Tag: AI tools

Bringing C.H.A.O.S to Chaos: Syllabi with an AI Usage Policy – Faculty Focus

It is no secret that Artificial Intelligence (AI) technology is transforming college classrooms. AI tools can easily and quickly assist students in various tasks such as essay writing, literature reviews, analyzing data, formulating code, solving equations, image generation, music composition, and so much more. With minimal or no effort, within minutes, students have most assignments, test questions, or discussion problems figured out and done…enter the chaos!

As educators, this means everything that we experienced in our own education and have worked years to develop is changing. We need to rethink our role, assignments, as well as formative and summative assessments of student learning all while AI capabilities change daily.

Through all this, we feel educators can take one concrete step to control the chaos by Communicating How AI Operates in the Syllabus (C.H.A.O.S). This article highlights the significance of AI usage policies and what elements should be considered in the policy. It also provides a few useful tips to implement AI policies.

Is there a need to specify the AI usage policy in the class syllabus?

Including an AI usage policy offers clarity to students regarding acceptable usage of AI tools. Although educators are making attempts to develop campus-wide consensus about AI tools, different sections of the same course may have different AI usage policies and practices depending on the instructor and their comfort level. When an explicit AI usage policy is stated in the syllabus, students (and faculty members) can be held accountable for upholding that policy. Any incidents of inappropriate AI usage or cheating can be dealt with within the framework of the policy mentioned.

What are the elements of an AI policy?

A clear AI usage policy needs to have at least three elements – acceptable use, citations necessary, and consequences of non-adherence to the policy. The first element, a well-formulated AI usage policy, specifies acceptable AI usage in learning processes, e.g. brainstorming and creating outlines. In the policy, it is beneficial to provide examples of unacceptable usage, e.g. using whole paragraphs of AI text or writing the whole essay. To develop the acceptable use portion, faculty need to take time and honestly reflect on how they see AI tools being integrated into classroom learning, career fields, and life in general. This can range from absolutely no AI usage to intentional integration of the tools in the classroom space. Your level of familiarity, acceptance of, and perceived value in AI will help shape what is allowed.

The second element is to determine how AI usage needs to be acknowledged and cited appropriately. Again, it is helpful to provide a sample citation as students may be unfamiliar with the norms of citations while taking initial undergraduate courses.

Furthermore, AI tools often include biases, inaccuracies, or sometimes hallucinations. Humans using AI tools, in this case students, need to be aware of these shortcomings and understand that even with proper citations they will be held accountable for not reviewing and verifying the accuracy of their work before submitting. So, the citation element in the syllabus must explain that the ultimate responsibility of academic rigor for the assignment submitted lies with the student.

The last element should outline information about how the instructor will deal with non-adherence to the policy. As an instructor, will you provide a chance for students to resubmit the work? Will the usage result in a loss of points, a zero for the assignment, or a failing grade in the course? How will AI usage be checked? How will you communicate it to students? What is the process and who is involved? These are a few questions that can help an educator shape their usage policy.

Implementing AI usage policy

A few additional steps can be taken when implementing an AI usage policy to make the process smoother. Given the varied nature of AI use policy in various courses, it would be helpful to have a classroom discussion early on (day 1) and throughout the semester with each assignment and exam. Researchers have suggested using a stoplight framework e.g., Red – No AI is allowed in this course; Green – AI tools are allowed and encouraged; and Yellow- AI is permitted for following activities only (Urbaczewski, 2025).

Regularly talking about AI usage may remove some of the pressure from students who want to express themselves through their essays or art and feel seen/heard but may be afraid that their grades will be poorer if they don’t use AI tools. Sharing stories of how past students have effectively, or ineffectively, utilized AI in their work can be helpful in mitigating inappropriate usage.

Another possible step is to introduce scaffolding in high stakes summative assignments such as end of the semester projects. Instead of a one-time submission worth 100 points, students could submit intermediate steps and include the prompts they may have used to get a response from AI tools such as ChatGPT or Perplexity. Some instructors may offer extra credit for such intermediate steps.

