Tag: pedagogy

High quality learning means developing and upskilling educators on the pedagogy of AI

There’s been endless discussion about what students do with generative AI tools, and what constitutes legitimate use of AI in assessment, but as the technology continues to improve there’s a whole conversation to be had about what educators do with AI tools.

We’re using the term “educators” to encompass both the academics leading modules and programmes and the professionals who support, enable and contribute to learning and teaching and student support.

Realising the potential of the technologies that an institution invests in to support student success requires educators to be willing and able to deploy it in ways that are appropriate for their context. It requires them to be active and creative users of that technology, not simply following a process or showing compliance with a policy.

So it was a bit worrying when in the course of exploring what effective preparation for digital learning futures could look like for our Capability for change report last year, it was noticeable how concerned digital and education leaders were about the variable digital capabilities of their staff.

Where technology meets pedagogy

Inevitably, when it comes to AI, some HE staff are enthusiastic early adopters and innovators; others are more cautious or less confident – and some are highly critical and/or just want it to go away. Some of this is about personal orientation towards particular technologies – there is a lively and important critical debate about how society comes into a relationship with AI technology and the implications for, well, the future of humanity.

Some of it is about the realities of the pressures that educators are under, and the lack of available time and headspace to engage with developmental activity. As one education leader put it:

Sometimes staff, they know that they need to change what they’re doing, but they get caught in the academic cycle. So every year it’s back to teaching again, really, really large groups of students; they haven’t had the time to go and think about how to do things differently.

But there’s also an institutional strategic challenge here about situating AI within the pedagogic environment – recognising that students will not only be using it habitually in their work and learning, but that they will expect to graduate with a level of competence in it in anticipation of using AI in the workplace. There’s an efficiency question about how using AI can reprofile educator working patterns and workflows. Even if the prospect of “freeing up” lots of time might feel a bit remote right now, educators are clearly going to be using AI in interesting ways to make some of their work a bit more efficient, to surface insight from large datasets that might not otherwise be accessible, or as a co-creator to help enhance their thinking and practice.

In the context of learning and teaching, educators need to be ready to go beyond asking “how do the tools work and what can I do with them?” and be prepared to ask and answer a larger question: “what does it mean for academic quality and pedagogy when I do?”

As Tom Chatfield has persuasively argued in his recent white paper on AI and the future of pedagogy, AI needs to have a clear educative purpose when it is deployed in learning and teaching, and should be about actively enhancing pedagogy. Reaching this halcyon state requires educators who are not only competent in the technical use of the tools that are available but prepared to work creatively to embed those tools to achieve particular learning objectives within the wider framework and structures of their academic discipline. Expertise of this nature is not cheaply won – it takes time and resource to think, experiment, test, and refine.

Educators have the power – and responsibility – to work out how best to harness AI in learning and teaching in their disciplines, but education leaders need to create the right environment for innovation to flourish. As one leader put it:

How do we create an environment where we’re allowing people to feel like they are the arbiters of their own day to day, that they’ve got more time, that they’re able to do the things that they want to do?…So that’s really an excitement for me. I think there’s real opportunity in digital to enable those things.

Introducing “Educating the AI generation”

For our new project “Educating the AI generation” we want to explore how institutions are developing educator AI literacy and practice – what frameworks, interventions, and provisions are helpful and effective, and where the barriers and challenges lie. What sort of environment helps educators to develop not just the capability, but also the motivation and opportunity to become skilled and critical users of AI in learning and teaching? And what does that teach us about how the role of educators might change as the higher education learning environment evolves?

At the discussion session Rachel co-hosted alongside Kortext advisor Janice Kay at the Festival of Higher Education earlier this month there was a strong sense among attendees that educating the AI generation requires universities to take action on multiple fronts simultaneously if they are to keep up with the pace of change in AI technology.

Achieving this kind of agility means making space for risk-taking, and moving away from compliance-focused language to a more collaborative and exploratory approach, including with students, who are equally finding their feet with AI. For leaders, that could mean offering both reassurance that this approach is welcomed, and fostering spaces in which it can be deployed.

In a time of such fast-paced change, staying grounded in concepts of what it means to be a professional educator can help manage the potential sense of threat from AI in learning and teaching. Discussions focused on the “how” of effective use of AI, and the ways it can support student learning and educator practice, are always grounded in core knowledge of pedagogy and education.

On AI in assessment, it was instructive to hear student participants share a desire to be able to demonstrate learning and skills above and beyond what is captured in traditional assessment, and find different, authentic ways to engage with knowledge. Assessment is always a bit of a flashpoint in pedagogy, especially in constructing students’ understanding of their learning, and there is an open question on how AI technology can support educators in assessment design and execution. More prosaically, the risks to traditional assessment from large language models indicate that staff may need to spend proportionally more of their time on managing assessment going forward.

Participants drew upon the experiences of the Covid pivot to emergency remote teaching and taking the best lessons from trialling new ways of learning and teaching as a useful reminder that the sector can pivot quickly – and well – when required. Yet the feeling that AI is often something of a “talking point” rather than an “action point” led some to suggest that there may not yet be a sufficiently pressing sense of urgency to kickstart change in practice.

What is clear about the present moment is that the sector will make the most progress on these questions when there is sharing of thinking and practice and co-development of approaches. Over the next six months we’ll be building up our insight and we’d love to hear your views on what works to support educator development of AI in pedagogy. We’re not expecting any silver bullets, but if you have an example of practice to share, please get in touch.

This article is published in association with Kortext. Join Debbie, Rachel and a host of other speakers at Kortext LIVE on Wednesday 11 February in London, where we’ll be discussing some of our findings – find out more and book your place here.

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December 1, 2025
From Lectures to Laughter: Strategies to Spark Joy in the Classroom – Faculty Focus
In the fast-paced, demanding world of college education, joy might not be the first thing that comes to mind when we think about teaching. But bringing joy into the classroom can make a real difference; it boosts student engagement, sparks creativity, and supports academic success. Joyful learning is about building a space where curiosity can thrive, where students feel safe making connections, asking questions, and taking intellectual risks. When instructors intentionally weave joy into their teaching, they help students tap into their own motivation and foster a stronger sense of community and belonging. This article takes a closer look at how joyful pedagogy can turn the college classroom into a vibrant, supportive environment where students truly flourish.

What is Joyful Pedagogy?

Joyful pedagogy is an approach to teaching that fosters enthusiasm, engagement, and a deep sense of connection to learning. It cultivates the emotional and intellectual well-being of students by integrating curiosity, creativity, and meaningful collaboration into the learning process. According to Zull (2011), when students experience joy in learning, their brains are more receptive to new information, leading to deeper understanding and retention. This approach moves beyond traditional lecture-based instruction, incorporating active learning strategies and, yes, even some laughter along the way. Joyful pedagogy recognizes that students are more likely to succeed academically and personally when they feel a sense of ownership over their learning and when the classroom environment is supportive and stimulating.

By incorporating joyful teaching practices, educators create welcoming classrooms that inspire critical thinking and collaboration. Additionally, joyful pedagogy embraces flexibility and adaptability, allowing for various instructional methods that meet students’ individual needs.

The Power of Play

One powerful tool for cultivating joy in learning is play, something often overlooked in higher education. Play isn’t just for children; it’s an essential part of learning at all ages. Forbes and Thomas (2022) highlight that play supports overall well-being and serves as a meaningful pathway to learning, even for adult learners. In the classroom, play sparks joy, which can help reduce stress, boost optimism and resilience, and foster a more positive learning environment (Rylance-Graham, 2024).

Importantly, bringing joy into the learning process doesn’t mean sacrificing academic rigor. High expectations and challenging coursework can be upheld while making room for playful, joyful moments. In fact, play can offer much-needed balance, especially when tackling serious or emotionally heavy content (Forbes, 2021).

Research shows that enjoyment during learning increases blood flow to the brain, enhancing cognitive functioning (Purinton & Burke, 2019). When play and joy are intentionally woven into instruction, students often feel more connected to their professors and peers, relationships that can, in turn, motivate them to meet and exceed academic expectations (Forbes et al., 2022).

Play promotes flexible and creative thinking and increases productivity (Brown, 2009). It naturally draws students in, helping them engage more deeply and look forward to class. Because play is intrinsically motivating, it can shift how students relate to learning itself (Whitton & Moseley, 2019). Simply put, when students experience joy in learning, their engagement and their performance can thrive.

Practical Strategies for Infusing Joy

How can instructors bring joy into their day-to-day teaching? Below are two simple yet effective and easily adaptable strategies I’ve used in my courses.

On the first day of my Introduction to Special Education course, I arrive with a large sheet of poster paper and a collection of colorful paper scraps varied in shape, size, and color. As students enter the room, I invite each of them to choose a scrap of paper, any one they like. Before we begin introductions or any of the “usual” first-day business, I ask them to tape their chosen piece onto the poster, wherever they feel it belongs. Once everyone has placed their scrap of paper, I ask the following questions:
- Why did you choose the piece you did?
- Why did you place it where you did?
- What do you notice about the overall design the group created?
- Why do you think I started class this way?
- What might this activity represent?
This simple yet engaging activity sparks curiosity and sets the tone for a semester rooted in exploration, connection, and reflection. While I can’t take full credit for the idea, it’s an activity shared by Marilyn P. Rice in the Professors at Play Playbook (2022), I’ve made it my own by adding a meaningful twist. I save the class poster and bring it back on the last day of the semester, hang it on the board, and ask the students if they remember creating it. Then I ask some reflective questions such as
- How has your understanding of individual learners evolved since placing your scrap of paper on this poster?
- How might this visual metaphor reflect the diversity you’ll encounter in your future classrooms?
- What can you do to ensure every student feels like their “scrap of paper” belongs in your classroom?
The responses are always thoughtful, creative, and deeply personal. It’s a powerful way to bookend our learning journey and leave students with a lasting impression of the value of joy, inclusion, and connection in education.

To adapt the end-of-semester reflective questions to be relevant to a broader range of disciplines, the following could be posed:
- How have you grown or changed since that first day?
- What did you learn from your peers that has stayed with you?
- Why do you think we’re revisiting this activity now, at the end of the course?
- How might this visual metaphor of “many parts making a whole” guide you in your future classes, work, or life?
Alternatively, students could choose a second paper scrap to place on the poster, symbolizing their “after” self, creating a kind of before-and-after reflection.

Another example of joyful pedagogy involves using visual exploration to spark creativity and deepen understanding. While exploring the question “What is literacy?” I asked students to work in teams of two or three and head outside with their phones. Their task was simple: find and photograph something they believed represented literacy. They had 15 minutes to explore and return to class.

Once they returned, each group uploaded their photo to our online discussion platform. I projected the images for the whole class to see, and each team took a moment to explain why they chose their particular representation. The results were diverse, creative, and often surprising, ranging from street signs and murals to sculptures or even a menu spotted in an unexpected place.

This activity was not only fun and energizing, but it also prompted meaningful discussion about how literacy can take many forms in our everyday lives. It encouraged students to think beyond traditional definitions and consider how literacy shows up in the world around them.

Even better, this type of activity is easy to adapt to nearly any course topic. Whether you’re exploring themes like equity, identity, community, or content-specific concepts, inviting students to find and share visual representations brings movement, creativity, and fresh perspective into the learning experience.

