Beyond the Proof: Bringing Writing into Real Analysis I

Posted on October 30, 2026 by abrusini

[Guest post by Richard Brown, Teaching Professor, Department of Mathematics, Johns Hopkins University]

Mathematics isn’t really just about solving problems—it’s about understanding and communicating how and why those solutions make sense. The ability to clearly communicate mathematical ideas can deepen students’ conceptual understanding, and also prepare them to write in different formats, for different purposes, and for different audiences, both within and beyond the mathematical community. Despite its importance, mathematical writing is rarely explicitly taught at the undergraduate level. Instead, students are often expected to develop these skills “along the way,” simply through the process of studying and doing mathematics.

With this in mind, I planned a redesign of AS.110.405 Real Analysis I, a core requirement for mathematics majors, as a writing-intensive course. As one of the first courses in which students transition from calculation-based mathematics to more conceptual, proof-based work, Real Analysis I seemed like a natural fit for incorporating writing instruction. In the spring of 2023, I was awarded a Teaching Innovation Grant from the Center for Teaching Excellence and Innovation (CTEI) which helped fund the course redesign.

In alignment with the goals of Johns Hopkins University’s Second Commission on Undergraduate Education (CUE2) and the Krieger School’s emphasis on writing within STEM disciplines, the redesign aimed to expand opportunities for students to develop as mathematical writers without sacrificing essential course content. I partnered with the University Writing Program (UWP) to ensure the course met UWP criteria for qualifying as writing intensive. I also incorporated the principles of Universal Design for Learning (UDL) to reduce barriers to learning and create inclusive opportunities for students to engage with course material. Informed by the Scholarship of Teaching and Learning (SoTL), I kept the focus on student learning, using evidence-based teaching practices to guide course activities, assignments, and assessments.

The existing version of Real Analysis I was taught in standard lecture style format with instructor-led lectures, weekly problem sets, midterms, and a final exam. The revised version was designed to be more engaging and included several active learning strategies:

Small group activities to practice definition writing, direct vs. reverse engineered proof-writing, example and counterexample development brainstorming, pop-math and other audience-specific expression.
Opportunities for short presentations of writing samples with peer review.
Writing workshops led by teaching assistants (who were trained by UWP staff) to focus on writing for different audiences.
Lecture elements, worksheets, assigned readings, and handouts highlighting stylistic elements involved in mathematical presentation, proof-writing, and concept development.

The midterms and exam were replaced with a mix of small-stakes, no-stakes, and large-stakes writing assignments that support both effective mathematical communication and conceptual understanding:

Problem Sets: weekly proof-writing exercises to demonstrate understanding of mathematical concepts as well as refine writing style.
Diary: short low and no stakes written assignments to reflect on growth in understanding.
Final Project: 3-5 page paper, written in peer-reviewed journal style on a mathematical topic related to the course (i.e. Taylor’s Theorem, Picard Iterations, etc.). Students worked in pairs to facilitate collaborative writing in a manner common in mathematical research.
Presentation: presentation to the class of final project allowing the authors to practice the transition from formal writing to formal presentation of mathematical material.
Portfolio: a set of polished artifacts geared toward a variety of audiences (e.g., peers, professor, textbook style, etc.).

Challenges
Although the course went well overall, there were several challenges in implementation:

Writing assignments can be particularly time-consuming to grade.
- The grant funded two additional course assistants and the UWP provided substantial help developing rubrics, but training TAs to evaluate student writing and grading the work itself still required significant time. The variety of assessments further increased the grading workload.
There were so many moving parts to the course.
- Students sometimes felt like they had something due every day, which was challenging to manage alongside the demands of other courses. The workload may have diminished some of the course’s intended benefits, and certain components, such as the Diary, received less attention as the semester progressed.

The variety of assignments and activities sometimes left students unclear about expectations.
Implementing this approach more broadly would require substantial training for faculty, TAs, and students who are new to the process.

Results
Midsemester and end-of-course student surveys were excellent, reflecting students’ strong appreciation for the course design and learning experience:

90% agree: The quality of the content of the overall design and organization of the course was good.
90% agree: Compared to a standard lecture course in mathematics, I found this course to be a much more valuable learning experience.

Student quotes:
“I like this course because you are not just teaching us math. You are teaching us how to be mathematicians.”

“I love this class, the content, the structure, and everything involved.”

“I appreciated that this course went beyond the usual lectures + exams because it felt like it prioritized an understanding of how to think about math.”

Outcomes
The department has established an ad hoc committee to examine writing within the discipline and identified courses across the curriculum in which to incorporate writing instruction and exercises. Elements of the course were adapted for use in several classes, including critical writing exercises in second-semester Analysis and Algebra, Topology, and Introduction to Proofs. Other examples include a mock journal in Topology, portfolios in Differential Equations and Vector Calculus, and written group project work in Calculus I and II for Biology and Social Sciences majors.

TA instruction has been expanded to include additional training on teaching, assessing, and grading mathematical writing, with continued collaboration with the UWP on rubric development. More formal training has been incorporated into the TA training program, and writing exercises have been implemented more broadly in section meetings. These efforts have helped foster a more active culture of TA-led instruction.

Conclusion
Ultimately, the redesign demonstrated that writing can and should be an integral part of learning mathematics, helping students develop not only their understanding of mathematical concepts but also their ability to communicate as mathematicians. While the approach required additional time, coordination, and support for both instructors and TAs, the positive student feedback and the department’s subsequent adoption of writing activities across the curriculum suggest that the benefits extended beyond a single course. By integrating writing into existing mathematics courses and strengthening TA preparation, the department continues to build a culture in which mathematical writing is taught intentionally rather than left for students to develop on their own.

Richard Brown
Teaching Professor, Mathematics
Johns Hopkins University

RIchard Brown is the Director of Undergraduate Studies and a teaching professor in the Department of Mathematics. He has been a member of the faculty at Johns Hopkins since 2005. His research interests include Hamiltonian dynamical systems, surface mapping class groups, character varieties of surfaces, and symplectic geometry.

Image source: Unsplash

Office Hours in the Age of AI: The Power of Human Connection

Posted on September 28, 2026 by abrusini

[Guest post by Christine Solan, Teaching and Training Specialist, Biology, Johns Hopkins University]

Over the past year, many instructors and teaching assistants (TAs) have been asking what office hours mean now that students can get instant answers from AI. The more this question is explored, the clearer it becomes: even with continuous advancements in AI, the human elements of teaching are more important than ever.

