Volume 10,Issue 8
Mechanisms and Reform Pathways for Integrating Generative AI into University Programming Education: A Task–Evidence–Assessment Chain Analysis
The integration of generative artificial intelligence (GenAI) into university programming education creates opportunities for timely feedback and differentiated support, but it may also intensify cognitive outsourcing, obscure learning processes, and weaken assessment validity. Using policy-text analysis, purposive literature retrieval, and comparative case analysis, this study reviews authoritative guidance and six empirical studies of GenAI-supported programming learning. The available evidence—predominantly small-sample and short-duration—suggests that GenAI can alter debugging behavior, improve selected code-quality and self-efficacy indicators, and enrich learning support; however, access alone does not consistently improve performance or transfer. Educational effects are conditioned by task structure, cognitive engagement, instructional scaffolding, and assessment design. To explain these conditions, the paper develops a coordinated task–evidence–assessment chain: staged tasks define AI-use boundaries; version histories, tests, and selected dialogues create process evidence; and authentic assessment combines products, process records, oral defense, and transfer tasks. It further proposes outcome redesign, progressive authorization, structured prompting, traceable learning portfolios, assessment reform, and multilevel governance. The framework is a theory-informed design proposition that requires validation in authentic classroom settings.
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