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GAI in Higher Education (Source CAIS JA 7 January 2025)

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GAI in Higher Education (Source CAIS JA 6 Sept 2023)

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GAI in Higher Education (Source CAIS JA 5 May 2025)

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Our mission is to curate a collection of openly licensed, peer-reviewed strategies that utilize generative AI tools to support learning in higher education.

The intention is that any faculty member, specialist, librarian, or other professional may contribute strategies that feature AI-infused learning, as well as adopt or adapt strategies contributed by others for use in their own unique educational contexts.

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Our mission is to curate a collection of openly licensed, peer-reviewed strategies that utilize generative AI tools to support learning in higher education.

The intention is that any faculty member, specialist, librarian, or other professional may contribute strategies that feature AI-infused learning, as well as adopt or adapt strategies contributed by others for use in their own unique educational contexts.

Browse the TRAIIL collection by category:

Don't miss this opportunity to be a TRAIILblazer at the forefront of teaching with AI!

Want to share your own AI-infused strategy with the world? Please visit the Submission Guidelines page in the Author Corner to get started. Also consider joining the TRAIIL mailing list to be kept in the loop about the annual call for submissions and the latest published entries.

New TRAIIL submissions will undergo a peer-reviewed process. While entries can be submitted year-round, priority will be given during the official Call for Submissions timeframe which re-opens June 3, 2025.

Authors will be notified October - December 2025 regarding their submissions. Please contact the Editors at TRAIIL@ucf.edu for any questions.
Review Submission Guidelines Join TRAIIL Mailing List Volunteer to Be a Peer Reviewer for TRAIIL
Submissions from 2025

AI-Infusion through a three-step model of assignment analysis, Steve Covello

AI-Infused Discussion Boards: Enhancing Engagement, Reflection, and Academic Integrity in Anatomy and Physiology Education, Wade Dauberman

How to Use Copilot and ChatGPT to Create Rubrics, Lina Eskew

Instruction in Synthesis Writing can be Augmented by Giving Students a Generative AI Prompt, Drew Loewe

AI-Integrated Cross-Disciplinary Liberal Arts Colloquium, Diana S. Perdue, Niloofar Gholamrezaei, Jennifer Krusinger, and Shannon Hogan

Sharpening Marketing Strategy Through Smart ChatGPT Prompts, Wenkai Zhou

Submissions from 2024

A Human-in-the-loop Approach: Designing an AI-Assisted Workflow, Abram Anders and Emily Dux Speltz

AI-Infused Learning: Unlocking Career Potential, Karen Haslett, Danielle M. Pratt, and Amanda Pacheco

Exploring Business Ideas and Opportunities, Mary Ann Hughes Butts

Experiencing AI: Learning through Exploration, Nicole Mishnick and Dana Wise

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Teaching Repository of AI-Infused Learning

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Our mission is to curate a collection of openly licensed, peer-reviewed strategies that utilize generative AI tools to support learning in higher education. The intention is that any faculty member, specialist, librarian, or other professional may contribute strategies that feature AI-infused learning, as well as adopt or adapt strategies contributed by others for use in their own unique educational contexts.

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In a world driven by algorithms, what makes you human is your greatest asset.

Authenticity: How to Thrive in the Age of AI is your essential guide to navigating an AI-driven workplace with confidence and clarity. As artificial intelligence reshapes job markets and automation accelerates, future-proof career strategies have never been more important.

This timely and empowering guide offers a clear answer: lead with authenticity.

This book empowers professionals to stand out by developing human skills that technology can’t replicate, like empathy, judgment, adaptability, and creativity. It also shows you how to build authentic personal branding across digital platforms and use AI tools for career growth without losing your voice or values.

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Sean Doyle (Author), Jean Kotsiovos (Author), Shaila Rana (Author) & 0 more Format: Kindle Edition

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Authenticity: How to Thrive in the Age of AI

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Authenticity: How to Thrive in the Age of AI - Kindle edition by Doyle, Sean, Kotsiovos, Jean, Rana, Shaila. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Authenticity: How to Thrive in the Age of AI.

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Business is changing, and the convergence of strategic management and artificial intelligence (AI) is creating unprecedented opportunities for innovation and success. "The Strategist's Companion" is a blueprint for transforming insight into action using artificial intelligence (AI). This comprehensive guide is designed to equip current and aspiring strategists with the knowledge, tools, and capabilities needed to harness the power of AI in their strategic roles.

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Sean Doyle (Author) Format: Kindle Edition

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The Strategist’s Companion: Transforming Insight into Action: Leveraging Artificial Intelligence (The Professional Edge Book 1)

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Amazon.com: The Strategist’s Companion: Transforming Insight into Action: Leveraging Artificial Intelligence (The Professional Edge Book 1) eBook : Doyle, Sean: Kindle Store

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Building Future-Ready Business Schools With Generative AI | AACSB

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Building Future-Ready Business Schools With Generative AI | AACSB

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The year 2023 has been heralded as the year of Generative AI (GenAI), sparking the question: will 2024 see a new wave of business schools fully embracing and leveraging this groundbreaking technology? This report distills insights from months of environmental scanning and rich discussions led by AACSB's Innovation Committee, alongside other visionary thinkers. It delves into the strategic pathways business schools can navigate to sustain their value creation in the GenAI era.

AACSB presents pivotal focus areas for business schools to explore, emphasizing ways they can enhance learning experiences, highlighting essential strategies for business school leaders to realize their ambitious organizational goals, and envisioning potential futures for business education. The recommendations feature adopting a purpose-driven, human-centric, and collaborative approach.

This report serves as a springboard for AACSB's ongoing work in this fast-moving development impacting business schools and their stakeholders worldwide.

Sponsored by:

Learn more about the sponsor.

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Building Future-Ready Business Schools With Generative AI | AACSB

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Discover how business schools can use generative AI to enhance learning and implement a human-centric and purpose-driven approach to achieving goals.

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TeachAI | AI Guidance for Schools Toolkit

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The AI Guidance for Schools Toolkit is designed to help education authorities, school leaders and teachers create their own thoughtful guidance to achieve potential benefits of incorporating artificial intelligence in primary and secondary K-12 education while mitigating risks.

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This is a new Padlet to assist with reducing the number of posts as well as more focus on specific tools and applications.

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Why you should take the AI self-assessment today! listen to the short podcast

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Why you should take the AI self-assessment today! listen to the short podcast

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Your AI Learning Path: An Educator's Self-Assessment

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AI Self-Assessment: Which AI Learning Path is Right for You?

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AI Self-Assessment: Which AI Learning Path is Right for You?

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Interpreting Your Responses:

1%–50%: The Beginner Learning Path: Exploring AI Basics could still be a good starting point to solidify your understanding. You might also find the initial steps of the Basic Learning Path: Exploring AI Applications relevant as you begin to consider practical uses. Click this link to start your learning journey: AI_Beginner

51% - 75%: The Basic Learning Path: Exploring AI Applications is probably the best fit for you. This path will delve into the practical ways AI tools can be used to enhance teaching and learning. Click this link to start your learning journey: AI_Basic

75%-100%: The Advanced Learning Path: Advanced AI engagement aligns with your current level of understanding and interest. This path will encourage you to critically analyze and contribute to the broader landscape of AI in education. Click this link to start your learning journey: AI_Advanced

This self-assessment is designed as a simple guide. You may find that your interests span multiple levels, and you are welcome to explore resources from different learning paths as needed. The goal is to find a starting point that builds upon your existing knowledge and helps you progress effectively in your understanding of AI in education.

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Self-Assessment: Which AI Learning Path is Right for You?

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Take this survey powered by surveymonkey.com. Create your own surveys for free.

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University of Florida: Building an AI University: An Administrator's Guide 

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University of Florida: Building an AI University: An Administrator's Guide 

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Navigating the Ethical Challenges of Artificial Intelligence in Higher Education - An Analysis of Seven Global AI Ethics Policies

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Navigating the Ethical Challenges of Artificial Intelligence in Higher Education - An Analysis of Seven Global AI Ethics Policies

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Navigating the Ethical Challenges of Artificial Intelligence in Higher Education - An Analysis of Seven Global AI Ethics Policies

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AI and ethics - Investigating the first policy responses of higher education institutions to the challenge of generative AI

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AI and ethics - Investigating the first policy responses of higher education institutions to the challenge of generative AI

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AI and ethics - Investigating the first policy responses of higher education institutions to the challenge of generative AI

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Generative AI in Higher Education Institutions’ Policies and Syllabi

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Generative AI in Higher Education Institutions’ Policies and Syllabi

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AI Subcommitte developed document to help guide your AI learning pathway.

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Dive into this series of 4 student use cases for AI to discover how generative AI tools like ChatGPT can be used as a feedback generator, tutor, team coach, and learner. Get sample prompts and shareable guidelines to help students use AI tools effectively.

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UT Austin has been ahead of the curve on the topic of AI in higher education.

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UT Austin has been ahead of the curve on the topic of AI in higher education.

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UT Austin has been ahead of the curve on the topic of AI in higher education.

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Office of the Executive Vice President and Provost

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Office of Academic Technology - Office of the Executive Vice President and Provost

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We are a community of people working together to amplify the teaching and learning mission through the strategic use of academic technology. As a leading public research institution, our university is renowned for its world–class teaching and learning experience for students from all over the world. The mission of the Office of Academic Technology (OAT) […]

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TurnItIn is now helping schools to work WITH AI for assessment.

