The CraFT

By Kate Cammack, CfT Co-Director

Like many of you (and this guy), I’m finding that many of my assignments and assessments, as I originally designed them, are now doable with GenAI 😭

As I wade through this strange academic grief process and consider whether I’m actually accurately assessing my students’ learning, I have an increasing sense of acceptance and am ready to start tackling this issue head-on this year.

Some questions that I’m using to audit my assignments:

1. Could AI perform much of this assignment/assessment?
2. To what extent would students still be engaging in important thinking?
3. Would I still have convincing evidence of students’ learning?
4. How confident would I be that this student understands / “got it”?

Then, I decide: do I leave this assignment unchanged, modify it, integrate AI more intentionally, or replace it? 

Our goal isn’t to outwit AI — it’s to revisit concrete aspects of our work, as educators, to ensure that students are still engaged in the most important parts of learning.

This is for everyone

We can all do this work, regardless of our current feelings about AI & student learning. Here are a few common AI stances:

• enthusiastic adopter
• cautious experimenter
• skeptical observer
• conscientious objector
• naive non-explorer

Note: none of these stances is better or worse than another. The work is simply to rethink our teaching from our current AI stance, whatever that stance may be.

Also, our stance will inevitably continue to evolve. FWIW last year, I was straddling conscientious objector and naive non-explorer. After a few workshops and deep soul-searching this summer, I think I’m now flirting with skeptical observer and cautious experimenter. TBH I don’t know that I’ll ever be an enthusiastic adopter, but who knows.

Join us to (re)align your assignments!

At the CfT, we’re dedicating time to revisiting our assignments and assessments so that, slowly but surely, they are re-aligned with our current AI stance, whatever that may be. 

This semester, we’ll be hosting a few (Re)Designing Assignments for Real Thinking mini-workshops, where you’ll have protected time to:

1. Articulate the key goals (learning objectives) of one of your assignments
2. Evaluate whether student work toward those goals (learning outcomes) is still being accurately represented/captured by that assignment 
3. Revise/adjust that assignment to re-align with your objectives/outcomes, based on your current GenAI stance

Please join us! We’ll add more if needed:

• Wed, Aug 12, 1-2pm (add to calendar)
• Thurs, Aug 13, 9-10am (add to calendar)
• Fri, Aug 21, 10-11am (add to calendar) and 11am-12pm (add to calendar)

For anyone looking for longer-term accountability and solidarity, we’ll also be facilitating a community of practice (CoP) on the same topic: (Re)Designing Assignments for Real Thinking. This CoP will meet regularly throughout the semester; each session will have time to:

1. have focused discussions on how/where/when GenAI is shifting our thinking about student learning (15-20min), and
2. update a specific assignment so that it is (re)aligned with your learning objectives and your current GenAI stance (30-40min)

Sign up for the (Re)designing assignments for real thinking CoP here.

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Finally, if you’re looking for a thought-partner, you can always book a one-on-one consultation with us – we’d love to ideate and troubleshoot together.

Related readings:

McMurtrie (2026) With AI in the classroom, professors are walking a tightrope

Hommel (2026) Beyond bans: Rebuilding teaching for a world with AI

UChicago – Strategies for designing AI-resistant assignments

Alkouk & Khlaif (2024) AI-resistant assessments in higher education: practical insights from faculty training workshops

Warner (2023) If ChatGPT can do it, it’s not worth doing

The AI Resistant Pedagogy Studio

More assessment/exam-specific resources can be found here

AI was not used to support/write this post.

By Kate Cammack, CfT Co-Director

Let’s imagine you just received this email:

What would you bring with you? How would you decide?

Maybe you’d make some assumptions, like “It’s still hot, so it must be a beach trip – better bring my snorkel gear!”

Or, maybe you made a different assumption, like that we were heading with Lauryl to the University Chalet in the heart of ski country.

It’s clearly important to know what we’re doing (our plan) and where we’re going (our destination) so that everyone can plan ahead, they know what to pay attention to, and they do what they’re supposed to do.

Teaching has a plan and a destination

In our teaching, we articulate what we’re doing (our plan) in our learning objectives. And, we have evidence that students learned (our destination) in our learning outcomes. 

