TutorUp: What If Your Students Were Simulated? Training Tutors to Address Engagement Challenges in Online Learning

要旨

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.

著者
Sitong Pan
Zhejiang University, Hangzhou, China
Robin Schmucker
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Bernardo Garcia Bulle Bueno
MIT, Cambridge, Massachusetts, United States
Salome Aguilar Llanes
MIT, Cambridge, Massachusetts, United States
Fernanda Albo Alarcón
ITAM, CDMX, Mexico
Hangxiao Zhu
Texas A&M University, College Station, Texas, United States
Adam Teo
Texas A&M University, College Station, Texas, United States
Meng Xia
Texas A&M University, College Station, Texas, United States
DOI

10.1145/3706598.3713589

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713589

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: AI in the Classroom

G303
6 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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