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A popular plagiarism checker has pulled a big about-face on AI for students

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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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3 Areas Where AI Will Impact Higher Ed Most in 2025

"In summary, AI will usher in a tipping point of change for students, faculty, and campus efficiencies. The years of ed tech advancements have helped higher ed reach this tipping point."
- Michael Mathews, VP for innovation and technology, Oral Roberts University

"2025 will be the year for AI to be more infused in education initiatives and platforms. AI-powered solutions have reached a tipping point from being a nice-to-have to a must-have in order to deliver compelling and competitive education experiences. When we look at the education sector, the use cases are clear. From creating content like quizzes, to matching students with education courses that meet their needs, to grading huge volumes of work, enhancing coaching and guidance for students, and even collecting, analyzing and acting on feedback from learners, there is so much value to reap from AI. Looking ahead, there could be additional applications in education for multimodal AI models, which are capable of processing and analyzing complex documents including images, tables, charts, and audio."
- Rachael Mohammed, corporate social responsibility digital offerings leader, IBM

"In 2025, my hope is that AI will transform the quality of online learning, particularly in asynchronous courses, by enhancing meaningful interactions and freeing up time for faculty and staff to dedicate more time to directly supporting students. Asynchronous learning often presents unique challenges for students, who may struggle to stay engaged and feel connected. AI can step in to provide real-time feedback on assignments, personalized learning pathways, and automated nudges to keep students on track. These capabilities can ensure that students feel supported and seen, even when learning on their own schedules. By taking on time-consuming tasks like formative assessments, answering routine questions, or monitoring progress, AI will allow faculty and staff to focus on deeper, more personalized connections with students, ensuring that asynchronous courses deliver the kind of support and engagement that leads to meaningful learning outcomes. The future play for AI is to support just-in-time learning opportunities as well as scheduled courses to connect and expand the learning experience to meet the ways students want and need to learn. By complementing human effort, it can empower educators to focus on what matters most - fostering connections and delivering the kind of high-quality learning experiences that truly make a difference in students' lives.
- Dr. Deborah Adair, chief executive officer, Quality Matters

"In 2025, AI will shift from concept to classroom staple and reshape education in a practical way. Faculty will use AI for personalized learning paths tailored to each student's progress, making education feel like a 'choose-your-own-adventure' experience. AI will also create immersive content, like virtual field trips, and streamline tasks like grading and feedback, allowing teachers to focus more on students. By analyzing student performance, AI will help identify learning gaps and suggest targeted support. As AI becomes more user-friendly, educators will be trained to seamlessly incorporate it into daily teaching, making 2025 a breakthrough year for meaningful AI adoption in classrooms."
- Chris Hess, director of AI product management, higher education, Pearson

"Gen AI and AI writing detection tools will evolve, adding advanced capabilities to match each other's detectability flex. End users are reaching higher levels of familiarity and maturity with AI functionality, resulting in a shift in how they are leveraged.

"Savvy users will take a bookend approach, focusing on early-stage ideation, organization, and expansion of original ideas as well as late-stage refinement of ideas and writing. Coupling the use of gen AI with agentic AI applications will help to overcome current limitations, introducing multi-source analysis and adaptation capabilities to the writing process.

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3 Areas Where AI Will Impact Higher Ed Most in 2025 -- Campus Technology

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What should colleges and universities expect from the evolving landscape of artificial intelligence in the coming year? Here's what the experts told us.

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2024 Creative AI Jobs Report - Adobe for Education

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Rather than avoiding the tech, educators should guide students in using it responsibly to help complete tasks, says one education innovation expert.

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A Framework for Human-Centric AI-First Teaching - February 2025

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A Framework for Human-Centric AI-First Teaching - February 2025

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With the rapid rise of generative artificial intelligence, higher education is shifting away from traditional teaching models and toward AI-integrated approaches that emphasize ethical application and personalized feedback.

Human-centric AI-first education encourages students to leverage AI to support their continuous learning journeys, as they develop critical thinking, ethical awareness, and real-world problem-solving skills.

While the adoption of AI might raise concerns related to ethics and accessibility, it also promises to democratize learning, improve engagement, and prepare future leaders for an AI-powered world.

The rapid rise of generative AI (GenAI) tools such as ChatGPT is forcing business educators to fundamentally rethink their curricula. Unfortunately, traditional teaching models are not sufficient to equip our students for a world where GenAI is rewriting the rules of work and leadership.

The challenge for business schools is clear: How can they adapt to this new reality while retaining their human-centric values and relevance to society? The answer lies in the adoption of a human-centric AI-first (HCAIF) pedagogical framework. This model offers a blueprint for leveraging AI to customize student learning, enhance feedback, and foster competency-driven growth.

By reimagining how we deliver education, we can prepare students to lead in an AI-driven future. The question is not whether we should embrace this transformation, but how-and how quickly-we can make this transformation happen.

Why We Must Innovate

As business educators, we work to accomplish five primary objectives in our classroom. These include:

Tailoring learning to individual student needs.

Offering continuous feedback to support summative assessment and iterative growth.

Developing competencies that are essential to applying knowledge in real-world contexts.

Teaching tacit knowledge that is required for opportunity recognition, venture creation, and strategic decision-making.

Promoting the ethical and appropriate use of GenAI, so that students do not compromise learning by using new technological tools to obtain easy, immediate answers.

Unfortunately, conventional teaching strategies make it difficult to achieve these goals. In fact, educators have been seeking ways to transform traditional teaching models long before the advent of ChatGPT.

For example, experiential learning theory, introduced by educational theorist David Kolb in the 1980s, emphasizes the importance of hands-on, real-world learning. Similarly, a 2007 research paper by Peter Brusilovsky and Eva Millán highlights how adaptive learning systems can address these challenges by tailoring content delivery and feedback to individual learners.

GenAI's emergence has only further exposed the limitations of traditional pedagogical frameworks. But GenAI also offers us unprecedented opportunities to achieve the transformation that scholars such as Kolb, Brusilovsky, and Millán describe. Not only that, as we experiment with AI tools in our classrooms, we also must delve more deeply into questions related to ethics, equity, and the preservation of human-centric values in learning.

Five Pillars of AI-Assisted Education

What, exactly, is a HCAIF framework? In short, it is a structured, ethical, and adaptive approach to integrating GenAI into business education. This approach was developed by faculty at Newcastle Business School at the University of Newcastle in Australia after 20 months of extensive research and experimentation related to using GenAI to teach entrepreneurship. The framework now underpins a new undergraduate program in innovation and entrepreneurship.
The Human-Centric AI-First Pedagogical Framework
As shown in the graphic above, the HCAIF framework is designed around five foundational pillars:

Preparation- In this phase, teachers "collaborate" with AI to design exercises and assessments that align with clearly defined goals and competencies. In the process, teachers work toward establishing a robust repository of resources that can be dynamically updated and tailored to evolving educational needs.

Personalized Learning- Here, students engage with content using AI-created exercises and tools, which supports individualized learning experiences. Students use GenAI tools to support their creativity, research, content creation, group formation, prototyping, and presentations. Additionally, they use AI to facilitate team-based projects; enable exploration and co-creation; and receive immediate, personalized feedback. Finally, students receive AI insights, along with peer reviews, which they can use to reflect on their progress, refine their understanding, and continuously improve their outcomes.

Classroom Engagement- Teachers, students, and AI collaborate to create immersive learning environments that foster active participation. Teachers act as coaches who provide targeted feedback, facilitate teamwork, and encourage peer review. Teachers also integrate reflection and planning into every class session to ensure that students apply their learning effectively.

Summative Assessment- Teachers use AI tools to generate personalized assessments, feedback, and grading. At this stage, students are assessed on both the process of learning and the quality of outcomes. Teachers oversee and refine the AI-generated assessments to ensure their accuracy and alignment with academic standards.

Teachers can use AI-generated data not only to identify students' strengths, but also to offer tailored support and guidance, so that all students receive the help they need when they need it.

Personalized Monitoring- For this final phase, teachers deploy AI to continuously monitor student progress and receive real-time insights into students' individual learning trajectories. Teachers use this data not only to identify students' strengths, but also to offer tailored support and guidance, so that all students receive the help they need when they need it.

The HCAIF framework is further underpinned by two essential factors:

Attribution-Students must clearly show how and where they used GenAI in their work. This allows educators to evaluate students' problem-solving capability, critical thinking, and proficiency in co-creating with AI tools.

Reflection-Students maintain journals in which they analyze and reflect on their learning and use of GenAI in every course. In their journals, students also assess GenAI's effectiveness, limitations, and impact.

This dual focus ensures that students develop the necessary metacognitive skills to engage ethically and effectively with GenAI in real-world contexts.

Examples From the Classroom

Nowhere is the need for personalized learning more apparent than in entrepreneurship education, where skills related to adaptability, creativity, ethical decision-making, and real-world problem-solving are paramount. At Newcastle Business School, we are taking a bold step to create immersive, impactful educational experiences for students in our innovation and entrepreneurship program.

Here are several examples of how we have adopted the HCAIF framework in our entrepreneurship courses:

Preparation. In Traction and Growth, a third-year undergraduate course, faculty use GenAI-powered tools to curate relevant case studies, examples, and exercises, each aligned with weekly learning objectives. Faculty also use the technology to create a repository of startup-specific insights (Canva's pricing strategies, for example), as well as to design pre-workshop activities and ensure consistency across learning stages.

Personalized Learning. In Venture Funding, students use GenAI tools to co-create pitch decks and refine their startup investment readiness plans. For example, during a week dedicated to the topic "investment readiness," students explore AI-generated templates to develop financial projections tailored to their chosen ventures. Students also receive peer feedback and AI insights, which they can reference in their reflections and use to improve their work.

In Traction and Growth, students write weekly entries in personal journals; in these entries, they reflect on their learning journeys, as well as on how GenAI insights and peer feedback have influenced their decision-making processes. Teachers review the journals periodically and provide personalized guidance to support ongoing development.

Classroom Engagement. In Sales Mastery, student teams practice sales pitches in workshops and receive AI-generated sentiment analyses to improve their messaging. Students also gain hands-on experience by completing role-playing exercises, such as negotiating with a real-life business. During these exercises, teachers use AI tools to provide targeted feedback on team dynamics and individual contributions.

Summative Assessment. In the last week of Traction and Growth, students present their final customer growth strategy reports. Teachers use AI tools to evaluate the reports, based on how well students demonstrate competencies such as clarity of strategy and innovation. Teachers then review AI-generated feedback, adding personalized comments to ensure a comprehensive evaluation-effectively blending AI's efficiency with their own human oversight.

Personalized Monitoring. In the Startup Fundraising Process module of Venture Funding, teachers use AI to continuously monitor students' progress. For instance, in a week covering the topic "strategic clarity," AI tracks students' engagement with pre-workshop activities and flags students who are struggling with specific concepts. Teachers use this data to provide targeted support during workshops and ensure that all students are prepared for subsequent activities.

As we adopt the HCAIF pedagogy, we are finding ways to overcome the limitations of conventional pedagogical methods. GenAI enables us not only to adapt our teaching to the learning needs of individual students, but also to align our curriculum with desired learning outcomes and real-world applications.