If the aforementioned activities don’t quite fit your style, there are infinite ways to infuse joy into your teaching. For example, send students outside with chalk to solve a problem or equation on the sidewalk. Having students take a photo of their completed work to share with the class adds a collaborative element. Another example is to ask small groups of students to create and perform a short skit or role play to demonstrate a concept. This activity can add some fun along with a deeper layer of learning and engagement.

Bringing in board games or puzzles that connect to your course content is another engaging way to reinforce key concepts and spark curiosity. You can also flip the challenge by asking students to design their own games that relate to what they’re learning.

Even a quick three-minute brain break after 30 minutes of teaching can make a big difference. Ask students to stand up if possible and set aside all devices before the break begins. A brain break might include box breathing, stretching, structured movement, or a brainteaser. There are many resources available for brain break ideas, and they range from seated breathing exercises to dancing and everything in between. These quick pauses support focus and concentration, reduce stress, and contribute to a more positive learning experience (Tapp, 2020). Brain breaks can also help combat mental fatigue and give students a much-needed cognitive recharge.

Scaling Joy for Large Classes

Joy isn’t just for small seminar rooms; it is equally powerful in large lecture halls. In fact, joyful teaching may be even more important when students risk feeling lost in the crowd.

Here are a few scalable strategies:
- Create a Class Playlist: Let students contribute songs and play them before class begins to set a positive tone.
- Quick Icebreakers or Polls: Start class with a low-stakes question or prompt that encourages interaction.
- Digital Scavenger Hunts: Use online tools for creative, content-linked challenges.
- Infographic Partner Work: Students create visual summaries of key concepts together.
- Pop Culture Connections: Use memes or relevant songs to reinforce course material and spark discussion.
These small touches can build community and make content feel more relevant without a full course redesign.

Key Takeaway

Joyful learning doesn’t have to involve elaborate plans or major changes to your teaching. In fact, it often emerges from small, intentional moments that make the classroom feel more human, more connected, and more alive. You don’t need to overhaul your entire lesson; just try one simple idea that adds a spark of joy to your students’ experience and see where it leads.

These joyful moments can increase engagement, deepen connection, and foster a more positive learning environment. Most importantly, by weaving joy into our teaching, we have the invaluable opportunity to inspire a lifelong love of learning, one that reaches far beyond the walls of the classroom.

AI Disclosure: ChatGPT was used for basic copyediting and general proofreading to check for redundancies and identify grammatical and word usage errors. It was also used to inspire ideas for the title of the article.

Robin Wolpinsky, EdD, is a clinical assistant professor in the Mary Lou Fulton College for Teaching and Learning Innovation at Arizona State University. Her background and expertise are in school psychology, human development, special education, and adult learning. Dr. Wolpinsky is deeply committed to cultivating student success.

References

Brown, S. (2009). Play: How it shapes the brain, opens the imagination, and invigorates the soul. New York: Penguin Group (USA) Inc.

Forbes, L. (2021). The process of play in learning in higher education: A phenomenological study. Journal of Teaching and Learning. 15(1), 57-73.

Forbes, L. & Thomas, D. (2022). Professors at Play Playbook. Pittsburgh, PA: Carnegie Mellon University: ETC Press.

Purinton, E. & Burke, M. (2019). Student engagement and fun: evidence from the field. Business Education Innovation Journal, 11(2), 133-140.

Rice, M. (2022). Human growth and learning: Setting the stage. In Forbes, L. & Thomas, D. Professors at Play Playbook (pp. 92-93). Pittsburgh, PA: Carnegie Mellon University: ETC Press.

Rylance-Graham, R. (2024). The lived experience of play and how it relates to psychological well-being: An interpretive phenomenological analysis (IPA) study amongst undergraduate students from medicine, nursing, and allied health professions’ programmes in the United Kingdom. Nursing Research and Practice. Apr 3;2024:7871499. doi: 10.1155/2024/7871499.

Tapp, F. (2020, January 9). Kids need brain breaks – and so do adults. Brain Facts. https://www.brainfacts.org/thinking-sensing-and-behaving/learning-and-memory/2020/kids-need-brain-breaks-010920

Whitton, N. & Moseley, A. (2019). Play and learning in adulthood. In Whitton, N. & Moseley, A. Playful Learning: Events and Activities to Engage Adults. (pp. 12-24). New York: Routledge.

Zull, J. E. (2011). From brain to mind: Using neuroscience to guide change in education. Stylus Publishing, LLC.
Source link
September 19, 2025
From Lectures to Laughter: Strategies to Spark Joy in the Classroom – Faculty Focus
In the fast-paced, demanding world of college education, joy might not be the first thing that comes to mind when we think about teaching. But bringing joy into the classroom can make a real difference; it boosts student engagement, sparks creativity, and supports academic success. Joyful learning is about building a space where curiosity can thrive, where students feel safe making connections, asking questions, and taking intellectual risks. When instructors intentionally weave joy into their teaching, they help students tap into their own motivation and foster a stronger sense of community and belonging. This article takes a closer look at how joyful pedagogy can turn the college classroom into a vibrant, supportive environment where students truly flourish.

What is Joyful Pedagogy?

Joyful pedagogy is an approach to teaching that fosters enthusiasm, engagement, and a deep sense of connection to learning. It cultivates the emotional and intellectual well-being of students by integrating curiosity, creativity, and meaningful collaboration into the learning process. According to Zull (2011), when students experience joy in learning, their brains are more receptive to new information, leading to deeper understanding and retention. This approach moves beyond traditional lecture-based instruction, incorporating active learning strategies and, yes, even some laughter along the way. Joyful pedagogy recognizes that students are more likely to succeed academically and personally when they feel a sense of ownership over their learning and when the classroom environment is supportive and stimulating.

By incorporating joyful teaching practices, educators create welcoming classrooms that inspire critical thinking and collaboration. Additionally, joyful pedagogy embraces flexibility and adaptability, allowing for various instructional methods that meet students’ individual needs.

The Power of Play

One powerful tool for cultivating joy in learning is play, something often overlooked in higher education. Play isn’t just for children; it’s an essential part of learning at all ages. Forbes and Thomas (2022) highlight that play supports overall well-being and serves as a meaningful pathway to learning, even for adult learners. In the classroom, play sparks joy, which can help reduce stress, boost optimism and resilience, and foster a more positive learning environment (Rylance-Graham, 2024).

Importantly, bringing joy into the learning process doesn’t mean sacrificing academic rigor. High expectations and challenging coursework can be upheld while making room for playful, joyful moments. In fact, play can offer much-needed balance, especially when tackling serious or emotionally heavy content (Forbes, 2021).

Research shows that enjoyment during learning increases blood flow to the brain, enhancing cognitive functioning (Purinton & Burke, 2019). When play and joy are intentionally woven into instruction, students often feel more connected to their professors and peers, relationships that can, in turn, motivate them to meet and exceed academic expectations (Forbes et al., 2022).

Play promotes flexible and creative thinking and increases productivity (Brown, 2009). It naturally draws students in, helping them engage more deeply and look forward to class. Because play is intrinsically motivating, it can shift how students relate to learning itself (Whitton & Moseley, 2019). Simply put, when students experience joy in learning, their engagement and their performance can thrive.

Practical Strategies for Infusing Joy

How can instructors bring joy into their day-to-day teaching? Below are two simple yet effective and easily adaptable strategies I’ve used in my courses.

On the first day of my Introduction to Special Education course, I arrive with a large sheet of poster paper and a collection of colorful paper scraps varied in shape, size, and color. As students enter the room, I invite each of them to choose a scrap of paper, any one they like. Before we begin introductions or any of the “usual” first-day business, I ask them to tape their chosen piece onto the poster, wherever they feel it belongs. Once everyone has placed their scrap of paper, I ask the following questions:
- Why did you choose the piece you did?
- Why did you place it where you did?
- What do you notice about the overall design the group created?
- Why do you think I started class this way?
- What might this activity represent?
This simple yet engaging activity sparks curiosity and sets the tone for a semester rooted in exploration, connection, and reflection. While I can’t take full credit for the idea, it’s an activity shared by Marilyn P. Rice in the Professors at Play Playbook (2022), I’ve made it my own by adding a meaningful twist. I save the class poster and bring it back on the last day of the semester, hang it on the board, and ask the students if they remember creating it. Then I ask some reflective questions such as
- How has your understanding of individual learners evolved since placing your scrap of paper on this poster?
- How might this visual metaphor reflect the diversity you’ll encounter in your future classrooms?
- What can you do to ensure every student feels like their “scrap of paper” belongs in your classroom?
The responses are always thoughtful, creative, and deeply personal. It’s a powerful way to bookend our learning journey and leave students with a lasting impression of the value of joy, inclusion, and connection in education.

To adapt the end-of-semester reflective questions to be relevant to a broader range of disciplines, the following could be posed:
- How have you grown or changed since that first day?
- What did you learn from your peers that has stayed with you?
- Why do you think we’re revisiting this activity now, at the end of the course?
- How might this visual metaphor of “many parts making a whole” guide you in your future classes, work, or life?
Alternatively, students could choose a second paper scrap to place on the poster, symbolizing their “after” self, creating a kind of before-and-after reflection.

Another example of joyful pedagogy involves using visual exploration to spark creativity and deepen understanding. While exploring the question “What is literacy?” I asked students to work in teams of two or three and head outside with their phones. Their task was simple: find and photograph something they believed represented literacy. They had 15 minutes to explore and return to class.

Once they returned, each group uploaded their photo to our online discussion platform. I projected the images for the whole class to see, and each team took a moment to explain why they chose their particular representation. The results were diverse, creative, and often surprising, ranging from street signs and murals to sculptures or even a menu spotted in an unexpected place.

This activity was not only fun and energizing, but it also prompted meaningful discussion about how literacy can take many forms in our everyday lives. It encouraged students to think beyond traditional definitions and consider how literacy shows up in the world around them.

Even better, this type of activity is easy to adapt to nearly any course topic. Whether you’re exploring themes like equity, identity, community, or content-specific concepts, inviting students to find and share visual representations brings movement, creativity, and fresh perspective into the learning experience.

If the aforementioned activities don’t quite fit your style, there are infinite ways to infuse joy into your teaching. For example, send students outside with chalk to solve a problem or equation on the sidewalk. Having students take a photo of their completed work to share with the class adds a collaborative element. Another example is to ask small groups of students to create and perform a short skit or role play to demonstrate a concept. This activity can add some fun along with a deeper layer of learning and engagement.

Bringing in board games or puzzles that connect to your course content is another engaging way to reinforce key concepts and spark curiosity. You can also flip the challenge by asking students to design their own games that relate to what they’re learning.

Even a quick three-minute brain break after 30 minutes of teaching can make a big difference. Ask students to stand up if possible and set aside all devices before the break begins. A brain break might include box breathing, stretching, structured movement, or a brainteaser. There are many resources available for brain break ideas, and they range from seated breathing exercises to dancing and everything in between. These quick pauses support focus and concentration, reduce stress, and contribute to a more positive learning experience (Tapp, 2020). Brain breaks can also help combat mental fatigue and give students a much-needed cognitive recharge.