Unlike grading or attendance policies in the syllabus, AI usage policies will be dynamic. As AI technology advances, the definition of academic integrity may need to be revisited. Faculty members may need to revise this policy each semester and update it to ensure that students are meeting learning outcomes. As the educational landscape continues to evolve, remember you can get a handle on the chaos by introducing C.H.A.O.S to keep your syllabi in alignment with the current times.

AI Disclosure: AI tools such as Editor in MS Word were used for checking grammar and spelling. No AI tools were used for brainstorming or writing this article.

Simantini Karve, PhD, is a professor of biology and professional development co-coordinator at Skyline College, San Bruno, CA.

Jessica Hurless was a professor of Communication Studies and currently serves as the Dean STEM division at Skyline College, San Bruno, CA.

Reference

Urbaczewski, Andrew, “Developing a syllabus for is course usage in the ai era” (2025). Proceedings of 2024 AIS SIGED International Conference on Information Systems Education and Research. 16. https://aisel.aisnet.org/siged2024/16

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August 25, 2025
Bringing C.H.A.O.S to Chaos: Syllabi with an AI Usage Policy – Faculty Focus

It is no secret that Artificial Intelligence (AI) technology is transforming college classrooms. AI tools can easily and quickly assist students in various tasks such as essay writing, literature reviews, analyzing data, formulating code, solving equations, image generation, music composition, and so much more. With minimal or no effort, within minutes, students have most assignments, test questions, or discussion problems figured out and done…enter the chaos!

As educators, this means everything that we experienced in our own education and have worked years to develop is changing. We need to rethink our role, assignments, as well as formative and summative assessments of student learning all while AI capabilities change daily.

Through all this, we feel educators can take one concrete step to control the chaos by Communicating How AI Operates in the Syllabus (C.H.A.O.S). This article highlights the significance of AI usage policies and what elements should be considered in the policy. It also provides a few useful tips to implement AI policies.

Is there a need to specify the AI usage policy in the class syllabus?

Including an AI usage policy offers clarity to students regarding acceptable usage of AI tools. Although educators are making attempts to develop campus-wide consensus about AI tools, different sections of the same course may have different AI usage policies and practices depending on the instructor and their comfort level. When an explicit AI usage policy is stated in the syllabus, students (and faculty members) can be held accountable for upholding that policy. Any incidents of inappropriate AI usage or cheating can be dealt with within the framework of the policy mentioned.

What are the elements of an AI policy?

A clear AI usage policy needs to have at least three elements – acceptable use, citations necessary, and consequences of non-adherence to the policy. The first element, a well-formulated AI usage policy, specifies acceptable AI usage in learning processes, e.g. brainstorming and creating outlines. In the policy, it is beneficial to provide examples of unacceptable usage, e.g. using whole paragraphs of AI text or writing the whole essay. To develop the acceptable use portion, faculty need to take time and honestly reflect on how they see AI tools being integrated into classroom learning, career fields, and life in general. This can range from absolutely no AI usage to intentional integration of the tools in the classroom space. Your level of familiarity, acceptance of, and perceived value in AI will help shape what is allowed.

The second element is to determine how AI usage needs to be acknowledged and cited appropriately. Again, it is helpful to provide a sample citation as students may be unfamiliar with the norms of citations while taking initial undergraduate courses.

Furthermore, AI tools often include biases, inaccuracies, or sometimes hallucinations. Humans using AI tools, in this case students, need to be aware of these shortcomings and understand that even with proper citations they will be held accountable for not reviewing and verifying the accuracy of their work before submitting. So, the citation element in the syllabus must explain that the ultimate responsibility of academic rigor for the assignment submitted lies with the student.

The last element should outline information about how the instructor will deal with non-adherence to the policy. As an instructor, will you provide a chance for students to resubmit the work? Will the usage result in a loss of points, a zero for the assignment, or a failing grade in the course? How will AI usage be checked? How will you communicate it to students? What is the process and who is involved? These are a few questions that can help an educator shape their usage policy.

