeur et sur quelle base ? Là encore, seule une compréhension profonde du monde, et des disciplines où notre créativité s'exerce, permet de trancher cette question.

Un air de déjà-vu ?

Bien que les espoirs et les peurs engendrés par ChatGPT puissent nous sembler sans précédent, en réalité, à chaque avancée significative de l'informatique, nous revisitons les mêmes mythes et les mêmes fantasmes.

Lorsque les premières calculatrices électroniques sont devenues accessibles au grand public, on s'est inquiété de savoir si on devait laisser les jeunes enfants les utiliser. Fallait-il encore enseigner les règles de base du calcul, comme les propriétés de commutativité ou d'associativité de certaines opérations arithmétiques ?

Les réponses à ces questions furent nuancées, mais dans les grandes lignes, on décida avec raison que les calculatrices étaient des outils fabuleux, pour qui savait calculer et avait assimilé les bases de l'arithmétique. Par conséquent, on continua à enseigner les principes du calcul, même si l'usage des calculatrices fut admis à partir d'un certain niveau d'étude.

Cette approche reposait sur le bon sens : comment notre système éducatif pouvait-il en effet former des personnes capables de faire progresser nos connaissances en mathématiques, ou en toutes autres sciences utilisant les mathématiques comme langage, si nous remplacions la compréhension de ses principes de base par la seule aptitude à appuyer sur des boutons ?

Plus récemment a contrario, pendant la période du COVID, on a pu constater à quel point la maitrise des outils numériques par les soi-disant " natifs du numérique " (digital natives) était un mythe. Contrairement à l'usage de la calculatrice, on était parti du principe depuis plusieurs années qu'il suffisait aux natifs du numérique d'avoir été exposés aux outils informatiques depuis leur plus jeune âge, pour qu'ils acquièrent une compréhension solide et approfondie des principes fondamentaux de l'informatique.

Or on s'est aperçu que les natifs du numérique n'avaient en réalité développé que des connaissances contingentes et superficielles, essentiellement liées à l'usage de certains outils spécifiques. Par exemple, de nombreux jeunes aujourd'hui n'ont aucune notion de la manière dont les données sont stockées, au point où les notions de fichier, de disque ou de serveur de stockages leur sont le plus souvent étrangères.

Un bon serviteur mais un mauvais maître

Comme tous les outils, l'intelligence artificielle en général et ChatGTP en particulier sont de fabuleux serviteurs pour qui sait les utiliser, mais de bien mauvais maîtres pour qui s'en rend totalement tributaire.

Là encore, cette situation n'est pas nouvelle : les traducteurs automatiques, les correcteurs orthographiques, les environnements de développement intégrés, Internet lui-même, sont des outils d'une efficacité redoutable aux mains d'utilisateurs avertis, mais peuvent être terriblement dangereux lorsqu'ils sont manipulés sans discernement par des utilisateurs candides.

Au final, il nous appartient de décider si l'intelligence artificielle s'inscrira dans la lignée de ce que l'ordinateur a été jusque-là, à savoir une forme de vélo pour l'esprit, qui ne se substitue pas à notre force musculaire mais permet de la multiplier et donc d'aller plus loin, selon une métaphore chère à Steve Jobs.

Avec l'intelligence artificielle, sans doute est-on en train de passer au vélo électrique : le facteur multiplicateur est certes plus important, mais le principe reste le même, puisque c'est toujours le cycliste qui fournit le mouvement de base sans lequel le vélo électrique reste immobile.

L'alternative à ce scénario serait que l'intelligence artificielle devienne une sorte de SUV autonome, écrasant tout sur son passage : les nuances infinies de notre intelligence humaine, notre intuition, notre créativité, notre sensibilité, notre empathie au monde.

Si nous voulons éviter ce second scénario, il nous appartient donc de déterminer non seulement quand et comment utiliser l'intelligence artificielle, mais aussi et surtout quand et comment ne pas le faire.

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Le Buzz autour de ChatGPT - Vers l'infini et au delà !

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On ne présente plus ChatGPT, tant ce service en ligne a fait le buzz en un éclair et est devenu la nouvelle star technologique des médias et des réseaux sociaux. Depuis, on nous annonce un monde nouveau, où le moindre de nos désirs sera exaucé littéralement dans la seconde.

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ChatGPT : de mauvais usages débusqués dans des articles scientifiques

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De nombreux scientifiques se servent de ChatGPT comme aide à la rédaction d’articles scientifiques. Seulement, certains n’ont même pas pris la peine de relire ce qu’avait généré l’intelligence artificielle et un chercheur français est allé débusquer ces erreurs grotesques dans des revues renommées.

