# TeachMateGPT: A Multi-Agent Knowledge-Grounded Framework for Pedagogical Assessment Generation from Science Curriculum Materials

arXiv: https://arxiv.org/abs/2608.13708 · HTML: https://mlsanity.com/paper/2608.13708/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation.
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 13 Aug 2026.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)

## Builds on (most cited first, 9 of 9)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity](https://mlsanity.com/paper/2403.14403/) (2024)
- [Enhancing textual textbook question answering with large language models and retrieval augmented generation](https://mlsanity.com/paper/2402.05128/) (2024)
- [Generating AI Literacy MCQs: A Multi-Agent LLM Approach](https://mlsanity.com/paper/2412.00970/) (2024)
- [Small Models, Big Support: A Local LLM Framework for Educator-Centric Content Creation and Assessment with RAG and CAG](https://mlsanity.com/paper/2506.05925/) (2025)
- [KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation](https://mlsanity.com/paper/2505.07618/) (2025)
- [NCTB-QA: A Large-Scale Bangla Educational Question Answering Dataset and Benchmarking Performance](https://mlsanity.com/paper/2603.05462/) (2026)
- [CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation](https://mlsanity.com/paper/2604.03926/) (2026)

## Abstract (arXiv)

Automatically generating textbook-grounded assessment items can reduce science teachers' workload, but existing retrieval-augmented generation (RAG) systems rely on flat retrieval, support only single-question generation, lack safeguards against weak evidence, and are ill-suited to low-resource, board-exam-structured curricula. We address these limitations with TeachMateGPT, a multi-agent system contributing four advances to curriculum-grounded science-assessment authoring. (i) COPE, a hierarchical knowledge base replacing token-window chunking with a multi-resolution index that segments documents along syllabus structure and links them at three granularities via a traversable graph-based lineage, matching evidence to each topic's instructional level. (ii) A staged, fail-closed agent pipeline replacing one-shot retrieve-then-generate: routing gates search, retrieval fuses dense and lexical evidence under a coverage gate that withholds generation on insufficient evidence, and specialist agents draft objective and constructed-response items. (iii) SAVER, a source-attributed verification protocol scoring faithfulness, relevance, and hallucination risk against retrieved evidence, applying stricter grounding checks across each creative question's four sub-parts, paired with teacher-in-the-loop evaluation rather than automatic filtering. (iv) NCTB-SciGen8, a curriculum-grounded dataset of 198 items (143 multiple-choice, 55 creative questions) spanning all 14 chapters of the NCTB Class 8 science textbook, produced by the pipeline and rated by three practicing teachers. TeachMateGPT raises faithfulness (0.68 rightarrow 0.96) and answer relevancy (0.60 rightarrow 0.89) over a vanilla RAG baseline.