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Abstract This systematic review examines how AI-driven tools and innovative pedagogical models influence key outcomes in programming education, with a focus on student engagement, comprehension, retention, and self-regulation. A structured search was conducted in the Scopus database for peer-reviewed journal articles published between 2010 and 2023, and the selection process followed the PRISMA 2020 guidelines. After applying predefined inclusion and exclusion criteria, 29 empirical studies on flipped classrooms, gamification, project-based learning, collaborative learning, and blended learning in programming or computational-thinking courses were included in the review. Across these models, adaptive learning platforms, automated feedback systems, and collaborative tools were found to enhance active learning and support more personalized and self-regulated study. At the same time, the effectiveness of AI-enhanced learning environments depends on careful scaffolding, attention to accessibility, and a balanced use of motivational strategies. For educators, a core implication of the review is that combining AI-based adaptive feedback with structured in-class practice and well-designed collaborative activities can help reduce dropout, sustain motivation, and foster durable programming competencies. The findings offer evidence-informed guidance for designing programming courses that leverage AI and innovative pedagogies to create more engaging and inclusive learning experiences.
Katona et al. (Mon,) studied this question.