AI in Programming Education: A Narrative Review and Framework for Hybrid Instruction

Abstract

Artificial intelligence (AI) has renewed debate about the relationship between personalized assistance and programming education led by instructors. Comparisons are complicated, however, by the treatment of adaptive learning, generative tutoring, student satisfaction, and demonstrated learning as interchangeable constructs. The article presents a narrative review of selected programming and contextual education literature published through 2025, supported by targeted verification of bibliographic details and individual claims. The synthesis examines student engagement, academic performance, educator perceptions, scalability, and implementation challenges while distinguishing evidence from programming contexts from contextual and conceptual contributions. Three recurring interpretive problems emerge: equating technology use with cognitive engagement, inferring independent competence from performance supported by AI, and presenting hybrid instruction as effective without specifying the coordination of instructional responsibility. Drawing on dynamic personalization and distributed scaffolding, the article proposes a framework for hybrid instruction organized around evidence of understanding. Movement between automated support, human dialogue, and independent work depends on observable learner performance. The framework connects diagnosis, adaptive practice, explanation, collaborative review, unaided transfer, and instructional adjustment. Four research propositions and a comparative evaluation agenda address learning, teacher agency, and equitable participation. The contribution is an analytical and design framework rather than a pooled effectiveness estimate or a validated intervention. Educational value should be assessed through independent learning and accountable instructional coordination, not automation alone.

Index Terms

Adaptive learning; artificial intelligence in education; educational equity; hybrid instruction; programming education; scaffolding

How to cite

Spector, A. (2026). “AI in Programming Education: A Narrative Review and Framework for Hybrid Instruction.” International Journal of Culture and Education, 3(1). DOI pending.