Why This Matters

AI is no longer a horizon issue for online programs: it is already reshaping how faculty teach, how students learn, and how operations run. The challenge now is that adoption has outpaced institutional infrastructure, with faculty skepticism creating friction in the instructional core (Quality Matters / Eduventures / EDUCAUSE, 2024), student AI literacy going largely unmeasured (Tyton Partners, 2026), and online education bearing a disproportionate share of academic integrity scrutiny (UPCEA, 2026). Institutions running online programs need a clearer picture of where AI is actually being used, where the gaps are, and what support structures are producing results.

The Current State: Uneven Adoption Across Teaching, Services, and Operations

The practical portrait of AI in online higher education right now is one of concentrated adoption in some functions and notable underinvestment in others. Across campus applications broadly, teaching and learning leads at 76% and administrative processes at 57%, while recruiting and enrollment (34%) and student support services (29%) remain underdeveloped relative to their potential (The Chronicle of Higher Education & P3•EDU, 2025). Among online enterprises specifically, the top AI applications are enhancing teaching practices (61%), administrative efficiency (58%), student support services (39%), and personalized learning (37%), with instructional efficiency and faculty support cited as the primary strategic driver by 67% of respondents (UPCEA, 2025). Individual use tells a similar story of momentum: 43% of administrators now use AI daily, compared to 32% of students and 25% of faculty, and over half of all three groups use AI at least weekly (Tyton Partners, 2026). What this distribution reveals is that administrative staff have largely adopted AI as a productivity tool, while faculty adoption remains slower and more contested. For institutions running online programs, where faculty often work remotely and course design decisions carry significant scale implications, the gap between administrator and faculty adoption is not just a culture issue. It shapes instructional quality and student experience at scale.

Faculty Support Structures: What Institutions Are Actually Offering

The faculty adoption gap has not gone unaddressed, though the interventions most commonly deployed tend toward structured programming rather than financial incentives. Among online enterprises, 77% offer structured training or workshops, 70% support communities of practice or faculty learning groups, and 62% provide internal showcases or sandbox environments (UPCEA, 2025). These are reasonable starting points. What is less common, and likely worth examining, is direct compensation: only 15% offer microgrants or stipends for AI-related work, and 13% use recognition mechanisms such as badging (UPCEA, 2025). Given that COLOs perceive faculty attitudes toward AI's educational applications as typically neutral or negative (Quality Matters / Eduventures / EDUCAUSE, 2024), relying almost entirely on voluntary participation in workshops and learning communities may not be sufficient to shift the instructional core. The resistance pattern mirrors what online learning itself encountered during its early adoption phases, which suggests the pathway forward likely involves sustained, structured engagement rather than one-time training events. Institutions should assess whether their current faculty support model is primarily informational or genuinely incentivized, and consider whether adding even modest stipends or course-release provisions for AI integration work changes faculty engagement patterns.

Student-Facing AI: Encouragement Without Measurement

Student use of AI is growing, but institutional approaches to supporting and measuring that use remain inconsistent. About one-third of COLOs (34%) reported that students are being encouraged to use AI to support learning, including generating content (32%), developing or editing code (29%), and using AI-powered adaptive learning tools (27%) (Quality Matters / Eduventures / EDUCAUSE, 2024). Encouragement is a starting point, but it is not a curriculum strategy. The measurement gap is significant: only 10% of administrators report that their institutions currently measure student AI literacy, though 42% expect to do so within three years (Tyton Partners, 2026). That three-year window suggests many institutions are aware they need a more structured approach but have not yet built one. For online programs, where demonstrating learning outcomes is already central to quality assurance and accreditation conversations, embedding AI literacy as a measurable competency is a concrete curricular decision, not just a values statement. Institutions should define what AI fluency means in the context of their disciplines and degree levels, then identify where in the curriculum or co-curriculum that competency can be taught, practiced, and assessed.

Planned Expansion and the Academic Integrity Burden Online Programs Carry

The near-term trajectory of AI in higher education points toward significant investment in student-facing functions. Institutions anticipate expanding AI use in recruiting and enrollment from 34% currently to 73% over the next two to three years, in student support services from 29% to 69%, and in fundraising and alumni engagement from 19% to 60% (The Chronicle of Higher Education & P3•EDU, 2025). That scale of planned expansion across the OPM and edtech sector is consistent: 70% of companies in that space are already using AI to enhance the learning experience, and 33% are using it to improve business operations (Validated Insights, 2026). Against this backdrop of accelerating adoption, one persistent friction point for online programs is worth naming directly. 55% of COLOs reported that academic integrity and AI concerns are raised first in the context of online learning, even though the risk is consistent across modalities (UPCEA, 2026). That asymmetry places a credibility burden on online programs that the evidence does not support. Institutions addressing AI integrity concerns through online-specific policy responses, rather than institution-wide ones, may be reinforcing a perception problem rather than solving an academic one. Institutions should position AI integrity policy as a campus-wide responsibility and advocate explicitly for that framing in faculty governance and senior leadership conversations.

Action Items

• Audit current AI applications across instructional, administrative, and student-facing functions to identify where deployment is active, where it is planned, and where gaps exist relative to peer benchmarks
• Map faculty AI support offerings against what the evidence shows as most common (structured training, communities of practice, sandbox environments) and identify whether incentive structures such as stipends or course releases are absent and worth piloting
• Define AI literacy as a measurable competency for students, specifying what it means in context, and identify where in existing curriculum or co-curriculum it can be embedded and assessed
• Work with academic affairs and faculty governance to frame AI academic integrity policy as institution-wide rather than online-specific, and request data disaggregated by modality before accepting modality-specific policy responses
• Establish a baseline for student and faculty AI use within online programs, using it to track adoption over time and inform resource allocation decisions
• Engage OPM and edtech partners about their current and planned AI applications in learning experience and operations, and assess how those capabilities align with institutional priorities

The evidence collectively suggests that institutions running online programs are in a middle stage of AI adoption: past the point of early experimentation, but not yet at systematic integration. Administrative adoption is outpacing faculty adoption, student encouragement is outpacing student measurement, and planned expansion in student-facing AI is outpacing current infrastructure. The online programs best positioned to benefit from AI investment over the next several years are likely those that treat faculty engagement as a change management challenge requiring incentives and not just training, that build AI literacy into their quality frameworks before external pressures require it, and that resist the pull toward online-specific AI policies that may inadvertently reinforce the credibility gaps the field has worked for decades to close.

References

1. The Future of the OPM Market in the United States. Validated Insights, 2026.
2. Time for Class 2026: The AI Tipping Point. Tyton Partners, 2026.
3. Benchmarking Online Enterprises: Insights into Structures, Strategies, and Financial Models in Higher Education. UPCEA, 2026.
4. Benchmarking Online Enterprises: Insights into Structures, Strategies, and Financial Models in Higher Education. UPCEA, 2025.
5. 2025 Public-Private Partnership Survey Key Findings. The Chronicle of Higher Education & P3•EDU, 2025.
6. CHLOE 9: Strategy Shift: Institutions Respond to Sustained Online Demand — The Changing Landscape of Online Education, 2024. Quality Matters / Eduventures / EDUCAUSE, 2024.