- Part I: Beyond the AI Efficiency Trap
- Part II: All Modalities Are Not Created Equal
- Part III: Orientation: More than a Campus Map
CHLOE10 REPORT RESPONSE (Part one of a three-part series)
Last year, our team of higher ed subject matter experts responded to the findings of the ninth installment of the Changing Landscape of Online Education (CHLOE9) by sharing their perspectives and insights in a series of articles. This year’s findings were no less intriguing, so we held a roundtable with Phil Autrey, Regina Law, Kevin Phang, and Lauren Dukes, EdD. Their feedback and ideas are captured in three articles covering the following topics:
- Utilization of AI continues to grow, but is its use too focused on efficiency?
- The growth of online learning has continued and accelerated, but are institutions delivering online instruction because it’s the best modality for a particular subject? Or just to meet student demand?
- Do current orientation programs prepare students (and faculty) for learning in a digital world? While the typical undergrad may be a “digital native”, do they have the learning-specific skills to be successful? And how do we support our adult learners who may not be as tech-savvy?
Are Institutions and Faculty Missing the Most Powerful Benefits of AI?
While CHLOE10 was filled with insights about the state of higher education, its findings regarding the utilization of AI are disappointingly familiar: “AI is emerging as a strategic priority for online learning, though most institutions are still in early stages. Only 23% report having an institution-wide AI strategy, while 66% describe localized efforts.”
In other words, much of higher education is using AI about as efficiently as using a smart phone exclusively to make calls, but it is a positive start. Realizing the full potential of AI will take time, but are institutions too focused on the promise of AI as a way to “do more with less”?
As the CHLOE10 Report finds, universities have made great forays into leveraging AI to enhance university operations—“the most frequently cited [AI] use cases were workload efficiency (49%) and course preparation (46%)”—and can begin to turn their attention to AI’s learning/teaching potential. Increasing comfort with AI through practical applications opens the door to thinking about how AI could be harnessed to transform learning itself, not just the operational mechanics behind the scenes.
Efficiency or Impact? Both, please.
As Lauren Dukes notes, “when institutions are thinking about AI, they’re not necessarily thinking about the students’ perspective, they’re thinking about driving efficiencies for themselves.” When faculty do use AI, it is often deployed for automated evaluation processes, resource management, and logistical support, rather than transforming learning activities or assessments.
Regina Law adds that while AI can function as a “connective tissue” for student support enabling “personalized experiences at scale,” most programs remain one-size-fits-all due to inertia or limited faculty bandwidth.
What’s Holding Us Back?
Hesitation and Policy Confusion
- As noted, only 23% of COLOs reported having an institution-wide approach while a majority (66%) indicated that AI planning occurs at the unit level, with individual departments or offices developing localized strategies. This creates an inconsistent patchwork approach to AI strategies.
- Survey data supports our team’s observation that both faculty and students face a policy vacuum—or a “mishmash”—when it comes to AI’s role in learning. As Lauren Dukes puts it, “Students are scared to get in trouble for using AI. Universities don’t have strong policies around AI use, and there’s not a lot of continuity in ‘this class I use it, this class I can’t use it.’ So there’s just a lot of hesitation.”
- Phil Autrey warns, “What we’re not doing is we’re not taking a look at how AI can help me as a faculty member drive my instruction, figure out what my students’ needs are, individualize for a specific topic, etc. And this goes for all kinds of courses, whether hybrid or fully online.”
Lagging Faculty Development
- Despite the expressed enthusiasm for AI-driven advancement in learning, only 28% of institutions say their faculty are fully prepared for online course design.
- Adjuncts—who shoulder most online teaching—often lack access to robust, ongoing training.
- Phil Autrey notes that “the opportunity and the priority of increasing your instructional skills is probably number three on the list, right? So, I have my research, I have my course load, and then how do I improve my own instructional abilities? Do I have time to do that?”
- That said, Autrey remains optimistic, “I think faculty are learners first and foremost. They got into academia because they enjoy learning. So, the idea that faculty don’t want to learn new things, I think that’s the misleading statement out of all of this. They’re extremely curious. They’re extremely open. But time and resources are scarce, so we need to prioritize that. And that’s where I think you’ll get this kind of exponential curve of once you get going, faculty are going to take off with it and really find new ways to use it.”
Moving Forward
To encourage this kind of creative exploration of AI’s capabilities, institutions must reorient their approach to its implementation:
- Clarify and unify AI policy: Unified, transparent guidelines on AI use for both students and faculty can reduce fear, confusion, and uneven adoption.
- Teach the teachers: Efficiency gains open windows of opportunity to invest in faculty development, helping them design and implement AI-powered learning.
- Leverage AI for meaningful assessment: Beyond identifying plagiarism or other “cheating” activities, AI offers unique opportunities to evaluate student success. Skilled instructors can create prompts and learning tasks that foster critical thinking, reflection, and creativity.
- Personalize at scale: AI enables personalized, one-on-one interaction even among student bodies numbering in the thousands. It can play a key role in student advising and support, tailoring not just content delivery but pathways, interventions, and “warm handoffs” throughout the student lifecycle.
- Ongoing data-driven reflection: While primarily viewed as an efficiency tool, AI’s data and analytics capabilities will be critical for continuous program improvement, not just for compliance or recruitment.
One particularly strong example of this kind of approach has been implemented by Unity Environmental University. Unity has endorsed “AI-First Design Principles” with a robust framework of guidance (following is a short summary) addressing a broad swath of issues that can/should be impacted by AI:
- I. Integration & Scalability
- 1. Mission Driven Integration
- 2. Scalable and Sustainable Deployment
- 3. Learner-Centered Access
- II. Operational Efficiency & Cost Control
- 4. Efficiency as a Core Outcome
- 5. Automation-First Mindset
- III. Intelligence & Optimization
- 6. AI-Driven Decision Intelligence
- 7. ROI and Impact Measurement
- 8. Agility and Continuous Optimization
- IV. Accountability & Responsible Use
- 9. Transparency and Accountability
- 10. Responsible and Compliant Use
- 11. Transparency and Explainability
- 12. Talent & Readiness
They even provide a guide to “Selecting AI Tools” to help students and faculty select the best fit for the task at hand.
Conclusion: Welcome Efficiency. Embrace Creativity.
While AI utilization in higher education remains in its infancy—as it does in many fields—there is reason to believe that institutions will move on from the logical first use of AI—as an efficiency tool—toward its full creative potential by integrating it into the learning process. As CHLOE10 reports, “Looking ahead, COLOs envision an increasingly hybrid student experience shaped by AI tools, personalized learning, and diminished reliance on lectures and traditional classrooms.”
This vision suggests a future where AI is utilized not just as an efficiency tool and certainly not simply as an honor code enforcement mechanism. As Regina Law puts it, “I think there are a lot of faculty out there that are being very creative in thinking about how they leverage AI in the classrooms versus trying to prevent it from being utilized.” Institutions that adopt this mindset and support these trailblazers with the resources and training they need will almost certainly see handsome rewards now and down the road.
If your institution is ready to move from efficiency to impact, it’s time to rethink how AI supports learning, not just operations.
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