Initial Lessons, Future Implications

We will continue to pilot, test, and refine the HCAIF framework at Newcastle Business School. Already, we have learned the following valuable lessons relevant to the broad adoption of AI in the business curriculum:

GenAI tools can enhance engagement. Students often embrace AI-generated content, but they require structured guidance on how to integrate AI into the learning process and evaluate its effectiveness.

GenAI can support adaptive learning pathways. Teachers can use GenAI tools to adjust the difficulty and focus of learning exercises based on each student's needs. According to a 2023 literature review led by Ilie Gligorea, such adaptive systems improve learning outcomes.

GenAI must be integrated in balanced ways. AI-assisted activities are most effective when they are complemented by human-led workshops. This dual approach supports the creation of holistic and collaborative learning environments.

Universities can proactively address ethical concerns arising from an AI-first pedagogical framework through targeted professional development, dedicated AI ethics courses, and interdisciplinary ethics committees.

We believe the HCAIF framework will have four key implications for the future of business education:

It will redefine pedagogical integrity. Even as GenAI plays a larger role in education, we must preserve human-centric values. The role of human educators as coaches and facilitators will remain indispensable. Human educators will ensure that AI fosters critical thinking and human connection, and that students' learning is meaningful, ethical, and deeply engaging.

It will expand accessibility. According to a 2019 literature review led by Olaf Zawacki-Richter, AI has great potential to help higher educational institutions bridge educational gaps regionally and globally. Schools can use the technology to cater to diverse student populations, including those in resource-constrained environments.

It can be adapted to different disciplines. While developed for entrepreneurship education, the HCAIF framework is based on principles that can be adapted to other business disciplines such as finance and marketing, as well as to fields such as medicine, engineering, and the arts.

It will prepare future leaders. By developing their GenAI proficiency, critical thinking, and ethical awareness, graduates will be positioned to improve efficiencies, generate value, and lead organizations in a complex, interconnected business landscape.

Opportunities for Transformation

Of course, adopting an AI-first pedagogical framework will pose challenges, but these challenges are not insurmountable. For example, the use of AI in the classroom raises significant ethical concerns, including those relating to data privacy, algorithmic bias, and equity. Universities can proactively address such concerns by integrating targeted professional development and dedicated AI ethics courses across disciplines. They can establish interdisciplinary ethics committees to oversee projects and policies, while also fostering collaborative partnerships with industry, policymakers, and nonprofit organizations to ensure real-world alignment.

In addition, staff and students will require comprehensive training to transition to this paradigm-and some might even resist the need for change altogether. Schools will need to prioritize clear communication and ongoing professional development to ensure a smooth adoption of new practices.

Resource-constrained institutions might have limited access to advanced GenAI technologies and expertise. However, they can form strategic academic partnerships with key technology players and take advantage of free large language models, or LLMs, such ChatGPT, Claude, or Gemini to democratize access. As Satish Nambisan of Case Western Reserve University highlights in a 2017 paper, being part of a collaborative ecosystem can help institutions navigate resource limitations and foster educational innovation.

Despite the challenges ahead, business schools are now in a transformative moment. We must continue to experiment and refine our pedagogical frameworks, as well as pursue future research that will allow us to validate these approaches and ensure their effectiveness across diverse educational contexts. By fostering innovative, adaptable, and scalable educational models, we will help our institutions maintain their relevance to business and society.

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A Framework for Human-Centric AI-First Teaching | AACSB

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Higher education is undergoing a paradigm shift toward more human-centric, adaptive learning models that will equip students for an AI-powered future.

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Authenticity and Ethics in Generative AI - June 2024

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Authenticity and Ethics in Generative AI - June 2024

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Authenticity and Ethics in Generative AI | AACSB

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For business schools to effectively leverage generative AI, they will need to help students become unique creators committed to responsible use of the tool.

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The AI Advantage in Teaching and Learning - August 2024

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The AI Advantage in Teaching and Learning - August 2024

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The AI Advantage in Teaching and Learning | AACSB

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By leveraging AI, educators can enhance their curricula, foster peer collaboration, and prepare students for AI-driven careers.

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Business School Innovation in the Age of Generative AI - November 2023

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Business School Innovation in the Age of Generative AI - November 2023

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Business School Innovation in the Age of Generative AI | AACSB

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Generative AI is here to stay. How can business educators best prepare learners for the possibilities—and the risks—of integrating this disruptive technology?

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Management or Mastery: How Important Is AI for MBA Students? - March 2024

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Management or Mastery: How Important Is AI for MBA Students? - March 2024

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As "AI mania" starts to mature, companies must demonstrate how it can drive not only share price but company growth, as well. These expectations have led to an increased demand for AI skills from employers.

Business schools have responded to the demand by rapidly integrating AI into programs, electives, and multidisciplinary projects.

MBA graduates must demonstrate a conceptual understanding of AI so they can incorporate it into their corporate strategy, demonstrate shareholder value, and drive business growth.

Nobody likes getting left behind.

So when ChatGPT first exploded onto the scene in November 2022, there was a global scramble to process, understand, and utilize this thrilling new technology. Nowhere more so than in the world of business education.

In the subsequent frenzied 15 months or so, business schools have rapidly integrated AI into their offerings. Schools want to teach it, students want to use it, and employers want their workers to master it.

As a result, AI has become the latest in a long line of must-have skills for MBA students. But how much proficiency are they expected to achieve?

Let's take a look.

What's Driving the Growth of AI?

Within management education, more factors are driving the growth of AI than just the fear of getting left behind.

Business schools have always adapted and evolved their offerings based on what the market demands from them. In 2024, it's becoming increasingly clear that the market demands AI skills.

According to the 2023 GMAC Corporate Recruiters Survey, 74 percent of employers believe that AI and machine learning are important skills for business school graduates to have. Around the same percentage believe these skills will only grow in importance over the next five years. Business schools are simply responding to that demand.

Yet, if the reasons behind management education's growing interest in AI are fairly clear, the motivations for major companies are a little more complicated. They also don't want to get left behind, of course, but another factor was driving so-called AI mania in 2023: shareholder value.

74 percent of employers believe that AI and machine learning are important skills for business school graduates to have.

In the U.S. last year, the Nasdaq 100 grew by a staggering 50 percent-its best year since 1999. Michael Landsberg of Landsberg Bennett Private Wealth Management commented that, in 2023, "most large tech was buoyed by the excitement and not necessarily the details of AI." Last year, simply showing an interest in AI was enough to drive up the share price of companies.

But in 2024, that won't be enough to satisfy shareholders. Organizations will have to start demonstrating a clear AI strategy to investors-one that will propel company growth and not just inflate share prices. They will therefore need to find leaders who are capable of powering growth through AI.

Enter MBA graduates.

How Business Schools Are Incorporating AI Into Their Programs

Within graduate management education, the shift to AI has been astonishingly quick.

In a little over a year, business schools have (in some cases) totally revamped their curricula to include AI. According to research from the Graduate Business Curriculum Roundtable, 74 percent of schools now teach generative AI as part of existing courses.

In Italy, the POLIMI Graduate School of Management has gone even further than that. "From this year, all of our MBA candidates will have to use generative AI software to conduct the admission test," says Professor Antonella Moretto, who is the associate dean for Open Programs at POLIMI.

"As a school dedicated to innovation, we do not want to demonize [generative AI software] but to teach students how to use it critically." -Sampsa Samila

"The reason we do this is to test their critical thinking. For this, we provide them with a prompt and ask them to report the solution that emerges from the generative AI software to then comment, discuss, and argue. This is done because, as a school dedicated to innovation, we do not want to demonize the tool but to teach students how to use it critically."

Other business schools are not only incorporating AI within their programs-they're working it into their wider approach to education. IESE Business School 's Artificial Intelligence Initiative is a multidisciplinary project that oversees the school's relationship with AI. It manages the integration of AI within programs and electives but also encompasses faculty research and engagement with policymakers.

It's an example of how schools are also starting to move past the initial AI boom and instead develop a streamlined approach to teaching it. Now, their expectations of what MBA graduates should know about AI are becoming much clearer.

What Does an AI-Ready MBA Graduate Look Like?

A lot of MBA students might look at AI as the latest in a long line of skills they're expected to master. In recent years that list has grown at an even quicker rate than normal, given the integration of skills such as big data, machine learning, and blockchain into business school curricula.

Although AI is a key part of business education in 2024, it's not necessarily a skill that MBA students must master. Like other parts of the program, it's about management rather than mastery.

"Students must firstly develop a 'clear, conceptual understanding' of how AI technologies work." -Antonella Moretto

"I believe that managers and future managers-the typical target of an MBA program-do not need to necessarily know the technical aspects of AI, both because that is not their role and due to strong technological evolution," says Moretto.

"On the other hand, however, it becomes fundamental to understand how it could be used, how it can become a tool for increasing human potential, like a real co-pilot, which no longer serves only as an enabler but as a true amplifier."

Professor Sampsa Samila, who is the academic director of the Artificial Intelligence Initiative at IESE Business School, believes students must firstly develop a "clear, conceptual understanding" of how AI technologies work.

"This needs to then be complemented with a conceptual understanding of how the business works across the different functions," he says. "We do cover concrete, practical applications as well, but those applications from a couple of years ago are now getting old-while the conceptual understanding is still entirely valid."

In short, MBA graduates must have a working knowledge of AI-one that allows them to understand its key concepts and how to incorporate them within an overarching business strategy. Crucially, they must also demonstrate how AI can go from interesting new technology to key driver of company growth. If they can manage that transition, they're likely to be in high demand for the next stage of AI mania.

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Management or Mastery: How Important Is AI for MBA Students? | AACSB

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As more and more business schools incorporate AI into their programs, to what extent will students have to master the technology?

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How Can Academics Use GenAI in Their Research? - July 2024

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How Can Academics Use GenAI in Their Research? - July 2024

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Academics can use AI tools as aids to challenge assumptions, generate hypotheses, develop ideas, critique research designs, generate synthetic data, discern patterns, and discover anomalies.

However, AI can return incorrect information, violate privacy standards, perpetuate biases, lead researchers to rely on it too heavily, and present ethical dilemmas.

In an ongoing project, researchers are using GenAI to develop a chatbot tutor that will provide personalized and affordable support to students at minority-serving institutions.

