Artificial Intelligence Adoption, Social Welfare, and Adult Learning Outcomes Among Continuing Education Students of Ibrahim Badamasi Babangida University, Lapai, Niger State, Nigeria

Abstract

This study examined the relationship between artificial intelligence adoption, social welfare, and adult learning outcomes among continuing education students of Ibrahim Badamasi Babangida University, Lapai, Niger State, Nigeria. The integration of artificial intelligence into higher education represents a significant pedagogical shift, yet its impact on continuing education students remains insufficiently explored in the Nigerian context. This study adopted a descriptive survey research design, employing both quantitative and qualitative methods. The population comprised all continuing education students enrolled in the Department of Continuing Education and Community Development, from which a sample of 350 participants was selected using stratified and simple random sampling techniques. Data were collected using a structured questionnaire titled "Artificial Intelligence Adoption, Social Welfare and Adult Learning Outcomes Questionnaire" (AIASWALOQ), supplemented by in-depth interviews. The instrument was validated by experts, while reliability yielded a Cronbach's alpha coefficient of 0.89. Data were analysed using descriptive and inferential statistics. The findings revealed that artificial intelligence adoption significantly influences social welfare and adult learning outcomes, with AI-powered learning tools, personalised adaptive systems, intelligent tutoring platforms, and automated assessment technologies recording varying mean scores above the criterion mean of 2.50. However, significant challenges were identified including poor digital infrastructure, inadequate funding, limited digital literacy, high data costs, and resistance to technological change. The study concluded that while artificial intelligence holds tremendous promise for transforming continuing education, its successful integration requires institutional intervention, investment in digital infrastructure, comprehensive training programmes, and locally relevant AI-powered content. The study recommended strengthened policy frameworks, increased budgetary allocation, public-private partnerships, indigenous language digital materials, and a national digital skills certification framework.