Lecturers’ Perceptions on the Integration of Generative AI in Higher Education: Demographic Influences, Opportunities and Challenges
TL;DR
Examination of lecturers’ perceptions of GenAI in higher education suggests that lecturers’ perceptions of GenAI are socially situated and systematically shaped by gender, age, and teaching experience, and that these perceptions in turn are strongly associated with frequency of use and depth of adoption.
Abstract
Aim/Purpose: This study examines lecturers’ perceptions of Generative Artificial Intelligence (GenAI) in higher education and how these perceptions vary with demographic characteristics, namely gender, age and teaching experience. It further investigates how such perceptions relate to lecturers’ frequency of GenAI use and stage of adoption, identifying the opportunities and challenges lecturers associate with the integration of GenAI in educational practice. Background: The rapid emergence of GenAI has introduced new possibilities and challenges for higher education, yet empirical research examining lecturers’ perspectives, particularly within diverse contexts, remains limited. While existing studies have largely focused on students’ use of GenAI or technical adoption models, less attention has been given to lecturers’ perceptions, demographic influences, and the ethical and pedagogical implications of GenAI integration Methodology: A mixed-methods research design was employed. Quantitative data were collected through an online survey administered to 98 lecturers from different higher education institutions spanning a range of disciplines (55% male, 45% female; 44% aged 25-45, 30% aged 46-55, 26% aged 56+). Qualitative data were gathered through semi-structured interviews with a purposive subsample of ten lecturers. Quantitative analyses included independent-samples t-tests, one-way ANOVA, and Spearman rank-order correlations, while qualitative data were analyzed using Braun and Clarke’s six-phase thematic analysis. Contribution: Theoretically, the study extends technology-adoption research by integrating the Technology Acceptance Model with an interpretive technology-in-practice perspective, suggesting that lecturers’ perceptions of GenAI are socially situated and systematically shaped by gender, age, and teaching experience, and that these perceptions in turn are strongly associated with frequency of use and depth of adoption. Practically, the findings highlight the need for differentiated professional development, updated ethical guidelines, and redesigned assessment practices that foster critical AI literacy, supporting responsible and sustainable GenAI integration in higher education. Findings: Quantitative findings revealed significant demographic differences in lecturers’ perceptions. Female lecturers reported more positive perceptions than male lecturers on several pedagogical dimensions. A clear age-related gradient emerged, with lecturers aged 25-45 expressing the most favorable perceptions and those aged 56+ the least. Strong positive correlations were observed between perceptions and frequency of GenAI use, between perceptions and stage of adoption (rs = 0.67, p< 0.01) and between frequency of use and stage of adoption (rs= 0.74, p<0.01). Also, the thematic analysis of interview data identified three opportunity themes. These included: empowering lecturers in teaching and time management, enhancing pedagogical practices and student learning and improving accessibility to information and knowledge creation, alongside three challenging themes involving ethical and academic integrity concerns, technological limitations and dependency and threats to professional identity. Recommendations for Practitioners: Institutions should implement differentiated professional development and update ethical governance frameworks, with particular emphasis on redesigning assessment to foster critical GenAI literacy and authentic engagement. Recommendation for Researchers: Further studies should investigate how GenAI evolves from a technological tool into a sustained technology-in-practice within higher education systems. Impact on Society: The study underscores the importance of responsible GenAI integration to safeguard educational quality, equity, and human-centered academic values. Future Research: Future research should incorporate a larger, cross-cultural sample to explore how differing institutional, national, and policy environments moderate GenAI acceptance. Furthermore, while this investigation indicates an association between positive perceptions and the frequency of GenAI use, a step for subsequent studies is to empirically validate the longitudinal relationship between initial positive attitudes and the deep, sustained integration of GenAI into core curricula and assessment practices.