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In November’s Active Teaching Lab, Stuart Moulthrop shared his experience using a custom GPT “agent” to support reading-based discussion and scholarly experimentation in a graduate-level theory course. What began for Stuart as a practical test – “What could AI do with a semester’s worth of dense theoretical readings?” – quickly evolved into a broader reflection on what it means to be a scholar, teacher, and creator alongside AI. His experiment showed that building a course-specific chatbot is surprisingly feasible and opens genuinely interesting pedagogical possibilities, from supporting discussion to modeling scholarly dialogue.

At the same time, the session underscored a crucial constraint: student attitudes toward AI ultimately determine what uses of AI are pedagogically appropriate. Stuart’s students raised thoughtful objections to the use of AI, even while engaging critically with it. The Lab discussion highlighted that while custom AI agents can support learning, their effectiveness depends on careful framing, transparency, and voluntary use that respects student concerns.

To continue exploring this month’s topic, start by watching the November 5th Active Teaching Lab recording below. Then, take a look at the key takeaways from our discussion and a few classroom experiments you can adapt right away. 

Lab Takeaways 

1. Building a course-specific chatbot is surprisingly feasible��
   • Creating a custom AI agent for your course requires far less technical friction than is generally expected. Stuart Moulthrop built SemBot in about a week, and it successfully processed more than a thousand pages of course readings – including scanned PDFs – to answer questions and support weekly discussions. These results were possible only because Stuart iteratively tested, refined, and expanded the agent based on how it behaved with real course materials.
   • AI’s usefulness depends on thoughtful instructional design. AI can supplement your teaching, but it cannot replace an instructor’s judgment, presence, or engagement, especially in classrooms where student trust and comfort with AI vary widely.
   • See more on creating your own AI Agent below!
2. Don’t assume students want AI in the classroom��
   • Stuart’s graduate students were broadly skeptical of AI for substantial ethical and political reasons. His students raised thoughtful concerns about: training data taken without consent, the hidden labor behind AI systems, AI’s high environmental costs, and the concentration of power in a few tech companies. Instead of shutting down the conversation, these concerns fueled strong, critical dialogue regarding AI use.
3. Students’ attitudes toward AI strongly shape what’s pedagogically appropriate in your classroom��
   • Because of this antipathy toward AI, Stuart found that what “good teaching with AI” looks like is very much determined by student buy-in. He therefore kept AI use optional, and spent time in class examining and contextualizing its use in literature and education more broadly.
   • Ultimately, even students who refuse to use AI can still learn from it and engage with its implications for their field. For Stuart, he leveraged AI to help students see how humans write, imitate, and construct meaning.

Experiments Worth Trying 

Build your own AI agent

The SemBot instructions and information included below can be used to help you build your own AI agent. . 

SemBot Resources

• – Stuart Mouthrop’s AI Agent
• – Stuart added precise dates and detailed instructions only after extensive trial-and-error, highlighting how crucial iterative testing is for accurate AI responses.
• – SemBot Mood Rules outline thirty distinct conversational “Moods,” each associated with a unique set of stylistic and structural constraints for generating responses. These rules dictate the bot’s persona and tone, ranging from “Scholarly” to “Salty” to “Dada,” and often include mandatory elements like specific greetings, closing phrases, or grammatical peculiarities.
• – Stuart reformatted this syllabus to improve SemBot’s ability to access it for answers��

In October’s Active Teaching Lab, Brian Thompson from the Office of Strategic Partnerships shared how regional partners – Northwestern Mutual, Rockwell Automation, Microsoft, Direct Supply, Harley-Davidson, and the Milwaukee Bucks – are actually using AI, what they expect from new hires, and where 51���� can better prepare students.

Our discussion emphasized AI’s current role as a tool for augmenting, not replacing, human work; the importance of redesigning workflows rather than simply “bolting AI on”; and the continued relevance of the humanities given the growing premium on communication, critical thinking, and domain expertise. We also explored how 51����’s own initiatives – such as the Connected Systems Institute, Microsoft AI Co-Innovation Lab, NMDSI coursework and grants, and enterprise tools like Copilot – can help students prepare for an AI-enabled workplace.

To continue exploring this month’s topic, start by watching the October 1 Active Teaching Lab recording below. Then, take a look at the key takeaways from our discussion and a few classroom experiments you can adapt right away. 