Scaling Joy for Large Classes

Joy isn’t just for small seminar rooms; it is equally powerful in large lecture halls. In fact, joyful teaching may be even more important when students risk feeling lost in the crowd.

Here are a few scalable strategies:
- Create a Class Playlist: Let students contribute songs and play them before class begins to set a positive tone.
- Quick Icebreakers or Polls: Start class with a low-stakes question or prompt that encourages interaction.
- Digital Scavenger Hunts: Use online tools for creative, content-linked challenges.
- Infographic Partner Work: Students create visual summaries of key concepts together.
- Pop Culture Connections: Use memes or relevant songs to reinforce course material and spark discussion.
These small touches can build community and make content feel more relevant without a full course redesign.

Key Takeaway

Joyful learning doesn’t have to involve elaborate plans or major changes to your teaching. In fact, it often emerges from small, intentional moments that make the classroom feel more human, more connected, and more alive. You don’t need to overhaul your entire lesson; just try one simple idea that adds a spark of joy to your students’ experience and see where it leads.

These joyful moments can increase engagement, deepen connection, and foster a more positive learning environment. Most importantly, by weaving joy into our teaching, we have the invaluable opportunity to inspire a lifelong love of learning, one that reaches far beyond the walls of the classroom.

AI Disclosure: ChatGPT was used for basic copyediting and general proofreading to check for redundancies and identify grammatical and word usage errors. It was also used to inspire ideas for the title of the article.

Robin Wolpinsky, EdD, is a clinical assistant professor in the Mary Lou Fulton College for Teaching and Learning Innovation at Arizona State University. Her background and expertise are in school psychology, human development, special education, and adult learning. Dr. Wolpinsky is deeply committed to cultivating student success.

References

Brown, S. (2009). Play: How it shapes the brain, opens the imagination, and invigorates the soul. New York: Penguin Group (USA) Inc.

Forbes, L. (2021). The process of play in learning in higher education: A phenomenological study. Journal of Teaching and Learning. 15(1), 57-73.

Forbes, L. & Thomas, D. (2022). Professors at Play Playbook. Pittsburgh, PA: Carnegie Mellon University: ETC Press.

Purinton, E. & Burke, M. (2019). Student engagement and fun: evidence from the field. Business Education Innovation Journal, 11(2), 133-140.

Rice, M. (2022). Human growth and learning: Setting the stage. In Forbes, L. & Thomas, D. Professors at Play Playbook (pp. 92-93). Pittsburgh, PA: Carnegie Mellon University: ETC Press.

Rylance-Graham, R. (2024). The lived experience of play and how it relates to psychological well-being: An interpretive phenomenological analysis (IPA) study amongst undergraduate students from medicine, nursing, and allied health professions’ programmes in the United Kingdom. Nursing Research and Practice. Apr 3;2024:7871499. doi: 10.1155/2024/7871499.

Tapp, F. (2020, January 9). Kids need brain breaks – and so do adults. Brain Facts. https://www.brainfacts.org/thinking-sensing-and-behaving/learning-and-memory/2020/kids-need-brain-breaks-010920

Whitton, N. & Moseley, A. (2019). Play and learning in adulthood. In Whitton, N. & Moseley, A. Playful Learning: Events and Activities to Engage Adults. (pp. 12-24). New York: Routledge.

Zull, J. E. (2011). From brain to mind: Using neuroscience to guide change in education. Stylus Publishing, LLC.
Source link
September 19, 2025
AI Teaching Learners Today: Pick Your Pedagogy
University budgets across the country are broken. Overall revenue and accumulated financial support appear to be declining for a wide variety of reasons.

New funding policies, administrative reorganizations such as those at the Department of Education, lean fiscal times for states, diminished regard for higher learning, fewer requirements for degrees among employers hiring for entry-level positions and the impact of artificial intelligence all come together to reduce the pool of new students, tuition revenues and grants. As a result, new initiatives are stifled unless they show promise to immediately reduce costs and generate new revenue.

The cost of developing, designing and teaching classes is often largely determined by the faculty and staff costs. Long-running lower-division classes at some universities may be taught by supervised teaching assistants or adjunct faculty whose salaries are lower than tenure-track faculty’s. However, we are now confronted with highly capable technologies that require little to no additional investment and can bring immediate revenue positive opportunities. Each university very soon will have to determine to what extent AI will be permitted to design and deliver classes, and under what oversight and supervision.

However, few of us in higher ed seem to realize that such technologies are freely available today. The tsunami of new and improving AI technologies has inundated us over the past three years so quickly that many in academe have not been able to keep up.

Case in point is the rapidly expanded and enhanced AI app. We know them by the names Gemini, ChatGPT, Claude and a few others that we tried out in 2023 or 2024. Early on, they were deficient in many ways, including hallucinations and a strictly limited number of words you could put into a prompt. That has largely changed, though hallucinations can still happen—which is why it is good practice to compare prompts submitted at multiple sites, as has been suggested previously in this column.

AI is now capable of teaching higher learners in a highly sophisticated way. Given a thoughtful, detailed prompt, it is able to implement most of the proven pedagogical approaches, many of which are unknown to our teaching faculty. It is able to dig more deeply into a topic with more recent data than many of the faculty members are using. It can interpolate, extrapolate and pursue conjectures to their statistical conclusions, revealing unexpected outcomes. It can be friendly, supportive, patient and also challenging at the appropriate times.

My point is that the prompt today can be a most powerful teaching tool that can consistently create a robust, engaging learning environment. It can even offer material through a variety of pedagogies. A well-written, detailed prompt can be the equal of many of our teaching assistants, adjunct faculty and, yes, full-time faculty members who have not been deeply trained in effective pedagogy and current practice.

Let’s test out one of these technologies right now!

This one below, shared by the “There’s an AI for That” newsletter, provides an online tutor to teach students using an assortment of time-tested technologies and pedagogies. The first step begins with merely a prompt, albeit not a short one. The Interactive Learning Tutor is a virtual instructor that runs on the leading frontier AI app models (Gemini, ChatGPT and Claude). Most interesting is not only the vast range of topics it is prepared to deliver, but the range of pedagogies it will enable at the request of the learner. So, the learner can pick their pedagogy rather than being subject to the designer’s and instructor’s choice of teaching and learning methods.

“This prompt turns AI into a dual-role system that first helps learners choose the most effective study techniques for their subject, then switches into live teaching using those chosen methods. In Navigator Mode, the system gathers details about what the user wants to learn and how they prefer to learn it, then presents a curated set of three to five evidence-based methods from its catalog. Each method is explained in depth, with strengths, limitations, step-by-step applications and concrete examples tied to the user’s subject. It also suggests learning archetypes, outlines pitfalls and fixes, creates an integrated action plan, and provides tools like reflection prompts, comparison tables, and pathways matrices to help learners see their options clearly.”

This is a highly qualified virtual teaching assistant, eager and well prepared to take on the task of teaching an individual in an online tutorial or class. Imagine 30 students, with a week or so initial orientation, running this app to achieve specified learning outcomes in an engaging, interactive way. The materials provided in the individual tutorial could be augmented through a syllabus of supplementary readings, reference materials and periodic assessments leading up to a final project or paper to be submitted individually or by groups in the class. The prompt and app take over the most important role of engaging and guiding the student in ways proven to be successful.

So, let’s give this a road test! Step one is to copy the 1,112-word prompt that begins with and ends with . Paste this into the input box of your chosen AI app and press enter. After a brief moment for processing, the app will reply with an invitation to share the topic about which you would like to learn. This would be defined by the instructor of the course along with the learning outcomes for the class. As an enthusiastic learner who is going to attend an outdoors bluegrass music festival today, I typed in, “I want to learn about bluegrass music, including how it evolved, its popularity, the geographic range of its performance, the instruments used and cultural impact.” Including such learning outcomes will streamline the process for later assessments.

Gemini replied, “That’s a fantastic topic! Bluegrass music has a rich history and a vibrant culture. We can definitely break down its evolution, instruments, and cultural impact.” Gemini went on to write,
Now, to help me suggest the best learning methods for you, could you tell me a bit about how you prefer to learn? For example, when you’re trying to understand something new, do you find it helps to:

Discuss it and ask lots of questions?

Organize information visually, maybe with timelines or diagrams?

Listen to examples (like different songs) and connect them back to the concepts?

Try to explain it to someone else to make sure you’ve grasped it?
Gemini also offered a personalized action plan to set the schedule.
Integrated Action Plan

Immediate Actions (First 1-2 Hours): Use the Socratic method to generate a list of foundational questions about bluegrass. Use dual coding to create a simple timeline of major events (e.g., Bill Monroe forms the Blue Grass Boys, Earl Scruggs joins, the Newport Folk Festival).

Medium-Term Actions (First Week): Use the Storytelling method to weave those timeline events into a narrative about the birth of the genre. Pick one specific concept (e.g., “the high lonesome sound”) and use the Feynman technique to explain it in your own words.

Long-Term Actions (Ongoing): Continue to build out your visual library (dual coding) with instrument diagrams and regional maps. As you learn more, continuously refine your core story (Storytelling) and test your understanding of complex subtopics (Feynman technique).
Further details were shared by Gemini in table format to ensure that the learner understood the different approaches. Then it gave me, the learner, the chance to affirm that I wanted to blend those modes. And then we were off and running.

This format seems ideal to prototype just-in-time certificate programs, over-demand programs where insufficient numbers of faculty are readily available and other instances where time or finances may not permit full design and teaching staffing. Carefully monitor each student by asking them to share their exchanges with the app. Be vigilant to protect the learner’s FERPA rights. Use secure methods for quizzing and other assessments. Compare the outcomes to other such classes and gather feedback from the participating learners. Modify the original prompt to meet your needs and refine the results for the next offering.
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September 17, 2025
How We Think, How We Teach: Five Ways to Think About AI in Faculty Work – Faculty Focus

Every new technology brings with it a moment of reckoning and a lot of noise. Higher education has always had its share of both. We’re good at asking questions, kicking the tires, holding things at arm’s length until we’re sure it’s worth leaning in. But AI hasn’t given us that luxury. It arrived fast, and it arrived everywhere. And so here we are: adapting syllabi, revisiting assessments, trying to imagine what teaching looks like when the work of thinking and writing and making can now be shared with a machine.

It’s easy to feel like we’re supposed to become AI experts overnight, or like our value is being called into question. But I don’t think either is true. The real challenge, the real opportunity, is to understand how this new partner might show up in the work we already do. The intellectual work. The teaching work. The deeply human work.

That starts by asking better questions – not just “What can AI do?” but “What kind of thinking does good teaching really require?” If we can name that, we can start to see where AI fits and where it doesn’t.

Jared Spataro, Microsoft’s Corporate Vice President for AI at Work, offered a helpful frame for understanding AI’s potential. He identifies five key cognitive tasks that define knowledge work: perceiving, understanding, reasoning, executing, and creating. I think this framework can be translated to the world of higher ed, and the work that faculty do. Because whether we’re designing curriculum, guiding discussion, mentoring students, or shaping institutional strategy, we’re doing some blend of those five things. And by looking closely at how they show up in our work, we can start to imagine how AI might support, not replace, the best of what we do.