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A specter is haunting the landscape-the specter of generative AI. First came fears that student cheating would explode, plus that artists and actors would be unemployed. Then the ante was upped: Some of the very technology's creators warned that AI's potential risk to humanity as we know it was on par with pandemics and nuclear war.

This cascade of angst was triggered by the launch of ChatGPT by OpenAI in November 2022. Among its wiles, the bot is best known for its prowess at churning out prose. On an existential level, does it matter if AI writes for us? For a non-kneejerk answer, we need to make a serious stab at understanding how writing affects us as people.

Most fundamentally, it changes our minds and brains. The classicist Eric Havelock argued in Preface to Plato that development of writing and concomitant spread of literacy in Archaic Greece, even in limited circles, enabled the flowering of Greek philosophical thought. Writing facilitated reflection, logical thinking, and production of tangible texts to foster rethinking.

While Havelock's argument for historical mind change has had its critics, it's incontrovertible that literacy changes our brains. Thanks to modern neuroscience, we know the brain is "plastic," meaning it is capable of reorganizing its structure or laying down new pathways, depending upon our physical or mental activities. London cabbies with "the Knowledge" of thousands of routes, streets, and landmarks have larger posterior hippocampi (the area responsible for physical navigation) than control groups. And people who are literate have different brains than those who aren't. Using MRI scans, Stanislas Dehaene taught us that adults who only learned to read and write later in life increased the density of white and gray matter in brain areas involved in reading.
If writing helps us think, what happens when we surrender the process to AI?
The literate brain empowers us to use writing as a canvas for witnessing our thoughts. Recall Flannery O'Connor's much-quoted remark that "I write because I don't know what I think until I read what I say." Her sentiment is hardly unique in literary annals. You'll find it echoed by Horace Walpole, E.M. Forster, Arthur Koestler, George Bernard Shaw, William Faulkner, and, of course, Joan Didion ("I don't know what I think until I write it down").

If writing helps us think, what happens when we surrender the process to AI? We risk becoming cognitively and expressively disempowered.

Start with AI as editor of text we write ourselves. Spellcheck and basic grammar and style edits from Microsoft Word are old news. But newer tools like Grammarly and Microsoft Editor (both now infused with OpenAI's GPT models) are at once more potent and perilous, especially for less confident writers.

Can AI edits be trusted? Some bones of contention are small beer. Word continually instructs me where to put my commas and to be more concise. Sentence-initial "Finally" must be followed by a comma; replace "in the near future" with "soon." A matter of personal choice, you say. Yet sometimes the advice is flat-out wrong. When I wrote

"However we might define 'good' writing, it's more than acing checklists."

Word scolded me for not inserting a comma after "however." Sorry, Word. A comma doesn't belong there, since the adverb "however" is modifying "define," not the whole sentence.

More troubling still was the "inclusiveness" flag that Microsoft Editor blandished at me when recently I wrote that Rameshbabu Praggnanandhaa (nickname: Pragg) was "the new Indian wunderkind," describing a sixteen-year-old who astounded the chess world in early 2022 by beating five-time world champion Magnus Carlsen. Microsoft's style cop warned that "This language may imply bias against indigenous populations" and suggested I substitute "Indigenous" or "Native American." Yes, there are indigenous populations in Pragg's home country. But they're indigenous to India, and I doubt that's how Pragg would characterize himself.

I knew to ignore Word's counsel. From Pragg's Hindu name alone, not to mention the long tradition of playing chess in the country that invented the game, it was obvious to me what "Indian" meant. But writers for whom it wasn't self-evident might have ended up substituting "Native American," which would have been absurd.

Beyond the issue of trust is that of personal writing voice. Take predictive texting (an early version of generative AI). Harvard research has shown that when we use predictive texting, our vocabulary tends to become more concise and less interesting. The philosopher Evan Selinger warns that this AI shortcut encourages us "not to think too deeply about our words" and to "give others more algorithm and less of ourselves." Describing predictive texting, a student in one of my studies complained that "I feel like the message I sent is not mine."

AI as editor and author is with us for the long haul. Writers need to make peace with the language genie, while holding fast to the opportunities for thinking and creating that writing bestows on us. Since we all have different aspirations when it comes to writing, the peace we broker must be individual. Approaching our own negotiating tables, here are two considerations to keep in mind.