AI can generate responses, but humans build thinkers. Humans can help students feel seen, capable, and connected to a discipline. That distinction is especially visible – and fragile – during office hours.

The Problem Behind the Problem

Office hours have long been underused. Hsu et al. (2022) showed that students often view office hours as narrowly useful and face real psychological barriers such as fear of looking incompetent, uncertainty about expectations, and the belief that asking for help signals weakness. In newer work (Hsu et al., 2025), first‑year STEM students describe office hours as intimidating or unclear, even when they know the sessions could help.

At the same time, students increasingly turn to generative AI as their first stop for academic support. Chatzopoulos et al. (2026) found that AI tools have become part of students’ everyday learning practice for things like clarifying concepts, preparing for exams, and smoothing out the friction of studying.

Put these findings together and the situation becomes clear: students aren’t avoiding office hours because they don’t need help, but because AI feels easier, safer, and more predictable. If office hours remain an unstructured drop‑in Q&A desk, AI will win every time.

The Human Advantage: What AI Can’t Replace

Instructors and TAs still offer support that AI cannot fully replicate:

Personalized guidance
Mentorship
Connection

These aren’t “extras.” They are central to how students learn to reason, ask better questions, and develop a sense of belonging in a learning community. AI can support learning, but it cannot replace the experience of being known, coached, and intellectually challenged by another human being.

This is why the human element of office hours matters more now, not less.

Moving Beyond the Help‑Desk Model

One way to keep office hours relevant is to think of them as structured, student‑centered learning experiences rather than troubleshooting sessions. The Stanford Teaching Commons (2023) reminds us that effective office hours require intentional facilitation, including clear expectations, active engagement, and inclusive practices. When instructors and TAs adopt these strategies, office hours can become spaces where students practice thinking rather than simply receiving answers, fostering the kind of learning and human connection that AI cannot reproduce.

Here are three shifts worth exploring:

Reframe and Rebrand Office Hours

Students often don’t know what office hours are for; renaming them can help communicate purpose and value.
Examples include:

“Weekly Recap”
“Course Check‑Ins”
“Guided Study Session”
“Supplemental Course Assistance”
“TA Feedback Session”
“Collaborative Study Session”

A clear title communicates purpose. A clear purpose demonstrates value.

Make the Value Explicit

Students need to hear – directly – why office hours matter.
Short, memorable value statements may help:

“AI generates responses. Office hours develop thinkers.”
“Students who connect, succeed.”
“When AI isn’t enough, conversation makes the difference.”
“Learn the why, not just the what.”
“Get ahead with tailored guidance.”
“Build deeper understanding for exams.”

These statements help students understand that office hours aren’t redundant with AI – they’re qualitatively different.

Design for Interaction

If office hours are passive, students may be more likely to default to using AI. Making them interactive gives students a reason to return.
Effective strategies include:

Using peer‑to‑peer problem solving
Incorporating mini‑activities
Meeting in new or more inviting spaces
Integrating technology intentionally (e.g., games, collaborative tools)
Facilitating collaborative learning

These small design choices transform office hours from transactional to relational.

Why This Matters for Faculty

Faculty set the tone for how students perceive office hours. When instructors model and endorse structured, interactive office hours, students may be more likely to see them as integral to the course rather than optional support. Faculty also play a crucial role in helping TAs shift from “answer‑givers” to learning facilitators. When TAs feel empowered to design meaningful office hour sessions, they build leadership, communication, and coaching skills that serve them far beyond their time in the classroom. Students, in turn, benefit from office hours as welcoming, purposeful spaces that offer something AI cannot provide on its own: deeper learning through human connection.

The Future of Office Hours Is Human

AI will continue to evolve, and students will continue to use it. That only increases the importance of the relational, interactive, and intellectually generative work that happens both inside and outside of the classroom.

If we want students to become scientists, researchers, critical thinkers, and thought leaders – not just answer‑seekers – we can design office hours that cultivate curiosity, belonging, and the kind of thinking AI cannot do for them.

Office hours aren’t obsolete. They are an opportunity. With thoughtful design, they can become some of the most meaningful learning experiences we offer.

Christine Solan
Teaching and Training Specialist, Biology
Johns Hopkins University

Christine Solan is a seasoned education professional with extensive experience in curriculum design, training, and development. She holds an M.S. from Johns Hopkins University and a B.A. in Biology Secondary Education. In her current role as a Teaching and Training Specialist (TTS) in the Department of Biology at Johns Hopkins University, Christine focuses on enhancing the undergraduate classroom experience in STEM courses where TAs play important teaching roles.

Image source: Unsplash

References:

Chatzopoulos, A., Zacharia, P., & Kantaros, A. (2026). Generative AI in higher education: A large-scale study of student usage patterns, applications and motivations. Applied Sciences, 16(12), 5972.

Hsu, J. L., Holick, G., Green, R. M., Schwartz, E. B., & Rowland-Goldsmith, M. (2025). “Asking for help feels like a weakness”: Factors shaping first-year students’ expectations of STEM course office hours. International Journal of STEM Education, 12, 40. https://doi.org/10.1186/s40594-025-00561-3

Hsu, J. L., Rowland-Goldsmith, M., & Schwartz, E. B. (2022). Student motivations and barriers toward online and in-person office hours in STEM courses. CBE—Life Sciences Education, 21(4), Article ar68. https://doi.org/10.1187/cbe.22-03-0048

Kogler, A. & Cao, Y. T. (2023). Strategies for Office Hours and Review Sessions for TAs. Stanford University, Teaching Commons. https://teachingcommons.stanford.edu/news/strategies-office-hours-and-review-sessions-tas

Authentic Assessments: Measuring What Students Can Do

Posted on August 25, 2026 by abrusini

One of the pillars of successful course design is creating assessments that align with clearly stated learning objectives; this alignment provides a clear connection between what students are expected to learn and how their achievement is evaluated. Traditional assessments, such as term papers, quizzes, and exams, typically ask students to demonstrate their knowledge by recalling information or applying concepts in controlled or simulated settings. These strategies can be very effective in some contexts. However, when the goal is to challenge students to apply their learning in meaningful, real-world scenarios, authentic assessments may be a better fit. By asking students to put their knowledge and skills into practice, authentic assessments can help students develop and demonstrate abilities that extend beyond the classroom.

