Foster communities of practice for AI governance instead of rigid top-down policies¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that universities move away from rigid, top-down AI policies and instead foster "communities of practice," collaborative spaces where faculty, staff, and students navigate AI implementation together. Three actionable recommendations are given: centering pedagogy and care over punitive approaches, sustaining leadership through relational peer-support networks, and building equitable infrastructure and policy with living, iterative guidelines. The approach is framed as a decolonial shift from universalizing mandates to contextually situated ways of governing technology.

Design Implications¶

Context¶

Requirements¶

• Institutional governance should be intentionally interdisciplinary, including library and humanities voices in unit-level working groups

Constraints¶

• The authors state that because AI implementation is highly context-dependent, there is no immediate solution or universal roadmap

Target Learners¶

• higher education leaders and administrators
• faculty
• staff
• students

Target Learning Goals¶

• AI literacy development
• ethical and equitable AI integration
• leadership resilience during technological change

Affordances¶

• Onto Epistemological Stewardship Ai Leadership

Related Strategies¶

• Sustain a faculty community of practice for creative problem-solving pedagogy
• Support faculty AI literacy through needs assessment, targeted training, teaching resources, and communities of practice
• Districts should build community-shared visions, evaluation infrastructure, and policies for procuring and using AI edtech
• School districts adopting AI should follow a five-step roadmap: engage communities, apply and develop policies, provide professional development, create supportive learning environments, and monitor and evaluate AI use
• Adopt a systems approach to AI integration rather than spotlights or gaps
• Co-create contextualized AI literacy policies via multi-stakeholder task forces with phased roadmaps
• Concentrate AI-edtech adoption priorities through collaborative networks that share tools, policies, and evaluation practices
• Implement sustained educator development programs including needs assessment, coaching, communities of practice, and experiential learning
• Establish comprehensive institutional AI governance frameworks including task forces, roadmaps, and transparent policies
• Higher education leaders should develop more inclusive and future-oriented GenAI policies integrating social equity, interdisciplinary experimentation, and sustainability considerations
• Institutional leaders should examine their own data, identify troublesome equity trends, and engage faculty, staff, and students in deliberate action to support all students
• Develop GenAI management strategies by identifying the contradictions GenAI access creates in the educational activity system
• Pursue inclusive digital strategies, educator professional development, and ethical AI governance for AI in adult education
• Address responsible design, learner experience, equity, validity, and relational assessment practice when implementing AI-agent-supported assessment
• Design task-sensitive institutional AI policies rather than blanket permitted/prohibited distinctions
• Embed AI policy literacy, peer-led communities of practice, and shared AI resources into professional development
• Embed anticipatory governance in institutional strategy via Chief AI Officers, AI task forces, faculty AI-literacy development, and student co-design mechanisms
• Build ethical AI integration on four coordinated levels: faculty professional development, ethics curricula, clear institutional guidelines, and interdisciplinary updating
• Build institutional AI literacy and transparent risk-communication governance for agentic AI browsers
• Four-question public template for high-stakes AI assessment tasks
• Reform academic governance through interventions on governance architectures, people, and technologies/resources
• Design profession-level governance arrangements, modeled on Ostrom's commons mechanisms, to sustain expertise regeneration
• Adopt a roadmap for ethical CAI implementation in education
• Researcher-oriented GenAI guidelines that foreground each AI literacy dimension across diverse research tasks
• Pursue sustainable AI integration in PBL through attention to design, ethics, and policy
• Create dialogic spaces where students and educators negotiate GenAI dilemmas together
• Institutions should respond to GenAI with clear governance frameworks and structured professional training programs for staff.
• Foster interdisciplinary collaboration and actively include student perspectives in institutional GenAI responses.
• Classify and design AI teaching tools by governance mechanism, using transferable design questions rather than generic tool categories
• Shift from punitive detection to proactive educative approaches, co-created detailed guidelines, and assessment designs GenAI cannot do well
• Address AI Anxiety through multidisciplinary solutions spanning educational, technological, regulatory, and ethical guidelines
• Embed automated question categorisation in learning management systems and develop ethical guidelines for AI in formative assessment
• Pursue ethical AI feedback use as a collective effort across multiple stakeholders in and beyond higher education
• Transition from restrictive AI governance to frameworks that formally integrate operational and epistemic workflow competencies with subject-sensitive guidance
• Address equity, transparency, and teacher professional judgment when deploying AI-mediated collaboration
• 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
• Co-design AI policies with students through open dialogue rather than enforcement and surveillance
• Recalibrate detection as a supplementary layer within multi-layer integrity governance rather than its foundation
• Incorporate AI literacy into educational-leadership preparation curricula
• Structure GenAI use guidelines as a scaffold progressing from what GenAI is, through affordances, to consequences and collective responsibilities
• Teach the pedagogical rationale behind AI policies rather than only stating rules
• Position AI detection tools as one component within a broader institutional decision-making process, with regularly updated benchmarks
• Staged institutional implementation: audit interactions, map to teaming levels, build AI literacy, co-design, and evaluate iteratively
• Educators share experiences, strategies, and insights about generative AI tools to keep the education community informed
• Professional development for AI ethics should create spaces for contextualised critique and resistance
• Combine bottom-up co-design with top-down executive sponsorship, cross-unit collaboration and dedicated R&D capacity to embed institutional GenAI
• Tripartite institutional response: AI literacy, ethical infrastructure, and competence-based educator training
• Develop student AI literacy and institutional GenAI policies, guidelines and safeguards for responsible integration
• Teachers adopting EAI should address digital literacy and ethical design challenges identified for each AI dimension
• Define institutional value from the bottom up, incorporating learning outcomes, community engagement, and career readiness beyond earnings thresholds
• Use consensus processes, editorial strategies, and institutional policies to make theory integration impactful

Examples¶

-

Key Sources¶

• Dozier, V., & Molina, S. C. (2026). Leading From the Precipice: How Academic Deans Are Navigating Artificial Intelligence in Higher Education. New Directions for Student Leadership. https://doi.org/10.1002/yd.70077