Document, Verify, Explain

A Transparent Accountability Framework for Equitable Generative AI Use in Computer Science Education

Abstract

The rapid adoption of generative AI tools in Computer Science (CS) education has created a tension between their potential to support learning and growing concerns about academic integrity, equity, fairness, and erosion of core skills. Attempts to prohibit or police AI use have proven difficult to enforce, inconsistently applied, and costly in instructional effort, often amplifying inequities arising from unequal prior experience, access, or confidence in AI tools. The main objective of this paper is to present a transparent AI accountability framework that integrates generative AI into CS courses in a structured, auditable, and equitable manner, enabling consistent assessment while promoting responsible use. The framework is built on three principles: explicit expectations for AI use, structured documentation and reflection, and mechanisms for student accountability. This paper presents the framework’s design and reports on its initial feasibility through pilot applications in an introductory programming course and an upper-level CS course. While exploratory in nature, these case studies demonstrate how the framework structures AI-supported problem solving with required logging, verification, and oral explanation, scaffolding responsible AI use for students with diverse preparation levels in the introductory course. In the upper-level course, it supported AI-assisted design, testing, visualization, and formal verification of complex systems. Across both cases, students demonstrated stronger engagement with reasoning, validation, and explanation, while faculty experienced reduced enforcement burden. The results provide a foundational proof-of-concept for scalable, transparent AI integration in CS curricula, offering a structured alternative to detection-based approaches.