AI-Enhanced Electronic Modules Bridge Mathematical Induction Learning Gap - Sari, Mayang; Lestari, Isnawati Lujeng; Uripno, Gusti; Suprihatiningsih, Siti; Hariyanti, Firda (Universitas Tidar, 2025)
This study develops and validates AI-integrated electronic modules (e-modules) for instruction in mathematical induction. Mathematical induction requires sophisticated logical reasoning, yet students often struggle to master this proof technique. The research employed a systematic three-phase approach: analysis of learning challenges, design of e-module architecture with AI integration, and development using the Canva platform with integrated AI tools including Wolfram Alpha, Interactive Theorem Provers, and ChatGPT. Validation used a 4-point Likert scale across four primary dimensions, including material aspect, media aspect, language aspect, and learning aspect. Unlike traditional materials, these AI-integrated e-modules provide personalized feedback, adaptive learning pathways, and real-time diagnostic support tailored to individual student needs. Results demonstrate the potential of AI-enhanced educational technology in bridging the gap between complex mathematical reasoning and student comprehension.