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Some instructors are feeling very concerned about students using Generative AI to, for example, write essays or other written assessments for them. There are some things we can do to design writing assessments that are more resistant to AI:

1. Evaluate students on process, not only on the final product. For example, you might collect essay outlines or research proposals for evaluation and place less weight on the final essay. Meeting with students to discuss their writing process is also helpful.

2. Include components of self-reflection, including reflection on prior learning or the student's own life or work contexts, in assessments.

3. Consider why you are assigning essays and whether the essay as an evaluative form reflects the learning objectives in your class. Could you explore project-based learning or the "unessay" instead?

It's also important to keep open lines of communication with students about these tools. Consider exploring the limitations of Generative AI with your students by, for example, asking it to create a bibliography for an assignment and then checking whether the sources it provides are reliable (or even real!).

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Some instructors are feeling very concerned about students using Generative AI to, for example, write essays or other written assessments for them. There are some things we can do to design writing assessments that are more resistant to AI: Evaluate students on process, not only on the final product.

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University of Glasgow - MyGlasgow - Learning and Teaching - AI Guidance

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Generative AI: Lifeline for students or threat to traditional assessment?

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Generative AI: Lifeline for students or threat to traditional assessment?

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By Marieke Guy, on 21 April 2023

Our increasingly complex world has made the potential impact of artificial intelligence on education more relevant than ever. Gone are the days when AI's role in academic assessment required extensive explanation; it has become embedded in our daily lives. This shift has caused a wave of concern in Higher Education as traditional assessment practices risk becoming obsolete.
This post is a version of a one that appears on the National Centre for AI blog. It was reframed using Chat-GPT4.
In March, Russell Group university leaders convened to discuss the impact of AI on education and the implications for the sector. The event, chaired by Kathy Armour, Vice-Provost (Education & Student Experience) at UCL, featured a panel of students from various disciplines, sharing their experiences and insights on how AI tools, such as ChatGPT, have transformed their approach to learning.

The students' accounts made it clear that the genie is out of the bottle; AI is now so deeply integrated into their learning experience that it would be futile and dangerous to resist the change. For many, AI has become a "lifechanging" educational companion, offering a level of support that is impossible to ignore. As such, the students argued, returning to traditional exam halls or engaging in an AI detection arms race would be detrimental to their future employability and wellbeing.

It is evident that a collaborative approach between students and educational leaders is necessary to navigate this brave new world.

Prior to the event the students contributed to the drafting of a set of future-proof principles related to AI and assessment, addressing concerns such as relevance, literacy, rigour, transparency, fairness, and human-centred education.

The students expressed a desire for their education to prepare them for the wider world and the future workplace, necessitating the adoption of AI in learning, teaching, and assessment. Additionally, students and staff must be supported in developing academic skills in relation to AI, ensuring that learning and development opportunities are not missed. The students pointed to friends who were already creating AI-based start-ups.

Transparency and fairness are crucial when AI tools are used in assessment and marking. Students are particularly concerned about the potential for a widening gap between those who can afford AI tools and those who cannot. This raises the question of whether universities should provide paid-for versions of AI tools as part of their standard IT provision.

Moreover, learning, teaching, and assessment must remain human-centred. AI should enhance, not replace, relationships between students and educators, and AI interactions should promote a pedagogy of care. If students rely on AI to bypass required academic work, it is essential to ask why and provide additional support as needed.

This thought-provoking event demonstrated the importance of engaging in open dialogue with students about the role of AI in education and assessment. As Kathy Armour noted, the challenges posed by AI and assessment are not new; they are rooted in longstanding issues of assessment and curriculum design that continue to challenge the sector. Embracing the potential of AI in education can offer a lifeline to students, but it requires a delicate balance between technological innovation and maintaining the integrity of traditional learning experiences. By working together, students and educators can create a path forward that incorporates AI in a way that benefits all.

The event also featured visionary case-studies from sector-experts on AI: Sue Attewell, Head of edtech and lead at Jisc's national Centre for AI in tertiary education, Professor Mike Sharples from the Institute of Educational Technology at the Open University and Michael Veale, Associate Professor and Deputy Vice Dean (Education) in the Faculty of Laws at UCL.
Thanks go to those involved in this work:
Students:

Matthew Banner - Postgraduate in the third year of a PhD in Biochemical Engineering, leading on a student-led partnership project considering assessment design and AI.

Sophie Bush - Undergraduate student on History and the Philosophy of Science BSc and lead course rep for Science and Technology studies.

Megan Fisher - Second-year undergraduate student studying Economics, with chosen modules in Environmental Economics and Algebra.

Rachel Lam - First-year undergraduate law student, serves as a student partner on the assessment design and quality review team.

Jennifer Seon - In last year of my part-time master's programme studying Education and Technology, dissertation will focus on collaborative problem-solving in assessment. Recently interviewed AI expert Wayne Holmes for a podcast with the UCL AI Society.

Bernice Yeo - Postgraduate student taking the MA in Education and Technology. Works as an examiner for the International Baccalaureate.

Sopio Zhgenti - Postgraduate student studying Education and Technology at the Institute of Education with special interest in Artificial Intelligence.

Staff:

Marieke Guy (Head of Digital Assessment), UCL

Zak Liddell (Director of Education & Student Experience, MAPS), UCL

Joanne Moles (Head of Assessment Delivery and Platforms), UCL

Jennifer Griffiths (Associate Director in the UCL Arena Centre for Research-based Education), UCL

Lizzie Vinton (Assessment Regulations and Governance Manager, Academic Services) , UCL

Chris Thomson (Programme lead for teaching, learning and assessment), Jisc

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Generative AI: Lifeline for students or threat to traditional assessment?

