Designing a Human Oversight Framework for Generative AI in Computer Science Education

Abstract

Generative AI (GenAI) tools have been adopted across all levels of computer science education, from K-12 through graduate study, offering learners opportunities to improve learning efficiency and support creative problem solving. However, this rapid adoption has outpaced critical evaluation: students demonstrate high acceptance of AI-generated outputs but significant difficulty correcting AI-generated code compared to instructor-designed tasks, an asymmetry that reflects a deeper structural gap where students are taught to use GenAI tools, but not to critically evaluate or correct what these tools produce. Existing learning theories such as constructivism, sociocultural theory, and connectivism presuppose human-centered epistemic agency and do not account for GenAI's role in simulating reasoning or co-constructing meaning with learners. At the institutional level, GenAI governance has been active but limited in scope: policy analysis shows that guidelines remain largely prescriptive and output-focused, emphasizing academic integrity, privacy, and security while offering little direction on how students should reason through, reflect on, or take responsibility for AI-assisted decisions. This rapid review synthesizes findings across GenAI adoption, human-AI collaboration, and institutional governance in computer science education to clarify the conditions under which human oversight should occur. We propose a framework, organized around task classification, output verification, confidence checking, correction and revision, and learning reflection that operationalizes a human-in-the-loop approach to GenAI use, positioning learners and educators as the final authority over AI-generated outputs rather than passive recipients of them.