Outcomes are tangible evidence of student learning – they might be activities, assignments, or something else that you use to assess students’ work toward a learning objective. I found Lang & Braseby’s recent post “If you teach it, can they do it?” and associated chart (below) to be particularly helpful:

As a fresh new semester approaches, it’s a great time to revisit your learning objectives and outcomes.

You can approach this work at the level of your entire course, a specific class session, or even one single assignment.

Take your students along for the ride

While we all (likely) list our course learning objectives in our syllabus, we all have learning objectives for each unit or even each class session — even if we haven’t written them down.

But articulating your learning objectives — whether for a unit and even a reading — helps your students know where you’re going and what they need to pay attention to.

And, it can be easy! This fall, I added them to the beginning of each of my Brightspace units, like this:

I’ve also rephrased a few learning objectives as more student-friendly “guiding questions” or “what to take notes on” below certain readings and videos, in the hopes that it helps students focus on key points and come prepared for class (!):

Also, while these are doable, student-centered things (and thus similar to the Learning Outcomes” column, above), a true learning outcome is something that the students would actually be producing/doing to demonstrate that knowledge (e.g., an assignment, an exam answer).

Join us to prep your Brightspace for the semester!

Drop by next week to zhuzh up your Brightspace! Our incredible Adam Hawkins (aka Sewanee’s “Brightspace Guy”), our amazing Matt Brown (aka “King of Digital Accessibility”), and I (aka “Links are her Love Language”) will be there to ideate, troubleshoot, and make your teaching life easier with Brightspace tips & hacks:

• Wed Aug 10th, 10-11:30am (add to your calendar)
• Thurs, Aug 11th, 10-11:30am (add to your calendar)

If you already have your Brightspace set up for this semester, that’s shamazing! I’m (hopefully) right behind you!

Related readings:

Nowak (2026) Taking the cotton candy out of your learning objectives

Hara (2010) Graphic display of student learning objectives

Davidson (2017) Design learning outcomes to change the world

UMass Amherst IDEAS (2025) Planning with precision: Developing learning objectives with AI

Duke CTL – Writing effective course learning objectives

Stachowiak (2011) Creating measurable learning objectives.

Chatterjee & Corral (2017) How to write well defined learning objectives

AI was not used to support/write this post.

by Kate Cammack, CfT Co-Director

How are we, as Sewanee faculty, thinking and feeling about GenAI? 

Where are our strengths, and what are our opportunities as we move forward?

A few months ago, we were invited to help pilot a new scale, developed by a research team in Minnesota, assessing our readiness and attitudes toward using and implementing GenAI. Last week, we received our site’s data. Thanks to the 26 Sewanee educators who took this survey — that’s a decent sample for this pilot dataset! While they didn’t collect disciplinary data (which likely matters), this provides us some initial, valuable insight into dimensions of our GenAI use that we feel more comfortable and less comfortable with.

Each of the five dimensions are listed below; here’s how they’re organized:

1. First is the researchers’ brief explanation. Each questions within that dimension is listed on the figure below each explanation. Responses were on a Likert scale of 1-5, with 5 = strongly agree. 
2. Then, I’ve graphed Sewanee’s responses, with the overall total scores within that dimension.
3. Finally, I’ve added a quick summary of these data. While there are many ways that we might interpret or speculate about these data, I’ve tried to keep my summaries purely descriptive.

GenAI Ethics

1. Topics that help educators understand implications of AI related to bias, privacy, integrity, and responsible use. Examples:

• Identifying and mitigating bias in GenAI writing and assessment tools
• Understanding student data privacy when using AI-enabled platforms
• Designing ethical AI use guidelines for courses
• Applying transparency and citation when using GenAI tools

2. Data time!

3. Sewanee faculty scored the highest on this dimension, suggesting this is an area of strength on our campus. Responses were also fairly consistent across all questions. Mean = 4.3, SD = 0.6; Median = 4; Mode = 4

GenAI Critical Thinking

1. Topics that develop educators’ ability to evaluate and question the quality, accuracy, and appropriateness of GenAI output. Examples:

