Enhancing programming education: The impact of AI-based pedagogical agents on student self-efficacy, engagement, and learning outcomes
Year: 2025
Type: article
Abstract: In recent years, learning programming has been a challenge for both learners and educators. How to enhance student engagement and learning outcomes has been a significant concern for researchers. This study examines the effects of AI-based pedagogical agents on students’ learning experiences in programming courses, focusing on web game development using JavaScript and Phaser. We developed two pedagogical agents: a debugger that provides context-sensitive assistance and a chatbot that offers guidance based on pre-configured Phaser knowledge. The experiment involved 60 sophomore students from a university in southern Taiwan, and they were randomly assigned to control and experimental groups. The study measured changes in students’ self-efficacy (creative, persuasive, and change dimensions), JavaScript proficiency, debugging efficiency, and overall engagement. Results show significant improvements in all self-efficacy dimensions and JavaScript proficiency for the experimental group. Debugging log analysis showed that students who used the pedagogical agents were able to fix bugs more quickly and more effectively. Qualitative analysis of student reflections indicated more positive learning experiences and deeper engagement with learning content in the experimental group. These findings suggest that integrating AI-based pedagogical agents can enhance students’ learning experiences in programming courses.
Author: Hao-Chiang Koong Lin, Chun-Hsiung Tseng, Nian-Shing Chen
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6908756697