1. Perceiving

Perceiving is about seeing what’s really there – what’s in front of us, and what might be hidden underneath. It’s the first move of any good teacher or designer: noticing. Noticing what students understand and what they don’t. Noticing patterns in discussion boards, in assignment uploads, in the quiet absence of a student who was once engaged. Perception is where reflection begins.

AI can help here by extending human observation. Imagine tools that model thousands of student submissions and flag potential misunderstandings. Or dashboards that surface patterns in feedback across multiple course sections. Or sentiment analysis that gives faculty a pulse on how students are responding to a unit in real time. These aren’t just speculative. Georgia State University’s implementation of predictive analytics has significantly improved student outcomes, especially for underrepresented groups (Dimeo, 2017).

And in my own work, I’ve seen how AI-powered tagging and clustering can help make sense of the digital exhaust students leave behind. During a review of some end of course survey responses, I used a language model to surface common themes in open-ended student responses. What might have taken hours of coding was compressed to minutes, giving me more time to focus on what really matters: how to respond, how to improve, how to connect.

When we talk about perceiving, we’re really talking about attention. AI can expand the reach of our attention—but it’s still up to us to decide where to look, and what to do with what we find.

2. Understanding

Understanding sits at the core of what faculty do. Whether we’re preparing to teach a new course or guiding a student through their first research project or writing up our own research, we’re spending time interpreting. This kind of work takes time and attention, a willingness to sit with uncertainty. And it’s where AI, when used carefully and with intention, can help.

In my own experience, I’ve used generative tools to scan large sets of institutional policy documents to better understand how decisions are communicated, and where inconsistencies emerge. What would have taken a full afternoon of toggling between tabs and highlighting paragraphs became a manageable, interpretable task, one that still needed my judgment, but got me there faster.

When AI can support us in making sense of large volumes of information, summarizing texts, comparing perspectives, identifying patterns, we free ourselves up for the more valuable intellectual work: asking better questions, and spending more time with the answers that matter. It’s about capacity.

Recent studies point to this as a growing area of impact. In a 2023 EDUCAUSE report, researchers note that AI’s ability to “curate and synthesize complex information” has emerged as a top priority for institutions looking to support both faculty productivity and student success (Pelletier et al., 2023). That doesn’t mean outsourcing the work of understanding, but it might mean sharing the load.

As Spataro puts it, these systems can “interpret, analyze, and generate vast amounts of text data,” but it’s up to us to bring interpretation, context, and care (2025). Used well, they don’t replace the act of understanding but they expand the space we have to do it well.

3. Reasoning

Tools that can break a complex task into parts, hold multiple threads in play, and adjust course as they go? That’s thinking and reasoning, and it can be put to work for us. Imagine planning a new course. You’re juggling student needs, institutional requirements, disciplinary content, pedagogical practices, assessment design, and accessibility considerations. AI can now meaningfully assist in that process by helping you reason your way through the options. I’ve used it to test weekly structures, re-sequence modules, generate alternate assessments keyed to different learning outcomes. It’s not always right. But it’s responsive.

This is what Spataro points to when he describes reasoning models’ capacity to navigate multistep challenges. And it’s what others are beginning to explore too. The 2024 Stanford Institute for Human-Centered AI report notes that models like GPT-4 are now outperforming the average human on tasks like LSAT logical reasoning questions, tasks that require inference, not recall (Stanford HAI, 2024). It’s not just that AI can make suggestions. It can anticipate consequences. It can debug your logic. It can help you think.

But it doesn’t replace the thinking. That means we need to stay in the loop. Because just like a teaching assistant who works fast but occasionally misses the nuance, these tools need supervision. The real value is in the collaboration. You bring the goals, the context, the judgment. The system brings the speed, the range, and the willingness to try again.

4. Executing

One of the most immediate shifts many faculty feel with these tools is in execution. Not in some futuristic, sci-fi sense, but in the simplest, most grounded way: things just get done faster. And not just routine things. Writing the first draft of an announcement. Reformatting a rubric. Creating a visual from a block of text. Summarizing student feedback across discussion boards. These are tasks that used to chip away at your time, that required a certain kind of attention and structure you didn’t always have at the end of the day. Now they can happen in seconds. Not perfect, but done. Or at least started, ready for you to refine, revise, and finalize.

In my own work, this means I don’t get stalled as easily. If a meeting runs long and I lose the hour I had planned to draft a guidance doc for a new course design initiative, I don’t start from zero later. I sketch the intent in a few lines, and the system scaffolds a first version. I get to come in as editor, refining and recentering. And yes, sometimes rejecting and starting over.

What’s changed isn’t just speed. It’s how close we can get from idea to action without needing to switch tools, start a new doc, find the right template. Execution becomes lighter. It gets folded into the flow. And for faculty navigating a day that might include grading, advising, committee work, and prepping for class, that lightness matters. But we should be careful here. The goal isn’t to turn every task into a race to the bottom. The speed is a gift only if we use the time it gives us well. Execution, in this new context, isn’t about doing more. It’s about clearing space to do what matters.

5. Creating

Creativity sits at the heart of so much work that faculty do. It’s how we see ourselves, not just as transmitters of knowledge, but as makers. We write, design, shape experiences. We revise courses to better fit the needs of a new cohort, craft discussion prompts that pull students deeper, build assignments that didn’t exist five years ago. Creativity is where our identities as scholars, teachers, and thinkers converge. So it’s no surprise that when people hear about AI “creating,” it sparks something between skepticism and alarm. And I get that.

But here’s where I land: this kind of creativity isn’t competition. These tools don’t originate like we do. They don’t generate ideas out of passion or lived experience. But they can be astonishingly good at offering sparks, those half-formed ideas, raw drafts, unexpected juxtapositions. In my own work, I’ve used them to draft module overviews that I later rewrite completely, but which help me see where I’m being too vague or too dense. I’ve used them to riff on potential assignment prompts, not to choose one blindly, but to scan for a new angle or a better tone. Sometimes, I reject it all. But I always walk away with more clarity about what I think.

That’s the shift: using the tools not to replace our voice, but to sharpen it. Not to outsource our thinking, but to reflect it back in new forms. Of course, this only works if we stay present in the process. If we hold fast to our criticality, our nuance, our sense of context. That’s the work. That’s the art. And as Spataro reminds us, the best ideas don’t care where they came from—they care what we do with them next (2024).

Why This Matters

I was working with a group of faculty from different disciplines demoing a few uses of generative tools in course development. We’d just finished a quick example, generating some low-stakes writing prompts for a discussion board. One person leaned back, arms crossed. “This is fine,” they said. “But the question is: what kind of teacher does this make me?”

It’s a great question. And I think the answer is: it makes you a teacher who’s choosing. Choosing how to spend your time and choosing where your expertise matters most. Choosing when to hand something off to the machine and, most importantly, choosing when to hold on tight because the human parts are the whole point.

That’s why I’ve stayed close to these five cognitive tasks. Because none of this matters unless we connect it to the real work we do. The knowledge work. The pedagogical labor. The thinking, the care, the creative decisions.

These five domains – perceiving, understanding, reasoning, executing, creating – aren’t abstract categories or corporate taxonomies. They’re a mirror of our everyday academic labor. They map how faculty prep a new course, evaluate student performance, write feedback, collaborate with colleagues, design new programs, interpret policy, serve on committees, apply for grants, rethink curriculum. This is what it means to work in higher ed. And these are the places where AI is entering.

So when we talk about adoption or training or integration, we’re not just talking about tools or workflows. We’re talking about how we think. How we value time. How we make meaning. And whether we can build systems, technological and human, that let us spend more of our energy on the parts of this job that matter most.

Dr. Nathan Pritts is Professor and Program Chair for First Year Writing at the University of Arizona Global Campus where he also serves as University Faculty Fellow for AI Strategy. He leads initiatives at the intersection of pedagogy, design, and emerging technologies and has spearheaded efforts in the strategic implementation of online learning tools, faculty training, and scalable interventions that support both educators and students. His work brings a humanistic lens to the integration of AI—balancing innovation with thoughtful pedagogy and student-centered design. As author and researcher, Dr. Pritts has published widely on topics including digital pedagogy, AI-enhanced curriculum design, assessment strategies, and the future of higher education.

References

Dimeo, J. (2017, July 18). Georgia State improves student outcomes with data. Inside Higher Ed. https://www.insidehighered.com/digital-learning/article/2017/07/19/georgia-state-improves-student-outcomes-data

Pelletier, K., Robert, J., Muscanell, N., McCormack, M., Reeves, J., Arbino, N., & Grajek, S., with Birdwell, T., Liu, D., Mandernach, J., Moore, A., Porcaro, A., Rutledge, R., & Zimmern, J. (2023). 2023 EDUCAUSE Horizon Report: Teaching and Learning Edition. EDUCAUSE.

Pritts, N. (2025, March 19). Supporting the Instructional Design Process: Stress-Testing Assignments with AI. Faculty Focus. https://www.facultyfocus.com/articles/teaching-with-technology-articles/supporting-the-instructional-design-process-stress-testing-assignments-with-ai/

Spataro, J. (2025). AI at Work: Coming to Terms with AI’s Cognitive Inflection Point. Microsoft WorkLab. https://www.microsoft.com/en-us/worklab/ai-at-work-coming-to-terms-with-ais-cognitive-inflection-point

Stanford Institute for Human-Centered AI. (2024). AI Index Report 2024. https://aiindex.stanford.edu/report/

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September 15, 2025
From Policing to Pedagogy: Navigating AI’s Transformative Power – Faculty Focus

The rapid integration of generative artificial intelligence (GenAI) into higher education has reignited a familiar moral panic around academic dishonesty. While much of the immediate institutional response has centered on detection and enforcement, this reaction echoes a familiar pattern—one that treats academic dishonesty as an individual moral failure rather than a symptom of broader systemic issues (Bertram Gallant, 2008). Students, faced with overloaded schedules, mounting debt, and an unforgiving emphasis on grades, often make pragmatic decisions about where to invest their time and effort gravitating toward efficiency and certainty over exploration and risk. In this environment, the ethical clarity around “cheating” becomes murky—not because students don’t understand right from wrong, but because the structures around them reward efficiency and penalize risk. AI tools such as ChatGPT or Grammarly simply fit into this equation as time-saving devices—much like calculators, Google, or even essay mills before them. What has shifted is not the motivation, but the accessibility. A student’s use of AI is a reflection of the transactional nature of how learning is often experienced in contemporary institutions.

The proliferation of AI tools has not so much introduced a new problem as it has exposed the fragility of existing educational structures. Before we focus on what AI changes, we must confront what it reveals: that higher education has long incentivized a culture of performance over learning. Research has shown that when students perceive academic work as irrelevant, overly procedural, or divorced from their goals and identities, they disengage—regardless of whether AI is available (Ambrose et al., 2010). In this light, the question is not “How do we catch them?” but rather, “What kind of learning environments have we built that make outsourcing feel logical or even necessary?” This reframing shifts the pedagogical conversation from one of compliance and control to one of capacity-building and curiosity. If we can use this moment to ask how to foster a culture of learning that values growth over grades, process over product, and understanding over output, then AI may serve not as a threat to education but as a catalyst for its reinvention.

