AI-CBL: A Technology-Enhanced Learning Model Combining Chatbot and Case-Based Pedagogy to Improve Critical Thinking in Physics Education

Abstract

In response to the pedagogical challenges of traditional physics education and the evolving demands of Education 4.0, this study introduces the AI-CBL model, a hybrid instructional approach integrating Case-Based Learning with an AI-powered chatbot. The research aimed to develop and evaluate the effectiveness of the AI-CBL model in enhancing critical thinking among undergraduate physics students. Employing Research and Development (R&D) within a pretest-posttest control group design framework, the AI-CBL model was implemented through an interactive e-learning platform across two sessions on the topic of electromagnetic induction. Seventy students were divided into two groups: a control group (CBL only) and an experimental group (AI-CBL). Data collection instruments included pre- and post-tests, expert validation sheets, observation protocols, and questionnaires. Expert validation results showed high feasibility (average score of 4.25). The AI-CBL group demonstrated significantly higher critical thinking gains (N-Gain = 0.9838, categorized as High) compared to the control group (N-Gain = 0.5212, categorized as Medium), with a t-test indicating a significant difference (p < 0.001). These results highlight the pedagogical effectiveness of the AI-CBL model in promoting deeper conceptual understanding and critical thinking. Additionally, students reported high levels of engagement, ease of use, and satisfaction with the AI chatbot’s interactive features. The study confirms that the AI-CBL model provides a viable, adaptive, and impactful approach to modern physics education, promoting deeper conceptual understanding and fostering 21st-century skills.