The AI Adoption Gap in Student Support

Why Read This Research

AI adoption in higher education is moving quickly. Student support is not. This research looks at the gap between how students now expect to interact with technology and how prepared institutions are to deliver AI-enabled support at scale.
It examines where institutions stand today, what is holding them back, and what separates a promising pilot from AI that is actually integrated into student support operations. The research also provides a practical framework for evaluating readiness, measuring impact, and deciding what to do next.

Key Questions Answered

• Where does higher education stand on AI adoption in student support?
• What do students actually expect from AI-enabled support?
• Why do SIS, LMS, identity, and financial aid systems need to connect with AI?
• What are the operational, financial, and retention implications of AI adoption?
• What is preventing institutions from moving beyond pilots?
• How should institutions approach AI governance, measurement, and human-AI collaboration?
• What should institutions do next?

Features

• Research Focus: AI Adoption in Student Supporty
• Framework: Adoption, Capability, Integration, Visibility
• Future Campus Impacts: Student Experience, Operational Efficiency, Retention, Governance, Institutional AI Readiness
• Research Approach: Analysis of published research, institutional data, sector intelligence, and peer-reviewed literature
• Published by: Campus Consortium Foundation
• Research Availability: June 2026

Table of Contents

1. Executive Summary
2. The Part of AI in Higher Education Nobody Is Talking About
3. What Students Actually Want: The Voice Behind the Data
4. How This Report Was Built
5. The Four Gaps Framework
6. The Real Cost of Falling Behind
7. What Gets in the Way: Barriers and What Breaks Through Them
8. The Human Side of AI in Student Support
9. Three Institutions That Figured This Out
10. A Practical Self-Assessment for IT Leaders
11. What Institutions Should Do Next
12. Where AI May Fail: A Contrarian Assessment
13. Looking Ahead: 2027–2028