First, beware of deskilling. Like retaining a foreign language, writing takes continuing practice. Nir Eisikovits, a philosopher, warns that the biggest near-term threat of AI is that it will lull us into degrading "abilities and experiences that people consider essential to being human." I used to ask my students, What do you know when the internet is down? Today's concern: Are you still capable of writing if an AI editor or text generator is unavailable?
Both as a form of personal expression and as an art form, writing is a craft.
Second, recognize your level of commitment, especially when something written bears your name. It's child's play for the likes of GPT-4 to concoct emails, blog posts, and article summaries. Is that OK with you? If you opt to view use of AI as a collaborative venture, how much are you willing to accept its algorithmic edits or text it has created out of predictive whole cloth? Research by Shakked Noy and Whitney Zhang found that ChatGPT reduced the time humans needed for a writing task, plus improved the quality of the end result. What's more, 68% of study participants were content to submit ChatGPT's initial output without doing editing of their own.

That's handing over the keys.

The bulk of writing that most of us produce skews impersonal. It's the everyday tasks of emails and memos, maybe churning out news stories or school assignments, even writing a guide to successful ChatGPT prompts. AI has already proven itself highly adept at such endeavors.

But human motivations for writing run deeper. We write to look outward, as with literary works that convey our perspective on the human condition. We write to look inward, including to find out what we're thinking. We write for personal release, be it a diary entry or angry letter to an employer. All this writing is grounded in human sentience, of which AI has none. AI has no drive to better people's lives, impart what it is thinking, or convey emotion.

When it comes to weighing commitment, also remember that both as a form of personal expression and as an art form, writing is a craft. How we chose our words and sentences is as vital as the meaning they convey. I keep harkening back to a passage in Francine Prose's Reading Like a Writer:

"Writing...[is] done one word at a time, one punctuation mark at a time. It require[s] what a friend called 'putting every word on trial for its life': changing an adjective, cutting a phrase, removing a comma, and putting the comma back."

If we cede to AI final say about words and even commas, we jeopardize more than artistic pride. We risk convincing ourselves that in the name of efficiency, it's harmless for AI to assume ever wider swaths of what we previously would have written ourselves.

Don't get me wrong. I'm no troglodyte when it comes to partnering with AI in the writing enterprise. Rather, my counsel is not to lose sight of the precious tool that writing offers us for shaping our minds and brains, for articulating our own ideas, and for sharing them with fellow humans.

_________________________

Naomi S. Baron's Who Wrote This?: How AI and the Lure of Efficiency Threaten Human Writing is available now from Stanford University Press.

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Why Human Writing Is Worth Defending In the Age of ChatGPT

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A specter is haunting the landscape-the specter of generative AI. First came fears that student cheating would explode, plus that artists and actors would be unemployed. Then the ante was upped: So...

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En date de l'été 2023, l'Oregon State University a élaboré une série de conseils et de guides pour l'utilisation d'outils d'intelligence artificielle (IA) par le corps enseignant. Partant de la prémisse que les technologies IA évoluent rapidement, il y a suggestion d'une approche basée sur l'usage de ces outils IA à partir de cadres et de valeurs de manière à maximiser les bénéfices pour les actrices et acteurs de l'éducation et la communauté étudiante, tout en minimisant les risques possibles.

Plus particulièrement, l'article présente de manière non exhaustive, des pistes d'analyse des tâches d'apprentissage et d'évaluation à partir de la taxonomie de Bloom révisée par Anderson et Krathwohl afin :

d'évaluer comment les étudiants et étudiantes pourraient utiliser les outils IA pour les activités d'apprentissage et les évaluations ;

d'identifier les compétences humaines distinctives que le personnel enseignant peut continuer à promouvoir et à évaluer dans l'apprentissage.

Voici une traduction libre de l'infographie originale de l'Oregon State University (2023). Cette infographie illustre une adaptation de la taxonomie de Bloom révisée pouvant être utilisée, dans un contexte éducatif, comme référence pour évaluer et apporter des modifications à des activités pédagogiques et à des évaluations en tenant compte de deux perspectives, soit : les capacités des outils d'intelligence artificielle (IA) générative et le caractère unique des compétences humaines.

[ Télécharger la version PDF]

L'analyse permet une réflexion sur l'impact de l'IA, peu importe que la personne enseignante soit ou non encline à intégrer l'IA dans ses activités, ou à demander aux personnes apprenantes d'intégrer ces outils dans leurs processus d'apprentissage.

Pour explorer en salle de classe

Les personnes enseignantes souhaitant aller de l'avant avec l'utilisation de l'IA en classe pourront ainsi mieux planifier leurs activités, l'implication possible de l'IA générative choisie et les compétences que devront développer les apprenants et apprenantes selon le niveau taxonomique de l'activité en question.

Par exemple, l'article propose qu'une activité du niveau taxonomique Comprendre pourrait inviter les personnes apprenantes à utiliser l'IA générative pour aider à définir des concepts. Il pourrait ensuite être demandé à ces personnes de contextualiser ces concepts avec les ressources présentes dans leur environnement ou en faisant des liens avec le domaine affectif (selon le sujet).

