--- title: "AI-Native University Maturity Model" slug: "appendix-a-ai-native-university-maturity-model" kind: "appendix" order: 23 label: "Appendix A" letter: "A" summary: "Universities’ transformation into AI-native will occur gradually, as different parts of the university develop new capabilities. A research division may advance quickly because faculty are already" words: 3291 --- Universities’ transformation into AI-native will occur gradually, as different parts of the university develop new capabilities. A research division may advance quickly because faculty are already using artificial intelligence in scholarship, while administrative departments remain cautious. Student services may develop sophisticated AI-supported advising before academic governance has established consistent expectations for classroom use. One college may redesign learning around intelligent support while another remains focused on early experimentation. Complex institutions rarely transform uniformly, and universities are among the most complex organizations in society. Their missions, traditions, governance structures, academic disciplines, and communities vary widely. The AI-Native University Maturity Model is designed to provide institutional leaders with that understanding along with a common language for evaluating current capabilities, identifying gaps, establishing priorities, and determining what must be developed before the university proceeds to a more advanced stage. The model is organized around the four phases introduced in Chapter 19: 1. AI-Enabled University 2. AI-Integrated University 3. AI-Orchestrated University 4. AI-Native University Each phase is evaluated across six dimensions that together describe institutional maturity: 1. Strategy and Leadership 2. Teaching and Learning 3. Research and Scholarship 4. Operations and Workforce 5. Technology and Institutional Knowledge 6. Governance, Ethics, and Trust The maturity model should be used as a framework for institutional reflection rather than as a mechanical scorecard. The objective is not to assign a single label to the university, but to understand the quality and coherence of its progress. ## Phase 1: The AI-Enabled University The AI-enabled university has begun experimenting with artificial intelligence but has not yet developed a coordinated institutional approach. Adoption is typically local and driven by individual faculty members, researchers, administrative teams, or technology leaders who recognize practical opportunities within their areas of responsibility. Innovation is visible, but fragmented. Some faculty incorporate generative AI into course preparation or student assignments, while others prohibit its use entirely. Researchers experiment with literature review, coding, or data analysis tools. Administrative offices test conversational assistants, drafting tools, or workflow automation. Students encounter inconsistent expectations depending upon the course, department, or service with which they interact. This stage is valuable because it creates institutional learning. Early adopters discover useful applications, identify risks, and develop practical experience that can inform future strategy. The danger lies not in experimentation itself, but in allowing experimentation to remain permanently disconnected from common institutional direction. ### Strategy and Leadership At this stage, senior leaders recognize that AI will affect higher education, but institutional strategy remains in its early stages. Discussions may be led by a temporary task force, technology committee, faculty working group, or innovation office. Leadership conversations focus upon understanding the technology, managing immediate risks, and identifying potential use cases. The university has not yet defined a clear institution-wide vision for AI. Responsibility may be distributed informally, and investment decisions may occur independently across departments. Progress is evident when leaders begin to connect AI to institutional priorities rather than treating it as a technology trend. ### Teaching and Learning Faculty experimentation defines the educational environment. Individual instructors use AI to develop course materials, generate examples, redesign assignments, support feedback, or explore new forms of student engagement. Professional development may be available, but participation is generally voluntary and concentrated among early adopters. Students often receive inconsistent guidance. One faculty member may encourage responsible AI use, another may prohibit it, and a third may not address it at all. Academic integrity policies may be under revision, and institutional expectations remain broad. ### Research and Scholarship Researchers are beginning to use AI for literature discovery, coding, data analysis, translation, proposal preparation, and other aspects of scholarly work. Adoption is largely investigator-led. Institutional guidance concerning attribution, authorship, research data, reproducibility, intellectual property, and disclosure may remain incomplete. Researchers often rely upon external consumer tools because approved institutional alternatives have not yet been established. ### Operations and Workforce Administrative use focuses primarily upon individual productivity. Staff may use AI