Generative AI (GenAI) is revolutionizing the landscape for academic researchers. By creating novel avenues for them to tackle complex problems-from ending poverty to mitigating climate change to promoting sustainability-it is expanding what researchers can achieve. At the same time, GenAI introduces new risks that faculty need to understand and manage.

The route to GenAI came straight through big data, which profoundly impacted business and social sciences scholarship. Researchers initially used big data analytics to harness large datasets pertaining to complex business and societal phenomena. By identifying patterns of correlations within the data, researchers could apply predictive analytics to precisely estimate the behaviors and outcomes of individuals, teams, and organizations.

To deepen understanding and extract meaningful insights, researchers combined machine learning and deep learning with causal inference methods. This approach allowed them to assess how various factors affected the behaviors and outcomes of entities and how these effects varied across types of entities in different contexts.

As digital technologies and analytical methods have advanced, GenAI has emerged as a new player. Through conversational prompt-based interactions with GenAI, users can create new humanlike content and derive nuanced insights from existing datasets. Academics have opportunities to make the research process more efficient and innovative by leveraging AI as an automator of routine tasks and an augmenter of creative endeavors.

However, with these opportunities come risks. Content generated by AI is sometimes of low quality and has the potential to breach legal and privacy standards. I believe researchers must carefully manage these risks if they want to realize the benefits of GenAI without suffering adverse consequences.

Automation and Augmentation With GenAI

Researchers can use GenAI to automate routine data collection and preprocessing and to capture and integrate multimodal data such as texts, images, voice recordings, and videos. They also can rely on it to write and debug code, assist in copyediting, and streamline workflows.

In addition, they can use GenAI to augment their capabilities and skills to carry out seven key research tasks:

1. Challenging assumptions. As academics formulate research problems, they can have GenAI take on a role that is critical of their viewpoints. They can invite it to argue against their theoretical lenses and point out blind spots. Researchers often are vulnerable to making " type three errors "-that is, asking questions that might not be relevant or useful. To avoid this trap, they can use GenAI as a partner that can challenge bounded rationality and ensure that they do not fix prematurely on the lenses they use to formulate the problem and develop the research question.

2. Developing hypotheses. Scholars can input a premise for a hypothesis and ask GenAI to generate counterclaims, alternative explanations, reservations, and qualifications. They even can ask it to limit the claims to certain contexts or control the extent to which it offers speculations.

GenAI can create synthetic data that mimics the characteristics of real data. This allows researchers to augment training datasets, design test models, and ensure data privacy by using proxy counterfactuals.

3. Conceiving novel research designs. Researchers can invite GenAI to envision alternative or complementary approaches to their research designs. They can ask it how to improve the discovery of causal mechanisms underlying various effects, perhaps by using natural language processing (NLP) techniques to conduct a large-scale qualitative analysis using computational methods. They can even ask it to integrate the data from different sources and apply machine learning and NLP techniques to illuminate those causal mechanisms.

As an example, say researchers are planning an event study to determine what impact a cybersecurity breach would have on the market value of a firm. They could ask GenAI to probe the heterogeneity of the effect by type of breach and type of firm. From there, they could ask GenAI how to leverage information about the breach in earnings calls and 8-K reports, which track unscheduled events that could impact a company. Additionally, they could ask GenAI to predict how the market would react if they disclosed the breach, apologized to customers, and took action to contain the impact.

4. Generating synthetic data. GenAI can create high-quality, high-volume synthetic data that mimics the characteristics of real data. This allows researchers to augment training datasets, design test models, and ensure data privacy by using proxy counterfactuals. For example, GenAI can generate synthetic medical records to preserve patient privacy or create new imagery for a facial recognition system when real images are limited.

5. Simulating future states of the world. GenAI can forecast future scenarios by using historical and current data. When it does this at scale, it can evaluate a large number of conditions and variables, incorporating elements of uncertainty to produce comprehensive and diverse scenarios.

For example, academics can examine potential threats such as economic downturns, supply chain failures, and new technological or policy developments. Given the highly uncertain nature of business, political, technological, and global environments, such simulations can inform theory, practice, and policy as organizations prepare for exogenous shocks and transitions to different possible states of the world.

6. Discerning patterns. Before researchers move on to confirmatory analysis, they can use the technology to detect any interesting patterns that might be early predictors of something else.

7. Discovering anomalies. Researchers can upload a sales dataset and ask GenAI to identify and explain any anomalies that deviate from theoretical expectations. For instance, after pointing to an unexpected sales spike in a particular region, GenAI might provide contextual insights on promotional campaigns or competitor issues that explain the spike. This knowledge could spark the creative process and cause scholars to pivot to a new theoretical perspective.

Responsible GenAI Practices

Despite its many uses, GenAI presents risks that researchers must carefully manage. Fortunately, governments and professional organizations are beginning to draft useful standards that address these risks. In the U.S., guidance on the responsible and transparent use of AI is expected from bodies such as the President's Council of Advisors in Science and Technology. Increasing numbers of discipline-based societies and academic journals also are developing GenAI policies and requiring researchers to disclose how they have used the technology in their scholarly work. We can expect considerable experimentation to shape these policies in the future.

Overall, I believe researchers face challenges and must learn to mitigate risks in five main areas:

Content quality and legality. While GenAI reduces many of the cognitive demands on researchers, it also dramatically increases the amount of effort academics must make to ensure AI's suggestions are high-quality and ethical. GenAI can go awry in several ways:

It sometimes "hallucinates," making up answers when it lacks information.

Unlike human researchers, AI can't use judgment to evaluate the quality of publications, so it indiscriminately includes studies of varying quality and reproducibility.

GenAI can miss newer publications because the pool of knowledge used to inform its responses might have a cutoff date.

AI's effectiveness is limited by the biases and gaps in its training datasets.

GenAI can generate text that closely mirrors copyrighted articles or books, leading to potential legal issues and plagiarism concerns.

The only way for researchers to avoid these problems is to verify the appropriateness of the content obtained from GenAI.

While GenAI reduces many of the cognitive demands on researchers, it also increases the amount of effort academics must make to ensure AI's suggestions are high-quality and ethical.

Privacy. Whether accessing data or interacting with individuals, GenAI has been known to violate privacy standards. These include company confidentiality requirements and governmental regulations enacted by the United States' Family Educational Rights and Privacy Act ( FERPA) and the Health Insurance Portability and Accountability Act ( HIPAA).

Many individuals do not fully understand how their personal information and social media activity can be mined by AI. Even anonymized data can lead to reidentification when GenAI cross-references multiple datasets, such as academic records and social media posts.

To avoid privacy violations, researchers can implement advanced anonymization methods, ensure informed consent, establish strict data access boundaries, undergo regular audits, generate synthetic data, and participate in ongoing training.

Synthetic data validity. While synthetic data isn't necessarily good or bad, it might oversimplify entities and their relationships to ensure they fit computational models. When individuals and organizations are represented as artificial instances with certain attributes, critical information about them can be lost. To reduce this risk, researchers should consider combining extensive synthetic data with intensive data that digs deep into a phenomenon.

Citation bias. GenAI can perpetuate and reinforce certain popular bodies of work, viewpoints, and scholars through the citations it recommends, which creates cognitive trapping. If researchers prompt GenAI to suggest less popular, atypical combinations of citations, it will generate a broader scope of perspectives that can be used to challenge, replace, or enrich conventional ideas.

Peer reviewing. Peer reviewers are selected on the basis of their individual expertise, and peer critiques require substantial, privileged, and detailed information. If reviewers uploaded an article into a GenAI tool, they could not be certain where the tool would send, save, or use the article's contents. This would absolutely violate confidentiality expectations for peer review.

Another problem is that GenAI tools are trained on data that exists and has been widely published. If GenAI is reviewing article submissions, it could default to common biases and homogenize original thought. Its critiques also could rise to the level of plagiarism.

To avoid these possibilities, it is best to leave reviewing tasks to humans for now. Journals, professional associations, and scholarly communities will need to carefully conceive, trial, and validate practices for human-AI collaborative reviewing before AI is involved in the reviewing process.

A Firsthand Example

When scholars are aware of and manage risks through responsible practices, they can leverage GenAI to automate tasks and enhance both the creative and analytical aspects of their research. Here's an example from my own work at Georgia State University's Robinson College of Business in Atlanta.

We are partnering with researchers from the Massachusetts Institute of Technology in Cambridge on an ongoing project sponsored by Axim Collaborative, a social enterprise dedicated to expanding access to education. The goal is to design and evaluate a large language model (LLM) tutor that promotes equitable student success in computing courses, specifically Python programming. The tutor will provide at-scale, personalized, and affordable support to students at minority-serving institutions (MSIs) across the learning levels of the revised Bloom's taxonomy: remembering, understanding, applying, analyzing, evaluating, and creating.

Innovation thrives on diverse, original thought. If we rely too heavily on GenAI, we lose the ability to make critical analyses and question information.

My colleagues and I have utilized GenAI to automate several tasks, including converting audio files from focus groups into text transcripts, summarizing key points from these transcripts, and extracting structured data from unstructured chat logs. Moreover, GenAI helped us identify specific information that was embedded in lengthy paragraphs, such as questions students had posed to the LLM tutor.

GenAI also enabled us to use chat logs to discover patterns and challenges in students' learning pathways. Using ChatGPT-4o, we classified messages between students and the LLM tutor according to the revised Bloom's taxonomy, then we clustered these interactions based on transitions across taxonomy levels. This approach allowed us to visualize how each student and each group transitioned within their clusters, which helped us identify anomalies for further investigation.

In addition, we used GenAI in a discursive manner to explore how the complementary insights of contemporary learning theories can illuminate the diverse learning pathways of students at MSIs and inform the design of the LLM tutor. We also asked GenAI to provide us with notifications and summaries of relevant research articles and reports from digital libraries such as JSTOR, Google Scholar, and arXiv.

As a final example, we had GenAI suggest ways to formulate and communicate the implications of our findings to audiences in information systems, education, and public policy. In all of our uses of GenAI, we routinely considered its risks, employed responsible practices, and verified the results and recommendations.

An Exciting Future

For scholars, the future of GenAI is promising. It can transform and empower our research processes by augmenting our creative activities while automating our mundane tasks. But we must understand its limits.

