Rather than assuming what faculty required, the researchers asked instructors directly about their needs, challenges and priorities for integrating AI into teaching and learning. Participants also expressed a strong preference for the form such support should take. They favored interactive and adaptable materials, openly licensed so they could be remixed, and grounded in practical classroom applications rather than abstract theory.

Those survey results fed directly into the design of an OER prototype. The open licensing choice was deliberate: by making the resource free to reuse and modify, the team aimed to support inclusive access and allow instructors in different teaching contexts to adapt the material to their own institutions, class sizes and disciplines. In the second phase, 75 instructors reviewed the prototype and provided 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, meaning the feedback was used to improve the resource during development rather than to measure final effectiveness in classrooms. The researchers describe their contribution as an educator-informed process for translating instructors' needs into both the design and the evaluation of a faculty development resource.

The team positions the approach as a flexible, scalable and community-oriented pathway for bridging institutional resource gaps. The study documents a development process, however, 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, so the priorities reported reflect those participants rather than a census of all higher education faculty. Even so, the study offers a concrete model for how institutions tasked with preparing instructors for AI can ground their offerings in documented demand: ask faculty what they need, build openly and adaptably around the answers, and iterate based on structured peer feedback.

Subject of Research: Instructor needs assessment and development of an open educational resource on generative AI in higher education teaching

Article Title: AI Resources in Higher Education: What Instructors Need and How We Deliver

Article References: Iskandarova, S., Yi, F., Taggart, J., Mickel, B., Bayraktar, B., & Henry, D. (2027). AI Resources in Higher Education: What Instructors Need and How We Deliver. Frontiers of Digital Education, 4(1), Article 1. https://doi.org/10.1007/s44366-026-0106-y

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0106-y

Keywords: generative AI, higher education, open educational resources, faculty development, AI literacy, pedagogy, ethics, needs assessment, mixed methods, instructional design, Resources, Higher