Institutional governance should establish practical conditions supporting AI literacy, learner agency, and critical evaluation rather than relying on prohibition and detection¶

Strategy · All strategies
Evidence · 4 claims (3 for, 1 mixed) · 4 studies (1 review, 1 associational, 1 qualitative, 1 theoretical), q1–q2 · 0 of 4 report an effect size · 4 claims rest on one study

Description¶

The article recommends that effective institutional governance extend beyond prohibition, detection, or general guidance toward establishing practical conditions that support AI literacy, learner agency, privacy protection, critical evaluation, and avoidance of overdependence. Institutions act as an enabling and governing layer through curriculum and assessment structures, policies, infrastructure, professional development, procurement, data-protection arrangements, and accountability mechanisms. The authors argue that without adequate institutional support, responsibility shifts unevenly onto individual instructors and students.

Design Implications¶

Context¶

Requirements¶

• Institutional arrangements must shape which forms of GenAI-mediated practice are recognized as legitimate and how responsibilities are distributed

Constraints¶

• Student GenAI use extends beyond institutionally managed environments, limiting regulation through approved platforms alone

Target Learners¶

• higher education students
• instructors
• institutional leaders

Target Learning Goals¶

• responsible GenAI use
• critical evaluation of AI outputs
• academic integrity

Affordances¶

• Nested Instructor Student Genai Triadic Model

Claims¶

• Institutions have expanded student access to generative AI tools faster than they have developed policies, guidance, and shared learning goals for their use [+W]
• AI outcomes in higher education are shaped by leadership capacity, infrastructure readiness, and governance structures rather than functioning independently [+W]
• Among the 50 most innovative U.S. universities, instructor-set syllabus-level governance is the dominant GenAI convergence, with 33 of 50 providing syllabus or course-policy guidance [~W]
• Existing generative AI guidance in higher education focuses on policy and academic integrity rather than learning mechanisms [+W]

Related Strategies¶

• Graduate AI literacy training should promote purposeful, task-oriented, critically supervised GenAI use rather than targeting overall use levels
• Institutions should respond to generative AI in education with guided critical engagement rather than prohibition
• Address generative AI in higher education through assessment redesign and proportional reflective practices rather than surveillance or punitive controls
• Design task-sensitive institutional AI policies rather than blanket permitted/prohibited distinctions
• Establish comprehensive institutional AI governance frameworks including task forces, roadmaps, and transparent policies
• Four strategic recommendations for supporting AI literacy across settings
• Build ethical AI integration on four coordinated levels: faculty professional development, ethics curricula, clear institutional guidelines, and interdisciplinary updating
• Ground AI integration in higher education on core principles of human agency, academic freedom, transparency, ethics, inclusivity, and critical thinking
• Build institutional AI literacy and transparent risk-communication governance for agentic AI browsers
• Teach mediation literacy and redesign assessment and evaluation to measure interaction, not only satisfaction
• Structure GenAI integration in higher education around feedback-literacy curricula and reflective course design
• Classify and design AI teaching tools by governance mechanism, using transferable design questions rather than generic tool categories
• Protect AI-mediated assessment through isolated environments, clear guidelines, resilience assessments, and teaching prompt-processing awareness
• Treat AI literacy as educational support by teaching students to question AI responses, attempt problems first, and avoid substituting AI output for engagement
• Develop clear institutional GenAI policies, communicate them, raise awareness, and promote digital literacy
• Integrate AI literacy instruction into study programs alongside responsible tool design
• Develop AI literacy frameworks that build metacognitive awareness distinguishing system-assisted performance from independent competence
• Transition from restrictive AI governance to frameworks that formally integrate operational and epistemic workflow competencies with subject-sensitive guidance
• Bridge the AI intention-practice gap through digital infrastructure, professional development, and supportive institutional policies
• Strengthen institutional policies, ethics education, verification, disclosure, and data protection to support ethical GenAI use
• Recalibrate detection as a supplementary layer within multi-layer integrity governance rather than its foundation
• Five design principles for responsible implementation of AI-generated course readings
• Structure GenAI use guidelines as a scaffold progressing from what GenAI is, through affordances, to consequences and collective responsibilities
• Embed structured AI literacy in business curricula: teach verification, comparison, and transparent AI-use declarations rather than restricting or unrestrictedly encouraging GenAI
• Position AI detection tools as one component within a broader institutional decision-making process, with regularly updated benchmarks
• Move beyond permit-or-restrict GenAI policies toward policies that clarify appropriate use, support equitable access, and extend rather than replace independent reasoning
• Strategic steps to move beyond informal AI innovation: recognise redesign in workload models, embed innovation in promotion criteria, set institutional AI principles, redesign assessment, and fund scaling pilots
• Provide explicit pedagogical guidance on effective and ethical GenAI use within specific assessment contexts, beyond prohibitions
• Institutions should define generative-AI prohibitions by function, distinguish generation from format conversion, and require validated evidential methods before sanctions
• Scaffold AI use without undermining student autonomy through structured prompts, ethical checklists, and reflection journals
• Build educator AI literacy and pedagogical competence alongside student critical engagement with AI-generated content
• Deploy GenAI for continuous longitudinal analysis of student development to enable ipsative feedback, and scaffold feedback literacies for all students
• Institutional safeguards for safe, transparent AI use in assessment
• Privacy-conscious, teacher-mediated GenAI implementation: anonymized feedback, pre-programmed prompts, and accessible original comments
• Tripartite institutional response: AI literacy, ethical infrastructure, and competence-based educator training
• Institutions should move toward deliberate, transparent policies governing AI use in admissions and reconsider what application essays should assess
• Deploy soft barriers transparently: institutional approval, policy-level disclosure, and accessibility testing
• Develop student AI literacy and institutional GenAI policies, guidelines and safeguards for responsible integration
• Universities should provide vivid guidelines and education on how and where ChatGPT may be used for learning

Examples¶

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Key Sources¶

• Moganadas SR, Marín-González F, Nun SH and Gan CL (2026) Generative AI as a didactic-pedagogical mediator: rethinking human roles and pedagogical design in higher education. Front. Educ. 11:1856839. https://doi.org/10.3389/feduc.2026.1856839