When students enter the workforce, they most likely will not be given a multiple-choice test to assess their performance. Instead, they will be evaluated based on their ability to complete tasks, solve problems, and respond appropriately to situations they encounter on the job. Authentic assessments prepare students for these situations by asking them to use higher-order thinking skills, such as analyzing information, developing and evaluating solutions, and making decisions both independently and collaboratively (Vlachopoulos & Makri, 2024). These types of assessments can help bridge the gap between learning in the classroom and what students will be expected to do in professional settings. Because these assessments connect directly to skills students may use in their future careers, they can make learning feel more relevant and purposeful. This connection may increase student engagement by helping students see the practical value of what they are learning.

Some examples of authentic assessments include:

Conducting a hands-on experiment
Engaging in role play
Performing or demonstrating a skill (playing music, delivering a speech, teaching a lesson, etc.)
Developing a website or app
Examining a case study
Creating a business or marketing plan
Producing a video or podcast
Creating a patient care plan

By requiring students to apply their knowledge in complex, realistic situations, authentic assessments provide instructors with a more complete picture of what students know and can do. Rather than focusing primarily on recall or selecting a correct answer, these assessments allow instructors to observe how students approach problems, justify their decisions, and communicate their ideas. This broader range of skills can help instructors more accurately evaluate student achievement of the learning objectives (Messier, 2022).

Additionally, authentic assessments may help address some of the challenges AI creates for traditional forms of assessment. AI tools are making it increasingly difficult for instructors to determine whether work submitted was actually completed by the student or by generative AI. Authentic assessments value the process in addition to the final answer, product, or solution. Giving students opportunities to explain their answers, reflect on their choices, or revise their work gives instructors additional evidence of learning that is less dependent on a single written response. Although authentic assessments do not eliminate the possibility of AI use, they can provide instructors with more varied and meaningful evidence when evaluating student learning.

Interested in learning more? The University of Illinois Chicago has a guide that can help you get started creating your own authentic assessments.

Do you have any authentic assessments you have used in class that you would like to share? Please add them to the comments section below.

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image source: Unsplash

References:

Messier, N. (2022). Authentic assessments. Center for the Advancement of Teaching Excellence at the University of Illinois Chicago. https://teaching.uic.edu/resources/teaching-guides/assessment-grading-practices/authentic-assessments/

Vlachopoulos, D., & Makri, A. (2024). A systematic literature review on authentic assessment in higher education: Best practices for the development of 21st century skills, and policy considerations. Studies in Educational Evaluation, 83, 101425. https://doi.org/10.1016/j.stueduc.2024.101425

Summer Course Refresh: Practical Ideas for Busy Instructors

Posted on June 8, 2026 by abrusini

As the academic year comes to a close, many instructors begin thinking about what worked well in their courses and what they might like to change. Making changes doesn’t necessarily mean starting over from scratch. Summer can be a great time to refresh your course by focusing on a few manageable updates without undertaking a complete course redesign.

One place to start is with your syllabus. Consider updating it with a more welcoming tone, clarifying course expectations, or adding information about available student support resources. You might also review your AI policy – or create one if you do not already have one – to ensure students clearly understand your expectations regarding AI use in your course. Other considerations include participation expectations and communication guidelines to ensure they still reflect your current teaching practices. Take a look at this article for more ideas about improving your syllabus.

Another area to review is your course website. Take a few minutes to review your site from a student’s perspective. Are course materials organized in a logical way? Are there any broken links, outdated information, or unclear instructions? A well-organized course site communicates to students that you are prepared and invested in their learning. Small adjustments can help students navigate the course more easily and reduce confusion throughout the semester. See this article for helpful tips about setting up a course in Canvas.

Summer is also a good time to review your course content and make sure it meets accessibility requirements. For example, you might need to review the accessibility of your slides, check the quality of your video captions, or ensure that PDFs are readable by screen readers. Take advantage of built-in accessibility checking tools in Microsoft Office and Canvas. Making incremental changes can improve access for all students and help create a more inclusive learning environment. Additional best practices in accessibility can be found in this article.

You may also want to reflect on student feedback from previous semesters. Were there assignments that students struggled with or frequently found confusing? Did the same questions arise repeatedly? Reviewing course evaluations, student emails, or your own notes can help identify areas where a few targeted revisions could improve clarity and support student success. The University of Wisconsin has put together some strategies for interpreting student feedback that may be helpful.

Finally, consider identifying one new teaching strategy you would like to try next semester. It could be as simple as incorporating a brief think-pair-share activity, using an exit ticket, or adding a reflection prompt to a class session. If you need ideas, the CTEI created a set of active learning cards containing several activities that engage students in online, in-person, and hybrid environments. Browse through the cards and contact our center if you have any questions.

Course improvement is an ongoing process. Rather than trying to change everything at once, focus on one or two updates that will have the greatest impact on your students. Small, intentional changes made over time can lead to meaningful improvements for both you and your students in the coming semester.

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image source: Unsplash

Lunch and Learn: Learning With AI – Student Voices on Studying, Research, and Writing

Posted on May 19, 2026 by abrusini

On Wednesday, April 22nd, the Center for Teaching Excellence and Innovation (CTEI) hosted a Lunch and Learn featuring a student panel on Learning With AI: Student Voices on Studying, Research, and Writing. The panel featured Alisa Hamilton, a PhD student in Systems Engineering, Alisha Mason, a senior in Molecular and Cellular Biology, and Natalia Als, a freshman in Medicine, Science and Humanities. Caroline Egan, Teaching Academy Program Manager, moderated the discussion.

The panelists began by introducing themselves and describing their history with AI, including how they use it in an academic context. Alisa Hamilton uses it all the time for coding assistance and says it has definitely made her a better engineer. She does not use it much in her personal life but noted that she did use AI to help tile a bathroom. Alisha Mason was very “anti-AI” at first and did not use it much as a freshman because she was afraid of becoming too dependent on it. Her views have gradually shifted and she has since learned to use it as an effective study tool. Additionally, Mason uses AI as a “personal assistant” to help with tasks such as writing emails and revising her resume. Natalia Als was strictly “anti-AI” in high school, and continues to mostly avoid using it, but has noticed that it is more normalized at JHU than in her high school. She notes that instructors here include AI statements as part of their course policies, but she still does not feel like she has the skills to use it properly. She has observed peers using it in unethical ways and becoming too reliant on it. Unlike Mason, Als does not use AI in her personal life, preferring to take the time to write her own emails and complete other everyday tasks herself.

