Higher Education AI Capability Maturity Model - Proposal

	A	B	C
1	Strategy and Governance	Response	Comments
2	Presence of a clear AI strategy aligned with institutional goals.
3	Is the AI strategy clearly documented, communicated, and aligned with broader institutional goals?
4	Are key stakeholders across the institution involved and supportive of the AI strategy?
5	Does a detailed, actionable roadmap exist outlining specific steps, milestones, and metrics for achieving the AI strategy?
6	Are resources (funding, personnel, technology) allocated effectively to support AI initiatives in line with strategic priorities?
7	Are there mechanisms in place for regularly reviewing and updating the AI strategy to reflect technological advancements and changing institutional needs?
8	Ethical AI Use and Data Governance Policies
9	Are policies covering ethical AI use, data privacy, and governance comprehensive?
10	Are there effective mechanisms for implementing and enforcing these policies?
11	Is there a high level of awareness and training among stakeholders regarding ethical AI use and data governance?
12	Does the institution practice transparency in AI applications and hold itself accountable for ethical AI use?
13	Is there a process for regularly reviewing and updating policies to adapt to new ethical considerations and technological developments?
14	Use Case Assessment and Approval Process to Ensure Alignment to Institutional Priorities
15	Are the criteria used to assess and approve AI use cases clear and comprehensive?
16	Does the assessment process ensure AI use cases are aligned with institutional priorities?
17	Are relevant stakeholders involved in the use case assessment and approval process?
18	Is the process for making and communicating decisions efficient and transparent?
19	Are there mechanisms for collecting and incorporating feedback on the use case assessment process?
20	Governance Exists to Ensure Projects Are Subject to Privacy, Ethics, Security, Accessibility Reviews
21	Is the governance structure for overseeing AI projects clear and effective?
22	Are privacy, ethics, security, and accessibility reviews conducted thoroughly for each AI project?
23	Are review processes applied consistently across different AI projects?
24	Are stakeholders engaged in conducting reviews?
25	Are there mechanisms for learning from reviews and continuously improving governance and review processes?
26	Infrastructure, Data, and Technology
27	Availability and Accessibility of Computational Resources, Data, and Technological Tools Necessary for AI Research and Development
28	Are computational resources, data, and technological tools for AI research and development readily available and accessible to researchers and developers?
29	Do the available resources meet the current needs for AI research and development in terms of capacity and capability?
30	Is there a process in place to regularly assess and update the computational resources, data, and tools to keep pace with AI advancements?
31	Are there any barriers to accessing these resources, and if so, are measures being taken to address them?
32	Is there training and support available for researchers and developers to effectively utilize these resources?
33	Enterprise Architecture Standards Include AI Related Capabilities
34	Do the enterprise architecture standards specifically include AI-related capabilities?
35	Are these AI-related standards comprehensive, covering aspects such as scalability, security, and interoperability?
36	Is there a mechanism for regularly updating these standards to reflect the latest AI technological advancements?
37	Are stakeholders across the organization aware of and trained on these AI-related standards?
38	Is compliance with AI-related enterprise architecture standards monitored and enforced?
39	UX design and research are integrated with a focus on understanding user needs and behaviors.
40	Are user needs and behaviors explicitly identified and integrated into all UX design and research processes?
41	Is continuous user feedback systematically incorporated into ongoing UX design and research efforts?
42	Are there specific metrics or KPIs in place to assess the effectiveness of UX design and research on enhancing user satisfaction and behavior?
43	Does the organization actively monitor and integrate the latest UX trends into its design and research methodologies?
44	Is there a structured approach to ensure collaboration among UX designers, researchers, and other stakeholders for a better understanding of users?
45	Talent and Expertise
46	Faculty Expertise and Development Programs in AI
47	Do faculty members possess expertise in AI relevant to their disciplines?
48	Are there ongoing development programs available for faculty to enhance their AI knowledge and skills?
49	Is faculty participation in AI development programs encouraged and supported by the institution?
50	Do these programs address current and emerging AI technologies and methodologies?
51	Is there a mechanism to assess the impact of faculty development programs on AI education and research?
52	Student Access to AI Education and Research Opportunities
53	Do students have access to AI education through courses, workshops, and seminars?
54	Are there research opportunities for students to engage in AI projects?
55	Do AI education and research opportunities cover a range of disciplines and skill levels?
56	Is there support available to students participating in AI education and research, such as mentorship and resources?
57	Are efforts made to ensure equitable access to AI opportunities for all students?
58	Staff Engagement and Expertise in AI
59	Are staff members engaged in the institution's AI initiatives?
60	Do staff members have access to training and development programs to build AI expertise?
61	Is there a recognition or incentive system in place for staff contributing to AI projects?
62	Do staff have the opportunity to collaborate with faculty and students on AI initiatives?
63	Is the impact of staff engagement and expertise in AI regularly assessed and utilized in strategic planning?
64	Cross-functional Teams and Collaborations
65	Are cross-functional teams formed to work on AI projects and initiatives?
66	Do these teams facilitate collaboration between faculty, students, and staff from different departments and disciplines?
67	Is there a structure in place to support and manage cross-functional AI projects?
68	Are collaborations with external organizations (industry, government, non-profits) encouraged and facilitated?
69	Is the effectiveness of cross-functional teams and collaborations in advancing AI goals regularly evaluated?
70	Research and Innovation
71	Quantity and Quality of AI Research Output
72	Is the quantity of AI research output from the institution substantial?
73	Does the AI research output meet high standards of quality, as evidenced by peer reviews, citations, and impact factors?
74	Are there mechanisms in place to encourage and support high-quality AI research across various disciplines?
75	Is the research output diverse, covering a wide range of AI topics and methodologies?
76	Does the institution regularly review and assess its AI research output to identify trends, strengths, and areas for improvement?
77	Collaboration with Industry and Academia in AI Initiatives
78	Are there active collaborations between the institution and industry partners on AI initiatives?
79	Does the institution engage in joint AI research and development projects with other academic institutions?
80	Are these collaborations leading to tangible outcomes, such as co-authored publications, patents, or technology transfers?
81	Is there a framework or support system in place to facilitate these collaborations, including legal, administrative, and financial aspects?
82	Does the institution actively seek new collaboration opportunities in AI with both industry and academia to enhance its research and educational offerings?
83	Curriculum and Learning
84	Integration of AI Topics Across Disciplines
85	Are AI topics integrated into the curriculum across various disciplines?
86	Is there a strategic approach to embedding AI education in non-technical disciplines?
87	Do interdisciplinary courses that include AI topics exist to foster a broader understanding of AI's impact?
88	Are faculty from different disciplines collaborating to integrate AI topics into their courses?
89	Is the effectiveness of AI integration across disciplines regularly evaluated and used for curriculum development?
90	Availability of Specialized AI Programs and Courses
91	Are specialized AI programs and courses available to students?
92	Do these programs and courses cater to different levels of expertise, from beginner to advanced?
93	Is there a mechanism in place to regularly update AI programs and courses to reflect technological advancements?
94	Are resources and support readily available for students enrolled in specialized AI programs and courses?
95	Is there a strategy to promote and increase student enrollment in AI programs and courses?
96	Ethics and Policy
97	Institutional Commitment to Ethical AI Development and Use
98	Is there a clear institutional commitment to ethical AI development and use?
99	Are ethical considerations integrated into the planning and execution of AI projects?
100	Is there institutional support for research and initiatives focused on the ethical use of AI?