Innovation thrives on diverse, original thought. If we rely too heavily on GenAI, we lose the ability to make critical analyses and question information. We risk becoming "research drones" who generate fewer original ideas and breakthroughs. While GenAI can provide us with quick answers, we will acquire deep expertise only through intensive study and extensive experience. If we are to sustain vitality and progress in any business field, we must foster critical thinking and creativity even while we leverage this technology.

We also must ensure that all researchers have access to foundation models, comprehensive datasets, advanced computing power, and training and skill development opportunities. If we don't, we risk significant inequities in the research process. Partnerships among academia, industry, and government will be vital for fostering a more inclusive environment for researchers. They will be able to solve complex problems in innovative ways with the power of their imagination and ideas.

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How Can Academics Use GenAI in Their Research? | AACSB

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AI can support researchers by automating routine tasks and augmenting their creativity, yet the use of the technology introduces new risks.

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AI Upskilling for Business School Faculty - June 2024

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AI Upskilling for Business School Faculty - June 2024

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AI Upskilling for Business School Faculty | AACSB

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If schools provide the proper support and resources, they will help educators move from anxiety to empowerment when integrating AI into the classroom.

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AI and Assessment: Where We Are Now - April 2024

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AI and Assessment: Where We Are Now - April 2024

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AI and Assessment: Where We Are Now | AACSB

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A recent webinar explores how schools and edtech companies are experimenting with new applications to find what works best in the classroom.

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AI Literacy: A Vital Skill for Tomorrow’s Business Leaders

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AI Literacy: A Vital Skill for Tomorrow’s Business Leaders

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Preneur World - Lifestyle Magazine for the Preneur Mindset

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AI Literacy: A Vital Skill for Tomorrow’s Business Leaders - Preneur World

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By Lily Bi, President and CEO of AACSB International. Artificial intelligence (AI) is transforming the way organisations do business. Newer, more advanced models are capable of automating repetitive tasks, generating content like sales pitches, or taking on customer-facing roles – consider how many websites are now equipped with a chatbot helper, including AACSB earlier this

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Building Future-Ready Business Schools With Generative AI | AACSB

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Building Future-Ready Business Schools With Generative AI | AACSB

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The year 2023 has been heralded as the year of Generative AI (GenAI), sparking the question: will 2024 see a new wave of business schools fully embracing and leveraging this groundbreaking technology? This report distills insights from months of environmental scanning and rich discussions led by AACSB's Innovation Committee, alongside other visionary thinkers. It delves into the strategic pathways business schools can navigate to sustain their value creation in the GenAI era.

AACSB presents pivotal focus areas for business schools to explore, emphasizing ways they can enhance learning experiences, highlighting essential strategies for business school leaders to realize their ambitious organizational goals, and envisioning potential futures for business education. The recommendations feature adopting a purpose-driven, human-centric, and collaborative approach.

This report serves as a springboard for AACSB's ongoing work in this fast-moving development impacting business schools and their stakeholders worldwide.

Sponsored by:

Learn more about the sponsor.

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Building Future-Ready Business Schools With Generative AI | AACSB

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Discover how business schools can use generative AI to enhance learning and implement a human-centric and purpose-driven approach to achieving goals.

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AI Search Has a Citation Problem - March 2025

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AI Search Has a Citation Problem - March 2025

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Klaudia Jaźwińska and Aisvarya Chandrasekar

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We Compared Eight AI Search Engines. They’re All Bad at Citing News.

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The data so far on AI-as-a-tutor shows just letting students use AI chatbots can undermine education because the AI gives the illusion of learning while actually just providing answers. But AIs properly prompted to act like tutors, especially with instructor support, seem to be able to boost learning a lot through customized instruction. As someone who spends a lot of time working on using AI in education, it is frustrating that the debate becomes "AI destroys teachers" vs "AI destroys learning". There is huge near-term promise, but most people are not autodidacts in every subject who will just learn all with AI. The opportunities are so huge (and current issues in school so big) that it seems imperative that we, as a society, figure out how to best use AI in education as soon as we possibly can. But we don't have all the answers yet. I wrote about this here: https://lnkd.in/dS7Rwd_6

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The data so far on AI-as-a-tutor shows just letting students use AI... | Ethan Mollick | 105 comments

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The data so far on AI-as-a-tutor shows just letting students use AI chatbots can undermine education because the AI gives the illusion of learning while... | 105 comments on LinkedIn

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INSTITUTIONAL POLICIES ON ARTIFICIAL INTELLIGENCE IN UNIVERSITY LEARNING, TEACHING AND RESEARCH

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INSTITUTIONAL POLICIES ON ARTIFICIAL INTELLIGENCE IN UNIVERSITY LEARNING, TEACHING AND RESEARCH

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INSTITUTIONAL POLICIES ON ARTIFICIAL INTELLIGENCE IN UNIVERSITY LEARNING, TEACHING AND RESEARCH

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A comprehensive AI policy education framework for university teaching and learning

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A comprehensive AI policy education framework for university teaching and learning

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A comprehensive AI policy education framework for university teaching and learning

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Generative AI in higher education - Seeing ChatGPT through universities' policies, resources, and guidelines

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Generative AI in higher education - Seeing ChatGPT through universities' policies, resources, and guidelines

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Generative AI in higher education - Seeing ChatGPT through universities' policies, resources, and guidelines

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Education for AI, not AI for Education - The Role of Education and Ethics in National AI Policy Strategies - July 2021

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Education for AI, not AI for Education - The Role of Education and Ethics in National AI Policy Strategies - July 2021

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Education for AI, not AI for Education - The Role of Education and Ethics in National AI Policy Strategies

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Artificial Intelligence in Higher Education - 2023

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Artificial Intelligence in Higher Education - 2023

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Artificial intelligence in higher education

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Prepare your institution for what’s to come with our collection of data, recommendations, and case studies on the top 2025 trends in higher education.

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The future is already here: AI and education in 2025 • Stanford Accelerator for Learning

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The third Stanford AI+Education Summit brought together researchers, K-12 leaders, educators, and technologists to explore how AI is shaping teaching and learning.

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What's Missing From Your School's AI Adoption Plan?

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What's Missing From Your School's AI Adoption Plan?

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What’s Missing From Your School’s AI Adoption Plan? A Roadmap for School Leaders

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A Practical Guide to Equitable, Safe, and Accessible AI in K-12 and University Education

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Your Stars Help Us Improve AI Resources for the ACBSP community!!

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Your Stars Help Us Improve AI Resources for the ACBSP community!!

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Please take a moment to check it out and fill out the short survey 📝 so we can gather your valuable input during this pilot stage. Thank you!

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Padlet Platform Feedback: Your Stars Help Us Improve AI Resources for the ACBSP community

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Padlet Platform Feedback: Your Stars Help Us Improve AI Resources for the ACBSP community

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General Directions

You can add posts by double-clicking the background or the Plus sign in the bottom right corner. If you click on the Plus sign under the category name, your post will be uploaded to the bottom of that category by default.

You can click on + Add comment to share your critique of any posts.

You can rate each post with 1-5 Stars, with 5 being the top ranking. Three to five stars will move up, one or two will be pushed down, and those with no stars will remain in the middle. The idea is to help others focus on each category's most current and insightful posts.

Top Picks will be rated as the top five, and all others will be moved down under Additional Resources.

The "Expand this section" will open a new window with just the items from that column.

Searching Assistance - By clicking the "magnifying glass icon" in the top right of your browser, you can type in key words and the Padlet will zero in on your specific area of focus. [Simply clear the search to return to the full site view.]

Color Coding - The color coding is an effort to assist you in your journey by categorizing artifacts by level. Remember, these are only suggestions to assist you in narrowing the focus or your review.

Blue - Beginner

Green - Learner

Purple - Teacher

Orange - Leader

Red - Innovator

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AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology - Digital Promise

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AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology - Digital Promise

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Strategies to Promote AI Literacy in PK-12 Education

Public schools have a critical role to integrate AI literacy throughout PK-12 education so that our current and future communities are aware and able to critically evaluate AI systems. Our paper outlines five strategies to promote AI literacy in PK-12 education, with examples and guidance for implementing each strategy.

Strategy 1: Provide Guidance for Adoption and Evaluation
Systems Leaders and Policymakers

Strategy 2: Integrate AI Literacy Across Grades and Subject-Area Learning
System and School Administrators

Strategy 3: Facilitate Ongoing, Just-In-Time Professional Learning
Instructional Leaders

Strategy 4: Design Powerful Learning Experiences
Teachers and Instructional Leaders

Strategy 5: Promote Awareness and Agency
Leaders, Teachers, Learners, Caregivers, and Community Members

Informed users are essential for the safe and effective use of AI. We call on school leaders and policymakers to champion AI literacy for all learners, educators, and community members. Together, we can ensure the responsible adoption of emerging technologies.

Want to know more about AI literacy? Find more resources here:

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AI Literacy: A Framework to Understand, Evaluate, and Use Emerging Technology - Digital Promise

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An expanded framework for AI literacy and emerging technology with strategies to integrate into PK-12 education

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It’s been over 12 months since the first blog post about the AI Assessment Scale, and a lot has changed, both with the technology and with our understandings of how it impacts assessments in …

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Stanford University - Understanding AI Literacy

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Stanford University - Understanding AI Literacy

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A framework that identifies and organizes skills and knowledge you and students can use to navigate the opportunities and challenges of generative AI.

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Conestoga - Building Educator AI Literacy

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Conestoga - Building Educator AI Literacy

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AI literacy is understanding, using, and critically evaluating AI technologies for various domains and contexts ( Ng et al., 2021). Building AI literacy can help faculty know more about the functions and limitations of generative AI, include AI in their teaching practices, and keep up with the latest developments in AI and education.

Your Learning

Learn about Copilot from LinkedIn Learning, free for Conestoga employees and students.

Take a free online tutorial on the basics of generative AI.

Look up unfamiliar terminology using Ultimate Generative AI Glossary (AIPRM, 2024), which provides simple definitions of 200+ AI-related concepts and terms.*

College Resources

The College provides resources to support students to build their AI literacy:

Exploring

Start using Microsoft Copilot (log in with your Conestoga ID/password). If you have difficulty, contact IT.

Explore more AI tools for creating lessons, learning activities, and more.

ChatGPT - OpenAI is a chatbot. Free and paid subscriptions are available.

