Assessing Faculty Investments in Emerging Education Technologies and Artificial Intelligence in Selected African Universities (London Journal of Research In Humanities and Social Sciences, 2025)
Educational technology and Artificial Intelligence (AI) have revolutionised various industries including education. New education technologies,sometimes, find the education sector ill-prepared, not only in their applications but also in the technical know-how of faculty. This is especially so for higher education institutions (HEIs) in developing countries. The socio-economic and digital divide between developed and low-income countries continues to factor in education disparities between the two worlds. Anchored on the technology acceptance model (TAM), the aim of this study was to assess faculty awareness, personal investments, and perspectives on the potential of AIs as an emerging educational technology in higher education. A descriptive survey was conducted on faculty (n=65) drawn from selected universities in Africa using purposive snowball sampling. Quantitative and qualitative methods of data collection were integrated into a structured online questionnaire and administered through emails and social media. Participants represented thirty-four (34) universities in eight (8) African countries. Four (4) themes emerged from the findings; i) A remarkable level of awareness, use, and investment of educational technology mainly from self-learning with a score of 74% (n=65) on a three-point Likert scale, ii) Perceived benefits of ed-tech included AI as useful study, writing and research companion for both faculty and students iii) Facultys belief on institutional support and investments indicated only 43% (n=65) affirmation and iv) Cost, speed and policies were the main challenges in adopting new ed-tech. These results should inform decision-making, policy formulation, administrative and budgetary priorities, faculty capacity needs, and adoption of ed-tech including generative AIs.
Keywords: brazil; multimorbidity; multiple long-term chronic conditions; socioeconomic positions; inequality measures; health inequalities; literature; image; Perrault; Dor; Ibarbourou; Higher education; developing countries; Educational technology; Generative AIs