• Detecting hallucinations, inaccuracies, and biased reasoning in AI responses
• Evaluating whether AI‑generated content is developmentally appropriate for learners
• Teaching students to critique and refine AI‑assisted work
• Analyzing when AI‑generated explanations oversimplify or distort disciplinary concepts

2. Data time!

3. Sewanee faculty also scored relatively high on this dimension, suggesting this is another area of strength on our campus. One question, about our comfort with the ambiguity associated with GenAI use, was lower than the other questions, suggesting that this is an area for all of us to explore and pay attention to. Mean = 3.8, SD = 0.5; Median = 4; Mode = 4

GenAI Pedagogical Proficiency

1. Topics that help educators integrate GenAI into learning experiences, including design, facilitation, and assessment. Examples:

• Designing assignments that incorporate AI responsibly and creatively
• Using GenAI to scaffold writing, problem‑solving, or feedback workflows
• Redesigning assessments to leverage GenAI while maintaining rigor
• Using GenAI to model disciplinary thinking (e.g., revision, lab procedures)

2. Data time!

3. Sewanee scores were fairly bimodally distributed within this dimension, with similar proportions of faculty disagreeing and agreeing to most statements. This is also the dimension where we saw the most strong disagreement. Everyone agreed that they recognized teaching challenges related to GenAI. Mean = 3.1, SD = 0.7; Median = 3; Mode = 3

Technology Self-Efficacy

1. Topics that build general confidence in learning and applying new technologies beyond AI. Examples:

• Developing digital confidence with LMS features (analytics, rubrics, templates)
• Learning new educational technologies (e.g., video feedback tools, annotation tools)
• Troubleshooting and experimenting with unfamiliar teaching technologies
• Building a growth mindset for tech adoption in the classroom

2. Data time!

3.  Sewanee faculty scored relatively high on this dimension, suggesting this is another area of strength on our campus. There was a bit of hesitation about integrating new technologies if we didn’t consider ourselves an expert. Mean = 4.0, SD = 0.6; Median = 4; Mode = 4

GenAI Self-Efficacy

1. Topics that help educators feel capable using GenAI tools and influencing output quality. Examples:

• Prompt‑crafting basics for educators new to GenAI
• Improving AI output using iterative prompting strategies
• Comparing different GenAI tools for instructional use
• Understanding limitations and boundaries of current GenAI systems

2. Data time!

3.  Sewanee faculty were again fairly divided on most of these questions. Many of us do not feel confident using GenAI tools in our teaching, and also reported that it was hard for us to use new GenAI tools. All of us recognized that GenAI capabilities differed from other educational technologies. Mean = 3.1, SD = 0.4; Median = 3; Mode = 3

Moving forward

We at the CfT are grateful to those of you who responded, as it provides some concrete areas for us to focus on in our GenAI-related programming next year.

Again, we’d love to hear what you’re thinking about, reading or trying, at any time and as your thinking/work evolves — the form is set to continuously accept responses, so please help us build this new community of practice!

AI was not used for any aspect of this blog post.

by Kate Cammack, CfT Co-Director

Many of us use this late-May window to reflect and think ahead to how we might adjust our fall courses, before mentally transitioning to summer activities. One thing that will change all of our fall syllabi is the presence of our newly approved university AI policy (in its working form). With that comes an invitation for us to revisit and update our own course AI policies for our fall syllabi.

A Berkeley researcher analyzed 3000+ course syllabi and found that instructors have seemed to “warm” to AI over the past 3-5 years. Here’s Beth McMurtrie’s summary.

A brief tour through his figures:

Course AI policies are becoming more common in syllabi, though this varies across discipline. Business, humanities, and social science courses are more likely to include AI policies in their syllabi; physical sciences are less likely. Notably, across disciplines in this study, ~40% of syllabi still didn’t contain AI policies.

The permissiveness of course AI policies varies across disciplines. In this study, humanities, life sciences & social sciences were least permissive; business, arts, and math/CS were more permissive.

The framing of course AI policies has evolved relatively quickly. Over time, fewer syllabus policies mention academic integrity and more policies mention AI’s impact on learning.

Permitted uses of AI depends heavily on the task/purpose. Nearly 80% of instructors restricted AI use for drafting/revising work, but a similar percentage permitted AI for editing/proofreading. Interestingly, instructors were almost evenly split about whether they permitted or restricted AI use in ideation/planning work.

