Digital Competence as a Predictor of Trust in Artificial Intelligence among University Teachers in Pakistan's Public Sector Universities

Abstract

The greater the accumulation of artificial intelligence (AI) in higher education, the more opportunities there are for improving both teaching and learning, although concurrently the need for university teachers to have digital competence and to place trust in AI technologies becomes important. Yet there is still very little empirical evidence regarding teachers' digital competence and their trust in AI, especially in developing countries like Pakistan. Therefore, this study examined digital competence as a predictor of trust in artificial intelligence among university teachers in Pakistan's public sector universities. A quantitative research approach was adopted using a cross-sectional correlational research design. The target population consisted of 12,073 university teachers, including 7,053 male and 5,020 female teachers employed in public sector universities across Punjab, Pakistan. A sample of 1201 was selected by using a multistage stratified simple random sampling technique. A total of 908 responses were received.  A structured questionnaire consisting of two standardized instruments: Teacher’s Digital Competence Questionnaire (TDCQ) (Redecker, 2017), which comprised 22 items with reliability α=0.703 and the Trust in Artificial Intelligence scale (Nazaretsky et al., 2022), which consisted of 23 items with reliability α=0.915, was adapted to collect the data. The results specified a significant and positive relationship between digital competence and trust in AI. Higher education institutions should pay special attention to developing their university teachers' digital competence by ensuring the unremitting professional development of teachers as well as organizing training courses on AI and expanding the capacity of teachers to use AI in education.