Caroline Egan asked panelists if they used AI in their learning, how has it been the most useful, and whether there have been instances in which it has hindered their learning. Hamilton finds it very helpful to upload PowerPoints or PDFs and ask AI to explain them, such as why certain parts of code work in an application. For her, this helps
“fill in the blanks” and she ends up getting more out of courses than if she did not use AI. Mason agreed, adding that ChatGPT helps explain concepts that she might not initially be able to grasp. She frequently has ChatGPT open as she’s listening to recorded lectures so she can ask questions. An ongoing hindrance is that the information from AI is not always correct, and she needs to check the sources. She says the pros outweigh the cons, however, and she plans to continue using it. Als described a situation where she was assigned a web-scraping exercise using AI and it produced a great deal of incorrect information. In the end, she had to go back and do the entire assignment manually. She supports those who use it and are still able to think critically, but says she has observed many of her classmates using it unethically usually because they are pressed for time. She says many freshmen lack time management skills and feel too overwhelmed to consider other solutions, therefore, they end up relying on AI to do their work. “They are not learning.”

Egan asked panelists if they thought AI was taking the place of office hours and if so, how could instructors entice students to continue attending. Hamilton suggested going through problem sets during office hours in a structured way. Mason says she does not attend office hours regularly, and that she always has ChatGPT pulled up when studying with friends. However, she does not find ChatGPT helpful with computational coursework, such as in Physics and Calculus, and feels AI cannot take the place of a tutor in these courses. In contrast, Als said she and her classmates frequently use Chat GPT in their Chemistry and Calculus courses. Even so, she finds the assistance too broad at times. As for office hours, Als says sometimes there is judgement from the TAs: “You don’t know this already??” She suggested possible training for TAs so students would not feel intimidated during office hours.

Egan continued by asking what things faculty have done to help guide students in their use of AI, and what might they want faculty to know about AI from the student perspective. Hamilton has only taken one class where AI was encouraged and feels there wasn’t enough guidance. She said it is a “slippery slope” asking students to use AI, but then to call them out for using it too much. In her experience, students do not always realize that they are cheating. She says faculty should be super-clear about what they expect, model what they want from students, and tell them explicitly what is not ok. Mason described a biology research course in which the instructor allowed students to use AI selectively on their projects and provided guidance on when its use was appropriate. Students grouped each step of their project into one of three categories: repetitive, editing, or facilitating. These categories helped determine whether AI could appropriately support a particular task. Mason noted that the exercise was especially valuable and continues to influence how she thinks about using AI in other courses. Als added a few suggestions: being open and honest with students, providing examples for them, and establishing a policy from the beginning and sticking to it. She appreciated one class where students were given the opportunity to create a policy collaboratively.

Egan raised the concern that instructors have with academic integrity violations when using AI. She asked panelists if this is a valid concern and how might instructors counteract those violations. Mason acknowledged that this is a valid concern and suggested that instructors emphasize to students that they are here to learn how to think critically. She also suggested possibly using fear to dissuade students from cheating. Als agreed that her peers don’t seem to grasp that they are here to learn and “not just press Submit,” but is not sure that fear is the best solution. She suggested having conversations about academic integrity in smaller groups among peers, such as during TA sessions. Hamilton noted that this is a good opportunity for instructors to discuss what academic integrity means and provide examples for students who may be unfamiliar with the concept.

Several questions from the audience followed the discussion:

Q: I’d like the students to share their thoughts on faculty using AI to assist in teaching a class. Are you comfortable with instructors using it? If faculty disclose their use, is that enough?

AH: I think disclosure is important. I would be bitter if I knew assignments were graded by AI solely. But I also know that TAs do most of the grading anyway. I feel like I can have AI grade my work myself, but that is not why I’m taking this course.
AM: Multiple choice is ok but for free response questions, I prefer human eyes, human feedback. Instructors are able to see how students come to certain conclusions. I would want instructors to regrade if there was a question about the response.
NA: As long as the professor is prepared for regrade requests and questions, that’s fine. With free response, I would prefer to have my work graded by a human since that’s part of the grading process. What about other parts of the class? Will AI be creating the rubric? The course material? Where do we draw the line? You need context to grade. Are you giving AI enough context?
AH: I think faculty can use it the same way as students. It’s ok to draft an email or syllabus. And it’s a good idea to disclose that.
AM: I would feel weird if I knew AI generated the whole exam. I’m not sure why, but it takes away the creativity.

Q: As a faculty member, I take it personally when students use AI. I put so much work into commenting on writing assignments. It feels disrespectful to my time. I’m giving feedback at a high level which is the level they should be writing, but they are not understanding it. I’m wondering if it’s a double empathy scenario – all parties not understanding each other. Any ideas about this?
AH: If I thought a student was cheating, I would start with talking to that individual student.
AM: For writing courses, I would go old school to gauge students’ writing style and abilities. Use pen and paper – no technology – to see where students are and establish a baseline. Maybe a homework assignment where they write things down.
NA: We used a blue book in one of my classes to establish a baseline. Now the rest of the assignments are online, but the instructor knows how we write. It would be foolish for students to start using AI in that class now, as it would be so obvious. I think having one on one conversations with individual students is best.

Q: Is it invasive for professors to examine version history, such as using Google Docs?
NA: I understand the need and I personally do not feel like it is invasive.
AM: Students will find a way around version history if they want. I think starting with pen and paper is best.
AH: I recommend starting with an explicit agreement about what is expected, including what is and is not considered cheating.

Q: Do you use other AI tools used besides ChatGPT?
AH: I use Cursor and Claude Code, which are paid for by JHU.
AM: I use ChatGPT, Claude, and Gemini. I do not use HopGPT. I do not pay for any of them.
NA: I have used HopGPT and ChatGPT.

Q: Are you seeing a falling off of peers joining you in study groups because they are going off on their own to use AI? Or has it stayed steady while you use AI in the group?
NA: We are still meeting up and some people have ChatGPT open, but we’re still having social interaction. I am not sure about outside of my group.
AM: People who are going to study independently are going to study that way regardless of AI. Office hours for computational classes are always full. I still see study groups and my friends and I get together before exams. We always ask a TA or a professor if we have questions about an exam. I don’t really see it [falling off].
AH: We study outside of the lab to try out new AI tools.

Q: How do you and your peers think about the environmental impact, human rights issues, and privacy issues of using an LLM? How does this play into people’s decisions to use it/when to use it?
AM: We are all fully aware of the environmental concerns. Some students won’t touch it for this reason. Some are cautious about privacy concerns. My biggest fear is overuse. I need to know how to learn how to think.
NA: People are aware of the environmental impact. I see it posted on social media a lot. I’m from Chicago and there is a big data center about to go up there, which is concerning. People are not going to care enough until it actually affects them.
AH: I agree with all of these comments. We are going to end up with data centers on the moon, seriously. In terms of copyright, I make sure not to upload to anything that does not have a paywall.