Lab Takeaways 

1. Augmentation rather than replacement (for now) 
   • Most near-term AI value lies in enhancing existing work, not eliminating jobs. Currently, companies are automating routine tasks while upskilling workers toward higher-value responsibilities. That said, several industries are already reporting declines in entry-level positions.
2. You can’t “AI” a broken process 
   • AI adoption often requires rethinking and re-engineering workflows, not simply bolting AI onto existing practices. This principle applies as much to education as to industry. As Brian noted, deliberation and iteration are the most sustainable paths to meaningful integration. For educators, that means starting small: choose one well-understood part of your course or workload, experiment thoughtfully with AI, observe what works (and what doesn’t), make adjustments, and repeat. Over time, these small-scale refinements add up to significant transformation. 
3. Hiring signals are shifting 
   • Employers increasingly ask all candidates (not just coders): “How have you used AI?” A strong response reveal not only technical competence but also critical judgment: an ability to evaluate AI outputs, verify accuracy, and adapt based on what one finds. Helping students practice this process – try, test, verify, and adjust – in a variety of AI tools will better prepare them for the expectations of today’s job market. 
4. Sifting and winnowing data drive results 
   • AI initiatives depend on clean, well-governed data and context-specific expertise. Yet data rarely arrives ready to use. It must be interpreted, structured, and refined. This process requires the ability to weigh competing information, apply logical judgment, and filter data through company-specific knowledge that AI does not possess. For example, understanding what “comfort” means to Harley riders or what distinguishes “good” from “bad” output on a production line requires human discernment and experience and cannot simply be offloaded to an AI agent.
5. The humanities remain vital 
   • Success with AI isn’t purely technical—it depends on weighing competing claims, navigating ambiguity, and exercising human judgment. These are precisely the habits of mind cultivated in the humanities, making such courses essential preparation for ethical, adaptable, and discerning AI use. Beyond analyzing data, students must also be able to interpret, explain, and collaborate around it. Employers consistently cite communication and teamwork as key differentiators in hiring decisions. It’s not enough to know how to use AI. Graduates must also be able to evaluate information, communicate insights clearly, and do so effectively within teams. 
6. 51���� AI on-ramps for future careers 
   • The Connected Systems Institute (CSI) fosters innovation by uniting industry and academia. CSI aids the digital transformation of manufacturers, and develops curriculum to prepare the future workforce. It develops testbeds, facilitates student projects, and hosts a�� monthly open house.
   • The (NMDSI) enables faculty to collaborate on multidisciplinary data science research, apply for grants, and access resources for curriculum development 
   • (enterprise) is FERPA-compliant and available for campus use.

Experiments Worth Trying 

Teach students to think critically about AI

Ask students to explain how they used AI for an assignment (Remember: Copilot is a FERPA compliant, 51����-supported tool – like Canvas or Microsoft Office). Ask students to narrate what they asked, what they got, how they checked the results, and what they changed as a result.

For example, you might ask students to provide a brief “AI Use Note” with assignments. They should include the prompt(s) they used, describe AI outputs, explain their verification steps, and the rationale behind their decision to accept/reject AI content. Reviewing these reflections together in class helps students develop the habits of transparency, evaluation, and judgment that define responsible AI use. 

Collaboration and Communication

Building off of experiment one, help students work as a team to explain how they used AI in their group projects.

To bring it into the class, ask student groups to deliver short, iterative briefings—1–3 slides, 3–5 minutes each—focused on their reasoning process rather than their final results. Have them repeat these mini-presentations at key points in a project to show how their thinking evolves over time.

Comfort with messy data

Real-world data is rarely clean or complete. In most workplaces, data sets are inconsistent, mislabeled, and siloed—requiring both technical skill and critical judgment to make them usable. Developing this mindset helps students see data not as fixed truth, but as information that must be interpreted and refined.

To make this concrete, design a short “messy-to-model” mini-lab. Ask students to document the steps they take to clean and organize the data, identify assumptions or biases introduced during the process, and note potential risks or gaps in what the data represents. Reviewing these reflections together can help students appreciate how much meaning-making happens before any analysis begins. 

Process thinking 

Successful AI integration begins with understanding how a process actually works. Before deciding where to add AI, they must be able to analyze workflows, identify where human expertise is essential, and recognize inefficiencies or pain points. Developing process thinking equips students to use AI intentionally rather than automatically, designing solutions that improve systems rather than simply automate them.

To make this concrete, ask students to map a course or workplace process from start to finish. Have them mark pain points or bottlenecks, then propose one specific point where AI could augment the process, defining the inputs, outputs, guardrails, and human roles involved. Discuss how these changes would affect quality, efficiency, and decision-making across the system.