Why Students Turn to AI

When students turn to generative AI to complete assignments, they are not necessarily demonstrating a lack of understanding or unethical behavior—they are often responding rationally to the conditions of their academic lives. In surveys and interviews, students routinely cite overwhelming workloads, unclear expectations, time scarcity, and mental health struggles as reasons they seek shortcuts or support tools (McCabe, Treviño, & Butterfield, 2001; Pascoe, Hetrick, & Parker, 2020). AI fits seamlessly into a landscape where efficiency is often more highly rewarded than curiosity. Faced with tasks that feel repetitive, decontextualized, or performative, many students learn that their survival depends on doing just enough, not necessarily doing it deeply.

When a student uses AI to write a discussion post or summarize an article, the act is not just a form of evasion—it’s often a judgment about the value of the task itself. If the assignment doesn’t ask them to do something personally meaningful, intellectually stimulating, or clearly useful for their future goals, it becomes an obligation to manage rather than a learning opportunity to embrace. This disconnect is further amplified by the unspoken norms that teach students to prioritize grades, speed, and performance over exploration, failure, and growth (Margolis, 2001). In such a climate, using AI can feel less like cheating and more like optimization. Students aren’t necessarily trying to game the system—they’re playing the game as they perceive it was designed.

The challenge, then, is not to eliminate AI from the learning process, but to design learning experiences that make authentic engagement the easier, more meaningful choice. Rather than dismissing AI use as evidence of student laziness or misconduct, educators might ask: What does the pattern of AI usage tell us about how students experience our courses? What if the real issue isn’t student behavior but assignment design and instructional culture? By attending to these questions, we shift the focus from enforcement to empathy—from trying to control student actions to trying to understand their motivations and constraints.

Process Over Product

If generative AI makes it easier for students to produce work without deeply engaging in the learning process, it invites faculty to reconsider what their assessments are really measuring. Too often, traditional assessments prioritize polished outputs—essays, quizzes, presentations—over the messy, iterative, and uncertain processes that lead to genuine understanding. These conventional formats may reward conformity and correctness, but they do little to cultivate the flexibility, reflection, and critical thinking required in a world shaped by automation and ambiguity.

In contrast, AI offers a lens through which educators can reassess their learning goals: Are students being asked to think or to comply? To demonstrate mastery or to demonstrate growth? Increasingly, the competencies that matter most in both life and work—such as collaboration, problem framing, ethical reasoning, and the ability to adapt—are not easily captured through static, time-bound tasks (National Academies of Sciences, Engineering, and Medicine, 2018). Nor are they easily outsourced to AI. These are precisely the skills that thrive when assessment shifts from evaluating finished products to examining students’ thinking and decision-making processes along the way.

Emerging research on assessment design suggests that when students are asked to reflect on how they used AI, evaluate its limitations, compare its outputs to their own, or build on AI-generated responses with original insights, they begin to see these tools not as shortcuts but as thought partners (Mollick & Mollick, 2023; Popenici & Kerr, 2017). This approach not only fosters academic integrity, but also helps students develop the judgment, discernment, and contextual understanding that AI cannot replicate. Moreover, process-oriented assessments are more inclusive. They allow students with diverse learning styles, linguistic backgrounds, and levels of prior knowledge to demonstrate learning in multiple ways. They also encourage metacognition—students thinking about their own thinking—which is known to enhance long-term retention and transfer of learning (Ambrose et al., 2010). Rethinking assessment does not mean lowering standards; it means realigning them with the kinds of learning we claim to value. When assessments emphasize authentic engagement over mere output, they become more resilient to automation—and more relevant to the lives students are preparing to lead.

Disciplinary Innovation

While discussions about AI in education often remain abstract or policy-focused, meaningful change is already taking root within disciplines as faculty reimagine how to integrate AI into the logic of their fields. Rather than banning its use outright, many instructors are embedding AI into course design in ways that mirror authentic disciplinary practices. The goal is not just to accommodate new tools, but to cultivate students’ judgment in using them—aligning learning with the actual thinking, creating, and problem-solving expected in professional contexts.

Creative Disciplines

In design, media production, and the arts, instructors are moving beyond static portfolio reviews and embracing reflective, process-based documentation. Students are asked to keep iterative journals that detail how their ideas evolved, what role AI tools played in generating or refining concepts, and how creative decisions were made at each stage. This encourages fluency with new tools while reinforcing core disciplinary values: originality, intentionality, and critique. As Sullivan (2010) and McArthur & White (2021) argue, creative practice is inherently dialogic, and integrating AI into that dialogue allows students to develop their own voices in conversation with machine-generated suggestions.

Simulating Complexity

In fields such as public administration, business, and marketing, instructors are shifting from case-study write-ups to scenario-based simulations and role-playing exercises. These assessments ask students to evaluate data, propose interventions, justify decisions, and communicate across stakeholder perspectives. AI-generated content may be introduced as one data source among many, prompting students to critique its assumptions, identify potential biases, or integrate it with qualitative insights. This simulates real-world complexity and ethical ambiguity—core aspects of professional judgment that cannot be easily automated (Farrell, 2023; Wiggins, 2016).

Modeling Scientific Reasoning

In the sciences, instructors are piloting assignments that blend AI-assisted analysis with human critique. For example, students might be given an AI-generated interpretation of experimental data and asked to assess its validity, identify methodological flaws, or propose alternative explanations. These tasks promote skills fundamental to scientific reasoning: skepticism, data literacy, and peer review. As Wieman (2017) and Holmes, Wieman, & Bonn (2015) have shown, such approaches encourage deeper engagement with the epistemological foundations of science—what counts as evidence, how claims are justified, and why precision matters.

Building AI Fluency Across the Curriculum

To meet the demands of a world increasingly shaped by automation, higher education must move beyond treating AI as an external threat and begin cultivating AI fluency as a foundational component of 21st-century learning. AI fluency goes beyond knowing how to use tools like ChatGPT or Midjourney—it encompasses the ability to understand how AI systems work, critically evaluate their outputs, use them ethically, and reflect on their broader social and disciplinary implications (Long & Magerko, 2020; Luckin et al., 2016).

Integrating AI fluency into the curriculum means designing assignments that treat AI not as a banned shortcut or unexamined assistant, but as a subject of inquiry. Students should be taught to interrogate the design of AI tools, including how they are trained, what kinds of data they rely on, and how they reflect or reinforce existing social biases. Courses across disciplines can include assignments that ask students to compare AI-generated outputs with human-created work, evaluate their quality and assumptions, or explore how different prompts lead to different results. These tasks cultivate what Holmes, Bialik, & Fadel (2019) describe as “cognitive partnership”—the ability to think with, through, and about technology, rather than simply accepting its outputs.

Building AI fluency involves normalizing reflective practice. Students should be encouraged to articulate how and why they used AI tools in the completion of an assignment: What choices did they make? What did the AI get right—or wrong? How did it shape their thinking? This kind of metacognitive work not only deepens learning, but also demystifies AI as a black box and empowers students to use it responsibly (Mollick & Mollick, 2023).

Crucially, AI fluency should not be siloed within STEM or computer science. Every field—from history to healthcare, literature to law—will be transformed by AI in some way, and students in every discipline deserve the opportunity to engage critically with those transformations. Developing AI fluency as a form of literacy positions students to become not just competent users of technology, but thoughtful citizens capable of shaping its role in society.

The task before educators is not merely to regulate AI use, but to reimagine what it means to be educated in an age of intelligent machines. By embedding AI fluency into course outcomes, assessment practices, and institutional values, higher education can help students navigate this new landscape with agency, insight, and integrity.

The text was generated in collaboration with Gemini (2.5 Flash), Google’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

References

Ambrose, Susan A., Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman. How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco: Jossey-Bass, 2010.

Bertram Gallant, Tricia. Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative. San Francisco: Jossey-Bass, 2008.

Farrell, Paul. “Integrating AI into Public Administration Education: Risks and Rewards.” Journal of Public Affairs Education 29, no. 1 (2023): 45–58.

Holmes, Wayne, Maya Bialik, and Charles Fadel. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019.

Holmes, N. G., Carl E. Wieman, and Doug A. Bonn. “Teaching Critical Thinking.” Proceedings of the National Academy of Sciences 112, no. 36 (2015): 11199–204.

Long, David, and Brian Magerko. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. New York: ACM, 2020.

Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. Intelligence Unleashed: An Argument for AI in Education. London: Pearson, 2016.

Margolis, Eric. The Hidden Curriculum in Higher Education. New York: Routledge, 2001.

McArthur, Janine A., and Betty White. “Redesigning Creativity: AI, Agency, and Reflective Practice in the Visual Arts.” International Journal of Art & Design Education 40, no. 2 (2021): 437–51.

McCabe, Donald L., Linda Klebe Treviño, and Kenneth D. Butterfield. “Cheating in Academic Institutions: A Decade of Research.” Ethics & Behavior 11, no. 3 (2001): 219–32.

Mollick, Ethan, and Lilach Mollick. “Assigning AI: Seven Approaches for Students with Prompts.” Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed July 8, 2025).

National Academies of Sciences, Engineering, and Medicine. How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: The National Academies Press, 2018.

Pascoe, Michaela C., Rosanna M. Hetrick, and Alexandra G. Parker. “The Impact of Stress on Students in Secondary School and Higher Education.” International Journal of Adolescence and Youth 25, no. 1 (2020): 104–12.

Popenici, Stefan C., and Sharon Kerr. “Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.” Research and Practice in Technology Enhanced Learning 12, no. 1 (2017): 1–13.

Sullivan, Graeme. Art Practice as Research: Inquiry in the Visual Arts. Thousand Oaks, CA: SAGE Publications, 2010.

Wieman, Carl E. “The Similarities between Research in Education and Research in the Hard Sciences.” Educational Researcher 46, no. 6 (2017): 319–29.

Wiggins, Grant. “A True Test: Toward More Authentic and Equitable Assessment.” Educational Leadership 70, no. 6 (2016): 28–33.

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September 5, 2025
From Policing to Pedagogy: Navigating AI’s Transformative Power – Faculty Focus

The rapid integration of generative artificial intelligence (GenAI) into higher education has reignited a familiar moral panic around academic dishonesty. While much of the immediate institutional response has centered on detection and enforcement, this reaction echoes a familiar pattern—one that treats academic dishonesty as an individual moral failure rather than a symptom of broader systemic issues (Bertram Gallant, 2008). Students, faced with overloaded schedules, mounting debt, and an unforgiving emphasis on grades, often make pragmatic decisions about where to invest their time and effort gravitating toward efficiency and certainty over exploration and risk. In this environment, the ethical clarity around “cheating” becomes murky—not because students don’t understand right from wrong, but because the structures around them reward efficiency and penalize risk. AI tools such as ChatGPT or Grammarly simply fit into this equation as time-saving devices—much like calculators, Google, or even essay mills before them. What has shifted is not the motivation, but the accessibility. A student’s use of AI is a reflection of the transactional nature of how learning is often experienced in contemporary institutions.

