pigeonhole

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Inspiration

Generative AI is now a default study tool. The issue is not use vs. no use, but alignment: general-purpose chatbots do not know a course’s syllabus, policies, or pedagogical intent, and they often optimize for answer completion rather than conceptual understanding. We built pigeonhole to make AI support instruction by design, with instructor control and learning-first interaction.

What it does

pigeonhole is a course-aligned AI TA that provides scaffolded help rather than direct solutions. For students, it guides problem solving through questions, hints, and stepwise decomposition. After a session, it produces a structured recap (what was confusing, how understanding changed, what to review) and enables quick review through a lightweight quiz. For instructors, it offers configurable guardrails (what help is allowed, what topics are off-limits) and aggregated insight into common misconceptions and friction points.

How we built it

We implemented a web app with distinct student and instructor workflows:

Instructor configuration: course context, policy constraints, and approved hint scaffolds.
Student chat: a guided dialogue loop that prioritizes questioning, partial hints, and checking student work before progressing.
Learning artifacts: automatic session summaries and exportable PDFs for later study or forum posting.
Analytics: aggregation of anonymized interaction signals to surface high-level confusion patterns.

Challenges we ran into

Defining “helpful but not too helpful” in a way that is consistent across topics and student behaviors.
Preventing accidental answer leakage while still allowing students to make progress under time pressure.
Translating open-ended chat transcripts into concise, accurate recaps without introducing new errors.
Designing instructor controls that are powerful but simple enough to set up quickly.

Accomplishments that we're proud of

Built an end-to-end experience that serves both students and instructors, not just a chatbot.
Implemented policy-aware scaffolding that reliably pushes for student work and reasoning before hints escalate.
Produced reusable learning artifacts (recaps + PDF export) that turn chat into study material.

What we learned

Guardrails are most effective when they are explicit, instructor-configurable, and enforced at the interaction level, not just as a disclaimer.
Students respond well to structured guidance when it reduces cognitive load (first step, plan, checkpoints).
Instructor trust depends on transparency, control, and clear evidence of learning alignment.

What's next for pigeonhole

Richer instructor tooling: assignment-specific rubrics, granular hint templates, and per-topic constraints.
Stronger analytics: confusion clustering by concept, time-to-first-progress, and pre/post understanding signals.
Personalized study support: adaptive review plans before exams, spaced repetition prompts, and targeted practice sets.
Multimodal outputs: short personalized explanation videos based on what a student struggled with.
Deeper integration: LMS and course content connections to make setup and compliance seamless.