AI-Powered Gamification in Computer Engineering Education: A Mixed-Methods Study on Engagement, Learning Outcomes, and Personalization - Patel, Chirag Rasikbhai; Patel, Sunil; Rathod, Chiragkumar B.; Vagadia, Abhipsa; Modi, Ronak R.; Kumar, Anuj (Journal of Engineering Education Transformations, 2026)
Purpose: Engineering, specifically Computer Engineering education faces the challenge of engaging digital- native learners who require interactivity and personalized feedback, as traditional teaching methods often lack these elements. This study aims to empirically quantify the impact of Artificial Intelligence (AI) and Machine Learning (ML)-driven personalization on learning outcomes, student engagement, and satisfaction in a gamified Computer Engineering learning environment. Design/Methodology/Approach: The research utilized a mixed- methods quasi-experimental design involving 200 computer engineering students. Participants were randomly assigned to an experimental group (n = 100) using an AI-powered gamified platform with adaptive algorithms, or a control group (n = 100) using a traditional Learning Management System (LMS). Data were analysed using one-way ANOVA, hierarchical multiple regression, and mediation analysis, supplemented by qualitative thematic coding of open-ended reflections. Findings: The results demonstrated that AI-powered gamification yielded significant improvements over traditional e-learning, with the experimental group achieving higher post-test scores compared to the control group. Personalization based on scores acted as a significant mediator, accounting for approximately 55% of the effect of gamification on satisfaction. Additionally, technological familiarity was found to moderate the relationship between gamification and engagement. Qualitative analysis revealed that learners valued autonomous motivation and adaptive feedback. Originality/Value: While previous studies often examine gamification and AI independently, this research addresses the lack of empirical support for their combined holistic outcomes in computer engineering education. The study contributes to Self- Determination Theory by linking algorithmic feedback with intrinsic motivation, providing practical implications for designing scalable, adaptive educational models.