The Impact of AI-Supported Case-Based Learning on Students' Self-Efficacy - Tiara Nove Ria; Rudi Hartono; Sri Wahyuni (Jurnal Pemberdayaan Masyarakat, 2025)
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.