Q: Do you think using AI reduces the depth of learning at all compared to before AI?
AH: No, I know what I want to learn and what I want to get out of it.
AM: It depends on how you use it. I feel like I use it to increase my depth of learning. For me, it is very efficient for troubleshooting. But we joke that there are so many people who overuse it and are going to have to rely on it for everything. It’s interesting to see underclassmen, how quickly they turn to AI for help. Upperclassmen take time to think, ask a TA. This is my observation.

Q: How do you feel about fairness, such as someone receiving the same grade as you by using AI?
NA: I think it goes back to an established policy that everyone is graded the same way. It has been frustrating when we know others have used AI, and we have not. I feel like I am being penalized for being honest and this person is not because they have submitted a perfectly written AI generated paper. I am not sure what else faculty can do. You don’t want to accuse people left and right.
AM: We will all have to go into professional fields and need to know certain things and how to communicate. Some people do not value this. Maybe faculty could emphasize this: “You are going to have to go out in the real world and you are not always going to be able to use AI.”

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image source: Lunch and Learn logo, Unsplash

Piloting a Well-Being Lab in the Public Health Studies Curriculum

Posted on April 2, 2026 by abrusini

[Guest post by Leslie Bauman, Junior Lecturer in the Department of Psychological and Brain Sciences, Johns Hopkins University]

Introduction
The academic public health community has long recognized the value in experiential learning when it comes to public health practice. Like programs at many other universities, PHS majors at JHU must complete at least 80 hours of field work in a professional public health setting and engage in reflection on how those experiences bring their coursework to life. At the same time, a gap exists in that public health curricula generally do not reflect the importance of experiential learning when it comes to the very concept of health and well-being itself. Enter the Well-Being Lab.

In 2023, we launched an ambitious 2-year pilot of an experiential Well-Being Lab designed to address this curriculum gap. The idea was simple but powerful—what if students not only learned about population health and well-being but experienced it firsthand?

Our goals for the pilot were to test feasibility, gather student input, and explore models for integrating the Lab into the curriculum. During the pilot, students selected from a “menu” of activities and workshops that were available on campus through the PHS program or one of our campus partners (Health Promotion and Well-Being, Life Design Lab, Counseling Center, etc.), participate in the activity, and then reflect on how those experiences connected to their personal health and to broader public health concepts. The activities are categorized into eight aspects of health and well-being established by JHU’s Health Promotion and Well-Being office:

Emotional/mental health
Physical health
Spiritual health
Social health

Sexual health
Environmental health
Financial health
Professional health

Students were required to complete at least two well-being labs, each addressing a different dimension of well-being.

Results of the Pilot
The results were encouraging. Students engaged deeply with well-being concepts and were able to clearly articulate how their activities connected to population health themes. Many participants reported meaningful gains in stress management skills and increased awareness of campus well-being resources.

Reflection activities—while in need of refinement—proved valuable. Students described them as a bridge between personal growth and public health theory, helping them see how well-being practices relate to professional practice.

Student Feedback
If there was one message that came through loud and clear, it was this: students want the Well-Being Lab to be a real course.

Across the board, participants endorsed:

Making the Lab required
Offering 1.0 academic credit
Structuring it as pass/fail
Keeping it in-person and experiential, not fully asynchronous

Students emphasized that without credit, the Lab would feel undervalued and less likely to be taken seriously. They also expressed a strong preference for interactive elements—guest speakers, group activities, and opportunities to build social connection. While students supported flexibility through hybrid formats, they were far less enthusiastic about fully asynchronous options.

Importantly, this preference was not just about format—it was about connection. Across the pilot and follow-up focus groups, students consistently shared that they do not want another set of online modules, resource lists, or one-off workshops. They want small, peer-supported spaces where they can talk, connect, and grow.

Students were particularly drawn to peer-driven models, such as small groups led by trained peer mentors (e.g., upperclassmen or graduate students). They felt peer leadership would reduce formality, increase relatability, and create the psychological safety needed for honest conversation, accountability, and skill-building. These spaces, students emphasized, should feel social—but still structured enough to foster inclusion and follow-through.

Results from the activities and reflections in the pilot further reinforced this theme: students most valued well-being experiences that felt practical, low-barrier, and embedded into everyday life.

Students also asked for:

Shorter, completion-based reflections
Options for anonymity in sensitive topics
“Looser,” everyday activities like walks, gratitude practices, or financial planning
Practical life-skills content—time management, cooking, budgeting, navigating campus resources

In other words, students want well-being education that feels relevant, doable, and grounded in real life.

Lessons Learned:

Provide more structure and built-in educational content
Build in small-group, peer-to-peer connection through structured check-ins and trained peer mentors to increase belonging, accountability, and engagement.
Expand the menu of qualifying activities
Add asynchronous modules (in moderation)
Integrate the Lab into the PHS curriculum as a required component
Start Lab participation earlier in the semester
Build in biweekly check-ins instead of only start and end reflections
Strengthen partnerships with campus offices

These changes would make the Lab more coherent, more flexible, and more aligned with students’ needs.

“…students most valued well-being experiences that felt practical, low-barrier, and embedded into everyday life.”

If you are considering launching a well-being or experiential learning initiative, here are a few takeaways from our pilot:

Build in flexibility, especially in recruitment and participation.
Credit matters—students engage more deeply when the work “counts.”
Balance rigor with feasibility: shorter reflections and a manageable credit load help.
Partner with existing campus resources to improve feasibility and avoid duplication.
Connect reflection activities to disciplinary content to enhance both learning and well-being.

Final Thoughts
The pilot confirmed what many educators already suspect: students are eager for learning experiences that integrate personal well-being with academic and professional growth. The Well-Being Lab not only helped students understand public health principles more deeply—it supported them in living those principles. With thoughtful refinement and continued collaboration, this Lab has real potential to become a signature element of the Public Health Studies experience.

Leslie Bauman
Junior Lecturer, Psychological and Brain Sciences
Johns Hopkins University

Leslie Bauman is a junior lecturer in the Department of Psychological and Brain Sciences. Her research interests include clinical psychology, research methods, resilience, well-being, and mental health in university settings.