Implementing AI usage policy

A few additional steps can be taken when implementing an AI usage policy to make the process smoother. Given the varied nature of AI use policy in various courses, it would be helpful to have a classroom discussion early on (day 1) and throughout the semester with each assignment and exam. Researchers have suggested using a stoplight framework e.g., Red – No AI is allowed in this course; Green – AI tools are allowed and encouraged; and Yellow- AI is permitted for following activities only (Urbaczewski, 2025).

Regularly talking about AI usage may remove some of the pressure from students who want to express themselves through their essays or art and feel seen/heard but may be afraid that their grades will be poorer if they don’t use AI tools. Sharing stories of how past students have effectively, or ineffectively, utilized AI in their work can be helpful in mitigating inappropriate usage.

Another possible step is to introduce scaffolding in high stakes summative assignments such as end of the semester projects. Instead of a one-time submission worth 100 points, students could submit intermediate steps and include the prompts they may have used to get a response from AI tools such as ChatGPT or Perplexity. Some instructors may offer extra credit for such intermediate steps.

Unlike grading or attendance policies in the syllabus, AI usage policies will be dynamic. As AI technology advances, the definition of academic integrity may need to be revisited. Faculty members may need to revise this policy each semester and update it to ensure that students are meeting learning outcomes. As the educational landscape continues to evolve, remember you can get a handle on the chaos by introducing C.H.A.O.S to keep your syllabi in alignment with the current times.

AI Disclosure: AI tools such as Editor in MS Word were used for checking grammar and spelling. No AI tools were used for brainstorming or writing this article.

Simantini Karve, PhD, is a professor of biology and professional development co-coordinator at Skyline College, San Bruno, CA.

Jessica Hurless was a professor of Communication Studies and currently serves as the Dean STEM division at Skyline College, San Bruno, CA.

Reference

Urbaczewski, Andrew, “Developing a syllabus for is course usage in the ai era” (2025). Proceedings of 2024 AIS SIGED International Conference on Information Systems Education and Research. 16. https://aisel.aisnet.org/siged2024/16

Source link

August 25, 2025

Smarter Support: How to Use AI in Online Courses and Teach Your Students to Use It Too – Faculty Focus

Let’s be real. AI tools like ChatGPT showed up in higher ed like that one student who joins the Zoom session with their mic off, camera off, and still somehow takes over the discussion. Whether we were ready or not, AI is in the room. And if you’re teaching online, you’ve probably already seen it at work in discussion posts, essays, or that strangely perfect email.

Instead of panicking or pretending it’s not happening, we’ve got a better option. We can help students learn how to use AI responsibly, because it’s not going away. Honestly, some of them are relying on it more than we realize (Colvard 2024).

If you’re going to teach with AI, you’ve got to know what it can (and can’t) do. I’m talking about tools like ChatGPT, Grammarly, QuillBot, or even Microsoft Copilot. Give yourself a little “playtime” with them. Open one up and ask it to write a discussion post. Then see what it gets right and what falls flat.

Ask yourself:
– How might a stressed-out student use this tool to survive my course?
– Where does it help, and where does it start doing too much of the thinking?

Once you understand how AI might tempt students, you’re better equipped to teach them how to use it well.

Step Two: Don’t Just Allow AI, Teach It

Telling students “don’t cheat” isn’t enough. They need to know what responsible AI use actually looks like. That means we have to talk about it early, often, and clearly. In one recent Faculty Focus article, Holbeck (2025) recommends teaching students prompting skills to foster critical thinking.

Here are some easy ways to build AI literacy into your course:
– Syllabus Tip: Add a simple statement explaining how AI tools can (or can’t) be used.
– Quick Start Assignment: Ask students to explore an AI tool and reflect on what it’s good for and where it falls short.
– Reflect and Revise: Let them submit an AI draft and a revised version, plus a reflection on what they changed and why.

By teaching them how to work with AI instead of around it, we create better thinkers, not just better writers.

