From technology to pedagogy: determinants of university faculty’s pedagogically relevant use of generative AI

Purpose This study aims to explain the factors associated with university faculty’s use of pedagogically relevant (PR) generative artificial intelligence (GAI). These included AI literacy (AIL), organizational guidelines (OG), previous experience (PE) of using artificial intelligence (AI) and frequent interaction (FI) with GAI. Design/methodology/approach The study used a cross-sectional quantitative approach and collected data from 650 university faculty members. The data was analyzed using a variance-based approach known as partial least squares structural equation modeling with SmartPLS4 software. Findings The results revealed that AIL has a significant effect on PR, while OG, PE and FI have a non-significant effect in this regard. OG and PE have a significant impact on AIL. The effect of PE on FI is also significant, while FI has a non-significant effect on AIL. Research limitations/implications The study has implications for the broader educational system, university administration and faculty professional development programs and organizational-level support such as developing guidelines. The study’s scope was limited to faculty’s responses. Future research should study the opinions of educational leaders, such as deans and vice chancellors, regarding organizational-level guidelines for faculty’s pedagogical use of GAI. Originality/value The results show that faculty can use GAI in a way that is useful for pedagogical purposes and student learning. They can enhance their own efficacy by learning how to use GAI and understanding how to adhere to AI-related rules and procedures.

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Publication Details

Journal
Quality Assurance in Education
Published
2025-04-17
DOI
https://doi.org/10.1108/qae-12-2024-0255
Citations
2
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
3.39
Controls
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article

From technology to pedagogy: determinants of university faculty’s pedagogically relevant use of generative AI

Nazir Ahmed Jogezai, Fozia Ahmed Baloch, Gulab Khilji, Mohammad Najib Jaffar
2 citations
Quality Assurance in Education
Online Learning and Analytics
3.39
article

From technology to pedagogy: determinants of university faculty’s pedagogically relevant use of generative AI

Nazir Ahmed Jogezai, Fozia Ahmed Baloch, Gulab Khilji, Mohammad Najib Jaffar
article en
2 citations

Abstract

Purpose This study aims to explain the factors associated with university faculty’s use of pedagogically relevant (PR) generative artificial intelligence (GAI). These included AI literacy (AIL), organizational guidelines (OG), previous experience (PE) of using artificial intelligence (AI) and frequent interaction (FI) with GAI. Design/methodology/approach The study used a cross-sectional quantitative approach and collected data from 650 university faculty members. The data was analyzed using a variance-based approach known as partial least squares structural equation modeling with SmartPLS4 software. Findings The results revealed that AIL has a significant effect on PR, while OG, PE and FI have a non-significant effect in this regard. OG and PE have a significant impact on AIL. The effect of PE on FI is also significant, while FI has a non-significant effect on AIL. Research limitations/implications The study has implications for the broader educational system, university administration and faculty professional development programs and organizational-level support such as developing guidelines. The study’s scope was limited to faculty’s responses. Future research should study the opinions of educational leaders, such as deans and vice chancellors, regarding organizational-level guidelines for faculty’s pedagogical use of GAI. Originality/value The results show that faculty can use GAI in a way that is useful for pedagogical purposes and student learning. They can enhance their own efficacy by learning how to use GAI and understanding how to adhere to AI-related rules and procedures.

Quality Assurance in EducationVol. 33(4)
National Research University Higher School of Economics (RU), Balochistan University of Information Technology, Engineering and Management Sciences (PK), University of Balochistan (PK)
Openalex Percentile: Top 7%
Online Learning and Analytics
3.39
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