Image source: BalanceFormCreative – stock.adobe.com, JHU Vice Provost website, peopleimages.com – stock.adobe.com

Lunch and Learn: Practical AI Pedagogies

Posted on March 19, 2026 by abrusini

On Thursday, February 19th, the Center for Teaching Excellence and Innovation (CTEI) hosted a Lunch and Learn featuring two faculty members discussing Practical AI Pedagogies. Emily Fisher, Director of Undergraduate Studies and Associate Teaching Professor of Biology, and Illysa Izenberg, Associate Teaching Professor in JHU’s Center for Leadership Education, presented two approaches to integrating AI into their courses. Caroline Egan, Teaching Academy Program Manager, moderated the discussion.

Fisher began the presentation by describing how she and co-instructors Nichole Broderick and Chiara De Luca redesigned an exam preparation assignment to intentionally incorporate generative AI, also known as the “AI Study Buddy,” in their Molecular Biology course. Their goal was not to outsource thinking to AI, but to foster metacognition; they wanted students to appreciate the role of retrieval in learning, view exam prep as a learning opportunity, and practice self-directed learning and studying.

In previous iterations of this assignment (2020 – 2024), students were asked to write and answer their own exam questions (without AI). While this exercise was useful, it was unclear how many students had improperly used AI to complete their work. Also, Fisher noted that many of the questions produced were generally of low quality.

In 2025, the instructors reframed the activity: instead of avoiding the use of AI, they integrated it deliberately. Students were asked to use HopGPT (GPT-4o mini) to complete the following steps:

Prompt AI to generate a challenging exam question about molecular biology (without providing the answer).
Answer the AI-generated question themselves.
Ask AI to answer the same question.
Evaluate the AI’s response for:
-Accuracy
-Similarity to their own answer
-Alignment with the style of course exams
-Overall usefulness as a study tool

The assignment was worth 2 points out of a total of 106 for the course. A non-AI alternative was available for students who preferred not to use generative AI. (Two students selected this option.)

Overall, students rated the AI answers as highly accurate – but many times the students’ answers were different than the AI answers. Questions and answers were often very broad; the course content is much more detailed and includes experimental techniques that the AI answers did not include. Still, student feedback was mostly positive, rating it about “medium” in usefulness in helping them study. Fisher added that it was a valuable exercise, moving the focus away from writing exam questions and instead towards answering, analyzing, and critically evaluating content.

There were a few questions from the audience about the AI Study Buddy:

Q: Are there any data or future plans to compare these results with having students study together, asking each other questions?
EF: Not at the moment. This would be a challenge. I would have to trust that they weren’t using AI.

Q: Did students do better on the exam after doing this exercise?
EF: Students usually do well on exams in this class. I did not look to compare.

Q: Have you thought about asking HopGPT for more topics for more study? For example, telling students, “Here is what we think you should focus on, how does that compare with what AI recommends?”
EF: This is a good idea. Students are telling us they use AI to help them study. I guess I just wanted them to wonder how well it was helping.

Q: What about student equity to AI platforms?
EF: That’s why I used HopGPT, since all students have access to that one. I would have selected Claude if I did it again.

Q: Next time, would you keep it the same or expand it? Also, next time would you continue to focus on the answers?
EF: I thought it was useful the way it went. I would keep it. I love that there are ways to go deeper, but this is the right number of points for this class.

Illysa Izenberg continued the presentation by sharing her approach to using AI in her Engineering Management and Leadership course. Like many instructors, she wants to help students use AI tools productively without becoming overly dependent on them. Izenberg believes in total AI transparency between instructors and students and provides very clear guidance for students about its use. She uses color-coded symbols to show when and how students may use AI, and requires them to cite all sources (including AI tools) while providing detailed instructions on proper citation.

Izenberg has developed a unique framework that encourages students to view AI as “teammates” they can rely on for different types of support, depending on the task. Each teammate has a clearly defined role:

The Tasker handles repetitive work like formatting citations, organizing notes, or cleaning data — tasks that are routine but still require students to verify accuracy.

The Draftsmith can help refine writing, generate study materials, or suggest improvements. Students need discipline when using this role, as allowing AI to draft too much early on can lead to missed learning opportunities. For example, Izenberg notes that students need to develop their own writer’s voice which will be severely impacted if they rely on AI to draft everything for them.

The Facilitator acts as a thinking partner, asking questions that help the student consider alternatives, evaluate plans, and expand and sharpen their analyses. This role also requires discipline so that learning is not undermined. When used thoughtfully, this role can promote reflection and deeper understanding.

In addition to the AI teammates framework, Izenberg also introduced students to an “AI Gatekeeper,” a tool similar to a rubric that guides students in regulating their use of AI. The AI Gatekeeper asks students to first define their own criteria for using AI and then rate each task on a scale from 1 to 5 to decide whether to proceed (1 = no AI use, 5 = AI use is appropriate). Izenberg recommends a rating of at least “3” for students to consider using AI for assistance. Example student criteria and ratings:

Practice and expertise:
- 1 if this task gives me essential practice in a skill I’ll need later.
- 5 if I’ve done it many times and already have the needed expertise.

Creativity vs. rote work:
- 1 if the task requires creativity or original thought.
- 5 if it is mostly rote.

Students were not required to use AI or these tools in class, but they were there if they chose to use them. According to Izenberg, students who used the tools realized that they knew less about AI than they initially thought and learned a great deal in the process: to push themselves when articulating their reasoning, to avoid the temptation of allowing AI to make decisions for them, and to resist using it too early in the process so their creativity would not be compromised. Several students found the tools extremely helpful and suggested that they be made available and integrated across all of their courses.

The presentation concluded with additional questions from the audience and facilitator Caroline Egan:

Q: Given that the paid-for LLMs seem like they do a better job, what are the equity issues around giving them GPTs and prompts to use for coursework?
II: This is something we need to acknowledge. First, no assignment required AI use and students could succeed in the course without it. For those who chose to use it, many didn’t realize that HopGPT can access various ChatGPT models as well as Claude and others. I tried to mitigate the equity issues by either giving them prompts that they could edit and use in HopGPT or giving them access to GPTs I created for my engineering students working on senior design. Every time I create a GPT for students, I hire 2-3 people to try out the GPT at least 3 times. At least one of those times has to be using the free version of ChatGPT. I made up a rubric to evaluate GPTs and so I can see where the GPT might not work well for someone with a free account before I launch it so I can edit it. For now, that’s the best I can do I think. I’m going to be working on porting over my GPTs to another tool during the next few months in the hopes of resolving this and other issues.

Q: Maybe [the AI Gatekeeper] could be addressed as part of the introductions for all classes.
II: I love that idea.  Students are harming themselves [with their unregulated approach to using AI] and they don’t know it.