Step Three: Use AI to Lighten Your Load

Let’s face it. Online teaching is no walk in the park. Between grading, discussion boards, and endless emails, your plate is full. AI can help, but only if we use it smartly. Online instructors report using AI for feedback and discussion prompts (Mora and Semingson 2023).

Here’s how I’ve used it:
– Weekly Announcements: I use AI to draft a message, then tweak it with my voice.
– Rubrics and Prompts: Need something fast? AI can give you a rough draft to build from.
– Translations or Simplified Content: Great for students who need extra support.
– Quiz Questions: AI can help generate practice questions based on your content.

The key? You’re still the guide. AI is just the assistant.

Step Four: Teach with Real-Life Scenarios

Want to show students what smart AI use looks like? Try this:

Scenario	Tool	Instructor Strategy
Struggling with clarity	Grammarly	Use for grammar and structure
Overwhelmed with research	Chat GPT	Use it to narrow topics, not write the full paper
Studying for quizzes	Quizlet	Generate flashcards or summaries
Time and project management	ChatGPT	Create a schedule or timeline, not to do the work

You can also give them an activity where they compare a human-written and AI-written response. Let them spot the differences. It’s eye-opening. Lam (2025) found that students are using generative AI not just for writing, but for visual content and presentations—tools that could empower creativity with proper guidance.

Step Five: Set the Boundaries

Students need clarity, not fear. Make your expectations simple and direct. Here’s an example:

AI Use Policy: In this course, you can use AI tools to help brainstorm, organize your thoughts, or check your grammar. But your final work must reflect your own thinking and voice. Submitting something entirely written by AI is considered academic dishonesty.

Set the tone early, and students will know where the line is. In fact, Hans (2025) argues for a values-based approach to GenAI—teaching students what’s appropriate and why.

Lead the Way, Don’t Chase the Problem

AI isn’t the enemy of education. Misuse is. Online instructors have a real opportunity to teach students how to think critically about the tools they use. We don’t have to be tech experts, just open-minded guides.

When we show students how to use AI responsibly, we’re preparing them for a world where these tools will be everywhere. It’s not about control. It’s about confidence.

Let’s meet this moment with clarity, creativity, and a whole lot of common sense.

Dr. Joel Greene is a higher education consultant, psychology instructor, and former Director of Student Engagement, with experience leading online learning strategies across multiple campuses. Her work focuses on student success, equity-minded leadership, and innovative teaching in digital spaces.

References

Colvard, Nicholas B. “Artificial Intelligence: The Rise of ChatGPT and Its Implications.” Faculty Focus, May 2024.

Hans, Heather. “A Values-Based Approach to Using Gen AI.” Faculty Focus, June 11, 2025.

Holbeck, Rick. “Helping Students Develop AI Prompting Skills for Critical Thinking.” Faculty Focus, June 27, 2025.

Lam, Aileen Wanli. “How Students Use Generative AI Beyond Writing.” Faculty Focus, June 4, 2025.

Mora, Rebecca, and Sam Semingson. “Promoting AI-Enhanced Performance in the Online Classroom.” Faculty Focus, April 2025.

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August 18, 2025

Smarter Support: How to Use AI in Online Courses and Teach Your Students to Use It Too – Faculty Focus

Ask yourself:
– How might a stressed-out student use this tool to survive my course?
– Where does it help, and where does it start doing too much of the thinking?

Once you understand how AI might tempt students, you’re better equipped to teach them how to use it well.

Step Two: Don’t Just Allow AI, Teach It

By teaching them how to work with AI instead of around it, we create better thinkers, not just better writers.

Step Three: Use AI to Lighten Your Load

The key? You’re still the guide. AI is just the assistant.

Step Four: Teach with Real-Life Scenarios

Want to show students what smart AI use looks like? Try this:

Scenario	Tool	Instructor Strategy
Struggling with clarity	Grammarly	Use for grammar and structure
Overwhelmed with research	Chat GPT	Use it to narrow topics, not write the full paper
Studying for quizzes	Quizlet	Generate flashcards or summaries
Time and project management	ChatGPT	Create a schedule or timeline, not to do the work

Step Five: Set the Boundaries

Students need clarity, not fear. Make your expectations simple and direct. Here’s an example:

Set the tone early, and students will know where the line is. In fact, Hans (2025) argues for a values-based approach to GenAI—teaching students what’s appropriate and why.