GitHub Copilot is a cloud-based AI tool developed by GitHub and OpenAI to assist users of Visual Studio Code, Visual Studio, Neovim, and JetBrains integrated development environments (IDEs) by autocompleting code. Free and paid subscriptions are available.

DALL·E 2 - OpenAI, developed by OpenAI, generates images from text prompts. Paid subscription is available. Other tools (e.g., GenCraft) may offer free options.

Speechify lets you convert any text into natural-sounding voices. Trial and paid subscription.

Remember, use your own personal device and ensure the security of your information, especially when signing up for third-party tools. If you have concerns, ask Conestoga an AI Question.

Prompting

Learn to craft and practice with effective prompts when using AI tools for better results.

Reading

Review resources with a higher education focus on integrating AI into teaching and learning.

Receiving

Sign up for newsletters to have AI news and information sent right to your email.

Dr. Phil's Newsletter: Connecting the science of learning & AI with the art of learning experience design.

AI + Education = Simplified: A newsletter by Lance Eaton that describes what's going on in genAI and higher education.

Ben's Bites: A newsletter that tracks the web, covering top product launches, research, and news in AI.

TLDR: A daily newsletter with links and TLDRs of the most interesting stories in AI, machine learning, and data science.

While not a newsletter, Professor Ethan Mollick's One Useful Thing blog aims to translate academic research into useful insights, focusing on AI.

What's Next

*This resource was suggested by the STEM club for girls, Fullers Library.

Elan Paulson, PhD, has been an educator in Ontario's higher education system since 2004. Before joining Conestoga as a Teaching and Learning Consultant, Elan was on the executive team at eCampusOntario. She previously served as Program Director and as an instructor in professional education programs at Western University's Faculty of Education. With a Master's in Educational Technology, Elan specializes in technology-enabled and collaborative learning to support diverse learners. She has also conducted research on faculty participation in communities of practice for professional learning and self-care.

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A selection of ways that faculty can build their AI literacy and upskill for teaching and learning.

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5 Strategies-Teaching AI Literacy to Students

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5 Strategies-Teaching AI Literacy to Students

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5 Strategies-Teaching AI Literacy to Students

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Practical AI for Educators Session 3 - January 2025

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Practical AI for Educators Session 3 - January 2025

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Practical AI for Educators Session 3 with Kimberly Becker of Moxie

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Practical AI for Educators Session 2 - January 2025

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Practical AI for Educators Session 2 - January 2025

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Practical AI  for Educators Session 2 Virtual Session

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Practical AI for Educators Session 1: Level Set With ChatGPT

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AI Literacy in Higher Education: Ethics, Applications, and Best Practices - January 18, 2025

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AI Literacy in Higher Education: Ethics, Applications, and Best Practices - January 18, 2025

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AI Literacy in Higher Education: Ethics, Applications, and Best Practices

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Expand this section

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AI Guide for Students - AI for Education

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AI Guide for Students - AI for Education

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Essential AI Literacy: Do's and Don'ts for Users - August 24, 2024

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Essential AI Literacy: Do's and Don'ts for Users - August 24, 2024

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Generative AI in a Nutshell - how to survive and thrive in the age of AI

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Cornell University: AI & Academic Integrity

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Cornell University: AI & Academic Integrity

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For more information on promoting academic integrity in your classroom, view our strategies.

Although the Artificial Intelligence (AI) landscape is evolving rapidly, one clear impact is that this technology is already being integrated into the academic lives of both students and faculty. As a result, all instructors should consider how they will provide clear guidelines concerning the use of generative AI in academic work for every class they teach.

Here are a few things to keep in mind when addressing AI and academic integrity in your course.

Clearly Communicate your Expectations

Students benefit from clearly communicated expectations for their coursework, and will likewise benefit from transparency regarding limits to and expectations for use of generative AI in their course assignments and exams.

To reduce the chance of violations of academic integrity, explicitly communicate your course's generative AI policies by:

Including in your syllabus clear expectations regarding the use of generative AI tools and any differentiation in the usage policy for specific assignments.

Clearly identify in what situations generative AI use is prohibited or permitted.

When generative AI is permitted, faculty should be clear about student expectations in terms of documentation and attribution, as well as what work is expected to be produced by the student themselves, and how the student is expected to validate or verify output from generative AI. See Crediting generative AI in an assignment, below.

Discussing the expectations spelled out in the syllabus during a class session.

Reviewing expectations and communicating how they apply to specific assignments.

Engaging in ongoing conversations about the importance of academic integrity, including the fact that basic academic integrity principles remain important and still apply regardless of the existence of generative AI tools.

Example Course Policy Language for Generative AI Use

Crediting Generative AI in an Assignment

When using material generated from an LLM in course materials or in assignments that students submit, transparency is key, and these instances should be properly referenced. When possible, you may want to select citation guidelines and share them with your students using the style guide appropriate to your discipline. Currently, the APA style guide, MLA style guide, and Chicago Manual of Style include recommendations and examples for citing LLM-generated materials that you can share with students.

For example, consider:

Including an in-text citation with reference to the LLM model, i.e. include text in quotes and reference the author, i.e. (OpenAI, 2023).

Asking students to include an AI-generated source in the methods section of their research paper.

Having students provide the text or prompt they used for the LLM to generate a response and include what LLM model, date, and version they used.

Similarly, if instructors choose to use LLMs to help in the preparation of course materials, such work should be acknowledged and attributed.

If you have specific questions and would like to schedule an individual or group consultation, please contact us.

Detecting AI-Generated Content

Generative AI does provide an increased risk that students may use it to submit work that is not their own. It is tempting to seek technology-based solutions to identify inappropriate use of generative AI tools such as ChatGPT.

Unfortunately, it is unlikely that detection technologies will provide a workable solution to this problem. It can be very difficult to accurately detect AI-generated content. Although some instructors have reported that the tone and feel of AI-generated content is different from the student work they normally receive, in some cases it is almost undetectable from the work produced by students. Detection tools claim to identify work as AI generated, but cannot provide evidence for that claim, and tests have revealed significant margins of error. This raises a substantial risk that students may be wrongly accused of improper use of generative AI.

We currently do not recommend using current automatic detection algorithms for academic integrity violations using generative AI, given their unreliability and current inability to provide definitive evidence of violations. We believe that establishing trusting relationships with students and designing authentic assessments will likely be far more effective than policing students.

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AI & Academic Integrity | Center for Teaching Innovation

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Although the Artificial Intelligence (AI) landscape is evolving rapidly, one clear impact is that this technology is already being integrated into the academic lives of both students and faculty. As a result, all instructors should consider how they will provide clear guidelines concerning the use o…

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Digital Education Council Global AI Faculty Survey 2025

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Digital Education Council Global AI Faculty Survey 2025

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Inducing Fabric - A Human AI Augmentation Framework

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Inducing Fabric - A Human AI Augmentation Framework

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Introducing Fabric — A Human AI Augmentation Framework

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And alternative suggestions for design research.

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The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence

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The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence

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The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence

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Insights The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence Erik BrynjolfssonDirectorStanford Digital Economy Lab January 12, 202220-min read DædalusSpring 2022 In 1950, Alan Turing proposed an “imitation game” as the ultimate test of whether a machine was intelligent: could a machine imitate a human so well that its answers to questions are indistinguishable from those of […]

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We are excited to share our guidebook for the responsible adoption of generative artificial intelligence (GenAI) across our District. GenAI offers unprecedented opportunities to enhance classroom interaction, personalize learning experiences, and foster an innovative learning environment for our students.

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Despite the buzz surrounding it, the prominence of prompt engineering may be fleeting. A more enduring and adaptable skill will keep enabling us to harness the potential of generative AI? It is called problem formulation — the ability to identify, analyze, and delineate problems.

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How the 20 60 20 rule transforms work with AI

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How the 20 60 20 rule transforms work with AI

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Artificial intelligence (AI) does not come to displace us in our work, but to free us from the burden of the most tedious tasks, allowing us to focus on what really brings value: creativity and decision making. This is the basis of the 20-60-20 rule, which explains how work is distributed between humans and machines in the creative and productive process.

The first 20% of the work is essentially human and focuses on creativity and ideation. At this stage, the challenge is to ask the right question or make the right request to the AI. ChatGPT, for example, cannot supplant the human creativity needed to initiate a project. This is where creative talent, a solid cultural background, and tenacity in research are required. This is the time to come up with the idea, grounded in deep knowledge and the ability to pursue a goal with determination.

The 60% intermediate is where AI shines for its efficiency, taking on repetitive, time-consuming and slow tasks. In this segment, AI is capable of writing, drawing, creating videos and, it is anticipated, will soon be developing games. This is because we already have tools such as ChatGPT, for writing; DALL-E, Midjourney and Stable Diffusion, for illustration; and video generators such as Sora, which facilitate the production of complex content combining text, images, video and game rules.

Finally, the latest 20% becomes the exclusive domain of human ingenuity. This phase focuses on the revision and refinement of the work done by the AI. It is here that the persistence of creativity, knowledge and tenacity are valued. The human being intervenes to correct, request adjustments, discard or, finally, approve and publish the result as his own.

The 20-60-20 rule clearly illustrates the symbiosis between humans and machines in the creative and productive sphere: while the first and last 20% underline the irreplaceable importance of human creativity, knowledge and perseverance, the intermediate 60% highlights the ability of AI to optimize processes, freeing us to focus on what really matters.

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How the 20 60 20 rule transforms work with AI

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Discover how the 20-60-20 rule redefines human-AI collaboration, boosting creativity and optimizing productivity.

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This is an AI i found that is all about visualization for reporting and mapping information. Could potentially increase our accuracy for advertising by informing the company where we should be looking to gain members in specific areas.

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"We use scikit-learn to support leading-edge basic research [...]" "I think it's the most well-designed ML package I've seen so far." "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]" "The great benefit of scikit-learn is its fast learning curve [...]" "It allows us to do AWesome stuff we would not otherwise accomplish."

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Anthology: AI in Higher Ed: Hype, Harm, or Help, Study 2024

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Anthology: AI in Higher Ed: Hype, Harm, or Help, Study 2024

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2024 Graduate Employability Report: Are Grads Prepared for GenAI in the Workplace? 

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2024 Graduate Employability Report: Are Grads Prepared for GenAI in the Workplace? 

