Structuring Transparency: Developing Domain-Specific Generative AI Declaration Frameworks in Higher Education

2026-06-11

AI Summary

Current binary declarations of generative AI use fail to accurately capture students’ nuanced engagement across diverse academic tasks, thereby hindering both academic integrity and the development of AI literacy. This work proposes a domain-specific disclosure framework for computer science education, integrating a generative AI usage taxonomy with educational assessment theory to address writing and programming assignments. The framework guides students—according to their cognitive developmental stage—to transparently report AI involvement in specific phases such as writing planning, content generation, and code refinement. Moving beyond the simplistic “used/not used” dichotomy, this structured, task-oriented declaration mechanism effectively distinguishes legitimate assistance from academic misconduct, fosters student metacognition, and lays the groundwork for institutions to implement honest assessment practices and future workplace standards for AI documentation.

Abstract

As Generative AI (GenAI) disrupts higher education, institutions increasingly require students to declare AI use. However, generic, binary declarations (e.g., "I used GenAI") fail to capture the nuanced application of these tools in different academic tasks. Establishing transparency is key to protecting academic integrity, promoting AI literacy, and shifting the focus from policing to professional practice. In response, this paper contributes a design artefact and an accompanying position: a framework of two task-specific declaration structures, one for writing-focused activities and one for coding assessments, developed for a Computer Science department on the basis of an existing taxonomy of GenAI usage, together with an argument that task-specific disclosure is needed to move beyond binary declarations. By categorising AI usage across specific cognitive and developmental stages, such as structural planning vs. Textual Content Generation, or code improvement vs. code generation, the framework encourages students to reflect on their own learning process and clarifies the boundary between acceptable assistance and academic misconduct. We propose this domain-specific approach as a foundation for fostering more honest assessment in Computer Science and other disciplines, aiming to better prepare students for professional environments where documenting GenAI workflows might be an essential job requirement.

Problem

• Generative AI
• academic integrity
• declaration framework
• higher education
• AI transparency

Innovation

• Generative AI
• domain-specific framework
• task-specific declaration
• academic integrity
• AI literacy