The proliferation of AI tools has not so much introduced a new problem as it has exposed the fragility of existing educational structures. Before we focus on what AI changes, we must confront what it reveals: that higher education has long incentivized a culture of performance over learning. Research has shown that when students perceive academic work as irrelevant, overly procedural, or divorced from their goals and identities, they disengage—regardless of whether AI is available (Ambrose et al., 2010). In this light, the question is not “How do we catch them?” but rather, “What kind of learning environments have we built that make outsourcing feel logical or even necessary?” This reframing shifts the pedagogical conversation from one of compliance and control to one of capacity-building and curiosity. If we can use this moment to ask how to foster a culture of learning that values growth over grades, process over product, and understanding over output, then AI may serve not as a threat to education but as a catalyst for its reinvention.

Why Students Turn to AI

When students turn to generative AI to complete assignments, they are not necessarily demonstrating a lack of understanding or unethical behavior—they are often responding rationally to the conditions of their academic lives. In surveys and interviews, students routinely cite overwhelming workloads, unclear expectations, time scarcity, and mental health struggles as reasons they seek shortcuts or support tools (McCabe, Treviño, & Butterfield, 2001; Pascoe, Hetrick, & Parker, 2020). AI fits seamlessly into a landscape where efficiency is often more highly rewarded than curiosity. Faced with tasks that feel repetitive, decontextualized, or performative, many students learn that their survival depends on doing just enough, not necessarily doing it deeply.

When a student uses AI to write a discussion post or summarize an article, the act is not just a form of evasion—it’s often a judgment about the value of the task itself. If the assignment doesn’t ask them to do something personally meaningful, intellectually stimulating, or clearly useful for their future goals, it becomes an obligation to manage rather than a learning opportunity to embrace. This disconnect is further amplified by the unspoken norms that teach students to prioritize grades, speed, and performance over exploration, failure, and growth (Margolis, 2001). In such a climate, using AI can feel less like cheating and more like optimization. Students aren’t necessarily trying to game the system—they’re playing the game as they perceive it was designed.

The challenge, then, is not to eliminate AI from the learning process, but to design learning experiences that make authentic engagement the easier, more meaningful choice. Rather than dismissing AI use as evidence of student laziness or misconduct, educators might ask: What does the pattern of AI usage tell us about how students experience our courses? What if the real issue isn’t student behavior but assignment design and instructional culture? By attending to these questions, we shift the focus from enforcement to empathy—from trying to control student actions to trying to understand their motivations and constraints.

Process Over Product

If generative AI makes it easier for students to produce work without deeply engaging in the learning process, it invites faculty to reconsider what their assessments are really measuring. Too often, traditional assessments prioritize polished outputs—essays, quizzes, presentations—over the messy, iterative, and uncertain processes that lead to genuine understanding. These conventional formats may reward conformity and correctness, but they do little to cultivate the flexibility, reflection, and critical thinking required in a world shaped by automation and ambiguity.

In contrast, AI offers a lens through which educators can reassess their learning goals: Are students being asked to think or to comply? To demonstrate mastery or to demonstrate growth? Increasingly, the competencies that matter most in both life and work—such as collaboration, problem framing, ethical reasoning, and the ability to adapt—are not easily captured through static, time-bound tasks (National Academies of Sciences, Engineering, and Medicine, 2018). Nor are they easily outsourced to AI. These are precisely the skills that thrive when assessment shifts from evaluating finished products to examining students’ thinking and decision-making processes along the way.

Emerging research on assessment design suggests that when students are asked to reflect on how they used AI, evaluate its limitations, compare its outputs to their own, or build on AI-generated responses with original insights, they begin to see these tools not as shortcuts but as thought partners (Mollick & Mollick, 2023; Popenici & Kerr, 2017). This approach not only fosters academic integrity, but also helps students develop the judgment, discernment, and contextual understanding that AI cannot replicate. Moreover, process-oriented assessments are more inclusive. They allow students with diverse learning styles, linguistic backgrounds, and levels of prior knowledge to demonstrate learning in multiple ways. They also encourage metacognition—students thinking about their own thinking—which is known to enhance long-term retention and transfer of learning (Ambrose et al., 2010). Rethinking assessment does not mean lowering standards; it means realigning them with the kinds of learning we claim to value. When assessments emphasize authentic engagement over mere output, they become more resilient to automation—and more relevant to the lives students are preparing to lead.

Disciplinary Innovation

While discussions about AI in education often remain abstract or policy-focused, meaningful change is already taking root within disciplines as faculty reimagine how to integrate AI into the logic of their fields. Rather than banning its use outright, many instructors are embedding AI into course design in ways that mirror authentic disciplinary practices. The goal is not just to accommodate new tools, but to cultivate students’ judgment in using them—aligning learning with the actual thinking, creating, and problem-solving expected in professional contexts.

Creative Disciplines

In design, media production, and the arts, instructors are moving beyond static portfolio reviews and embracing reflective, process-based documentation. Students are asked to keep iterative journals that detail how their ideas evolved, what role AI tools played in generating or refining concepts, and how creative decisions were made at each stage. This encourages fluency with new tools while reinforcing core disciplinary values: originality, intentionality, and critique. As Sullivan (2010) and McArthur & White (2021) argue, creative practice is inherently dialogic, and integrating AI into that dialogue allows students to develop their own voices in conversation with machine-generated suggestions.

Simulating Complexity

In fields such as public administration, business, and marketing, instructors are shifting from case-study write-ups to scenario-based simulations and role-playing exercises. These assessments ask students to evaluate data, propose interventions, justify decisions, and communicate across stakeholder perspectives. AI-generated content may be introduced as one data source among many, prompting students to critique its assumptions, identify potential biases, or integrate it with qualitative insights. This simulates real-world complexity and ethical ambiguity—core aspects of professional judgment that cannot be easily automated (Farrell, 2023; Wiggins, 2016).

Modeling Scientific Reasoning

In the sciences, instructors are piloting assignments that blend AI-assisted analysis with human critique. For example, students might be given an AI-generated interpretation of experimental data and asked to assess its validity, identify methodological flaws, or propose alternative explanations. These tasks promote skills fundamental to scientific reasoning: skepticism, data literacy, and peer review. As Wieman (2017) and Holmes, Wieman, & Bonn (2015) have shown, such approaches encourage deeper engagement with the epistemological foundations of science—what counts as evidence, how claims are justified, and why precision matters.

Building AI Fluency Across the Curriculum

To meet the demands of a world increasingly shaped by automation, higher education must move beyond treating AI as an external threat and begin cultivating AI fluency as a foundational component of 21st-century learning. AI fluency goes beyond knowing how to use tools like ChatGPT or Midjourney—it encompasses the ability to understand how AI systems work, critically evaluate their outputs, use them ethically, and reflect on their broader social and disciplinary implications (Long & Magerko, 2020; Luckin et al., 2016).

Integrating AI fluency into the curriculum means designing assignments that treat AI not as a banned shortcut or unexamined assistant, but as a subject of inquiry. Students should be taught to interrogate the design of AI tools, including how they are trained, what kinds of data they rely on, and how they reflect or reinforce existing social biases. Courses across disciplines can include assignments that ask students to compare AI-generated outputs with human-created work, evaluate their quality and assumptions, or explore how different prompts lead to different results. These tasks cultivate what Holmes, Bialik, & Fadel (2019) describe as “cognitive partnership”—the ability to think with, through, and about technology, rather than simply accepting its outputs.

Building AI fluency involves normalizing reflective practice. Students should be encouraged to articulate how and why they used AI tools in the completion of an assignment: What choices did they make? What did the AI get right—or wrong? How did it shape their thinking? This kind of metacognitive work not only deepens learning, but also demystifies AI as a black box and empowers students to use it responsibly (Mollick & Mollick, 2023).

Crucially, AI fluency should not be siloed within STEM or computer science. Every field—from history to healthcare, literature to law—will be transformed by AI in some way, and students in every discipline deserve the opportunity to engage critically with those transformations. Developing AI fluency as a form of literacy positions students to become not just competent users of technology, but thoughtful citizens capable of shaping its role in society.

The task before educators is not merely to regulate AI use, but to reimagine what it means to be educated in an age of intelligent machines. By embedding AI fluency into course outcomes, assessment practices, and institutional values, higher education can help students navigate this new landscape with agency, insight, and integrity.

The text was generated in collaboration with Gemini (2.5 Flash), Google’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

References

Ambrose, Susan A., Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman. How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco: Jossey-Bass, 2010.

Bertram Gallant, Tricia. Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative. San Francisco: Jossey-Bass, 2008.

Farrell, Paul. “Integrating AI into Public Administration Education: Risks and Rewards.” Journal of Public Affairs Education 29, no. 1 (2023): 45–58.

Holmes, Wayne, Maya Bialik, and Charles Fadel. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019.

Holmes, N. G., Carl E. Wieman, and Doug A. Bonn. “Teaching Critical Thinking.” Proceedings of the National Academy of Sciences 112, no. 36 (2015): 11199–204.

Long, David, and Brian Magerko. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. New York: ACM, 2020.

Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. Intelligence Unleashed: An Argument for AI in Education. London: Pearson, 2016.

Margolis, Eric. The Hidden Curriculum in Higher Education. New York: Routledge, 2001.

McArthur, Janine A., and Betty White. “Redesigning Creativity: AI, Agency, and Reflective Practice in the Visual Arts.” International Journal of Art & Design Education 40, no. 2 (2021): 437–51.

McCabe, Donald L., Linda Klebe Treviño, and Kenneth D. Butterfield. “Cheating in Academic Institutions: A Decade of Research.” Ethics & Behavior 11, no. 3 (2001): 219–32.

Mollick, Ethan, and Lilach Mollick. “Assigning AI: Seven Approaches for Students with Prompts.” Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed July 8, 2025).

National Academies of Sciences, Engineering, and Medicine. How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: The National Academies Press, 2018.

Pascoe, Michaela C., Rosanna M. Hetrick, and Alexandra G. Parker. “The Impact of Stress on Students in Secondary School and Higher Education.” International Journal of Adolescence and Youth 25, no. 1 (2020): 104–12.

Popenici, Stefan C., and Sharon Kerr. “Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.” Research and Practice in Technology Enhanced Learning 12, no. 1 (2017): 1–13.

Sullivan, Graeme. Art Practice as Research: Inquiry in the Visual Arts. Thousand Oaks, CA: SAGE Publications, 2010.

Wieman, Carl E. “The Similarities between Research in Education and Research in the Hard Sciences.” Educational Researcher 46, no. 6 (2017): 319–29.

Wiggins, Grant. “A True Test: Toward More Authentic and Equitable Assessment.” Educational Leadership 70, no. 6 (2016): 28–33.

Source link

September 5, 2025
AI in the Classroom: Panic, Possibility, and the Pedagogy in Between – Faculty Focus
“Now is the time to understand more, so that we may fear less.”
—Marie Curie

In my role as a fellow at my university’s Center for Teaching and Learning, I’ve had dozens of conversations with faculty across disciplines—and one pattern has become impossible to ignore. The gulf between those working to integrate AI into their teaching and those swearing off its use entirely is growing wider by the month. It’s not just about comfort with technology; it’s about pedagogical identity, ethics, trust, and the role of higher education in a rapidly changing world.

