AI Adoption Framework for Higher Education
A Comprehensive AI Partnership Model
SCENARIO 1
Foundation First
Establishing values, principles, and guardrails before technical implementation.
AI POLICY & GOVERNANCE
Institutions need clear values, principles, and guardrails before adopting tools. This scenario explores established responsible AI policies that address:
Academic Integrity: Defining boundaries for AI use in research and coursework.
Data Privacy: Ensuring ethical handling of student and institutional data.
Accessibility: Guaranteeing equitable tool access for all learners.
WHY THIS COMES FIRST:
Strong policy foundations ensure that all subsequent AI initiatives align with institutional values and protect students, faculty, and staff.
SCENARIO 2
Building the System
Strategic technical infrastructure and processes to support AI at scale.
ENTERPRISE AI INFRASTRUCTURE
Technical Foundations
Cross-Divisional Coordination
Covers vendor evaluation, procurement, and data governance. Establishing sustainable systems ensures the institution isn't just "chasing the latest tool."
Integration across administrative and academic silos to create a unified system that scales without fragmentation.
WHY THIS COMES SECOND:
With clear policies in place, institutions can make infrastructure decisions that align with their values rather than chasing the latest shiny tool.
SCENARIO 3
Preparing People
Foundational understanding for students and faculty before full integration.
AI LITERACY ACROSS CURRICULUM
Foundational Understanding: Moving beyond "how to use" into "when and why" to use AI tools responsibly.
Ethical Considerations: Embedding critical evaluation of AI outputs into the core of student learning.
Curriculum Embedding: Systematically integrating literacy requirements into various disciplines rather than separate courses.
WHY THIS COMES THIRD:
Infrastructure without literacy leads to underutilization or misuse. People need competency before implementation.
SCENARIO 4
Enabling Educators
Ongoing support structures to help faculty redesign teaching for the AI era.
FACULTY & PEDAGOGICAL SUPPORT
Faculty need more than one-off workshops; they require sustainable professional development systems to:
Redesign assessment models.
Develop AI-integrated curriculum.
Join community support structures.
Guidance must be rooted in pedagogy, not just technology training.
WHY THIS COMES FOURTH:
Even AI-literate faculty need pedagogical guidance and institutional resources to effectively redesign curriculum for the AI era.
SCENARIO 5
Scaling to Practice
Integrating AI into services and operations to enhance human connection.
INTEGRATION IN SERVICES & OPERATIONS
Student Success
Operational Scale
Enhancing advising, tutoring, and accessibility support through intelligent, AI-driven assistants.
Implementing AI in ways that enhance—not replace—human connection within institutional workflows.
WHY THIS COMES LAST:
Service integration without policy, infrastructure, literacy, and faculty support leads to fragmented efforts, equity problems, and student mistrust.
AI Adoption in Higher Education
Lauren Kelley
Created on April 8, 2026
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Transcript
AI Adoption Framework for Higher Education
A Comprehensive AI Partnership Model
SCENARIO 1
Foundation First
Establishing values, principles, and guardrails before technical implementation.
AI POLICY & GOVERNANCE
Institutions need clear values, principles, and guardrails before adopting tools. This scenario explores established responsible AI policies that address:
Academic Integrity: Defining boundaries for AI use in research and coursework.
Data Privacy: Ensuring ethical handling of student and institutional data.
Accessibility: Guaranteeing equitable tool access for all learners.
WHY THIS COMES FIRST:
Strong policy foundations ensure that all subsequent AI initiatives align with institutional values and protect students, faculty, and staff.
SCENARIO 2
Building the System
Strategic technical infrastructure and processes to support AI at scale.
ENTERPRISE AI INFRASTRUCTURE
Technical Foundations
Cross-Divisional Coordination
Covers vendor evaluation, procurement, and data governance. Establishing sustainable systems ensures the institution isn't just "chasing the latest tool."
Integration across administrative and academic silos to create a unified system that scales without fragmentation.
WHY THIS COMES SECOND:
With clear policies in place, institutions can make infrastructure decisions that align with their values rather than chasing the latest shiny tool.
SCENARIO 3
Preparing People
Foundational understanding for students and faculty before full integration.
AI LITERACY ACROSS CURRICULUM
Foundational Understanding: Moving beyond "how to use" into "when and why" to use AI tools responsibly.
Ethical Considerations: Embedding critical evaluation of AI outputs into the core of student learning.
Curriculum Embedding: Systematically integrating literacy requirements into various disciplines rather than separate courses.
WHY THIS COMES THIRD:
Infrastructure without literacy leads to underutilization or misuse. People need competency before implementation.
SCENARIO 4
Enabling Educators
Ongoing support structures to help faculty redesign teaching for the AI era.
FACULTY & PEDAGOGICAL SUPPORT
Faculty need more than one-off workshops; they require sustainable professional development systems to:
Redesign assessment models.
Develop AI-integrated curriculum.
Join community support structures.
Guidance must be rooted in pedagogy, not just technology training.
WHY THIS COMES FOURTH:
Even AI-literate faculty need pedagogical guidance and institutional resources to effectively redesign curriculum for the AI era.
SCENARIO 5
Scaling to Practice
Integrating AI into services and operations to enhance human connection.
INTEGRATION IN SERVICES & OPERATIONS
Student Success
Operational Scale
Enhancing advising, tutoring, and accessibility support through intelligent, AI-driven assistants.
Implementing AI in ways that enhance—not replace—human connection within institutional workflows.
WHY THIS COMES LAST:
Service integration without policy, infrastructure, literacy, and faculty support leads to fragmented efforts, equity problems, and student mistrust.