Gen Mentor AI Agent

<div align="center"> <p align="center"> <img src="resources/logo.svg" alt="GenMentor" width="360"/> </p> <p><b>LLM-powered & Goal-oriented Tutoring System</b></p> <p> <a href="https://genmentor.aurax.live">Live app</a> &nbsp;·&nbsp; <a href="https://tianfu.wang/gen-mentor">Website</a> &nbsp;·&nbsp; <a href="https://arxiv.org/pdf/2501.15749">Paper</a> &nbsp;·&nbsp; <a href="https://youtu.be/vTdtGZop-Zc">Video</a> </p> </div>

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GenMentor is the official implementation of LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System (WWW 2025, Industry Track, oral). You tell it where you want to be; a set of coordinated agents refines the goal, finds the gap between it and your background, schedules a path of sessions, writes each session's reading and quiz for you, and rebuilds your learner profile from what you actually did.

This repository is a single Next.js application. There is no account and no server-side database: the learner's archive lives in the browser and can be exported and imported as one JSON file. Agent calls run inside the app's API routes against any OpenAI-compatible endpoint or the Anthropic API.

Paradigms and agents

<div align="center"> <img src="resources/its-paradigms.png" alt="ITS paradigms" width="500"/> </div>

Paradigm	Typical characteristics	Primary focus
Traditional MOOC	Static syllabus; pre-recorded lectures; fragmented learning	Broad access, low personalization
Chatbot ITS	Reactive Q&A; rule/LLM-driven; session-based help	Instant support, limited long-term adaptation
Goal-oriented ITS	Proactive planning; personalized paths; goal-aligned assessments	Targeted skill acquisition, continual adaptation

<div align="center"> <img src="resources/genmentor-framework.png" alt="GenMentor framework" width="700"/> </div>

Agent	Route	What it does
Goal Refiner	POST /api/refine-goal	Turns a vague goal into an actionable one
Skill Mapper + Skill Gap Identifier	POST /api/identify-skill-gap	Maps the goal to skills, infers the learner's level for each
Adaptive Learner Profiler	POST /api/profile	Builds the profile, then rebuilds it after every session with quiz evidence
Learning Path Scheduler	POST /api/schedule-path	Creates, refines and reschedules the session sequence (streamed)
Knowledge Explorer	POST /api/explore-knowledge	Picks the knowledge points a session needs
Knowledge Drafter	POST /api/draft-knowledge	Drafts each point, optionally grounded in web search (streamed)
Document Integrator	POST /api/integrate-document	Synthesizes the drafts into one document (streamed)
Quiz Generator	POST /api/generate-quiz	Writes questions from the document
Learner Feedback Simulator	POST /api/simulate-feedback	Role-plays the learner to critique a path or content
AI Chatbot Tutor	POST /api/tutor	Streams grounded answers during a session

The prompts are the paper's prompts. The verbatim port is preserved at git tag prompts-baseline; later edits are evaluated side by side against it (scripts/eval-prompts.sh, wiki/reviews/).

Quick start

Requirements: Node 24, pnpm 11, and one LLM credential.

make install                      # pnpm install in app/
cp app/.env.example app/.env.local
# set LLM_API_KEY and LLM_BASE_URL (or OPENAI_API_KEY / ANTHROPIC_API_KEY)
make dev                          # http://localhost:3000

Learners can also bring their own key from the app (the key icon in the sidebar): it stays in their browser and is sent with each request, so a public deployment can run without a server key.

LLM_FAST_MODEL and LLM_SMART_MODEL split the work between a cheap model (tutor, quizzes, goal refinement) and a stronger one (skill gap, scheduling, content). Defaults and the other knobs are documented in app/.env.example.

No key at hand? GENMENTOR_LLM_MODE=replay make dev serves the recorded journeys in e2e/fixtures/llm for the sample goal used by the tests.

Deploy

Target	How
Vercel	The public instance at genmentor.aurax.live ships without a server key: visitors add their own provider, key and models in Model settings. To run your own, import the repo with Root Directory app and optionally add the environment variables
Container	docker build -t genmentor app && docker run -p 3000:3000 --env-file app/.env.local genmentor

Repository layout

app/          the Next.js application (routes, agents, prompts, schemas, UI)
e2e/          Playwright journeys, axe checks, screenshot evidence, recorded LLM fixtures
wiki/         product proposal, roadmap, architecture, design principles, API reference, pitfalls
scripts/      gate.sh and verify-ui.sh (acceptance gates), eval-prompts.sh (prompt evaluation)
docs/rebuild/ the plan this rebuild followed

make gate runs the static acceptance gate (layout, docs, size budgets, banned patterns, types, lint, unit tests, build). make verify-ui builds the app, drives every journey in the browser, checks accessibility in both themes and screenshots every page in three viewports. AGENTS.md holds the constraints every contributor, human or agent, works under.

Reproducing the paper

The Python implementation used for the paper (FastAPI backend, Streamlit frontend, LangChain agents) lives on the v0.1-demo branch, also tagged v0.1.0 and paper-python-v1:

git checkout v0.1-demo

Its README describes how to run it. That branch is frozen as the demo version; all further work happens on main, which keeps the same nine agents and the same prompts (see prompts-baseline), rewritten in TypeScript.

Citation

@inproceedings{wang2025llm,
  title={LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System},
  author={Wang, Tianfu and Zhan, Yi and Lian, Jianxun and Hu, Zhengyu and Yuan, Nicholas Jing and Zhang, Qi and Xie, Xing and Xiong, Hui},
  booktitle={Companion Proceedings of the ACM Web Conference},
  year={2025}
}

License

See LICENSE.