Lead the Way, Don’t Chase the Problem

When we show students how to use AI responsibly, we’re preparing them for a world where these tools will be everywhere. It’s not about control. It’s about confidence.

Let’s meet this moment with clarity, creativity, and a whole lot of common sense.

References

Colvard, Nicholas B. “Artificial Intelligence: The Rise of ChatGPT and Its Implications.” Faculty Focus, May 2024.

Hans, Heather. “A Values-Based Approach to Using Gen AI.” Faculty Focus, June 11, 2025.

Holbeck, Rick. “Helping Students Develop AI Prompting Skills for Critical Thinking.” Faculty Focus, June 27, 2025.

Lam, Aileen Wanli. “How Students Use Generative AI Beyond Writing.” Faculty Focus, June 4, 2025.

Mora, Rebecca, and Sam Semingson. “Promoting AI-Enhanced Performance in the Online Classroom.” Faculty Focus, April 2025.

Source link

August 18, 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
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
How Students Use Generative AI Beyond Writing – Faculty Focus
Over the last two years, I’ve witnessed the rise in students’ use of generative AI as whole. Not surprisingly, more students are using generative AI to assist them in writing.

In an undergraduate business communication course that I oversee, the percentage of students who declared their use of generative AI for a writing assessment (i.e. business proposal) increased steadily over four semesters from 35% in 2023 to 61% in 2025. What’s more fascinating is the corresponding increase in the reported use of generative AI for their spoken assessment – their presentation (i.e. pitch) – from only 18% in 2023 to 43% in 2025.

*Note that there were about 350 students per semester and a total of about 1,400 students over four semesters/two years.

You may be wondering, how exactly are these students using generative AI for presentations?

They reported using generative AI to:
- Create and edit visuals (e.g. images, prototypes/ mockups, logos)
- Get inspiration for rhetorical devices (e.g. taglines, stories, alliterations)
- Prepare for the Q&A (e.g. generate questions, review/structure answers)
Beyond verbal language, visuals are an important facet of communication and students need to be prepared for more multimodal communication tasks at the workplace (Brumberger, 2005). With digital media, there has been a shift in balance between words and images (Bolter, 2003) which can be seen in websites, reports and even manuals. A students’ ability to communicate in writing and speaking must now be complemented with a proficiency in visual language. Now, generative AI can reduce those barriers to creative visual expression (Ali et al., 2004).

For example, students on my business communication course use AI tools to create prototypes and mockups of their project ideas to complement their explanations. When they are unable to generate exactly what they need, they edit those images with traditional editing software or more recently, software with generative AI editing abilities such as Adobe firefly which allows users to select specific areas of an image and use “generative fill” to brainstorm and edit it without advanced technical skills. This and other AI text generators including Dall-e (Openart) and Midjourney have opened up possibilities for communicators to enhance their message using visuals.

Here are the AI Visual Tools students have reported using in their written and spoken assessment over two years:
What’s interesting from the list is not only an increase in the number of AI tools used but the type of tools used (1) for specific purposes like Logopony, for the creation of logos, Usegalileo, for app Interface designs, and Slidesgo, for the creation of slides, as well as AI tools (2) for editing such as Photoshop AI, Adobe Firefly, and Canva. Beyond that, we can see how students are using different tools from companies that are constantly evolving such as Canva with Magic Studio and Dream Lab, OpenAI who has integrated Dall-e into ChatGPT, as well as their latest offering, Sora, and even Google who now has Gemini Flash 2.0. Generative AI is also becoming more accessible on different platforms with the integration of Meta AI to WhatsApp, a cross-platform messaging app.

Ultimately, this list provides a glimpse of what some undergraduate business students are dabbling with and educators should consider trying them out. More importantly, we could guide students in thinking about the visuals and graphics that they ultimately use because not all graphics are equally effective (Mayer and Moreno 2003).