CE: A question for Emily and Illysa: both of you have had students critically reflect on the use of AI. What would you recommend for faculty who haven’t done that yet?
II: It is important to decide for yourself and your course where the lines are. Ask yourself, what do students need to be able to do? From there, draw the lines. Then talk to the students about what happens if they go outside of the lines.
EF: I agree. In another course, I showed students data from a paper about what happens when you use AI summaries vs. a web search. Participants in the experiment learned less overall with the AI summaries.
II: Also, in my opinion, students are overloaded with classes, credits, and extracurriculars. It is costing them the ability to reflect and learn. So we have to help them and ourselves by planning ahead on what we want to turn to AI for and what we want to do on our own. Without clear guidance from faculty, students are never sure if they are doing something they should hide. This harms trust. Make the lines clear, relate them to your learning outcomes, and then teach them Total AI Transparency. You don’t have to hide what you’re allowed to do!

CE: Emily, the case study you brought up was on metacognition. It was an opportunity for students to practice metacognition. Illysa, did you also do this?
II: Yes – anytime students used AI, they had to explain why they chose to use it, why they made those choices, and why they used or didn’t use the output. They had to evaluate the output compared to their original goal. They weren’t just doing it, they were explaining why and how. The point is to teach them discernment.

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image source: Lunch and Learn logo, Unsplash

Contributions Beyond Grading: Why Teaching Assistants Matter More than We Often Acknowledge

Posted on February 20, 2026 by abrusini

[Guest post by Christine Solan, Teaching and Training Specialist, Biology, Johns Hopkins University]

In many classrooms, there is someone quietly shaping the learning environment in ways most students never notice—and most institutions never measure. We often talk about Teaching Assistants (TAs) in terms of tasks: grading, proctoring, helping labs or discussions run smoothly. But anyone who has taught a complex course knows that TAs often do far more than that. They help translate expectations, humanize the course, and create the small moments of connection that influence whether a student feels capable, welcomed, or overwhelmed.

Research in higher education has long shown that students’ sense of belonging, access to timely feedback, and perception of instructor approachability are among the strongest predictors of academic success. TAs often sit right at the intersection of these experiences. Students tend to see TAs as more approachable, more available for questions, and more attuned to the emotional ups and downs of learning. In large or fastmoving courses—where faculty are balancing significant teaching, research, and service responsibilities— a TA can be the person who notices confusion early, encourages a hesitant student, or reinforces the tone and expectations the faculty member has established.

None of this replaces faculty expertise; if anything, it reinforces it. TAs are close enough to students to feel accessible, yet experienced enough to guide thinking, model disciplinary reasoning, and support the inclusive norms faculty work hard to cultivate. When TAs receive even a small amount of structure or guidance, their work becomes more aligned with course goals and the whole learning environment benefits.

Because faculty carry the intellectual and pedagogical leadership of a course, the way they frame the TA role matters enormously. This doesn’t require major time commitments or extra meetings. Sometimes it’s as simple as sharing the “why” behind an instructional choice, asking TAs what they’re noticing in student questions, or clarifying the tone the faculty member hopes to set. These small moments of alignment can have an outsized impact on how effectively TAs support a course.

When faculty choose to engage TAs as collaborators—even briefly—teaching becomes more reflective, course design more intentional, and students experience a more cohesive learning environment. Many faculty also find that this partnership lightens the cognitive load of managing a complex classroom, allowing them to focus more fully on the aspects of teaching that require their expertise.

TAs do not simply help faculty teach; they help faculty reach—reach more students, more consistently, and with greater humanity. They are part of the hidden architecture that helps make excellent teaching possible. When faculty include TAs in their instructional planning, even in small ways, the partnership can enhance their own teaching while supporting the kind of learning environment they are working so hard to create.

Christine Solan
Teaching and Training Specialist, Biology
Johns Hopkins University

Image Source: Pexels

Starting the Semester with Accessibility in Mind

Posted on January 30, 2026 by abrusini

As the semester gets underway, this is a gentle reminder that faculty are responsible for creating course materials that are accessible to all students. While the start of the term is often packed with course preparation, Canvas/LMS setup, and student communication, building accessibility into materials from the beginning can prevent barriers before they arise. University-wide guidelines were published in 2021 to ensure that all new content aligns with Web Content Accessibility Guidelines (WCAG) 2.1 AA. Faculty are expected to make a good-faith, proactive effort to ensure their materials meet these standards and support equitable access for all learners.

A few best practices to help you get started:

Use built-in formatting tools for styles, headings (Heading 1, Heading 2, etc.), and lists (bullets, numbers, etc.) to provide structure and easier navigation.
Ensure PDFs are searchable and selectable (not scanned images).
Provide meaningful text for all hyperlinks, describing the link’s destination or purpose.
Use tables for displaying data, not for page layout; be sure to include header rows and defined borders in tables.

For multimedia:

Use high contrast visuals: dark text on a light background or light text on a dark background; avoid using color as the only way to distinguish highlighted areas of your document/slide.
Provide Alt Text (short written descriptions) for all non-decorative images, graphs, charts, or other non-textual elements.
Include captions and transcripts with all videos, including short clips and instructor-created recordings.

Other considerations:

If you use Canvas, take advantage of the built-in accessibility checker as well as Ally, a third-party tool that flags accessibility issues course-wide, including those found in uploaded external content (such as PDFs).
In addition to accessibility, applying Universal Design for Learning (UDL) principles can further support diverse learners by offering flexible ways to engage with course content and demonstrate learning:
- Provide materials in more than one format when possible, such as text, images, or video.
- Offer a choice in how students demonstrate their learning: essay, group presentation, written vs. oral exam, etc.
- Provide multiple opportunities for students to engage with course content; some examples include case studies, class discussion, collaboration with peers, guest speakers, and field trips.

This is not a complete list – faculty are encouraged to explore in more detail the university’s resources on how to create an accessible learning environment:

Do you have additional questions about accessibility? Contact your division’s teaching and learning center for more assistance.

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image source: magele-picture – stock.adobe.com, Canvas screenshot

Lunch and Learn: The Ethics of Using AI as an Instructor

Posted on December 18, 2025 by abrusini

On Wednesday, November 19th, the Center for Teaching Excellence and Innovation (CTEI) hosted a Lunch and Learn featuring faculty panelists discussing the Ethics of Using AI as an Instructor. Faculty panelists included Dan Ryan, Teaching Professor of Computer Science, Louis Hyman, Dorothy Ross Professor of Political Economy in History and Professor at the SNF Agora Institute, and Steven Gross, Professor and Chair of Philosophy. Caroline Egan, Teaching Academy Program Manager, moderated the discussion.

