TutorUp: What If Your Students Were Simulated? Training Tutors to Address Engagement Challenges in Online Learning
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Abstract (arXiv)
With the rise of online learning, many novice tutors lack experience engaging students remotely. We introduce TutorUp, a Large Language Model (LLM)-based system that enables novice tutors to practice engagement strategies with simulated students through scenario-based training. Based on a formative study involving two surveys (N1=86, N2=102) on student engagement challenges, we summarize scenarios that mimic real teaching situations. To enhance immersion and realism, we employ a prompting strategy that simulates dynamic online learning dialogues. TutorUp provides immediate and asynchronous feedback by referencing tutor-students online session dialogues and evidence-based teaching strategies from learning science literature. In a within-subject evaluation (N=16), participants rated TutorUp significantly higher than a baseline system without simulation capabilities regarding effectiveness and usability. Our findings suggest that TutorUp provides novice tutors with more effective training to learn and apply teaching strategies to address online student engagement challenges.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2025.
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Citing papers per year: 2025: 2; 2026: 9.
11 papers.
- 2026Simulated Students in Tutoring Dialogues: Substance or Illusion?cited by 14
- 2026A Survey on LLM-based Conversational User Simulationcited by 14
- 2025Simulating Students with Large Language Models: A Review of Architecture, Mechanisms, and Role Modelling in Education with Generative AIcited by 5
- 2025Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Stylecited by 4
- 2026Simulating Human Memory with Language Modelscited by 2
- 2026DrawSim-PD: Simulating Student Science Drawings to Support NGSS-Aligned Teacher Diagnostic Reasoningcited by 1
- 2026AI Meets Mathematics Education: A Case Study on Supporting an Instructor in a Large Mathematics Class with Context-Aware AIcited by 0
- 2026DeepTutor: Towards Agentic Personalized Tutoringcited by 0
- 2026Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulatorscited by 0
- 2026AgentSchool: An LLM-Powered Multi-Agent Simulation for Educationcited by 0
- 2026Simulating Students' Java Programming Errors with Large Language Modelscited by 0
- 2026Simulating Students' Java Programming Errors with Large Language Models12 Jun 2026
- 2026AgentSchool: An LLM-Powered Multi-Agent Simulation for Education28 May 2026
- 2026Simulating Human Memory with Language Models25 May 2026
- 2026Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators12 May 2026
- 2026A Survey on LLM-based Conversational User Simulation27 Apr 2026
- 2026DeepTutor: Towards Agentic Personalized Tutoring10 Apr 2026
- 2026AI Meets Mathematics Education: A Case Study on Supporting an Instructor in a Large Mathematics Class with Context-Aware AI9 Mar 2026
- 2026DrawSim-PD: Simulating Student Science Drawings to Support NGSS-Aligned Teacher Diagnostic Reasoning2 Feb 2026
- 2026Simulated Students in Tutoring Dialogues: Substance or Illusion?7 Jan 2026
- 2025Simulating Students with Large Language Models: A Review of Architecture, Mechanisms, and Role Modelling in Education with Generative AI8 Nov 2025
- 2025Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style25 May 2025