Some faculty are experimenting with co-written essays and AI-graded orals. Others are defaulting to analog tools like in-class handwritten exams. Still others are choosing not to address AI at all—perhaps hoping it will fade, or that someone else will lead the conversation. But as AI tools become more deeply embedded in how students learn, communicate, and imagine their futures, “not engaging” isn’t neutrality. It’s a message. And our students are listening.

AI may or may not upend higher education, but in the meantime, it’s prompting urgent questions: What are we assessing? What do we value? How do we prepare students not just to perform, but to think, reflect, and adapt in a world where generative tools are the norm?

This isn’t a call to panic, or to blindly adopt new technologies. Instead, it’s an invitation: to engage, to listen, and to rethink what meaningful learning looks like in the age of AI.

Head in the Sand Isn’t Helping

Faculty skepticism toward AI isn’t unfounded. Many of the concerns I’ve heard from colleagues are serious and principled: worries about surveillance and data privacy, the environmental toll of training large language models, the ethical murkiness of how datasets are scraped and whose labor powers “intelligent” systems. Others raise deeply humanistic questions: What happens to student creativity when a machine can draft a paper in seconds? What becomes of learning when shortcuts are so easy to take?

These aren’t trivial. But neither is pretending this technology doesn’t exist.

I recently reached out to a student who had taken a “W” in one of my courses. When I asked why he withdrew, he cited discomfort with the role AI was playing in the class. Not because of cheating or confusion, but because the presence of AI made him question the point of the course—and maybe of college altogether. If AI could do the work, what was the value of his contribution? Why were we still doing things the old way if the world had already changed?

That moment hit hard. Not because it revealed something broken in my class, but because it surfaced the very questions many students are too hesitant—or too disillusioned—to ask aloud.

Higher education is at an inflection point. If we refuse to engage with the forces shaping our students’ futures, we risk becoming irrelevant not because AI replaces us, but because we’ve chosen not to show up.

The Concerns Are Real—and Bigger Than Cheating

It’s easy to reduce the AI debate in education to one issue: cheating. And yes, generative AI makes it easier than ever to outsource writing, coding, or even lab reports. But the ethical landscape of AI is much broader, and in many ways, more troubling, than academic dishonesty alone.

There’s the environmental cost. Training a single large language model can consume more electricity and water than some small towns (though geography plays a key role in the cost and availability of these resources). As educators asking students to confront the realities of climate change, it’s fair to ask whether adopting energy-intensive technologies contradicts our values.

There’s also the labor behind the intelligence. Many AI models rely on vast troves of human-generated data, scraped without consent, and cleaned or labeled by underpaid workers in precarious conditions. Even as these systems are hailed as revolutionary, they’re often built atop invisible, exploitative structures.

And then there are the questions of voice and bias. Who gets to decide what “good writing” or “correct” analysis looks like when the algorithms were trained on dominant cultural norms? What knowledge gets privileged, and what gets erased?

These are valid reasons to hesitate. To question. To push back.

But they’re also reasons to talk—to surface the complexities with our students rather than shielding them from the conversation. Because AI isn’t just a technological shift; it’s a mirror reflecting what we value in education, labor, and society at large. And the only way to use that mirror well is to look into it—together.

Engagement Starts with Transparency

Even if you don’t use AI in your teaching—and even if you don’t want students to use it—ignoring it isn’t a neutral act. In today’s classroom, silence sends a message. And more often than not, students interpret that message as either indifference or confusion.

That’s why the most important place to start is also the simplest: your syllabus.

Whether you fully embrace AI, permit it in limited ways, or prohibit it entirely, make your expectations visible. Be specific about when, how, and why students are or are not allowed to use generative tools. If AI is restricted for certain assignments, explain the rationale. If it’s allowed, clarify what constitutes appropriate use—and what crosses the line into misrepresentation.

This isn’t just about compliance or classroom management. It’s about modeling critical thinking. When we articulate our stance on AI, especially with nuance, we teach students how to approach emerging technologies with intention rather than fear or opportunism. We show them that tools are never neutral, and that ethical use requires context, purpose, and reflection.

A well-crafted AI policy in the syllabus isn’t just a rules section. It’s a pedagogical opportunity. It invites students to see learning as more than task completion—and faculty as more than enforcers of boundaries.

Co-Creating AI Policies with Students

One of the most illuminating experiences I’ve had with AI in the classroom wasn’t about a tool—it was about a conversation.

At the beginning of the term, I asked my students to help draft our class AI policy. Not just to vote on what was allowed or not, but to actually engage with the deeper questions:
- When does using it help us learn, and when might it interfere?
- Why might we choose not to use it, even if it’s available?
Some students were hesitant. Others were skeptical. A few openly admitted they hadn’t given the issue much thought. But the discussion that unfolded was rich, not because we arrived at a perfect consensus, but because we didn’t.

In fact, when I posed the question, “Can we come to a single, consensus class policy we all agree on?” several students said no. That moment, of disagreement, was a gift. It opened up a space to explore divergent opinions and values. What emerged was a shift from a single rule to individually described and justified policies: each student reflecting on how they might or might not use AI in their own work, and under what conditions.

The results were powerful. Students who had initially seen AI as a shortcut began to see it as a tool, one that like any other required skill, responsibility, and judgment. Others who feared AI’s presence in the classroom said they felt more respected, more seen, and more in control after having the chance to express their discomfort and shape the rules that would govern their learning.

Co-creating policy didn’t make the complexity go away. But it did something more important: it positioned students as thinking partners, not passive recipients. It made clear that education is not about enforcing compliance—it’s about wrestling with hard questions together.

Rethinking Assessment in an Age of Automation

If there’s one place where AI has rattled the foundations of higher education, it’s assessment. From auto-generated essays to AI-coded solutions, the fear is clear: how do we know students are actually doing the work themselves?

Some instructors have responded by doubling down: returning to blue books, in-class timed writing, or closed-note exams. But as one colleague of mine pointed out, even that has limits. Most of our students aren’t used to writing by hand anymore. For some, these methods feel less like rigor and more like an arbitrary barrier to demonstrating what they know.

Others are experimenting. A colleague now uses oral exams (with AI tools assisting in grading) to reduce bias and create a more authentic picture of student understanding. The approach is labor-intensive, but it’s also more human. It asks students to speak to what they’ve learned, to explain their reasoning, to think on their feet.

Is that scalable for every class? No. But it raises an important question: What exactly are we assessing, and why? Are we testing memory, or synthesis? Output, or process? Compliance, or curiosity?

AI is forcing us to reconsider not just how we assess learning, but what we think learning is.

Maybe it’s time to move beyond one-size-fits-all exams and instead explore multimodal assessments: things like revision portfolios, audio reflections, collaborative projects, or scaffolded assignments that make “cheating” harder not through restriction, but through design.

Yes, that takes time. Yes, it takes creativity. But perhaps what’s most needed isn’t new tools or tighter rules, but the courage to leave behind methods that no longer serve our students or our goals.

AI as Catalyst, Not Catastrophe

It’s tempting to view AI as the problem. And in some ways, it is, especially when it accelerates inequities, obscures labor or undermines trust. But it’s also a mirror. It reflects our uncertainty about what we’re teaching, how we’re measuring it, and why any of it matters.

For those of us in higher education, that reflection can feel uncomfortable. But discomfort isn’t the enemy. It’s often the beginning of clarity.

AI has surfaced long-standing tensions, between efficiency and depth, standardization and creativity, performance and learning. It’s challenged our assumptions about what knowledge looks like and who gets to demonstrate it. And in doing so, it’s offered us a rare opportunity: to rethink what we’re really doing in the classroom.

This doesn’t mean rushing to adopt every new tool. It doesn’t mean ignoring legitimate ethical concerns. It means refusing to retreat into defensiveness or nostalgia. It means showing up, for our students, for each other, and for the evolving work of teaching and learning.

Our students don’t need us to have all the answers. They need us to model how to live with the questions. They need to see that thoughtful, ethical, human learning is still possible, especially in a world full of algorithms.

So no, this moment doesn’t call for panic. But it does call for pedagogy. And purpose. And the willingness to meet the future, not with fear but with imagination.

Demian Hommel, PhD, teaches introductory and upper-division human geography courses in the College of Earth, Ocean, and Atmospheric Sciences at Oregon State University. He is also a fellow with the institution’s Center for Teaching and Learning, working to push the mission of excellence in teaching and learning across his campus and beyond.
Source link
August 13, 2025
AI in the Classroom: Panic, Possibility, and the Pedagogy in Between – Faculty Focus
“Now is the time to understand more, so that we may fear less.”
—Marie Curie

In my role as a fellow at my university’s Center for Teaching and Learning, I’ve had dozens of conversations with faculty across disciplines—and one pattern has become impossible to ignore. The gulf between those working to integrate AI into their teaching and those swearing off its use entirely is growing wider by the month. It’s not just about comfort with technology; it’s about pedagogical identity, ethics, trust, and the role of higher education in a rapidly changing world.

Some faculty are experimenting with co-written essays and AI-graded orals. Others are defaulting to analog tools like in-class handwritten exams. Still others are choosing not to address AI at all—perhaps hoping it will fade, or that someone else will lead the conversation. But as AI tools become more deeply embedded in how students learn, communicate, and imagine their futures, “not engaging” isn’t neutrality. It’s a message. And our students are listening.

AI may or may not upend higher education, but in the meantime, it’s prompting urgent questions: What are we assessing? What do we value? How do we prepare students not just to perform, but to think, reflect, and adapt in a world where generative tools are the norm?

This isn’t a call to panic, or to blindly adopt new technologies. Instead, it’s an invitation: to engage, to listen, and to rethink what meaningful learning looks like in the age of AI.

Head in the Sand Isn’t Helping

Faculty skepticism toward AI isn’t unfounded. Many of the concerns I’ve heard from colleagues are serious and principled: worries about surveillance and data privacy, the environmental toll of training large language models, the ethical murkiness of how datasets are scraped and whose labor powers “intelligent” systems. Others raise deeply humanistic questions: What happens to student creativity when a machine can draft a paper in seconds? What becomes of learning when shortcuts are so easy to take?

These aren’t trivial. But neither is pretending this technology doesn’t exist.

I recently reached out to a student who had taken a “W” in one of my courses. When I asked why he withdrew, he cited discomfort with the role AI was playing in the class. Not because of cheating or confusion, but because the presence of AI made him question the point of the course—and maybe of college altogether. If AI could do the work, what was the value of his contribution? Why were we still doing things the old way if the world had already changed?

That moment hit hard. Not because it revealed something broken in my class, but because it surfaced the very questions many students are too hesitant—or too disillusioned—to ask aloud.

Higher education is at an inflection point. If we refuse to engage with the forces shaping our students’ futures, we risk becoming irrelevant not because AI replaces us, but because we’ve chosen not to show up.

The Concerns Are Real—and Bigger Than Cheating

It’s easy to reduce the AI debate in education to one issue: cheating. And yes, generative AI makes it easier than ever to outsource writing, coding, or even lab reports. But the ethical landscape of AI is much broader, and in many ways, more troubling, than academic dishonesty alone.