Some graphics are:

Decorative
They are neutral and may enhance the aesthetics but is not interesting or directly relevant.

Seductive
They may be highly interesting but may not be directly relevant and can distract the audience and cause their cognitive processing to focus on irrelevant material.

Instructive
They are directly relevant to the topic (Sung and Maye, 2012).

However, it doesn’t mean that all visuals should be instructive because it depends on the goal of the communicator. For example, if the main goal is for enjoyment, then decorative visuals can enhance the aesthetics and seductive visuals can be so interesting that it leads to higher satisfaction, so we should remind students to be intentional in their use of visuals and AI tools. For example, AI tools tend to create visuals with a lot of extraneous details that may be distracting and lead to cognitive overload (Deleeuw and Mayer 2008) so students should refine their prompts by being more specific and precise (Hwang and Wu 2024) and they should be prepared to use editing software which can include other AI enhanced software like Adobe firefly and Imagen to achieve their final goal.

There are limitations to what AI can do at the moment.

It cannot be truly innovative because it learns from existing data.

It cannot fully understand subtle aspects like culture, values or emotional nuances (Hwang and Wu 2024).

But it can provide the stepping stone for students to visualize their ideas.

Let’s encourage our students to be aware of what they want to achieve when using AI tools and be proactive in selecting, rearranging, editing and refining the visuals to suit their purposes.

Aileen Wanli Lam is a Senior Lecturer and technology enthusiast at the National University of Singapore. She is fascinated by education technology and enjoys conversations about the latest industry developments. She is also passionate about professional communications, student engagement and educational leadership.

References

Ali, Safinah, Prerna Ravi, Randi Williams, Daniella DiPaola, and Cynthia Breazeal. “Constructing dreams using generative AI.” In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 21, pp. 23268-23275. 2024.

Bolter, Jay David. “Critical theory and the challenge of new media.” (2003).

Brumberger, Eva R. “Visual rhetoric in the curriculum: Pedagogy for a multimodal workplace.” Business Communication Quarterly 68, no. 3 (2005): 318-333.

DeLeeuw, Krista E., and Richard E. Mayer. “A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load.” Journal of educational psychology 100, no. 1 (2008): 223.

Hwang, Younjung, and Yi Wu. “Methodology for Visual Communication Design Based on Generative AI.” International journal of advanced smart convergence 13, no. 3 (2024): 170-175.

Mayer, Richard E., and Roxana Moreno. “Nine ways to reduce cognitive load in multimedia learning.” Educational psychologist 38, no. 1 (2003): 43-52.

Sung, Eunmo, and Richard E. Mayer. “When graphics improve liking but not learning from online lessons.” Computers in human behavior 28, no. 5 (2012): 1618-1625.
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June 4, 2025

Tag: AI tools

Is there a need to specify the AI usage policy in the class syllabus?

What are the elements of an AI policy?

Implementing AI usage policy

Is there a need to specify the AI usage policy in the class syllabus?

What are the elements of an AI policy?

Implementing AI usage policy

Step Two: Don’t Just Allow AI, Teach It

Step Three: Use AI to Lighten Your Load

Step Four: Teach with Real-Life Scenarios

Step Five: Set the Boundaries

Lead the Way, Don’t Chase the Problem

Step Two: Don’t Just Allow AI, Teach It

Step Three: Use AI to Lighten Your Load

Step Four: Teach with Real-Life Scenarios

Step Five: Set the Boundaries

Lead the Way, Don’t Chase the Problem

Head in the Sand Isn’t Helping

The Concerns Are Real—and Bigger Than Cheating

Engagement Starts with Transparency

Co-Creating AI Policies with Students

Rethinking Assessment in an Age of Automation

AI as Catalyst, Not Catastrophe

Head in the Sand Isn’t Helping

The Concerns Are Real—and Bigger Than Cheating

Engagement Starts with Transparency

Co-Creating AI Policies with Students

Rethinking Assessment in an Age of Automation

AI as Catalyst, Not Catastrophe