{"id":262,"date":"2026-05-27T22:14:33","date_gmt":"2026-05-27T22:14:33","guid":{"rendered":"https:\/\/sites.clarkson.edu\/ai-institute\/workbench\/teach-2\/"},"modified":"2026-06-25T20:41:48","modified_gmt":"2026-06-25T20:41:48","slug":"teach","status":"publish","type":"page","link":"https:\/\/sites.clarkson.edu\/ai-institute\/workbench\/teach\/","title":{"rendered":"Teaching & Learning"},"content":{"rendered":"\n\n\n\n\n\n\n
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\n ←  Workbench<\/a>\n <\/div>\n\n \n
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AI Institute Workbench  \/  01  Teach<\/div>\n

Teaching & Learning<\/h1>\n

A living studio for teaching with, against, and about AI. Experiments, assignment designs, policies, reflections, tools, and practices from across Clarkson \u2014 updated as the community builds.<\/p>\n

\n Borrow an Idea<\/a>\n Share an Experiment<\/a>\n Get Help<\/a>\n <\/div>\n <\/div>\n\n \n\n
\n Work in progress<\/strong>\n Everything below this point is actively being built. Sections, examples, tools, and patterns will be added as the Clarkson community contributes.\n <\/div>\n\n
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What this space is for<\/h2>\n

A practical home for faculty, staff, and students who want better teaching rather than louder panic.<\/p>\n <\/div>\n

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Teach with AI<\/h3>\n

Use AI for feedback, drafting, simulation, analysis, tutoring, design, coding, or reflection.<\/p>\n <\/article>\n

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Teach about AI<\/h3>\n

Help students understand AI’s social, ethical, technical, historical, and professional consequences.<\/p>\n <\/article>\n

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Teach against misuse<\/h3>\n

Design assignments that reduce shallow automation<\/a> and invite real intellectual work.<\/p>\n <\/article>\n

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Teach beyond panic<\/h3>\n

Move from detection and prohibition toward clear expectations and stronger student agency.<\/p>\n <\/article>\n <\/div>\n <\/div>\n\n \n

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Framework<\/div>\n

TRAIL<\/h2>\n

Transparent · Responsible · Auditable · Integrative Learning<\/p>\n

If AI contributes materially to student work, the student must be able to explain how the work came into being.<\/blockquote>\n
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TRAIL is a lightweight framework for responsible AI-assisted academic work. It does not ban AI or rely on AI detection. It asks students to work within clear rules, document meaningful AI assistance, verify what matters, and remain accountable for the final work.<\/p>\n

The framework centers on two documents: a Minimum Institutional Standard<\/strong> that sets the floor for AI use across courses, and a Lab Notebook<\/strong> students keep during AI-assisted sessions—recording what the AI contributed, what the student decided, and what still needs checking.<\/p>\n

▶ Watch: How do you teach TRAIL?<\/a><\/p>\n <\/div>\n

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For Faculty<\/div>\n

TRAIL Faculty Introduction<\/h3>\n

A concise overview of the TRAIL framework—what it is, how it works, and how to implement it without policing students.<\/p>\n ↓  Download Deck<\/a>\n ▶ Watch Video<\/a>\n <\/div>\n

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For Students<\/div>\n

TRAIL Student Guide<\/h3>\n

A student-facing overview of TRAIL—what is expected, how to keep a Lab Notebook, and why accountability matters in the AI era.<\/p>\n ↓  Download Deck<\/a>\n ▶ Watch Video<\/a>\n <\/div>\n <\/div>\n <\/div>\n\n\n \n

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Teaching Showcase<\/h2>\n

Concrete enough to borrow, honest enough to revise. Click any card to expand full details.<\/p>\n <\/div>\n

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Teaching Patterns<\/h2>\n

Reusable designs adaptable across courses, disciplines, and comfort levels.<\/p>\n <\/div>\n

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AI as Drafting Partner<\/h3>\n

Generate weak drafts, compare versions, or diagnose structure before human revision begins.<\/p>\n <\/article>\n

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AI as Debate Partner<\/h3>\n

Challenge, interrogate, or improve AI-generated claims. Builds critical reasoning under pressure.<\/p>\n <\/article>\n

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AI as Simulator<\/h3>\n

Practice interviews, troubleshooting, role-play, or design review in low-stakes environments.<\/p>\n <\/article>\n

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AI as Feedback Layer<\/h3>\n

Use formative AI feedback before instructor evaluation<\/a> to reduce surface-level revision loops.<\/p>\n <\/article>\n

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AI as Object of Critique<\/h3>\n

Examine bias<\/a>, labor, surveillance, authorship, or expertise embedded in AI systems.<\/p>\n <\/article>\n

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AI as Workflow Tool<\/h3>\n

Build rubrics, cases, examples, lesson plans, and instructional supports faster.<\/p>\n <\/article>\n <\/div>\n <\/div>\n\n \n

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Start Here<\/h2>\n

Different visitors arrive with different needs. Give yourself a door.<\/p>\n <\/div>\n

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I am new to AI in teaching.<\/h3>\n

Start with one low-risk classroom activity, a sample syllabus statement, and a short student conversation guide.<\/p>\n <\/article>\n

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I am worried about cheating.<\/h3>\n

Start with assignment redesign, process evidence, transparency<\/a> policies, and reflective disclosure.<\/p>\n <\/article>\n

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I want to redesign an assignment.<\/h3>\n

Use the LEAN Teaching Loop and submit one course problem for feedback from the AI Institute.<\/p>\n <\/article>\n

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I want students to use AI well.<\/h3>\n

Start with prompt<\/a> literacy, verification habits, citation norms, and judgment-focused reflection activities.<\/p>\n <\/article>\n <\/div>\n <\/div>\n\n \n

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House Rules<\/h2>\n

Practical, transparent, human-centered, and shareable.<\/p>\n <\/div>\n

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1<\/div>\n
Learning before tools<\/strong>AI use should serve clear intellectual, professional, or ethical goals \u2014 not replace them.<\/span><\/div>\n <\/div>\n
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2<\/div>\n
Transparency over surveillance<\/strong>We prefer clear expectations, disclosure, and reflection over hidden policing.<\/span><\/div>\n <\/div>\n
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3<\/div>\n
Human judgment remains central<\/strong>Students and instructors remain responsible for claims, evidence, interpretation, and consequences.<\/span><\/div>\n <\/div>\n
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4<\/div>\n
Access matters<\/strong>Teaching designs should consider uneven student access, disability, language, cost, and confidence.<\/span><\/div>\n <\/div>\n
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5<\/div>\n
Experiments should be shareable<\/strong>Small documented improvements are more useful than private perfection.<\/span><\/div>\n <\/div>\n <\/div>\n <\/div>\n\n \n
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Add to the Workbench<\/div>\n

Share what you’re trying.<\/h2>\n

Have a classroom experiment, assignment, policy, prompt, failure, reflection, or question? The AI Institute is especially interested in small, practical teaching moves that others can adapt.<\/p>\n

\n Submit a Teaching Item<\/a>\n Request a Consultation<\/a>\n Join the Teaching Community<\/a>\n <\/div>\n <\/div>\n\n<\/div>\n\n