More instructors are exploring AI-based tasks. Overall, 11% of syllabi mentioned at least one AI-based task, such as an output verification activity or AI-integrated assignment. Again, this ranged across disciplines.

One caveat is that these syllabi were pulled from a large university in Texas. Before you say, “But big universities are entirely different from Sewanee!”, I think the data remain worth considering in the context of broader, overall patterns that point to disciplinary differences, student assumptions, faculty understanding of AI, and other “big-picture” issues that are worthy of consideration. If you’re interested in more context, the methods begin on page 12.

A few take-home considerations:

• Adopt a course AI policy, if you don’t already have one.
• In your coursework, consider how/where AI might augment students’ learning experience and where it poses risks to their learning and skill-building. Then, set clear rationale and guidelines for AI use on those specific tasks.
• The disciplinary issues that you face in your teaching are different from those faced by your colleagues in different departments. Remember that we’re all approaching these issues with different perspectives and seeing different concerns and opportunities.
• Our pedagogical choices can impact labor markets. As stated on page 29:
  • “The task-based framework highlights a potential feedback loop between AI in education and AI in production. If AI displaces skill-building tasks in educational settings, students may graduate with weaker capabilities in precisely the areas where AI is most capable. This erosion of human skill would further shift comparative advantage toward AI, potentially accelerating automation in those tasks.”
  • “Consider writing as an example. If students practice writing less because AI can draft text, they may enter the labor market with weaker writing skills. Weaker human writing skills make AI-generated writing relatively more attractive to employers, reinforcing the displacement of human labor in writing tasks. The same logic applies to coding and other domains where AI capabilities are strong.”
  • “This feedback loop suggests that decisions made in classrooms today may shape the trajectory of automation tomorrow. Instructor responses to AI are not merely pedagogical choices but upstream determinants of skill supply that feed into labor market dynamics.”

The full paper is worth a read for more details, nuance, and thoughtful consideration of these data. 

Full citation: Chirikov, I (2026). How Instructors Regulate AI in College: Evidence from 31,000 Course Syllabi. CSHE Higher Education Working Paper Series, Vol. 26-1. Retrieved from: escholarship.org/uc/item/9c51s3gs

AI was not used for any aspect of this blog post.

by Kate Cammack, CfT Co-Director

Now that my final grades are in, I’m starting to sift through all of the AI articles and links that I’ve been saving “for summer” in my inbox. I thought it might be worth sharing this list, in case anyone else has been putting off any AI-thinking for the past few months, like I have.

My disclaimer is that this list does not imply endorsement or opinion-sharing – just sources that I hope to use as a launch-point this summer.

First, if you’re not already signed up for Beth McMurtrie’s free Teaching newsletter from the Chronicle of Higher Education, it’s one of my favorites. She’s been producing a lot of relevant AI content, and I’ve been inspired to track down some research/studies mentioned in her newsletters (see below). (You can also share your perspectives on AI & teaching with her)

The Chronicle also now has a new free e-newsletter, Jagged Intelligence, focused on the impact of AI on higher ed; I’ve just signed up.

ICYMI / good to be aware of

• How accurate are Google’s AI overviews? (here)
• Students are developing AI tools to do their homework for them
  • One tool, called Einstein, retrieved assignments from the LMS and completed them well (here)
  • A different AI tool was promoted as “giv[ing] you a precise roadmap to get an A with the least amount of work possible.” (here)
• Your course materials could end up chopped up and inserted into AI-generated courses…without your permission (here)
• Is AI contributing to “cognitive surrender”? (here)
• The founder of Khan Academy is developing a new online college with a degree in applied AI (here)
• Princeton has moved back to proctored, in-person exams (here)
• Recent college graduates think their degree was worth it, despite AI concerns (here)
• How AI is encouraging us to rethink our purpose in higher ed (here)
• AI killed the take-home essay. Covid killed attendance. Now what? (here; thanks, Karen!)
• How college faculty are using AI (here)
• When AI cheating becomes a legal risk (here; thanks, Clint!)

