University teachers’ behavioral intentions of generative AI instructional use: the effects of AI literacy, GAI pedagogical relevance and organizational AI guidelines
Purpose This study is aimed at explaining university teachers’ behavioral intentions to use generative artificial intelligence (GAI) in their instruction, considering the effect of their AI literacy (AIL), organizational guidelines (OG), pedagogical relevance (PR) of GAI and their previous experiences (PE) with AI. Design/methodology/approach Using an online survey instrument, 493 university faculty members across Pakistan provided data for this cross-sectional quantitative study. Findings We found AIL, PR and OG as key factors that influenced teachers’ intentions to use GAI in their instruction. In addition, their prior experience of AI usage was found to be significant in predicting their intentions to use GAI. OG did not have a significant effect on the PR of AI. Originality/value The results suggest that universities should assist teachers in building their AI-related capacity. They can offer relevant professional development opportunities and on-the-job support. To ensure the relevant and meaningful use of GAI, they must also consider formulating OG. These guidelines must accommodate the pedagogical parameters, improve teachers’ pedagogical practices and overcome the negative consequences of AI for learners.
Authors
- Nazir Ahmed Jogezai (ORCID: https://orcid.org/0000-0003-0849-5282)
- Fozia Ahmed Baloch (ORCID: https://orcid.org/0000-0002-2279-3553)
- Gulab Khilji
- Muhammad Jaffar (ORCID: https://orcid.org/0009-0002-0349-5531)
Institutions
- National Research University Higher School of Economics (RU)
- Balochistan University of Information Technology, Engineering and Management Sciences (PK)
- University of Balochistan (PK)
- State Board of Education (US)
Publication Details
- Journal
- International Journal of Information and Learning Technology
- Published
- 2025-09-18
- DOI
- https://doi.org/10.1108/ijilt-10-2024-0229
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00