Hybrid LLM-Embedded Dialogue Agents for Learner Reflection: Designing Responsive and Theory-Driven Interactions
On the map: LLM agents and tool use → Educational agents and math problem solving
Abstract (arXiv)
Dialogue systems have long supported learner reflections, with theoretically grounded, rule-based designs offering structured scaffolding but often struggling to respond to shifts in engagement. Large Language Models (LLMs), in contrast, can generate context-sensitive responses but are not informed by decades of research on how learning interactions should be structured, raising questions about their alignment with pedagogical theories. This paper presents a hybrid dialogue system that embeds LLM responsiveness within a theory-aligned, rule-based framework to support learner reflections in a culturally responsive robotics summer camp. The rule-based structure grounds dialogue in self-regulated learning theory, while the LLM decides when and how to prompt deeper reflections, responding to evolving conversation context. We analyze themes across dialogues to explore how our hybrid system shaped learner reflections. Our findings indicate that LLM-embedded dialogues supported richer learner reflections on goals and activities, but also introduced challenges due to repetitiveness and misalignment in prompts, reducing engagement.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. Builds on 11 of its 39 references that are in the map. First posted on arXiv 24 Feb 2026.
Builds on
11 of 39 references.
References · 11
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 428
- 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 265
- 2025Training LLM-based Tutors to Improve Student Learning Outcomes in Dialoguescited by 40
- 2023ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Eventscited by 31
- 2024Evaluating the Effectiveness of LLMs in Introductory Computer Science Education: A Semester-Long Field Studycited by 26
- 2024Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classroomscited by 20
- 2024Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape Appcited by 14
- 2024Combining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Supportcited by 11
- 2025Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learningcited by 5
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- 2025Dialogic Pedagogy for Large Language Models: Aligning Conversational AI with Proven Theories of Learning
- 2025Simulating Students with Large Language Models: A Review of Architecture, Mechanisms, and Role Modelling in Education with Generative AI
- 2025A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents
- 2023The Use of Multiple Conversational Agent Interlocutors in Learning
- 2025Building Knowledge from Interactions: An LLM-Based Architecture for Adaptive Tutoring and Social Reasoning
- 2025EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework
- 2025Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents
- 2025Playpen: An Environment for Exploring Learning Through Conversational Interaction