A few articles/reports that you might consider spending time with

HEPI Report of Undergraduate AI Use

• Lots of rich data (from 1000 UK university students) to consider, including:
  • In 2026, 95% of students reported using AI in at least one way.
  • Most students reported using AI to generate text (e.g., ChatGPT) and enhance/edit their writing.
  • ~40% of all students reported that AI-generated content would receive a good grade, though more students agreed that this was the case in STEM fields than arts and humanities
  • The most common reasons that students use AI are to “improve the quality of my work” and “to save me time”

How instructors regulate AI in college: Evidence from 31,000 course syllabi

• Again, valuable data to consider; some of my take-aways:
  • Course AI policies are becoming more common in syllabi, though this varies across discipline. Business, humanities, and social science courses are more likely to include AI policies in their syllabi; physical sciences are less likely. Notably, across disciplines in this study, ~40% of syllabi still didn’t contain AI policies
  • The permissiveness of course AI policies varies across disciplines. In this study, humanities, life sciences & social sciences were least permissive; business, arts, and math/CS were more permissive.
  • The framing of course AI policies has evolved relatively quickly. In this study, syllabi policies were less likely to mention academic integrity and increasingly likely to mention AI’s impact on learning; these changes were seen over six semesters, or three academic years.
  • Permitted uses of AI depends heavily on the task/purpose. Nearly 80% of instructors restricted AI use for drafting/revising work, but a similar percentage permitted AI for editing/proofreading. Interestingly, instructors were almost evenly split about whether they permitted or restricted AI use in ideation/planning work.
• Here’s Beth McMurtrie’s summary.

Artificial intelligence, cognitive offloading, and implications for education

• “There is a growing body of evidence that using AI can short-circuit the cognitive effort required for sustainable, deep learning, thus creating “false mastery” with potentially long-term consequences (OECD 2026). This cognitive offloading from human to AI is especially risky for school students (‘novice’ learners who are building foundational knowledge and skills) when they turn to AI as a tempting substitute, not an amplifier, increase their dependency on the tool and lose access to deeper learning.”
• “Cognitive outsourcing also introduces extra equity risks. Research suggests that students who possess high levels of content knowledge and strong metacognitive skills are better able to leverage AI to accelerate and deepen their learning and critical thinking (Hong et al. 2025). Conversely, students lacking such skills, often those already experiencing disadvantage, are potentially more susceptible to harmful offloading and missing the learning they need. The unstructured use of AI risks even wider equity divides (Loble & Stephens 2024).”
• Here’s Beth McMurtrie’s summary
• Here’s a watch-on-demand video of a virtual forum on teaching critical thinking skills in the age of AI, hosted by the Chronicle
• Here’s an article on the concept of “cognitive surrender” in the context of GenAI.
• Here’s a 2025 Science article on how an AI tutor designed to minimize cognitive offloading was linked to more self-reported engagement and motivation than in-class active learning (specific AI prompts/constraints found in their supplementary materials)

AI detection pushed my students to use AI

• “In 2023, researchers at Stanford University warned that detection tools were biased against nonnative English writers. Their study…found that detectors misclassified more than half of TOEFL essays written by nonnative speakers as AI-generated, while nearly all essays by native speakers passed. The authors anticipated that such bias could encourage defensive or evasive behavior.”
• “Detection tools communicate, even when instructors do not, that writing is a performance to be managed rather than a practice to be developed. Students learn that style can count against them, and that fluency invites suspicion. In that environment, AI becomes a way to smooth uncertainty. It offers reassurance that language will appear ordinary enough to pass.”
• Here’s another article on cultural biases in AI tools

Artificial intelligence & grade inflation

• “This paper provides novel evidence that generative AI has become a new mechanism of grade inflation in higher education. [C]ourses with higher shares of writing and coding tasks saw substantial grade increases after ChatGPT’s release, accompanied by compression of the grade distribution. These effects are concentrated in courses where homework carries greater assessment weight, consistent with AI substituting for student effort on unsupervised take-home work rather than improving underlying skills.”
• Here’s Beth McMurtrie’s summary

