Who Am I? History-Aware Profiles for Student Simulation in Tutoring Dialogues

arXiv:2605.30051 · cs.CL, cs.CY · Submitted 2026-05-28 · Read on arXiv

cs.CL, cs.CY

Submitted: 2026-05-28

Updated: 2026-08-30

Terminology

• GRPO — 27× in this paper · explained in 6 episodes
• Ablation — 10× in this paper · explained in 1 episode
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Ablation study — 5× in this paper · explained in 1 episode
• Alignment — 4× in this paper · explained in 1 episode
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Commentary — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 5 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Scaffolding — 1× in this paper · explained in 3 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• Developing Authentic Simulated Learners for Mathematics Teacher Learning: Insights from Three Approaches with Large Language Models
• The Llama 3 Herd of Models
• LoRA: Low-Rank Adaptation of Large Language Models
• Efficient Memory Management for Large Language Model Serving with PagedAttention
• AI tutoring can safely and effectively support students: An exploratory RCT in UK classrooms
• Which Type of Students can LLMs Act? Investigating Authentic Simulation with Graph-based Human-AI Collaborative System
• Explainable Few-shot Knowledge Tracing
• TeachLM: Post-Training LLMs for Education Using Authentic Learning Data
• Simulated Students in Tutoring Dialogues: Substance or Illusion?
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise
• Embracing Imperfection: Simulating Students with Diverse Cognitive Levels Using LLM-based Agents
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

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