Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2025

Abstract

This study explores the integration of a Large Language Model (LLM) based chatbot, PromptTutor, into flipped classrooms (FC) for undergraduate Computer Science (CS) education. PromptTutor is designed to provide personalized, immediate feedback to support student learning in FC by incorporating reflective learning and scaffolding strategies. The traditional FC typically lacks this immediate feedback during the pre-class learning phase, risking decreased student motivation according to existing literature. This study examines if students improve in learning outcomes and motivation after using PromptTutor. Through a controlled crossover experiment with 50 students, the study demonstrates statistically significant improvements in students' quiz performance and motivation compared to traditional FC. Our work underscores the potential of LLM-based tools in addressing FC challenges, offering actionable insights for educators and institutional leaders in technology-enhanced learning environments.

Keywords

computer science education, flipped classrooms, LLMs, programming design patterns, prompt engineering

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Educational Methods

Publication

ITiCSE 2025: Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education, Nijmegen, June 27 - July 2

Volume

1

First Page

445

Last Page

451

ISBN

9798400715679

Identifier

10.1145/3724363.3729095

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3724363.3729095

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