to draft communications, summarize documents, organize information, or respond to routine inquiries. Selected departments may pilot conversational assistants or process automation, but workflows themselves remain largely unchanged. Professional development for staff is limited, and questions concerning changing roles, accountability, and workforce implications may not yet have been addressed systematically. ### Technology and Institutional Knowledge Most AI activity occurs through standalone tools with limited integration into institutional systems. Identity management, permissions, data governance, interoperability, and centralized monitoring remain underdeveloped. Different departments may purchase or use separate technologies without common standards. The institution may possess many AI tools yet lack a shared AI infrastructure. ### Governance, Ethics, and Trust Initial governance focuses upon immediate concerns such as confidential information, acceptable use, academic integrity, procurement, and approved tools. Policies tend to describe what users should avoid rather than articulating a broader institutional philosophy for responsible AI. Governance is usually advisory rather than operational. Decisions may still be made locally without consistent risk classification or formal review. ### Indicators of Readiness for Phase 2 An AI-enabled university is prepared to move toward integration when experimentation has generated sufficient knowledge to support coordinated institutional direction. Typical indicators include: - Senior leadership has articulated why AI matters to the university’s mission. - Faculty and staff champions have emerged across multiple areas. - Early pilots have demonstrated educational, research, or operational value. - Basic guidance concerning privacy, security, and academic integrity exists. - The institution recognizes that disconnected experimentation will not scale responsibly. - Leadership is prepared to establish a common strategy, governance, investment, and evaluation. ## Phase 2: The AI-Integrated University The AI-integrated university moves from local experimentation toward intentional institutional adoption. Artificial intelligence is integrated into strategic planning, professional development, selected core workflows, and the university’s technology environment. The defining characteristic of this stage is that AI is no longer something individuals merely choose to use. It becomes something the institution formally supports, governs, and integrates into selected areas of academic and administrative work. The principal challenge is ensuring that integration does not simply place AI inside outdated processes. The university must begin asking whether those processes themselves should be redesigned. ### Strategy and Leadership Artificial intelligence becomes part of institutional strategy. Leaders identify a limited number of priorities directly connected to the mission, such as improving student support, strengthening teaching, accelerating research, reducing administrative burden, expanding access, or developing lifelong learning. Executive sponsorship is established, and responsibility becomes clearer. Budgeting, procurement, risk management, and evaluation increasingly reflect common institutional direction. AI is understood as a university-wide issue rather than solely an information technology initiative. ### Teaching and Learning Faculty development becomes systematic and available across the institution. Programs address not only how to use AI tools, but also how to rethink pedagogy, assessment, course design, student mentoring, and academic integrity. Academic units begin developing discipline-specific expectations. AI literacy is incorporated into selected programs or general education requirements. Students learn how to evaluate generated content, verify sources, understand model limitations, protect privacy, and use intelligent systems responsibly within their fields. Selected courses and programs integrate AI-supported tutoring, adaptive resources, accessibility assistance, or formative feedback while preserving faculty control over curriculum and academic standards. ### Research and Scholarship The university establishes clearer expectations for AI-assisted scholarship. Guidance addresses disclosure, attribution, authorship, intellectual property, data protection, reproducibility, and responsible use in grant development or publication. Research support offices provide approved tools, training, and secure environments. AI use is beginning to shift from informal individual experimentation toward institutionally supported scholarly practice. ### Operations and Workforce Administrative departments begin redesigning selected workflows around intelligent support. Admissions, advising, financial aid, finance, human resources, communications, and IT support may incorporate AI into established processes. Staff receive role-specific professional development. Leaders begin considering how responsibilities will evolve as routine cognitive work becomes increasingly automated or assisted. Success is measured not only through efficiency but also through service quality, employee experience, student outcomes, and the redirection of human attention toward higher-value work. ### Technology and Institutional Knowledge