GENERATIVE AI LITERACY, OUTPUT VERIFICATION AND RESPONSIBLE USE AMONG UNDERGRADUATE PRE-SERVICE TEACHERS AT THE UNIVERSITY OF ILORIN, NIGERIA

This study examined generative artificial intelligence (AI) literacy, output verification and responsible use among undergraduate pre-service teachers at the University of Ilorin, Nigeria. The study adopted a descriptive survey research design. The population comprised 10,377 pre-service teachers, from which a sample size of 370 respondents was determined using the Krejcie and Morgan sample size table. Data were collected using a researcher-developed questionnaire titled Generative AI Literacy, Output Verification and Responsible Use Questionnaire (GAILORUQ). The instrument was validated by experts to ensure clarity, relevance and alignment with the study objectives. A pilot test involving 37 pre-service teachers outside the main study sample was conducted to establish the instrument’s internal consistency. Cronbach’s alpha yielded a reliability coefficient of 0.80. Data were analysed using mean and standard deviation to answer five research questions on generative AI literacy, output verification, responsible AI use, instructional applications and challenges encountered by pre-service teachers. The results indicated a moderate level of generative AI literacy, with a grand mean of 2.50, while output verification practices and awareness of responsible AI use were low, with grand means of 2.32 and 2.40, respectively. The extent of generative AI use for teaching and learning activities was high, with a grand mean of 3.15. The challenges encountered when applying generative AI to academic and instructional activities were also high, with a grand mean of 3.16. The findings established a gap between pre-service teachers’ use of generative AI tools and their practices for verifying AI-generated information and applying responsible AI principles. The study recommended that the University of Ilorin strengthen practical AI literacy training, promote systematic verification of AI-generated content, provide clear guidelines on responsible AI use, and integrate ethical generative AI practices into undergraduate teacher education programmes.