Develop clear institutional GenAI policies, communicate them, raise awareness, and promote digital literacy¶

Strategy · All strategies
Evidence · 2 claims (2 for) · 2 studies (1 associational, 1 qualitative), q2 · 0 of 2 report an effect size · 2 claims rest on one study

Description¶

The article recommends that institutions respond to student confusion about GenAI by developing clear institutional policies, communicating and adhering to them, creating awareness, and promoting digital literacy. It states that "developing clear institutional policies, communicating and adhering to the policies, creating awareness, and promoting digital literacy seem to be essential." It further cautions that having a policy is not sufficient: policies must be comprehensive, clear, and well communicated, and rules must be reinforced and their effectiveness continuously assessed.

Design Implications¶

Context¶

Requirements¶

• Policies that are comprehensive, clear, and well communicated to all stakeholders
• Continuous reinforcement and assessment of policy effectiveness

Constraints¶

• The article notes that with existing policies students found the rules vague and faculty lacked training in enforcing them

Target Learners¶

• undergraduate students in higher education
• higher education instructors and institutions

Target Learning Goals¶

• ethical use of generative AI
• academic integrity

Claims¶

• Most undergraduate students in computer science–related programs are unaware of their institution's GenAI regulations (62.25% not aware; 25.83% aware) [+W]
• Academic integrity was the panel's most frequently cited GenAI policy concern, requiring clear definitions of misconduct and enforceable consequences [+W]

Related Strategies¶

• Institutional governance should establish practical conditions supporting AI literacy, learner agency, and critical evaluation rather than relying on prohibition and detection
• Institutions should respond to GenAI with clear governance frameworks and structured professional training programs for staff.
• Build ethical AI integration on four coordinated levels: faculty professional development, ethics curricula, clear institutional guidelines, and interdisciplinary updating
• Institutional integrity programs combining ethical AI-use guidelines, clear definitions of dishonesty, and risk-awareness education
• Implement preventative measures: student awareness programs and regular professional development training for educators
• Prevent AI-assisted plagiarism through student education, signed declarations, draft submissions, clear guidelines, and close monitoring

Examples¶

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Key Sources¶

• Feyisa Mulisa and Tesfa Mezgebu. (2026). Tools facilitate cheating, or partner supports learning? GenAI and academic integrity issues from students’ perspectives. International Journal for Educational Integrity. https://doi.org/10.1007/s40979-026-00231-z