The university begins integrating AI with existing institutional platforms through secure interfaces, identity management, role-based access, and approved services. Reliance upon unmanaged consumer tools decreases for sensitive institutional work. Knowledge systems become more organized. Documents, policies, procedures, and institutional resources are curated to enable intelligent systems to retrieve reliable information. Technical leaders begin establishing the foundations for institutional memory. ### Governance, Ethics, and Trust Formal governance structures are established, with representation from academic affairs, faculty, students, IT, research, legal, privacy, accessibility, security, and administration. Use cases are assessed based on risk, and high-impact applications require more rigorous review and human oversight. Policies become more precise. Procedures are created for approving vendors, reporting errors, monitoring performance, reviewing outcomes, and responding to unintended consequences. Governance begins shaping procurement and implementation rather than reacting afterward. ### Indicators of Readiness for Phase 3 An AI-integrated university is ready to move toward orchestration when it possesses enough institutional discipline to connect intelligent capabilities across departments. Typical indicators include: - AI strategy is linked to institutional priorities and supported by executive leadership. - Faculty, staff, and student development programs operate at a meaningful scale. - Approved AI services integrate securely with major institutional platforms. - Governance includes defined authority, risk classification, and review procedures. - Multiple departments have redesigned workflows and demonstrated measurable value. - Data, identity, permissions, and knowledge foundations are sufficiently reliable to support cross-functional collaboration. - Leaders recognize that greater value will come from connecting intelligent capabilities rather than merely expanding the number of tools. ## Phase 3: The AI-Orchestrated University The AI-orchestrated university connects specialized capabilities across institutional boundaries. Artificial intelligence no longer operates primarily within individual departments. Agents, knowledge systems, workflows, and human teams begin functioning as parts of a coordinated institutional ecosystem. This stage represents a significant organizational transition. Universities have historically designed systems and responsibilities around departmental structures. Orchestration requires institutions to design experiences around the learner, researcher, employee, or strategic objective instead. The central question is no longer whether each department has access to AI, but whether the university can coordinate intelligence coherently, securely, and responsibly across the institution. ### Strategy and Leadership Leadership begins treating AI as institutional infrastructure rather than a portfolio of innovation projects. Executive teams establish shared priorities for agent orchestration, workflow redesign, institutional memory, and decision intelligence. Investment increasingly supports common platforms and capabilities rather than separate departmental solutions. Leaders are beginning to reconsider organizational structures, decision rights, accountability, and the relationship between human and digital work. ### Teaching and Learning Students experience increasingly coordinated support across courses and services. Personal mentors, advising systems, tutoring resources, accessibility services, degree planning, and career guidance share appropriate context within institutional permissions. Faculty gain integrated support for teaching, research, and administration while retaining academic authority. Educational insights can be shared responsibly across relevant systems rather than remain confined to individual courses or platforms. ### Research and Scholarship Research support becomes more connected. Literature review, data services, grant development, compliance, research administration, publication, and interdisciplinary collaboration operate as parts of a coordinated environment. Institutional knowledge connects faculty expertise, facilities, funding opportunities, research outputs, and partnership possibilities. The university begins learning from its research activity as a whole rather than treating each project as an isolated effort. ### Operations and Workforce Specialized agents coordinate work across admissions, registrar functions, finance, human resources, student services, IT, legal, compliance, marketing, advancement, and facilities. Routine processes move through governed workflows, while human intervention is concentrated at points requiring judgment, exception handling, or accountability. Staff roles increasingly emphasize interpretation, relationships, strategy, creativity, and service design. The institution begins managing a combined human and digital workforce. ### Technology and Institutional Knowledge The university develops or adopts the central components of an AI Operating System. These include institutional memory, governed knowledge systems, role-based identity and permissions, agent orchestration, workflow automation, interoperability, monitoring, and evaluation. Existing enterprise