Teaching & Learning
A living studio for teaching with, against, and about AI. Experiments, assignment designs, policies, reflections, tools, and practices from across Clarkson — updated as the community builds.
What this space is for
A practical home for faculty, staff, and students who want better teaching rather than louder panic.
Teach with AI
Use AI for feedback, drafting, simulation, analysis, tutoring, design, coding, or reflection.
Teach about AI
Help students understand AI’s social, ethical, technical, historical, and professional consequences.
Teach against misuse
Design assignments that reduce shallow automation and invite real intellectual work.
Teach beyond panic
Move from detection and prohibition toward clear expectations and stronger student agency.
TRAIL
Transparent · Responsible · Auditable · Integrative Learning
If AI contributes materially to student work, the student must be able to explain how the work came into being.
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.
The framework centers on two documents: a Minimum Institutional Standard that sets the floor for AI use across courses, and a Lab Notebook students keep during AI-assisted sessions—recording what the AI contributed, what the student decided, and what still needs checking.
TRAIL Faculty Introduction
A concise overview of the TRAIL framework—what it is, how it works, and how to implement it without policing students.
↓ Download Deck ▶ Watch VideoTRAIL Student Guide
A student-facing overview of TRAIL—what is expected, how to keep a Lab Notebook, and why accountability matters in the AI era.
↓ Download Deck ▶ Watch VideoTeaching Showcase
Concrete enough to borrow, honest enough to revise. Click any card to expand full details.
Teaching Patterns
Reusable designs adaptable across courses, disciplines, and comfort levels.
AI as Drafting Partner
Generate weak drafts, compare versions, or diagnose structure before human revision begins.
AI as Debate Partner
Challenge, interrogate, or improve AI-generated claims. Builds critical reasoning under pressure.
AI as Simulator
Practice interviews, troubleshooting, role-play, or design review in low-stakes environments.
AI as Feedback Layer
Use formative AI feedback before instructor evaluation to reduce surface-level revision loops.
AI as Object of Critique
Examine bias, labor, surveillance, authorship, or expertise embedded in AI systems.
AI as Workflow Tool
Build rubrics, cases, examples, lesson plans, and instructional supports faster.
Start Here
Different visitors arrive with different needs. Give yourself a door.
I am new to AI in teaching.
Start with one low-risk classroom activity, a sample syllabus statement, and a short student conversation guide.
I am worried about cheating.
Start with assignment redesign, process evidence, transparency policies, and reflective disclosure.
I want to redesign an assignment.
Use the LEAN Teaching Loop and submit one course problem for feedback from the AI Institute.
I want students to use AI well.
Start with prompt literacy, verification habits, citation norms, and judgment-focused reflection activities.
House Rules
Practical, transparent, human-centered, and shareable.
Share what you’re trying.
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.