Key points:
- Can institutions recognize and support instructional designers as part of their AI infrastructure?
- The hidden link between AI strategy and student retention
- Schools are building AI rules before they know the destination
- For more news on AI strategy, visit eCN’s AI in Education hub
Higher education institutions are investing significant time in selecting AI tools, developing policies, and encouraging faculty experimentation. Yet one of the most important parts of an institutional AI strategy may be receiving far less attention: the people who help educators connect technology to the realities of teaching.
Giving faculty access to AI does not mean they will immediately understand where it fits within their courses, which instructional problems it might help solve, or where their own judgment should remain central. General training can introduce capabilities, but responsible use develops through practice, reflection, and support grounded in an educator’s actual context.
Instructional designers are already doing much of this work. The question is whether institutions recognize and support them as part of their AI infrastructure.
Adoption often begins with a real problem
This became clear in a research-practice partnership conducted by Digital Promise with support from D2L. The study examined how higher education faculty, staff, and instructional leaders were using D2L Lumi and what helped or hindered its integration into instructional practice.
The research found that instructional designers played a key role in introducing faculty to Lumi and supporting its use in timely, relevant, and often one-on-one ways.
One participant described being overwhelmed by the professional development opportunities available. When training did not address an immediate need, the participant set it aside. The turning point came while working on weekly course content overviews. An instructional designer demonstrated how Lumi could help with that specific task, creating what the participant called the “I-need-it” moment.
This is not evidence that faculty are uninterested in learning. It suggests that professional learning becomes more meaningful when it connects to work educators are actively trying to accomplish.
Faculty rarely approach AI adoption as an abstract institutional objective. They approach it through practical questions: Can this help me build an assessment? Can it improve feedback without compromising my voice? Can it make this material more accessible? Can I trust what it produces? Will it strengthen learning, or simply generate more content for me to review?
Instructional designers can help faculty work through those questions because they understand both the capabilities of the technology and the instructional decisions surrounding its use.
The goal cannot simply be greater adoption
It would be easy to interpret these findings as a call for instructional designers to become campus AI champions. The Digital Promise report does recommend making their expertise more visible and supporting them as bridges between faculty and emerging tools.
But their role should be broader than promoting adoption.
Instructional designers can help faculty determine whether AI is appropriate for a particular task in the first place. They can examine whether an output aligns with course objectives, reflects an educator’s instructional intent, meets accessibility expectations, and creates a learning experience faculty can confidently stand behind.
They can also help educators recognize where efficiency may come into tension with pedagogy.
The same Digital Promise study found that participants had mixed reactions to AI-generated feedback. Some valued its speed, detail, and consistency. Others encountered overly positive language, inaccuracies, limited customization, and a significant need for review. More fundamentally, educators questioned what they might lose if they stepped away from reading and responding to student work. That process helped them identify misconceptions and understand how students were experiencing their courses.
An instructional designer’s contribution in that situation is not to persuade a faculty member to use the technology. It is to help the educator examine the tradeoffs and make an intentional decision.
That is the difference between tool adoption and institutional capacity.
Do not turn a strategy into an unfunded role
Institutions should also be careful about describing instructional designers as essential while failing to give them the resources, preparation, or authority to perform this expanding role.
Instructional designers are already supporting course development, accessibility, faculty development, quality assurance, and technology implementation. Adding AI leadership to that portfolio without adjusting capacity may create another institutional priority sustained by invisible labor.
If instructional designers are expected to help operationalize AI strategy, institutions need to invest accordingly. That includes:
- Ongoing professional learning that extends beyond individual products
- Meaningful involvement in AI governance and technology decisions
- Access to opportunities for experimentation and evaluation
- Time for consultation with faculty
- Partnerships with academic leaders, faculty development teams, accessibility experts, information technology, and students
- Clear measures of success that extend beyond tool usage
Institutions should also build faculty and instructional design capacity together. Faculty bring disciplinary knowledge, relationships with learners, and responsibility for instructional decisions. Instructional designers bring expertise in learning design, accessibility, assessment, and the thoughtful integration of technology. Neither should be expected to navigate this transition alone.
Build a learning system, not a training calendar
The research also challenges institutions to reconsider how they approach AI professional learning.
Optional workshops and resource libraries have value, but they are unlikely to be sufficient. Faculty need foundational opportunities to build AI literacy, as well as support when a real instructional question emerges. They need spaces to learn from colleagues, examine unsuccessful uses, discuss pedagogical tensions, and revisit their practices as the technology changes.
The strongest approach will combine shared foundations with contextual support:
- Establish a common understanding of the institution’s purpose for using AI.
- Develop practical use cases tied to course design, assessment, accessibility, feedback, and student support.
- Create shared guardrails while leaving room for disciplinary and pedagogical differences.
- Provide just-in-time consultation through instructional designers and other trusted partners.
- Build communities where faculty and staff can share evidence, questions, and lessons from practice.
- Evaluate success through instructional quality, educator confidence, accessibility, student engagement, and learning outcomes, not adoption numbers alone.
AI adoption is often treated as a technology rollout followed by faculty training. The Digital Promise findings point toward a different model. Meaningful adoption depends on a human infrastructure that helps educators interpret new capabilities, exercise judgment, and connect experimentation to instructional purpose.
Instructional designers are an important part of that infrastructure. But institutions cannot simply call them champions and hope adoption follows. They must give them the preparation, resources, partnerships, and institutional voice to help shape what responsible AI use becomes.
The goal is not to get more faculty using AI. It is to help educators and institutions make better decisions about when, where, and why to use it.
Dr. Cristi Ford is Chief Learning Officer at D2L.
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