The Impact of AI-Supported Case-Based Learning on Students' Self-Efficacy

Tiara Nove Ria(1), Rudi Hartono(2), Sri Wahyuni(3),

DOI: https://doi.org/10.46843/jpm.v4i4.588


(1) Language Education, Faculty of Arts and Languages, Universitas Negeri Semarang, Indonesia
(2) English Literature, Faculty of Arts and Languages, Universitas Negeri Semarang, Indonesia
(3) English Literature, Faculty of Arts and Languages, Universitas Negeri Semarang, Indonesia

Abstract

This study investigates the impact of Artificial Intelligence (AI)-supported Case-Based Learning (CBL) on students' academic self-efficacy in English for Business learning of economics students at STIE Semarang. The research was motivated by the growing demand for student-centered approaches that integrate real-world problem-solving with intelligent technological support to enhance engagement, motivation, and confidence. The study aims to determine whether incorporating AI tools into CBL could effectively strengthen students' academic self-efficacy. A total of 112 students participated in a three-month intervention: 53 in a control group receiving traditional CBL and 59 in an experimental group using AI-powered tools, particularly ChatGPT, to support case discussions, generate feedback, and facilitate reflection. Using a quantitative design, data were collected through pre- and post-tests with an academic-specific self-efficacy scale. The findings revealed a significant improvement in the experimental group's self-efficacy, attributed to adaptive feedback, dynamic modelling, and emotionally responsive AI support aligned with Bandura's four sources of self-efficacy. This study concludes that pedagogically grounded AI integration can transform CBL by enhancing students’ confidence, motivation, and autonomy, thereby advancing technology-enhanced language learning research and practice.

Keywords

AI; Case-Based learning; ESP; Self-Efficacy

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References

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