A few videos/podcasts, for those who need a break from reading

• NPR Up First: Higher Education’s AI Problem (Nov 2025)
• NPR Morning Edition: More college students are using AI for class. Their professors aren’t far behind (Oct 2025; 3min)
• Chronicle’s “When AI cheating becomes a legal risk” (May 2026; 6min)
• 10 Podcasts on AI & Education (special disclaimer: I love podcasts but haven’t listened to any of these yet – let me know if you do/have reviews!)
• On-demand videos of AAC&U’s AI Week webinar series:
  • The future of AI and implications for higher education and the world of work (here)
  • Academic integrity in higher education: Practical responses to generative and agentic AI (webinar video here)
  • Faculty perspectives on AI (webinar video here)
  • How campuses are facilitating change regarding AI (webinar video here)

Student guides & self-assessments

• AAC&U & Elon University’s guidebook “AI-U: A student guide to navigating college in the AI era” (free)
• AAC&U & Elon University’s field guide “Human wisdom for the age of AI” (free)
  • There are also companion teacher’s guides to learning modules that contain some promising content
• AAC&U & Elon University’s AI Self-Assessment tool to help students identify how they use and think about AI in their work (quick & free)
  • potentially helpful to ask your students to take, then discuss!

What are you reading, thinking about, or engaging with, AI-wise?

This post is not a curated collection, but rather my personal inbox detritus.

We at the CfT are interested in how all of us are thinking about and/or using AI tools to support our students’ learning.

As these tools continue to evolve, so will our thinking about them. It’s valuable for us all — as a community of educators — to share our evolving ideas, thinking, questions, and approaches, so that we can learn from and support each other as we navigate this new landscape. No question or approach is too basic — there’s value in simply exploring, and in trying things out to see what works.

You can give us a sense of what you’re thinking about, reading or trying, at any time and as your thinking/work evolves — this form is set to continuously accept responses, so please help us build this new community of practice!

AI was not used for any aspect of this blog post.

by the Rev. Britta Meiers Carlson, PhD, Visiting Assistant Professor in Practical Theology

The School of Theology’s Beginning Pastoral Spanish II class visited El Jardín in Cowan in late April for an opportunity to use Spanish in a real-world context, connect with the local community, and reflect on their learning from the year. Students ordered their meals in Spanish and had basic conversations with restaurant staff. Over lunch, they discussed strategies for maintaining their language skills during the summer interim. Dr. Meiers Carlson shared insights with students about coursework and contextual education opportunities available through the Hispanic/Latino Ministry Certificate program, sharing recommendations for planning out their degree programs to take advantage of these offerings.

At the heart of the Pastoral Spanish program in the School of Theology is a concern for the spiritual well-being of Spanish-speaking people and their descendants, especially when their lives are touched by ministries in the Episcopal Church. The class outing gave students an opportunity to hear stories and ask pointed questions about the linguistic and cross-cultural challenges of parish ministry as well as accompaniment work with Spanish speakers and immigrant communities. In a surprising coincidence, students witnessed such ministry in action in the parking lot following our meal.

Many thanks to the Center for Teaching. Our experience demonstrates the learning and connection that can happen when a classroom becomes a community over a shared meal.

by Amy Patterson, Department of Politics

During April, two speakers shared their expertise on global health with Amy Patterson’s POLS 321 course (Global Health Governance). On April 14, Dr. David Wilson, Senior Programs Officer at the Gates Foundation, spoke to the class about his work managing health systems projects in Pakistan, Afghanistan, and Nigeria. He highlighted the Foundation’s emphasis on innovation and investment in new technologies to address challenging health questions such as infant mortality and maternal mortality in low-income countries. The second speaker—Kate Rademacher–previously directed projects on sexual and reproductive health in Africa for the international NGO Family Health International (now FHI 360). Currently a student at the School of Theology, Ms. Rademacher focused on the market forces that contribute to inadequate supply of affordable and effective family planning products in low-income countries. She discussed her involvement in a quality assurance project for contraceptive products that increased market competition and thus, lowered prices.

The speakers helped students to more deeply appreciate the multiple actors involved in global health governance, and they showed the nuanced role of philanthropy, market considerations, and context in providing health access to all.  In subsequent discussions, students linked points from the presentations to course concepts, such as the role of data in setting global health priorities. They also commented on the Gates Foundation’s focus on a biomedical, technical approach to health, which contrasted with the focus in the course on the social determinants of health and human rights. They responded to Rademacher’s presentation with comments about about family planning projects in light of the move to decolonize global health.  

