Design task-sensitive institutional AI policies rather than blanket permitted/prohibited distinctions¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article argues that because faculty and administrative staff use AI while also holding stronger integrity concerns and norm endorsement, policy "requires a task-sensitive design, with different rules for idea generation, text editing, material preparation, assessment, fully AI-generated answers, and disclosure of AI use". It recommends that university policy address practices that have already developed rather than rest on assumptions about future adoption, and that stakeholder-aware policies address academic integrity requirements and transparent disclosure.

Design Implications¶

Context¶

Requirements¶

• Policy must differentiate rules by task type, including assessment and disclosure of AI use

Constraints¶

• The article states the cross-sectional data do not demonstrate causal effects of policy on trust

Target Learners¶

• university students
• faculty
• administrative staff

Target Learning Goals¶

• responsible and institutionally appropriate AI use in learning, teaching, and administration

Related Strategies¶

• Designate permitted AI use per assignment using color-coded levels (red, yellow, green, blue)
• Institutions should respond to generative AI in education with guided critical engagement rather than prohibition
• Foster communities of practice for AI governance instead of rigid top-down policies
• Prepare teachers to use AI expeditiously and provide AI-text detectors for evaluation, alongside updated academic-integrity rules
• Institutional governance should establish practical conditions supporting AI literacy, learner agency, and critical evaluation rather than relying on prohibition and detection
• Teach differentiated trust in knowledge sources as part of the social-institutional dimension of science education
• Teach the pedagogical rationale behind AI policies rather than only stating rules
• Graduate the AI policy across the curriculum so that permitted AI use tracks each course's pedagogical purpose
• Institutions should define generative-AI prohibitions by function, distinguish generation from format conversion, and require validated evidential methods before sanctions

Examples¶

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Key Sources¶

• Braun, Y. S., & Khafizov, S. M. (2026). The AI Adaptation Gap in Higher Education: Students, Faculty, and Administrative Staff: AI Use, Trust, and Institutional Policy at a University Specializing in Teacher Education. https://arxiv.org/abs/2608.25063. doi:10.48550/arxiv.2608.25063