Des exemples sont donnés pour chacun des niveaux dans l' article de l'Oregon State University afin d'inspirer ceux et celles qui aimeraient explorer différentes idées.

Un domaine en constante évolution

Bien entendu, au fur et à mesure que les capacités de l'IA augmentent et évoluent, les compétences humaines distinctives à mettre en avant auront besoin d'être modifiées en conséquence. Il s'agit donc de pistes d'analyse contextualisées aux outils disponibles à l'été 2023.

Comme on peut le constater, l'article conclut que toutes les activités et évaluations menées dans un contexte éducatif bénéficieront d'une révision compte tenu des capacités des outils IA, mais, celles aux niveaux Mémoriser et Analyser sont plus susceptibles de nécessiter des modifications.

Des ressources à explorer à ce sujet dans notre réseau

Références

OpenAI. (2023). ChatGPT 4 (version du 3 août 2023) [Traduction anglais-français]. https://chat.openai.com/chat

Oregon State University, (2023). Artificial Intelligence Tools. Advancing meaningful learning in the age of AI. Consulté le 13 septembre 2023 au https://ecampus.oregonstate.edu/faculty/artificial-intelligence-tools/meaningful-learning/

Siemens, G., Marmolejo-Ramos, F., Gabriel, F., Medeiros, K., Marrone, R., Joksimovic, S., & de Laat, M. (2023). Human and artificial cognition. Computers and Education: Artificial Intelligence 3.

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En date de l'été 2023, l'Oregon State University a élaboré une série de conseils et de guides pour l'utilisation d'outils d'intelligence artificielle (IA) par le corps enseignant. Partant de la prémisse que les technologies IA évoluent rapidement, il y a suggestion d'une approche basée sur l'usage de ces outils IA à partir de cadres et ...

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Cet article intitulé "Assigning AI: Seven Approaches for Students with Prompts" explore les différentes manières d'utiliser l'Intelligence Artificielle (IA) comme outil d'apprentissage dans les salles de classe. Les auteurs proposent sept approches différentes, allant de l'utilisation de chatbots pour aider les étudiants à poser des questions, à l'utilisation de l'IA pour évaluer les compétences des étudiants. L'article aborde également les risques et les limites de l'utilisation de l'IA dans l'éducation, tels que la complaisance, les erreurs et les biais. Les auteurs proposent des solutions pour atténuer ces risques, notamment en encourageant les étudiants à réfléchir de manière critique sur les résultats de l'IA. Enfin, l'article présente des exemples de mise en œuvre réussie de l'apprentissage assisté par l'IA dans les salles de classe.

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48 Pages Posted: 21 Jun 2023 Last revised: 26 Sep 2023

Date Written: September 23, 2023

Abstract

This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and limitations. The authors propose seven approaches for utilizing AI in classrooms: AI-tutor, AI-coach, AI-mentor, AI-teammate, AI-tool, AI-simulator, and AI-student, each with distinct pedagogical benefits and risks. Prompts are included for each of these approaches. The aim is to help students learn with and about AI, with practical strategies designed to mitigate risks such as complacency about the AI's output, errors, and biases. These strategies promote active oversight, critical assessment of AI outputs, and complementarity of AI's capabilities with the students' unique insights. By challenging students to remain the "human in the loop," the authors aim to enhance learning outcomes while ensuring that AI serves as a supportive tool rather than a replacement. The proposed framework offers a guide for educators navigating the integration of AI-assisted learning in classrooms.

Keywords: AI, LLM, Education, Prompts

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assigning ai: seven approaches for students

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Assigning AI: Seven Approaches for Students, with Prompts

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This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and

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Une librairie de prompts remarquables pour les enseignant·e·s

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We have a variety of prompts to help you lesson plan and do administrative tasks with GenAI chatbots like ChatGPT, Claude, Bard, and Perplexity.

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Open IA publie son guide de conseils aux enseignant·e·s

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We’re releasing a guide for teachers using ChatGPT in their classroom—including suggested prompts, an explanation of how ChatGPT works and its limitations, the efficacy of AI detectors, and bias.

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Un rapport de l'UNESCO tout frais

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The recently published Jisc report looks at how students are currently using generative AI and explores its potential impact on their learning experience. To ensure a broad range of perspectives, they conducted both online and face-to-face sessions, allowing students to engage and express their thoughts anonymously. The discourse revolved around five focal points: existing usage trends, student apprehensions, the integration of generative AI in assessments, optimal methods for student support, and the broader implications of generative AI on learning.

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Jisc publishes report on student perceptions of generative AI

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The recently published Jisc report looks at how students are currently using generative AI and explores its potential impact on their learning experience. To ensure a broad range of perspectives...