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2024 Graduate Employability Report - The Cengage Blog

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The Cengage Group 2024 Graduate Employability Report reveals the growing focus on workplace skills & GenAI’s impact on career readiness.

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Generative AI and Postsecondary Instructional Practices, Study 2024

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Generative AI and Postsecondary Instructional Practices, Study 2024

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The commercial release of ChatGPT 3.5 in November 2022 put a powerful new tool into the hands of a mass market, and higher education quickly became a key cultural flashpoint for public discussion of this novel technology. Instructors and administrators at colleges and universities around the world found themselves in a new technological environment that challenged long-standing norms around academic integrity and raised serious questions about when and how learning happens in a college course.

Understanding how instructors are (or are not) using generative AI in their classrooms is vital because most college and university guidelines leave decision making about how, when, and if generative AI use is permitted to the discretion of individual instructors. Provosts and presidents will spend the coming years grappling with equipping students with AI skills and literacies and assessing the most appropriate and ethical ways to harness this technology to promote teaching and learning. CIOs and IT directors are beginning to make financial commitments to specific generative AI platforms. Centers for teaching and learning, libraries, and other university offices are developing service models and articulating best practices. The success of all these initiatives will hinge on the instructional practices of individual instructors.

Understanding how instructors are (or are not) using generative AI in their classrooms is vital because most college and university guidelines leave decision making about how, when, and if generative AI use is permitted to the discretion of individual instructors.

To gain insight into evolving instructional practices, we included a short four-question section dedicated specifically to generative AI as part of a national survey of instructors. The survey was sent to postsecondary instructors from a wide range of disciplines and at institutions in every Carnegie Classification, yielding 2,654 responses-the largest survey of US instructors about the adoption of generative AI for teaching purposes of which we are aware. Our survey was in the field from February 7, 2024, to March 10, 2024. As such, it provides a snapshot of instructional practices two semesters after the dramatic advent of generative AI as a cultural and educational force.

Our survey provides compelling evidence that instructors are exploring instructional uses of generative AI in large numbers. It also highlights ongoing uncertainty about how best to use the technology and indicates that many instructors do not allow students to use generative AI tools. Our primary findings are:

The majority of instructors have at least passing familiarity with generative AI tools. But many, especially older instructors, are not confident in their abilities to use them for pedagogical purposes or in their value in educational contexts.

A large majority (72 percent) of instructors have experimented with using generative AI as an instructional tool. Yet while instructors are using generative AI in many different ways, no individual use case has become particularly well established.

Most instructors want some kind of institutional support to help them integrate generative AI into their courses. But only a minority of them are looking for any specific support service, likely creating a dilemma for those investing in providing such services.

Many faculty, especially in the humanities, still prohibit student use of generative AI.

Methodology

The analysis at hand is part of the larger national 2024 US Instructor Survey, an instructor-focused edition of the national US Faculty Survey Ithaka S+R has been fielding for over 20 years on a triennial basis. The population for this survey consists of faculty members at four-year postsecondary institutions in the US. The survey was fielded through Qualtrics in February and March 2024 to a sample of 135,284 faculty members.

We received 5,259 completed responses for a total response rate of 3.9 percent. Respondents taking the survey were randomly assigned to one of two additional blocks of questions representing topical deep dives-one on generative AI and one on academic freedom and censorship. While findings from the overall survey, as well as the censorship and academic freedom topical deep dive are forthcoming, here we report on the findings related to the generative AI topical deep dive. For the purposes of this survey, we defined "generative AI" as "AI models that can create ("generate") original content (e.g., text, images, code), for example, ChatGPT, Midjourney, Google Bard, etc."

A subsample of 2,654 individuals were randomly assigned to, and completed, four questions on the use of generative AI in instructional contexts. It is worth noting that the response pool skews white (75 percent) and 45 years and older (75 percent). Fifty-one percent of respondents are women, and 61 percent are tenured or tenure-track. The plurality of respondents worked in the social sciences (44 percent), followed by humanities (29 percent), and sciences (24 percent). Fifty-four percent of respondents were employed at doctoral universities.

Finding 1: The majority of instructors have at least passing familiarity with generative AI tools. But many, especially older instructors, are not confident in their abilities to use them for pedagogical purposes and their value in educational contexts.

A majority (66 percent) of instructors indicated being at least somewhat familiar with generative AI tools, while only 16 percent reported having little to no familiarity. These are remarkable numbers given that OpenAI released ChatGPT just 13 months prior to the launch of our survey, underlining the speed at which generative AI is spreading. Instructors have clearly been paying attention.

However, general familiarity with these tools has not yet translated into deep understanding about how to use them in pedagogical contexts. As one respondent noted, "My negative answers on AI reflect a lack of knowledge of what is possible and effective, rather than a negative view on implementation of AI in some aspects of teaching." Only 18 percent of respondents agreed or strongly agreed that they understand teaching applications of generative AI, and only 14 percent agreed or strongly agreed they feel confident in their ability to use generative AI in their instruction. In contrast, 28 percent of instructors disagreed or strongly disagreed that they understand the potential applications of AI in teaching, and 38 percent felt little to no confidence in their ability to incorporate generative AI into their instructional practices. Despite the rapid proliferation of tips, guidelines, workshops, and other resources focused on instructional uses of generative AI, instructors are still struggling to integrate the technology into their courses. As one respondent noted, "I think that faculty members need to be trained in how to teach students to use AI materials ethically and correctly." We suspect many of this respondent's colleagues would agree.

Familiarity with generative AI is not necessarily associated with enthusiasm for it. Indeed, our survey found high levels of uncertainty among instructors about whether generative AI's net effects will be positive or negative.

Familiarity with generative AI is not necessarily associated with enthusiasm for it. Indeed, our survey found high levels of uncertainty among instructors about whether generative AI's net effects will be positive or negative. Only 19 percent of instructors agreed or strongly agreed that generative AI adoption would benefit teaching in their fields, while 25 percent disagreed or strongly disagreed that its impact would be positive. The clear majority-56 percent-were on the fence.

Skepticism of generative AI's value was highest among humanities faculty, where 45 percent disagreed or strongly disagreed that AI's impact on instructional practices in their field would be positive. This sentiment was further elucidated in the open comments:

"LLMs and Generative AI are going to undermine the trust between faculty and students. This is serious for all courses that involve writing, so a major blow to the humanities. Students need to read and think for themselves, not rely on algorithmically generated guesses."

"Philosophy is a discipline that teaches thinking as a practice, and the use of AI to do thinking for you destroys this practice."

"In Foreign Languages class AI is an issue. There is a huge difference between output at home and in class."

A large body of anecdotal evidence has suggested that familiarity with, and adoption of, generative AI is at least in part a generational issue. Our survey findings buttress these claims. Familiarity with generative AI, understanding of its teaching applications, and confidence in using it for either general or pedagogical purposes all correspond with age. Faculty skepticism and uncertainty are cross-generational concerns.

Familiarity with generative AI, understanding of its teaching applications, and confidence in using it for either general or pedagogical purposes all correspond with age. Faculty skepticism and uncertainty are cross-generational concerns.

Finding 2: A large majority of instructors (72 percent) have experimented with using generative AI as an instructional tool. Yet while instructors are using generative AI in many different ways, no individual use case has become particularly well established.

To understand how instructors were using generative AI, we asked whether respondents were using generative AI for one or more instructional purposes such as designing course materials or assessing student work. Seventy-two percent of respondents reported having used generative AI for at least one instructional activity. While our findings do not explore how often faculty are using generative AI in instructional contexts, the large percentage of those reporting having at least experimented with it underscores how rapidly the technology is spreading within higher education.

The large percentage of those reporting having at least experimented with generative AI underscores how rapidly the technology is spreading within higher education.

Our findings also indicate that instructors are using generative AI in a variety of ways, and that no individual use case has become particularly well established. The most common use cases-designing course materials, helping with email or other administrative tasks, and creating images or visualizations-were all used by only 22 percent, 16 percent, and 15 percent of instructors, respectively. The remaining five use cases that we identified were each used by only about 1 in 10 instructors.

The most common use case-designing course materials-was used by only 22 percent of instructors.

The instructors who have not engaged with generative AI in their teaching are most often faculty at doctoral institutions and are in the social sciences. Forty-one percent of social scientists have not engaged with any generative AI for their teaching, followed by 30 percent of humanists, and 27 percent of scientists. Additionally, 58 percent of respondents who have not engaged with any generative AI in their teaching are from doctoral universities followed by instructors at master's colleges (28 percent) and baccalaureate institutions (13 percent).

Finding 3: Most instructors want some kind of institutional support to help them integrate generative AI into their courses. But only a minority of them are looking for any specific support service, likely creating a dilemma for those investing in providing such services.

While most instructors have made at least some efforts to incorporate generative AI, they also report relatively low levels of understanding about how it could be used in instructional contexts, and even lower levels of confidence in their personal ability to do so (see finding 1). However, our survey revealed broad interest among instructors in building skills in this area.

Instructors expressed particular interest for support in three areas: creating tutorials and study guides (38 percent rated very or extremely valuable), creating images or visualizations for classroom use (36 percent rated very or extremely valuable), and using generative AI to design syllabi, assignments, and other course materials (36 percent rated very or extremely valuable). However, roughly half of instructors indicated that they would find support moderately to extremely valuable for every use case we identified except the use of generative AI to record audio for lectures (54 percent rated not at all or slightly valuable). While this means that approximately half of instructors see some value in instructional support for engaging with generative AI, it indicates that universities that build out services to support a range of AI-informed instructional uses will have a meaningful audience.

Universities that build out services to support a range of AI-informed instructional uses will have a meaningful audience.

Interest in support services varies by discipline. Humanities instructors were generally more skeptical of generative AI than faculty from other macro-disciplines and also less likely to see value in instructional support regardless of use case. In contrast, social science instructors were most likely to see at least moderate value in a wide range of support types.

Overall, 42 percent of instructors completely prohibit their students from using generative AI, which suggests that many faculty who have personally experimented with using the technology for instructional purposes do not allow their students similar experimental latitude. Postsecondary instructors have a well-earned reputation as late adopters and have expressed a wide range of concerns about generative AI, so it is not surprising that many of them do not allow their students to use generative AI.

Forty-two percent of instructors completely prohibit their students from using generative AI.

