Instructors Want Open, Practical AI Resources, Not Abstract Theory, Study Finds
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As generative artificial intelligence reshapes higher education, instructors are increasingly being asked to integrate AI into their teaching. But what do they actually need to do so effectively? A new study published in Frontiers of Digital Education provides a direct answer: trusted, timely, and adaptable resources focused on AI literacy, pedagogical strategies, and ethics -- materials that are openly licensed, interactive, and grounded in practical classroom applications rather than abstract theory.
The research, led by Sevinj Iskandarova of Bridgewater College and colleagues from the University of Virginia, George Mason University, and James Madison University, employed a multiphase, mixed-methods design. Rather than assuming what faculty required, the team first conducted a statewide needs assessment survey of 149 instructors across diverse institutional types and academic disciplines.
Participants prioritized three core content domains: AI literacy, pedagogical strategies, and ethics. They also expressed a strong preference for the form such support should take. Instructors favored interactive and adaptable materials, openly licensed so they could be remixed and modified, and rooted in real classroom scenarios. The open licensing choice was deliberate: By making the resource free to reuse and adapt, the team aimed to support inclusive access and allow instructors in different teaching contexts to tailor the material to their own institutions, teaching contexts, and disciplines.
Those survey results fed directly into the design of an open educational resource prototype. In the prototype evaluation phase, 75 instructors completed an evaluation survey on the prototype and provided structured feedback. According to the authors, reviewers affirmed the utility of the resource while also suggesting areas for further refinement. The study frames this as formative evaluation -- feedback used to improve the resource during development rather than to measure final effectiveness in classrooms.
"This study contributes an educator-informed process for translating instructors' needs into the design and formative evaluation of an adaptable, openly licensed faculty development resource," the authors write. They position the approach as a flexible, scalable, and community-oriented pathway for bridging institutional resource gaps and advancing inclusive faculty development in the age of AI.
The study documents a development process and does not report measured effects of the resource on teaching practice or student outcomes. The scope of the evidence is also bounded by its sample: the needs assessment drew 149 instructors from a single statewide survey, and the prototype evaluation involved 75 reviewers. The priorities reported reflect those participants rather than a census of all higher education faculty. Nonetheless, the research offers a concrete model for institutions tasked with preparing instructors for AI: ask faculty what they need, build openly and adaptably around the answers, and iterate based on structured peer feedback.