mlsanityData to 14 Oct 2026

Personalizing Student-Agent Interactions Using Log-Contextualized Retrieval-Augmented Generation (RAG)

arXiv 2505.17238 ↗First posted 22 May 2025cs.CLcited by 6

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

also in 1 more
Abstract (arXiv)

Collaborative dialogue offers rich insights into students' learning and critical thinking, which is essential for personalizing pedagogical agent interactions in STEM+C settings. While large language models (LLMs) facilitate dynamic pedagogical interactions, hallucinations undermine confidence, trust, and instructional value. Retrieval-augmented generation (RAG) grounds LLM outputs in curated knowledge, but requires a clear semantic link between user input and a knowledge base, which is often weak in student dialogue. We propose log-contextualized RAG (LC-RAG), which enhances RAG retrieval by using environment logs to contextualize collaborative discourse. Our findings show that LC-RAG improves retrieval over a discourse-only baseline and enables our collaborative peer agent, Copa, to deliver relevant, personalized guidance that supports students' critical thinking and epistemic decision-making in the collaborative computational modeling environment C2STEM.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 15 of its 45 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

15 of 45 references.

References · 15

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  5. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
  6. 2023State of What Art? A Call for Multi-Prompt LLM Evaluationcited by 310
  7. 2024Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?cited by 232
  8. 2024Unfamiliar Finetuning Examples Control How Language Models Hallucinatecited by 87
  9. 2024Towards Responsible Development of Generative AI for Education: An Evaluation-Driven Approachcited by 54
  10. 2024Enhancing LLM-Based Feedback: Insights from Intelligent Tutoring Systems and the Learning Sciencescited by 37
  11. 2024A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Sciencecited by 31
  12. 2023A Large Language Model Approach to Educational Survey Feedback Analysiscited by 14
3 more
  1. 2024Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse Analysiscited by 7
  2. 2024RAMO: Retrieval-Augmented Generation for Enhancing MOOCs Recommendationscited by 6
  3. 2024Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedbackcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2026Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directionscited by 14
  2. 2025Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrievalcited by 11
  3. 2025A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agentscited by 7
  4. 2026Evidence-Decision-Feedback: Theory-Driven Adaptive Scaffolding for LLM Agentscited by 3
  5. 2026Using Large Language Models to Detect Socially Shared Regulation of Collaborative Learningcited by 1
  6. 2026A Theory-Guided LLM Pedagogical Agent for STEM+C Scaffolding Without Over-Reliancecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style
  2. 2025From Misunderstandings to Learning Opportunities: Leveraging Generative AI in Discussion Forums to Support Student Learning ↗
  3. 2025Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning
  4. 2025Fine-tuning with RAG for Improving LLM Learning of New Skills
  5. 2025EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework
  6. 2025Interact-RAG: Reason and Interact with the Corpus, Beyond Black-Box Retrieval
  7. 2025Simulating Students with Large Language Models: A Review of Architecture, Mechanisms, and Role Modelling in Education with Generative AI
  8. 2026RAGShaper: Eliciting Sophisticated Agentic RAG Skills via Automated Data Synthesis