When instructors do allow or encourage students to use generative AI, they are most likely to permit use of generative AI as a brainstorming tool: 37 percent of instructors allow students to use generative AI for this purpose. Other use cases that instructors allow with some frequency are outlining (23 percent), drafting or revising written assignments (23 percent), and using generative AI as a study guide (21 percent). Other ways instructors encourage or allow students to use generative AI include generating practice questions, presentations or slide decks, and sample writing for critical analysis. In the open responses, some instructors also indicated that while they do not actively encourage students to use generative AI, they also do not actively prohibit its use in their courses.

It is noteworthy that the most commonly encouraged/allowed use cases were those related to writing, a finding that is somewhat surprising given that much of the early controversy around generative AI focused specifically on its abilities to enable plagiarism. One likely explanation is that writing occupies a unique role as a sort of default assignment modality in a wide number of academic disciplines. The relatively small number of instructors allowing students to use generative AI to write code, or for language instruction, for example, is likely in part a reflection of the relatively small number of courses where such activities would take place. In contrast, writing is a common activity in courses across disciplines.

Unsurprisingly, a larger share of instructors in the humanities (53 percent) do not allow students to use generative AI in their courses relative to their colleagues in other disciplines: 45 percent of faculty in the sciences and 40 percent of faculty in the social sciences do not allow students to use generative AI. However, a larger share of instructors in the social sciences allow their students to use generative AI for brainstorming ideas (40 percent compared to 30 percent of instructors in the humanities and 27 percent of instructors in the sciences), as well as creating outlines (24 percent compared to 17 percent of instructors in the sciences and 15 percent of instructors in the humanities).

Conclusion

As has been the case across sectors, the potential impacts of generative AI have been a major strategic question in higher education since the beginning of the spring 2023 semester. Our survey is one of several that indicate high levels of uncertainty and deep pockets of pessimism about whether those impacts will be positive or detrimental to teaching and learning. Serious concerns about academic integrity, ethics, accessibility, and educational effectiveness are contributing to this uncertainty and hostility. These concerns, along with usage of generative AI tools for instructional purposes, are most widespread among older faculty, but are shared by faculty across disciplines and professional ranks.

Even so, the number of faculty who have at least tested the waters of using generative AI for instructional purposes is remarkable and may have grown rapidly since last fall. A September 2023 survey found that only 22 percent of faculty were using generative AI for such purposes. While our findings are not directly comparable, it seems plausible to suggest that many faculty who had witnessed the explosion of generative AI technology over the spring had been persuaded to at least test the waters by the end of the year. One significant question that our survey leaves unanswered is how many of those faculty found their experiments fruitful enough to warrant further exploration.

Institutions are allocating significant resources to enabling and encouraging GAI use by instructors under the reasonable assumption that generative AI can't be put back in the bottle, and the arguably less reasonable assumption that widespread, transformative use of generative AI is inevitable. This may well prove to be the case. Certainly, university administrators have largely moved away from prohibition-based approaches to generative AI in favor of more nuanced consideration of what an effective AI-informed pedagogy would be. Our findings suggest that roughly four in 10 faculty are still relying on prohibitionist approaches, but that the vast majority have also taken at least modest steps towards integrating generative AI into their teaching. Whether those steps portend more openness to generative AI or confirm existing skepticism will play an important role in determining how far faculty will go on the road on which administrators hope to lead them.

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Generative AI and Postsecondary Instructional Practices - Ithaka S+R

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Understanding how instructors are (or are not) using generative AI in their classrooms is vital because most college and university guidelines leave decision making about how, when, and if generative AI use is permitted to the discretion of individual instructors.

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The Chronicle of Higher Education: Perspectives on Generative AI, Study 2024

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The Chronicle of Higher Education: Perspectives on Generative AI, Study 2024

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LinkedIn & Microsoft: 2024 Work Trend Index Annual Report

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LinkedIn & Microsoft: 2024 Work Trend Index Annual Report

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Deloitte: The state of AI in Early 2024: Gen AI Adoption Spikes and Starts to Generate Value

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Deloitte: The state of AI in Early 2024: Gen AI Adoption Spikes and Starts to Generate Value

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If 2023 was the year the world discovered generative AI (gen AI), 2024 is the year organizations truly began using-and deriving business value from-this new technology. In the latest McKinsey Global Survey on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents' expectations for gen AI's impact remain as high as they were last year, with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI-most notably, inaccuracy-as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents' organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. Looking by industry, the biggest increase in adoption can be found in professional services.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations-and they as individuals-are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development-two functions in which previous research determined that gen AI adoption could generate the most value-as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents' personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia-Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents-67 percent-expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations-in line with what we found last year -as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they're also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions: takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs-though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents' business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it's little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations' EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations' EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions-though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions-an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they're much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They're more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions-that is, to " shift left." They're also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent's nation to global GDP.

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

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As generative AI adoption accelerates, survey respondents report measurable benefits and increased mitigation of the risk of inaccuracy. A small group of high performers lead the way.

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EDUCAUSE: 2024 EDUCAUSE AI Landscape Study - The Future of AI in Higher Education

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EDUCAUSE: 2024 EDUCAUSE AI Landscape Study - The Future of AI in Higher Education

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The higher education community is still looking for common ground on how AI should and should not be used for learning and work.

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DeVry University 2024: Closing the Gap Upskilling and Reskilling in an AI Era

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DeVry University 2024: Closing the Gap Upskilling and Reskilling in an AI Era

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Higher Education’s Opportunity to Foster an AI-Skilled Workforce

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By Newsroom Staff LISLE, Ill. – October 15, 2024 – The high volume of data analytics stemming from artificial intelligence (AI) continues to shift business and societal norms, disrupting many traditional jobs while also creating new ones. Given this landscape, DeVry University partnered with an independent research firm to survey more than 1,500 U.S. employees and over 500 U.S. employers to learn about current approaches to training and skill building, including in AI. While more than half of employers acknowledge they hold responsibility for upskilling related to AI, 42% say they are not confident that their organization understands how to effectively train workers on the technology. Initiating these types of training programs can be intimidating for employers, especially when navigating the current acceleration of AI and varying employee skills, across levels. This presents a unique opportunity for higher education institutions to make learning more accessible and relevant for employers so they can keep pace and ensure their workers are using AI responsibly. Creating Personalized and Flexible Learning Pathways While nearly three in four workers agree that investing in education will help them advance their careers, colleges and universities can offer more personalized, flexible and accessible pathways to help them achieve this goal. In addition, academic institutions should ensure that learning pathways are anchored by education that addresses real skills gaps and industry needs, including the need for effective AI training. “Upskilling and reskilling should not only be tailored to each employer’s skills needs but it should also be tailored in a way that meets workers where they are and where they want to go in their careers,” said Shantanu Bose, Ph.D., provost and chief academic officer at DeVry. “Educators should cater to a diverse set of individuals by providing a range of learning pathways, including traditional degrees, short-form courses and certificates for specific skills.” Part of creating personalized learning pathways also includes offering flexibility, which is fundamental to ensuring ongoing access to education. This ensures that individuals have ample bandwidth to invest in themselves amid other priorities and responsibilities. “Institutions should make certain that any professional, no matter their skill set or schedule, can easily pursue upskilling and reskilling opportunities that accommodate their shifting career aspirations, life situations and work experience,” Bose noted. “Technology plays a key role in this with online and hybrid courses allowing professionals to learn on their own time and at their own pace.” In addition to offering hybrid learning formats, there are other approaches educators can take to ensure adult learners remain engaged in continuing education. This includes offering curricula that is highly relevant for their current or desired roles and by integrating activities that involve experientia, hands-on learning opportunities. Developing a Qualified Workforce Notably, the survey found that 60% of employers believe AI advancement is leaving some workers behind and making their skills outdated. While higher education institutions have been focused on several other challenges related to AI, the data underscores a need for the industry to lean in and foster the development of a skilled workforce as the job market continues to shift and AI becomes more prominent. “Educators have a unique role to play by ensuring employers aren’t being left alone when it comes to training their teams in an AI era,” Bose said. “Universities should be working side-by-side with employers to develop the talent that’s essential to succeed in the future, tech-focused environment, before and after an individual enters the workforce.” Academic institutions and businesses should maintain strong partnerships to align skills-based education with changing workforce and industry requirements, including use of AI. This proactive approach ensures that talent is well-prepared to embrace the new technology-centric landscape and that higher education meets the economy’s future demands. As a result, learners will have the skills they need to excel going forward, while organizations will be equipped with the workforce they need to innovate and remain competitive. “Creating a qualified workforce now requires more than traditional degrees given how rapidly technology continues to change,” Bose added. “Higher education and corporate America are at a pivotal moment that calls for more collaboration and intentional skills development from both parties.” DeVry operates with a specific purpose: to help learners at any stage in their academic journey and to prepare them for thriving careers in an economy transformed by technology. To learn more about how DeVry is working to help learners advance their skills in AI, visit devry.edu.

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Digital Education Council: DEC Global Survey Digital Education Council Global AI Student Survey 2024

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Digital Education Council: DEC Global Survey Digital Education Council Global AI Student Survey 2024

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DEC Global Survey

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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AI Governance and Student Perspectives: Democratizing Higher Education through Technology

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Digital Education Council: AI Governance and Student Perspectives: Democratizing Higher Education through Technology, Study 2024

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Digital Education Council: AI Governance and Student Perspectives: Democratizing Higher Education through Technology, Study 2024

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AI Governance and Student Perspectives: Democratising Higher Education through Technology

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Digital Education Council member Amity University Online (AUO) is using AI to expand access to quality education for India’s underserved 90%, with an aim to make learning more accessible and inclusive.

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2024 Creative AI Jobs Report - Adobe for Education

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The 2024 Creative AI Jobs Report shows how higher education can prepare students for the changing world of work with generative AI and essential skills.

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Expand this section

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Integrates 10+ Major AI into a single chat format

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Empower faculty and students to collaborate responsibly with AI using BoodleBox which enables effective, ethical GenAI integration into every classrooms.

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Not your computer? Use Guest mode to sign in privately. Learn more about using Guest mode

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Four Priorities for Human-Centered AI in Schools

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Four Priorities for Human-Centered AI in Schools

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Four Priorities for Human-Centered AI in Schools

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Or, what I’m going to be working on this coming year