Faculty Training for AI in Higher Education

Mark Mabrito

International Journal of Technology, Knowledge and Society2026https://doi.org/10.18848/1832-3669/cgp/a1021article
ABDC C
Weight
0.50

What the paper says

This article examines the challenges of integrating generative artificial intelligence (GenAI) into higher education through faculty training and development. As large language models like ChatGPT and Gemini become increasingly common in classrooms, many instructors report low confidence in how to use them effectively. In response, a three-part workshop titled Collaborating with Generative AI in the Classroom was developed and implemented at a regional university to provide faculty with practical skills and pedagogical strategies for AI-enhanced instruction. The workshop emphasized hands-on engagement and interdisciplinary perspectives, encouraging faculty to share ideas and co-develop classroom strategies. This interactive format fostered a sense of shared purpose and reduced the isolation some instructors may feel when experimenting with new technologies. The workshop focused on prompt crafting, transparency, critical thinking, and assignment redesign. Faculty response was mostly positive, with suggestions for a few areas of improvement. Additionally, faculty reported through anonymous surveys that they would attempt to implement some of the workshop strategies into their classrooms.

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https://doi.org/https://doi.org/10.18848/1832-3669/cgp/a1021

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@article{mark2026,
  title        = {{Faculty Training for AI in Higher Education}},
  author       = {Mark Mabrito},
  journal      = {International Journal of Technology, Knowledge and Society},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.18848/1832-3669/cgp/a1021},
}

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Faculty Training for AI in Higher Education

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.