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Journal of Medical Ethics - Ethics of generative AI 

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Journal of Medical Ethics - Ethics of generative AI 

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Artificial intelligence (AI) and its introduction into clinical pathways presents an array of ethical issues that are being discussed in the JME.1-7 The development of AI technologies that can produce text that will pass plagiarism detectors8 and are capable of appearing to be written by a human author9 present new issues for medical ethics.

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AI should cause us to adopt more progressive approaches to learning

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AI should cause us to adopt more progressive approaches to learning

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Higher education can no longer be limited to traditional didactic pedagogies and classroom settings.

Students are now necessarily seeking opportunities that bridge the gap between theoretical knowledge and real-world applications.

For over twenty years, I have used and implemented "service learning", "enterprise education", and "authentic learning" - three progressive approaches that empower students to develop practical skills and make a tangible impact.

However, the development of Artificial Intelligence (AI) some fear to be threatening the very existence of higher education, let alone outdated pedagogies such as one-way teaching.

An important question therefore has to be whether impactful pedagogies such as service learning, enterprise education and authentic learning - which have proved to develop work-ready, socially aware game changer graduates - can continue to show their worth in tandem with AI.

Getting real

Service learning involves students in community-based projects that address real societal issues.

By combining academic content with service opportunities, students not only gain a deeper understanding of the subject matter but also develop a sense of empathy, social responsibility, and civic engagement.

Service learning promotes active citizenship and encourages students to apply their skills to benefit others. For example, some of my engineering students developed a Talking Glove for people who had suffered a stroke, giving them the opportunity to use their skills whilst developing social awareness.

Enterprise education, in the meantime, equips students with the knowledge and skills necessary for entrepreneurship and innovation. By encouraging students to think critically, take risks, and embrace creativity, enterprise education nurtures an entrepreneurial mindset.

It empowers students to identify opportunities, develop business plans, and launch ventures, fostering a culture of innovation and economic growth. Combining service learning with enterprise education gives a powerful learning experience. An example is the creation of exyo, which was a company created as a result of a service learning project, or Handy-Fasteners.

Arguably both frameworks come under the umbrella of my favourite pedagogical approach, authentic learning, which focuses on providing students with opportunities to engage in real-world tasks and challenges.

By immersing themselves in authentic experiences, students can apply their knowledge, skills, and creativity to solve problems that mirror those found in professional environments.

From designing prototypes and conducting scientific experiments to creating multimedia presentations, authentic learning emphasises hands-on engagement, critical thinking, and collaboration.

And of course, I could not talk about authentic learning without making reference to the New Model Institute for Technology and Engineering, NMITE, an exemplar of authentic learning from beginning to end.

It is no news that policy pressures are pushing HEIs to reconsider their traditional approaches, and consequently, more and more, academics talk about these types of pedagogies as a way forward.

As someone whose career has been built on these trains of thoughts, I am all for universities transforming themselves - and I have argued for two decades that we should do that, hence the creation of NMITE.

However, AI is here to stay, and we must create new paradigms and take a new turn, rather than fight it or put our heads in the sand.

So, how could the above pedagogies be integrated with AI?

The robots are here

AI can play a pivotal role in service learning by analysing vast amounts of data and generating insights. It can help identify pressing community needs, propose innovative solutions, and optimise resource allocation.

AI can also facilitate communication and collaboration among students, community partners, and experts, fostering a networked approach to addressing social challenges.

Enterprise education could be revolutionised by providing students with access to real-time market insights, consumer trends, and predictive analytics. AI-powered platforms can already simulate business environments, allowing students to experiment with different strategies, assess risks, and make informed decisions.

And already many universities around the world are enhancing authentic learning by providing students with access to cutting-edge technologies and tools.

Through AI-powered simulations and virtual reality, students can engage in lifelike scenarios that mimic real-world challenges. AI can also facilitate personalised feedback and adaptive learning experiences, ensuring that students receive tailored guidance and support throughout their educational journey.

As a proponent of the use of authentic learning, service learning, and enterprise education as transformative educational models that prepare students for the complexities of the modern world, I also believe that by integrating AI into these approaches, we can unlock a wealth of opportunities for students to engage in immersive, impactful, and future-focused learning experiences.

As AI continues to evolve, its potential to enhance education will undoubtedly grow - paving the way for a generation of creative, smart, and socially responsible individuals ready to tackle global challenges head-on.

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AI should cause us to adopt more progressive approaches to learning | Wonkhe

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As students seek ways to bridge gaps between theoretical knowledge and real-world application, Elena Rodríguez Falcón senses opportunities amidst the "threats" of generative AI

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Generative AI can Help with Course Design

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Generative AI can Help with Course Design

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Generative AI can help with course planning | Wonkhe

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Course planning takes time and effort. Doug Specht and Gunter Saunders argue that large language models can serve as a critical friend in the process

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Times Higher Ed - Four Directions for Reassessment with Generative AI

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Times Higher Ed - Four Directions for Reassessment with Generative AI

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Four directions for assessment redesign in the age of generative AI

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The rise of generative AI has led universities to rethink how learning is quantified. Julia Chen offers four options for assessment redesign that can be applied across disciplines

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AI Text Detectors aren't Working - Is Regulation the Answer?

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AI Text Detectors aren't Working - Is Regulation the Answer?

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AI text detectors aren't working. Is regulation the answer?

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Tools developed to stamp out misconduct have been shown to be biased and inaccurate. Will AI creators themselves be forced to do it better?