systems remain authoritative sources of data, while institutional knowledge becomes accessible through a shared intelligence layer. Agent activity is logged, reviewed, and measured. ### Governance, Ethics, and Trust Governance becomes embedded within the architecture. Policies are translated into permissions, approval requirements, escalation rules, audit trails, and continuous monitoring. High-impact decisions remain subject to meaningful human oversight. The university continuously evaluates accuracy, bias, privacy, cybersecurity, accessibility, explainability, and educational impact, rather than only during initial approval. ### Indicators of Readiness for Phase 4 An AI-orchestrated university is prepared to become AI-native when intelligent collaboration has become reliable, trusted, and deeply aligned with mission. Typical indicators include: - Cross-functional agent workflows operate securely across major institutional domains. - Institutional memory and knowledge systems are trusted sources for people and agents. - Faculty, staff, students, and leaders understand their responsibilities within human-AI collaboration. - Governance is enforced through both policy and technical architecture. - Executive leadership uses institutional intelligence for planning and decision support. - Measurable improvements appear in learning, research, service, employee capacity, or institutional resilience. - AI is no longer perceived as a separate innovation program but as part of the ordinary work of the university. ## Phase 4: The AI-Native University The AI-native university has redesigned itself around the assumption that human expertise and artificial intelligence will work together across the institution. AI is no longer an external tool, a special initiative, or a collection of isolated applications. It has become a foundational institutional capability embedded within teaching, learning, research, operations, governance, and leadership. Technology becomes less visible because intelligent support has become part of the normal institutional environment. Students receive continuous, personalized guidance. Faculty work with integrated teaching and research support while preserving academic authority. Staff collaborate with specialized agents that reduce repetitive work and expand professional capacity. Leaders govern with a richer understanding of institutional history, conditions, risks, and possibilities. The defining characteristic of this stage is not the ubiquity of AI. It is the maturity with which intelligence has been organized around an institutional mission. ### Strategy and Leadership Institutional strategy assumes the presence of intelligent systems, just as contemporary strategy assumes the presence of digital infrastructure and the internet. Leaders evaluate major initiatives according to how human expertise, institutional knowledge, and artificial intelligence should work together. Progress is measured through mission outcomes rather than technology adoption. Leadership remains accountable for ensuring that AI strengthens access, learning, scholarship, trust, service, and long-term resilience. ### Teaching and Learning Personalization becomes an ordinary institutional capability rather than a special intervention. Every student can receive continuous learning support, while education remains grounded in faculty mentorship, intellectual community, disciplined inquiry, and shared academic standards. Curricula evolve through faculty leadership informed by emerging knowledge and institutional evidence. Assessment increasingly emphasizes mastery, authentic performance, judgment, creativity, and responsible use of intelligent systems. ### Research and Scholarship AI-augmented scholarship becomes standard across disciplines while transparency, attribution, reproducibility, and research integrity remain deeply embedded. Researchers gain access to sophisticated intellectual support regardless of department or institutional scale. The university becomes better equipped to recognize interdisciplinary opportunities, preserve research knowledge, translate discovery into impact, and extend the reach of scholarship. ### Operations and Workforce The human-AI workforce model becomes an established organizational reality. Faculty, staff, students, and AI coworkers contribute different capabilities to the institutional mission. Routine processes occur with minimal friction, while people devote greater attention to judgment, mentorship, relationships, creativity, discovery, strategy, and leadership. Departments remain important centers of expertise, but institutional experiences are designed across boundaries. Students and employees experience one coherent university rather than a collection of disconnected systems and offices. ### Technology and Institutional Knowledge The AI Operating System functions as a trusted institutional platform. Knowledge, identity, permissions, workflows, agents, and enterprise systems operate within a coherent architecture. Interoperability allows the institution to evolve without becoming dependent upon a single model, platform, or vendor. Institutional memory becomes cumulative. The university preserves what it learns, connects knowledge across generations, and improves continuously. ### Governance, Ethics, and Trust