Embracing AI Go Grant

Adopt a three-year roadmap defining goals, timelines, responsible units, and annual reporting metrics.

Rationale: A roadmap supports coherence, resource prioritization, and accountability. It also ensures CMU can communicate progress to internal and external stakeholders and adapt strategically as technologies evolve. Annual progress updates reinforce transparency and continuous improvement. 

Strategic Recommendation 2: Establish AI governance with decision authority and dedicated staffing

Formalize an AI Governance & Innovation Council with a senior executive, cross-unit membership (including representation from Academic Affairs, OIT, General Counsel, Research, Student Affairs, CIS, and students) and dedicated operational staffing to establish appropriate guidelines and policies. The AI Governance & Innovation Council will have defined decision authority over AI policy, tool approval, risk thresholds, and institutional standards, while maintaining advisory input from campus stakeholders. 

Rationale: AI integration requires coordination of policy, tools, infrastructure, training, risk management, and curricular practice. Successful institutions treat AI as a strategic capacity-building effort, not a series of independent initiatives. Dedicated staffing ensures momentum translates into sustainable institutional capability. 

Strategic Recommendation 3: Expand external partnerships to align with career pathways

Deepen partnerships with employers and community organizations to connect AI learning with internships, authentic projects, tool access, and applied research. 

Rationale: AI is a workforce expectation across sectors. Partnerships strengthen enrollment value propositions and improve student outcomes while positioning CMU as a regional leader. This recommendation supports career readiness, community-connected learning, and institutional relevance. 

Strategic Recommendation 4: Support career readiness with required AI literacy education for all students

Ensure every student receives foundational AI literacy education, ideally embedded in General Education or an equivalent shared learning requirement. 

Rationale: Students have uneven access to AI tools and an uneven understanding of responsible AI use. Embedding digital literacy into a required competency for all undergraduates and into all graduate programs promotes equity, academic confidence, workforce readiness, and ethical use. Core learning topics should include limitations, bias, verification, ethics, and discipline-relevant applications. 

Strategic Recommendation 5: Invest in secure AI infrastructure to enable broad use while maintaining compliance and privacy

Continue building secure AI infrastructure, including institutionally controlled environments for AI-assisted teaching, research, and operations. 

Rationale: Secure infrastructure is foundational. It allows CMU to use AI for high-value applications (student support workflows, analytics, research), while minimizing risk by maintaining data stewardship and compliance. Institutional infrastructure also reduces dependency on vendor changes and supports adaptability. 

Strategic Recommendation 6: Implement job-specific AI training sessions for faculty and staff

Provide a series of training sessions that include updated policies, privacy/data handling, accessibility considerations, responsible-use decision-making, and practical applications required for the role (faculty, academic staff, operational staff). 

Rationale: Role-based training reduces risk and supports confidence. Faculty need practical guidance on data stewardship, course design, learning outcomes, and assessment practices. Staff need guidance on data stewardship, communication standards, and workflow integration. Consistent training will support equitable student experiences and improved quality of service.  

Strategic Recommendation 7: Create transparent expectations for use of AI in coursework through standardized communication norms

Establish a university expectation that course syllabi and assignments clearly describe AI use expectations: permitted/prohibited/required uses, documentation expectations, and how AI use aligns with learning outcomes. 

Rationale: Lack of clarity creates confusion, inequity, and integrity concerns. Transparent guidance reduces disputes and improves trust, while helping students develop ethical and informed AI engagement. 

Strategic Recommendation 8: Fund a unified AI support hub for guidance, resources, and consulting

Create a visible, centralized “AI Support Hub” consolidating guidance, training, approved tools, pilot projects, and consultative support across CIS/OIT/Libraries/Research. 

Rationale: A centralized support ecosystem improves visibility and usability, reduces confusion, accelerates adoption, and ensures guidance remains current. It also provides a consistent campus-facing message that CMU supports AI thoughtfully – with guardrails and practical help. 

Strategic Recommendation 9: Support course redesign to promote authentic assessment of learning

Discourage reliance on AI detection tools and instead invest in assessment redesign that supports authentic learning and evidence of process (drafting, reflection, oral defenses, in-class work, portfolio approaches). 

Rationale: Detection tools are unreliable and risk false accusations and inequitable harm. A design-based integrity approach is more sustainable and educationally sound to support high-quality learning while acknowledging the realities of AI in contemporary work and writing contexts. 

Strategic Recommendation 10: Develop an AI tool portfolio within data stewardship guardrails to meet campus constituent needs

Establish a small portfolio of supported AI tools categorized by acceptable use (public/protected/restricted), including clear guidance on use of tools. Additionally, CMU should develop a formal evaluation pipeline for AI tools, including privacy/security review, cost modeling, accessibility compliance, training needs, and impact assessment.

Rationale: Many of the most serious AI risks stem from informal use with sensitive data. A campus-supported system reduces risk, improves training capacity and increases equitable access. It also enables governance decisions that balance innovation with compliance. Institutions adopting AI without a mechanism to evaluate tools before adoption experience tool sprawl, inconsistent privacy practices, and escalating support costs. A standard process promotes responsible decision-making, allows innovation via pilots, and enables scalable adoption of proven solutions.