There’s the environmental cost. Training a single large language model can consume more electricity and water than some small towns (though geography plays a key role in the cost and availability of these resources). As educators asking students to confront the realities of climate change, it’s fair to ask whether adopting energy-intensive technologies contradicts our values.

There’s also the labor behind the intelligence. Many AI models rely on vast troves of human-generated data, scraped without consent, and cleaned or labeled by underpaid workers in precarious conditions. Even as these systems are hailed as revolutionary, they’re often built atop invisible, exploitative structures.

And then there are the questions of voice and bias. Who gets to decide what “good writing” or “correct” analysis looks like when the algorithms were trained on dominant cultural norms? What knowledge gets privileged, and what gets erased?

These are valid reasons to hesitate. To question. To push back.

But they’re also reasons to talk—to surface the complexities with our students rather than shielding them from the conversation. Because AI isn’t just a technological shift; it’s a mirror reflecting what we value in education, labor, and society at large. And the only way to use that mirror well is to look into it—together.

Engagement Starts with Transparency

Even if you don’t use AI in your teaching—and even if you don’t want students to use it—ignoring it isn’t a neutral act. In today’s classroom, silence sends a message. And more often than not, students interpret that message as either indifference or confusion.

That’s why the most important place to start is also the simplest: your syllabus.

Whether you fully embrace AI, permit it in limited ways, or prohibit it entirely, make your expectations visible. Be specific about when, how, and why students are or are not allowed to use generative tools. If AI is restricted for certain assignments, explain the rationale. If it’s allowed, clarify what constitutes appropriate use—and what crosses the line into misrepresentation.

This isn’t just about compliance or classroom management. It’s about modeling critical thinking. When we articulate our stance on AI, especially with nuance, we teach students how to approach emerging technologies with intention rather than fear or opportunism. We show them that tools are never neutral, and that ethical use requires context, purpose, and reflection.

A well-crafted AI policy in the syllabus isn’t just a rules section. It’s a pedagogical opportunity. It invites students to see learning as more than task completion—and faculty as more than enforcers of boundaries.

Co-Creating AI Policies with Students

One of the most illuminating experiences I’ve had with AI in the classroom wasn’t about a tool—it was about a conversation.

At the beginning of the term, I asked my students to help draft our class AI policy. Not just to vote on what was allowed or not, but to actually engage with the deeper questions:
- When does using it help us learn, and when might it interfere?
- Why might we choose not to use it, even if it’s available?
Some students were hesitant. Others were skeptical. A few openly admitted they hadn’t given the issue much thought. But the discussion that unfolded was rich, not because we arrived at a perfect consensus, but because we didn’t.

In fact, when I posed the question, “Can we come to a single, consensus class policy we all agree on?” several students said no. That moment, of disagreement, was a gift. It opened up a space to explore divergent opinions and values. What emerged was a shift from a single rule to individually described and justified policies: each student reflecting on how they might or might not use AI in their own work, and under what conditions.

The results were powerful. Students who had initially seen AI as a shortcut began to see it as a tool, one that like any other required skill, responsibility, and judgment. Others who feared AI’s presence in the classroom said they felt more respected, more seen, and more in control after having the chance to express their discomfort and shape the rules that would govern their learning.

Co-creating policy didn’t make the complexity go away. But it did something more important: it positioned students as thinking partners, not passive recipients. It made clear that education is not about enforcing compliance—it’s about wrestling with hard questions together.

Rethinking Assessment in an Age of Automation

If there’s one place where AI has rattled the foundations of higher education, it’s assessment. From auto-generated essays to AI-coded solutions, the fear is clear: how do we know students are actually doing the work themselves?

Some instructors have responded by doubling down: returning to blue books, in-class timed writing, or closed-note exams. But as one colleague of mine pointed out, even that has limits. Most of our students aren’t used to writing by hand anymore. For some, these methods feel less like rigor and more like an arbitrary barrier to demonstrating what they know.

Others are experimenting. A colleague now uses oral exams (with AI tools assisting in grading) to reduce bias and create a more authentic picture of student understanding. The approach is labor-intensive, but it’s also more human. It asks students to speak to what they’ve learned, to explain their reasoning, to think on their feet.

Is that scalable for every class? No. But it raises an important question: What exactly are we assessing, and why? Are we testing memory, or synthesis? Output, or process? Compliance, or curiosity?

AI is forcing us to reconsider not just how we assess learning, but what we think learning is.

Maybe it’s time to move beyond one-size-fits-all exams and instead explore multimodal assessments: things like revision portfolios, audio reflections, collaborative projects, or scaffolded assignments that make “cheating” harder not through restriction, but through design.

Yes, that takes time. Yes, it takes creativity. But perhaps what’s most needed isn’t new tools or tighter rules, but the courage to leave behind methods that no longer serve our students or our goals.

AI as Catalyst, Not Catastrophe

It’s tempting to view AI as the problem. And in some ways, it is, especially when it accelerates inequities, obscures labor or undermines trust. But it’s also a mirror. It reflects our uncertainty about what we’re teaching, how we’re measuring it, and why any of it matters.

For those of us in higher education, that reflection can feel uncomfortable. But discomfort isn’t the enemy. It’s often the beginning of clarity.

AI has surfaced long-standing tensions, between efficiency and depth, standardization and creativity, performance and learning. It’s challenged our assumptions about what knowledge looks like and who gets to demonstrate it. And in doing so, it’s offered us a rare opportunity: to rethink what we’re really doing in the classroom.

This doesn’t mean rushing to adopt every new tool. It doesn’t mean ignoring legitimate ethical concerns. It means refusing to retreat into defensiveness or nostalgia. It means showing up, for our students, for each other, and for the evolving work of teaching and learning.

Our students don’t need us to have all the answers. They need us to model how to live with the questions. They need to see that thoughtful, ethical, human learning is still possible, especially in a world full of algorithms.

So no, this moment doesn’t call for panic. But it does call for pedagogy. And purpose. And the willingness to meet the future, not with fear but with imagination.

Demian Hommel, PhD, teaches introductory and upper-division human geography courses in the College of Earth, Ocean, and Atmospheric Sciences at Oregon State University. He is also a fellow with the institution’s Center for Teaching and Learning, working to push the mission of excellence in teaching and learning across his campus and beyond.
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August 13, 2025
To ‘think like a lawyer’: some thoughts on the pedagogy of international law

by Paolo Amorosa & Sebastián Machado

Most law professors face a similar challenge when designing their courses: how to explain to students the enduring gap between what the law says and how it functions in reality. One of the foundational assumptions of legal education is that law is more than just the written rules found in statutes, bills, or constitutions. Without an understanding of how these rules influence a judge’s decision-making, they remain little more than pretty playthings: abstract ideas with no real-world impact. This realist approach in domestic legal education helps bridge the divide between legal theory and practice; the same arguments might apply in most disciplines and fields with a similar divide between theory and practice. If you can examine a rule and confidently predict how it will be applied, you are engaging in the most basic form of legal research. But consider a legal system without a centralised rule-making authority or a single, binding interpreter – no supreme legislature or final court to settle disputes definitively. This is the reality of international law. While there are many judicial and quasi-judicial bodies, there is no universal, mandatory forum for resolving disputes, and most conflicts never reach a formal judgment. Instead, states, international organizations, and individuals all contribute to shaping the rules by advocating for their preferred interpretations, hoping to sway the broader consensus. International lawyers refer to this evolving consensus as the ‘invisible college of international lawyers’, a term that captures the discipline’s informal, socially constructed boundaries. In essence, international law is what international lawyers do.

Teaching international law, then, comes with an added layer of complexity: the lack of formal structures undermines legal certainty. Every international lawyer, to some degree, can influence the field. Through journal articles, blog posts, social media debates, or legal practice, they argue for their version of the correct interpretation of a rule. Academics may even challenge established meanings, making persuasive cases that defy the literal text of foundational documents like the UN Charter.

This is why international lawyers often say that the law is made, not found. Unlike domestic legal systems, where rules are either codified (as in civil law) or derived from judicial precedent (as in common law), international law is fundamentally discursive. This creates a twofold problem. First, without an authoritative interpreter, there is no clear way to separate theory from practice. A legal advisor in a Foreign Ministry might frame a state’s actions as part of a new trend that modifies a rule (such as pre-emptive self-defense), while others denounce it as a violation (like Article 51 of the UN Charter). In this environment, the line between legal theory and practice dissolves. Second, with no objective boundaries to the discipline, the distinction between mainstream international law and critical approaches collapses. What remains is the professor’s choice: which version of the law to teach.

Yet teaching international law does not require taking a stance on the theory-practice divide, because that divide is not inherent to the discipline. Law professors are not bound by the same rigid distinctions as, say, natural scientists, who must separate theoretical models from empirical observation. Instead, legal education can bypass this dichotomy entirely by focusing on the deeper conditions that shape how we understand both theory and practice. Rather than treating practice as a constraint on theory, students can learn to apply theoretical insights pragmatically. This approach allows law schools to teach practical skills without forcing an artificial separation between legal thought and legal action, following larger trends in pedagogical training outside legal academia.

Still, many international law professors struggle with curriculum design because of these perceived divides. On one hand, students must master a baseline of doctrinal knowledge to enter legal practice. On the other, mere knowledge acquisition is not enough – students must also develop the ability to analyse, synthesise, and critically evaluate legal arguments. A well-rounded legal education should cultivate these higher-order skills, enabling students to engage in meta-cognitive reflection about the law they are learning.

Moreover, there is no strong evidence that ‘thinking like a lawyer’ is a unique cognitive skill. Legal reasoning shares much with other forms of reasoning, meaning that better teaching methods alone will not necessarily produce better lawyers. Instead, what matters is equipping students with evaluative tools to interpret and refine legal arguments. By treating core legal knowledge as a foundation rather than a rigid boundary, and critical thinking as a method for engaging with that knowledge, the supposed divide between mainstream and critical approaches begins to fade.

The same logic applies to the theory-practice debate. The tension between these approaches persists only if we assume they are mutually exclusive. Law schools often face criticism from practitioners who argue that graduates lack practical skills, while academics defend the importance of theoretical training. But must these roles be in conflict?

Perhaps the real issue in international law is not the existence of these divides, but our insistence on treating them as inevitable. If there is little evidence that ‘thinking like a lawyer’ is a distinct cognitive skill, there is even less reason to impose it as a rigid framework for international legal education. Instead, we might focus on cultivating adaptable, reflective practitioners who can navigate both theory and practice – not as opposing forces, but as complementary dimensions of the same discipline. This is a lesson relevant for many if not all professional disciplines.

Sebastian Machado Ramírez is Postdoctoral Researcher at the University of Helsinki, where he works on the PRIVIGO project examining private governance and international law. He holds a PhD from the University of Melbourne, where his dissertation analyzed interpretive approaches in the law governing the use of force.

Paolo Amorosa is University Lecturer in International Law at the University of Helsinki. He holds a PhD from the same institution and specializes in the history and theory of international law and human rights. His monograph Rewriting the History of the Law of Nations (OUP 2019) critically re-examines the ideological foundations of international law’s canon.

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Categories: Teaching Learning & Assessment | Tags: adaptability, discourse, education, international, interpretation, Legal, practice, reasoning, structure, theory | Permalink.

Author: SRHE News Blog

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July 30, 2025