Harnessing Generative Artificial Intelligence in Computer Science Education: Pedagogical Innovation, Ethical Responsibility, and the Future of Assessment

Abstract

Artificial Intelligence (AI), particularly Generative AI (GenAI), is profoundly reshaping assessment practices in computer science education by enabling automation, scalability, and personalized feedback mechanisms. AI-enhanced tools facilitate adaptive testing, real-time learner support, and data-driven insights that foster deeper engagement and learning outcomes. However, the integration of AI also raises critical concerns related to academic integrity, algorithmic bias, transparency, and ethical implications of AI-driven evaluation. This theoretical paper critically examines the opportunities and complexities of GenAI-enabled assessment in CS education. Drawing on contemporary literature, pedagogical theory, and emerging use cases, the paper explores how GenAI can enhance student engagement, support equitable learning, and relieve instructional burden—while also highlighting the need for ethical safeguards and pedagogically grounded frameworks. It argues that the successful integration of GenAI depends not solely on technological capability but on deliberate, human-guided design that upholds transparency, fairness, and educational purpose. By advancing a nuanced and critically reflective perspective, this paper contributes to the evolving discourse on AI in education and provides actionable insights for educators, researchers, and policymakers seeking to harness GenAI responsibly in the future of computer science education.