AutoPBL: An LLM-powered Platform to Guide and Support Individual Learners Through Self Project-based Learning

要旨

Self project-based learning (SPBL) is a popular learning style where learners follow tutorials and build projects by themselves. SPBL combines project-based learning’s benefit of being engaging and effective with the flexibility of self-learning. However, insufficient guidance and support during SPBL may lead to unsatisfactory learning experiences and outcomes. While LLM chatbots (e.g., ChatGPT) could potentially serve as SPBL tutors, we have yet to see an SPBL platform with responsible and systematic LLM integration. To address this gap, we present AutoPBL, an interactive learning platform for SPBL learners. We examined human PBL tutors’ roles through formative interviews to inform our design. AutoPBL features an LLM-guided learning process with checkpoint questions and in-context Q&A. In a user study where 29 beginners learned machine learning through entry-level projects, we found that AutoPBL effectively improves learning outcomes and elicits better learning behavior and metacognition by clarifying current priorities and providing timely assistance.

著者
Yihao Zhu
Tsinghua University, Beijing, China
Zhoutong Ye
Tsinghua University, Beijing, China
Yichen YUAN
University of California, Berkeley, Berkeley, California, United States
Wenxuan Tang
School of Management and Engineering, Nanjing, Jiangsu, China
Chun Yu
Tsinghua University, Beijing, China
Yuanchun Shi
Tsinghua University, Beijing, China
DOI

10.1145/3706598.3714261

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714261

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: Innovative Learning Apporaches

G318+G319
7 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
日本語まとめ
読み込み中…