Finally, the speakers also shared information about their career paths: how they started working in global health, what they enjoy about the work, and what they have learned throughout their careers. Students found their stories inspirational, with many hanging around after class to ask additional questions and to seek career advice. 

Thank you to the Center for Teaching for a mini-grant to make these speakers possible!

by Teri Terigele, Assistant Professor of Psychology

With the support of the CfT mini grant, I was able to invite a guest speaker to my class as part of the faculty-led study abroad program in Japan. The overall goal of the course is to explore how individuals who are exposed to multiple cultures navigate their identities and cultural experiences, and how broader sociocultural environments affect their experiences and psychological wellbeing. One section of the course focuses on English education in Japan and how English teachers navigate their identities as cultural brokers. Through our host institution, Kansai Gaidai University (KGU), we invited a guest speaker who is a KGU alum and a former English teacher at a prestigious, highly selective high school in Osaka, Japan.

During the session, the guest speaker shared engaging experiences learning and teaching English, as well as his own study abroad experience in Australia. He also discussed the less visible challenges of English education in Japan. A significant portion of the session focused on the reality that learning and teaching English is often approached as a means to gain admission to prestigious universities or secure better employment, rather than as a tool for meaningful communication. As a result, many Japanese students achieve high exam scores but struggle to communicate confidently in English. The speaker also addressed the mental health challenges faced by students in this high-pressure high school environment, as well as by teachers working in a toxic environment with limited support. He ultimately left the job after grinding for four years due to these conditions.

My class includes 20 international students studying abroad in Japan, representing more than five countries and collectively speaking over 10 languages. Following the guest lecture, we held a discussion on students’ own language learning experiences, examining how their positionality, privilege, and disadvantage shape the ways they learn and use language, as well as how assumptions based on appearance and ethnic identity influence their experiences. For this group, the session and the follow-up discussion provided a rare opportunity to look beyond rose-colored glasses or tourist perspectives and reflect on more complex, and at times challenging, aspects of Japanese society.

Notably, the guest speaker has since transitioned from his role as an English teacher at a competitive high school to working as a Kobe beef chef in one of Osaka’s busiest tourist areas. He now enjoys his work as a private English tutor for those interested in language learning, while also engaging with international tourists who appreciate his enthusiastic introduction to Japanese culture in fluent English.

by Kate Cammack, CfT Co-Director

What a wonderful, invigorating, and inspiring week at the CfT!

On Monday, Evan Joslin (Chemistry) taught us how to calculate the half-life of a radioactive material, using M&Ms. We got to eat the decayed/safe M&Ms, so participant motivation and investment was high.

Then, Leslie Todd (Art History) taught us how to look for political and social clues in Baroque paintings, such as Still Life with “Moor,” Peaches and a Lemon (below), and how she’s “flipped” her classroom so that students can create meaningful & artistic analyses together during class time.

On Tuesday, Lhakpa Sherpa (Economics) taught us how comparative advantage depends on the opportunity cost, i.e., what you give up in order to produce something of value. He joked that there were opportunity costs to coming to the CfT event that day, but they were worth it — we agree!

Then, Lily Thompson (Geology/EES) showed us how to calculate the epicenter of an earthquake using data from three seismographs. It was in San Francisco! Here she is, checking the VC’s work 👍

On Wednesday, Chris Van de Ven (Geology/GIS) asked each of us to draw a map of Sewanee, to illustrate how maps always contain an individual’s perspective and an awareness of an audience. For instance, outdoorswoman Emily’s map contained lots of trails. Deon impressed us all by including a compass on his map.

Lisa Burner (Spanish/EAH) spoke to us almost exclusively in Spanish throughout her slice! We practiced complementos directos, or shorter ways of referencing an object. “Quieres los dulces?” “Los quiero, professora!” Here she is, rewarding an eager attendee with candy.

On Thursday, Chris McDonough (Classics) got us all deeply invested in the Greek word kunopidos / κυνώπα.* The roots of the word are “dog” and “circle/round”, so it has been translated as “dog-faced” until the most recent, and perhaps controversial, translation by Emily Wilson. Spirited debate ensued about translation, interpretation, and meaning. (*sorry, Chris — I tried!).

