A new report from D2L and Digital Promise offers a clear-eyed look at what higher education faculty and instructional staff actually want from AI: tools that fit their existing workflows, handle practical tasks like quiz creation, and keep educators firmly in control of the teaching process.
The research, released September 1, 2026, examined how instructors and staff used D2L Lumi within the Brightspace learning environment. The findings push back against the idea that AI adoption hinges on flashy features. Instead, educators responded best when the technology addressed specific, clearly defined instructional needs without forcing them to leave their familiar digital workspace.
Practical tasks build confidence faster than broad promises
Among participants, two use cases dominated: creating assessment questions and providing student feedback. D2L Lumi Quiz drew particularly strong interest. Instructors valued its ability to generate multiple questions quickly while still allowing them to review and approve every output before it reached students.
"What this research makes clear is that meaningful AI adoption is not driven by what the technology can produce. It is shaped by what educators can confidently stand behind," said Dr. Cristi Ford, Chief Learning Officer at D2L. "Educators saw the greatest value when AI fit naturally into their work and allowed them to inspect, refine and remain accountable for the output."
Embedding AI in existing platforms reduces friction
The study found that keeping AI inside the learning management system mattered. Participants valued using D2L Lumi within Brightspace rather than exporting course materials to standalone AI tools and then re-importing them. This integration also appeared to build trust - educators reported greater confidence in AI outputs when the system drew from course materials they had already uploaded, with fewer concerns about bias or inaccuracy than they had experienced with other AI tools.
Course context proved to be a quiet but critical factor. When AI referenced materials the instructor had selected, skepticism dropped. The technology felt less like a black box and more like an assistant working from the same source documents the educator already trusted.
Educators want support, not replacement
Participants consistently drew a line between tasks they wanted AI to handle and work they wanted to protect. Routine, repetitive jobs - generating question banks, drafting initial feedback - were welcomed as time-savers. But instructors were clear that this freed-up time should go toward course design, student interaction, mentorship, and other work requiring professional judgment.
Instructional designers emerged as essential bridges between the technology and the classroom. Faculty described one-on-one help from designers as especially useful because it connected AI capabilities to immediate, specific instructional problems at the moment they arose. For institutions exploring AI for Education, the finding suggests that software alone is not enough - targeted human support can determine whether tools get used or ignored.
The report also identified a potential evaluation framework that moves beyond simple adoption metrics. Participants said AI should be assessed on efficiency, effectiveness, and accessibility - whether it reduces instructor workload, produces useful outputs, supports student learning, and contributes to inclusive course experiences.
Why this matters for educators
This research confirms that faculty are not resistant to AI - they are selective. The tools that gain traction will be those that slot into existing platforms, handle specific instructional chores, and leave educators with final say over what students see. For teachers and instructional designers building AI Learning Path for Teachers skills, the message is practical: start with the repetitive tasks that eat up planning time, keep the human review step intact, and don't ask instructors to learn a whole new system just to experiment.
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