The session began with the panelists introducing themselves and discussing how they integrate AI into their course content and whether they’ve received feedback, positive or negative, from students. Dan Ryan uses AI “all the time and for everything.” He described how he uses it as a thought partner when building and designing his courses; he has also created tools for students to use, such as a custom GPT that helps them prepare for oral exams or assesses their answers to particular questions. Ryan has always been very open with his students about his use of AI and so far has not received any negative feedback.

Louis Hyman uses AI in varying degrees depending on the course he is teaching. In his AI methods and history course, his students use AI to teach themselves how to write code and then use the code to analyze historical data. This semester, he’s teaching a social theories course that involves much less interaction with AI. However, Hyman encourages his students to use AI where it makes sense, for “boring, tedious, non-creative work,” such as helping with grammar. He frequently uses it himself in this way to offload mundane tasks, such as extracting keywords from his lectures to create study handouts or revising dates in his syllabus. He does not support using AI when creative thinking is required, such as when writing a paper. Hyman is encouraged that the students in his class do not seem to be overly reliant on AI.

In his course, Philosophy and AI, Steven Gross and his students spend a lot of time discussing the philosophical underpinnings of artificial intelligence but use ChatGPT sparingly for in-class demonstrations of its capabilities. Gross expressed concern that students may be tempted to use AI, especially in Humanities courses that require a lot of dense reading and writing assessments. He designed his course to reduce this temptation by using in-class blue-book exams and discussions to encourage critical thinking without the use of AI. Gross does, however, support the use of AI as a study companion or for help working through difficult concepts.

Egan continued the discussion by asking panelists what ethical considerations they addressed, if any, related to using AI to support the creation of instructional materials. The discussion started with their own use but evolved into a larger discussion that included student use, indicating the interrelated questions and complexity of this topic. All panelists agreed that they see AI as a tool, like any other, that has its advantages and disadvantages. Hyman is fully transparent with his students and encourages them to use AI in situations where they think it would be helpful, such as helping to interpret difficult readings. He reinforces to students that it’s not “an oracle” but that they should treat it as “another point of contact” for helping to understand course material. Ryan holds class discussions about AI’s capabilities and appropriate applications and emphasized the importance of being explicit with students regarding AI usage. Gross agreed with the need for transparency and being upfront about when students can or cannot use it. He also stated that instructors have an obligation to prepare students to use and understand AI.

All panelists agreed that full disclosure about their own use of AI as instructors is essential. Hyman noted the “sacred bond” that exists between teachers and students that must be preserved through mutual trust, which includes being open and honest about the use of AI. Ryan commented that deception of any kind is off-limits, saying one should no more present AI-generated work as their own than present another person’s work as their own. He also does not support the use of AI for grading. Gross agreed that using AI for grading is not a good strategy, although he feels this may change at some point in the future for certain specific applications, such as logic problem sets. For now, Gross said instructors should err on the side of greater transparency and help students understand “why AI is ok here and not ok there.” Another point that was mutually agreed upon was that it is both reasonable and appropriate to place different limits of AI usage on learners than on instructors, just as we already do with other tools and resources.

A question was raised by an audience member who requested “good practices” for instructors and students when using AI. Responses from the panel included:

Use the “project” feature inside of a LLM to keep prompts and chats organized. The LLM will increase its knowledge base about your topic as more interactions occur within the project space. (Note: this feature is not available in HopGPT, the Hopkins AI platform).
Group interaction idea: Have students invite the LLM to join a study group with them. Before doing a pair and share in class, ask students to do a pair and share with the LLM as a warm-up.
Experiment with more than one LLM: ChatGPT, ClaudeAI, Google Notebook LLM, HopGPT, etc. Ask ChatGPT (or other LLM) what other tools are out there, what tools are best suited for what tasks, etc.
Get in the habit of asking the model what else it needs from you. The more detail you can provide, the better the responses will be.
Clearly outline your expectations for students regarding AI usage. Explain why you allow or prohibit it in specific situations and for what purpose.

Another question from the audience asked panelists how they handle various reactions and attitudes from students about using AI, particularly those who have concerns about its environmental impact. Gross stated that his impression was that the environmental impact of individual users is fairly small, but the training of the model is what has the larger impact. Ryan stated that we need to put this issue into context, and that perhaps it is not as large of an issue as it is made out to be. Hyman described a recent meeting he had with someone from the Baltimore Gas and Electric company (BG&E) who stated that the price of electricity is going to increase about ten times what it is now in just a few years, due in part to demands from a large data center in Maryland that is helping to fuel AI. He acknowledged that this is concerning but stated that this should not be a reason to not use AI. He tried to provide context by comparing this issue to other environmental concerns, such as the use of water on golf courses or the power required for cell phones. Gross agreed and encouraged audience members to consider the consequences of choosing not to use AI, asking whether it could help generate alternative approaches to solving global problems like climate change – and what might be lost if climate scientists were denied access to a tool that could potentially contribute to solutions.

Some audience members pushed back, arguing that AI’s environmental impact is far greater than the other issues mentioned – especially for people living near data centers – and that students should be able to assert their individual political agency in terms of using AI. One faculty member described having a student who refused to complete an assignment that required AI, so she prepared an alternative assignment for that situation. The panelists agreed that the environmental impact of AI is not trivial and needs to be addressed. Hyman suggested that we insist data centers find ways to use alternative energy sources, such as solar energy. Ryan and Gross agreed, with Ryan questioning why we are allowing a few big companies to make all of the decisions regarding regulation and argued for overall better governance of AI use.

Panel members wrapped up by sharing final thoughts with the group. Ryan encouraged everyone to remember that we have an established set of principles in place within academia (honesty, integrity, etc.) that we should use to help guide us and answer questions about AI usage. Gross encouraged responsible use of AI, pointing out that we would be failing pedagogically if we all use AI to represent ourselves in our work. Hyman echoed this response, expressing the need for intentional educational experiences with students, while also recognizing the value of AI in certain situations.

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image source: Lunch and Learn logo, Unsplash

The Innovative Instructor

Pedagogy – Best Practices – Technology

Author Archives: abrusini

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Lunch and Learn: Learning With AI – Student Voices on Studying, Research, and Writing

Piloting a Well-Being Lab in the Public Health Studies Curriculum

Lunch and Learn: Practical AI Pedagogies

Contributions Beyond Grading: Why Teaching Assistants Matter More than We Often Acknowledge

Starting the Semester with Accessibility in Mind

Lunch and Learn: The Ethics of Using AI as an Instructor