Then, Andrea Mansker (History) presented a brief history of man-midwives and we interpreted this image from 1793. Of note, the bottom shelf of vials/tinctures reads “This shelf for my own use” 🤨

On Friday, we enjoyed another lively round of slideshow karaoke, where volunteers “presented” slides that they’d never seen before, on themes such as “My stump-speech for the department chairship” and “How to make assessment fun”. Guess who made our very playful and absurd slide decks:

Here is Maria, explaining why she should receive IRB approval for a new project:

And here is Matthew, describing his ambitious plans for his proposed 2-year sabbatical:

And here are Lily & Pepe, making a compelling pitch for a new, team-taught course:

And here are Katy & Kate presenting reasons that prospective students should join the Psychology Department. It included a “Lil Buddies” mentoring initiative and lessons in duck-walking, which was demonstrated by one of the presenters – can you guess who?

Thank you all for a delightful, engaging, and celebratory week, and special thanks to our “slice” presenters for sharing some of your teaching worlds with us. There is such heart and meaning in the teaching that happens on our campus, and Lauryl and I are moved by the thoughtful, creative, playful, and responsive teaching embodied in our community.

We’re grateful for the opportunity to continue learning and growing together, as educators.

Kate Cammack, CfT Co-Director

As we head into midterm grading, I wanted to share one of my struggles with grades, as I imagine I’m not alone in this.

Our family was caught up in Winter Olympics fever and we were cheering on the women’s cross-country skiers. When the first skier crossed the finish line, my younger son declared her “the best skier.” But was she?

What about the other women who were racing, who didn’t earn a medal? 

What were their stories?

What if:

• One of them was coming back from a devastating injury?
• One of them didn’t get any sleep last night?
• One of them had the flu?
• One of them was the first-ever representative of their country in this sport?
• One of them was using borrowed skis, because hers were lost at the airport?
• One of them has been working to overcome performance anxiety?
• One of them had to swerve to avoid a dog on the course? (actually happened)

I wanted to tell my son, “They’re all so capable and have worked really, really hard. A gold medal reflects that work, to some extent. It also might have meant that, for that person on that day, conditions were ideal and everything went their way. Does it mean they’re the ‘best’? I’m not sure, because that suggests that everyone else wasn’t as successful as they were.”

One of my struggles with grading is that we can quickly lose the nuance of our students’ lived experiences. Students are humans, just like us, and they are often going through a lot more than we can see on the surface. And, these experiences can have very real impacts on the way that they’re able to engage with assessments, particularly one-time, high-stakes versions (e.g., a midterm exam). To what extent does a grade that they receive indicate their actual learning, in a way that’s not obscured by the complexities of life?

I’ve found myself spending a lot of time considering grading/assessment strategies that feel more humanizing and also hold students accountable for their learning. How might we prioritize students’ progress and growth in an area, and how might we rethink our assessment practices so that they actually reflect those processes? How can we (un)structure grades to help students focus more on their own learning processes and less on comparing/ranking themselves against each other? Can we expand our thinking about grades, so that every person is able to show their learning in accurate, meaningful, and achievable ways?

I realize that one of the exciting parts of the Olympics is the high level of competition and that (typically) only one gold medal is awarded for each event.

I also think that, in academia, we can build structures that more accurately reflect deep learning and growth, and don’t penalize students for having an off-day. Maybe when we’re talking about leveling the playing field, we’re actually talking about increasing access to the podium.

In May, we’ll be re-launching a popular Brightspace mini-course called “The Grading Question”, designed by Stephanie Batkie. It is an opportunity for us to reconsider what grading means and how we might more accurately assess our students’ learning. Many of us are rethinking our current approaches in the context of GenAI. This mini-course invites us to re-evaluate what we value, what assessment structures are possible, and how a fresh approach can benefit our students.

Additional recommended reading (please email me if you’d like to borrow any of these books!):

• A Pedagogy of Kindness by Cate Denial
• Grading for Growth by Clark & Talbert
• Ungrading edited by Susan Blum
• Failing our Future by Josh Eyler