Responsible AI becomes part of institutional culture. Governance is integrated into academic policy, research practice, procurement, cybersecurity, leadership, professional development, and technical architecture. Students, faculty, staff, and external partners understand how AI is used and where human accountability resides. Trust is treated not as a communications objective but as an institutional condition that must be continually earned. ### Evidence of AI-Native Maturity No institution becomes permanently or completely AI-native. The fourth phase is not a final destination at which transformation ends. It represents a mature capacity for continuous adaptation. Evidence of that maturity includes: - AI supports every major institutional mission without displacing human accountability. - Personalized learning and student support operate at institutional scale. - Faculty and researchers experience meaningful expansion of academic capacity. - Human and digital workforces collaborate through established organizational practices. - Institutional intelligence supports leadership without substituting for judgment. - Governance, privacy, security, transparency, and ethics are integrated into everyday operations. - Technology is coherent enough to recede into the background of institutional life. - The university learns continuously from its own experience. ## The Maturity Matrix The following matrix provides a concise summary of the four phases across the six dimensions. It is intended to support institutional discussion rather than produce a simplistic score. |**Dimension**|**AI-Enabled**|**AI-Integrated**|**AI-Orchestrated**|**AI-Native**| |---|---|---|---|---| |**Strategy and Leadership**|Local experimentation and emerging executive awareness|Institution-wide strategy, sponsorship, and prioritized use cases|Shared infrastructure and organizational redesign|AI is assumed as a foundational institutional capability| |**Teaching and Learning**|Faculty pilots and inconsistent student guidance|Scaled development, AI literacy, and selected integrations|Coordinated learning, advising, and support ecosystems|Personalized learning and human mentorship at institutional scale| |**Research and Scholarship**|Investigator-led use of external tools|Approved platforms, guidance, and shared support|Connected research agents, knowledge, compliance, and data services|AI-augmented scholarship embedded across disciplines| |**Operations and Workforce**|Individual productivity improvements|Departmental workflow redesign and staff preparation|Cross-functional agents and coordinated human-AI work|Mature human-AI workforce organized around mission| |**Technology and Knowledge**|Standalone tools and limited integration|Secure platforms, identity, APIs, and emerging knowledge systems|AI Operating System, institutional memory, and orchestration|Coherent, interoperable, continuously learning institutional platform| |**Governance and Trust**|Basic acceptable-use guidance|Formal governance, risk review, and approved tools|Embedded permissions, oversight, audit, and monitoring|Responsible AI integrated into institutional culture and operations| ## Applying the Model The maturity model is most useful when evaluated by a cross-functional group rather than by a single office. Presidents, provosts, faculty leaders, students, researchers, CIOs, legal counsel, student success professionals, and administrative leaders will often perceive the institution differently. Those differences are valuable because they reveal gaps between formal strategy and lived experience. A university may discover that teaching and research are already operating at the integrated stage while governance remains enabled and technology remains fragmented. Another institution may possess sophisticated technical infrastructure but limited faculty participation or student trust. Such findings should not be compressed into a single institutional score. They should guide priorities. Institutions should also distinguish between aspiration and evidence. A strategic plan may describe AI-native ambitions, but maturity must be evaluated through observable practice. Are faculty and staff development programs operating at scale? Are permissions technically enforced? Can agents collaborate across departments? Do students understand how AI affects their educational experience? Are outcomes being measured? Is institutional knowledge sufficiently reliable to support intelligent systems? The model should be revisited regularly because both artificial intelligence and institutional practice will continue evolving. Its purpose is not to freeze the definition of an AI-native university. It provides a stable framework through which institutions can evaluate a changing environment while remaining focused upon enduring educational purposes. The most useful question is therefore not whether a university has reached the fourth phase. It is whether the institution understands its present stage, recognizes what must be strengthened, and is prepared to move responsibly toward the next. The AI-native university will not be built by those who move fastest. It will be built by institutions that understand why they are changing and develop each new capability with enough discipline that technology, trust, mission, and human purpose advance together.