Relationship Between Students’ Academic Motivation and Perceived Usefulness of Artificial Intelligence in Learning Educational Psychology Among University Undergraduates in Northeast Nigeria

This study examined the relationship between students’ academic motivation and their perceived usefulness of artificial intelligence (AI) in learning Educational Psychology among university undergraduates in Northeast Nigeria. As higher education increasingly integrates emerging technologies, AI-based tools and platforms are positioned to transform pedagogical practices and learner engagement. However, the extent to which students’ motivational orientations influence, or are influenced by, their perceptions of AI’s usefulness remains underexplored in the Nigerian context. Drawing on self-determination theory and the technology acceptance model, this research employed a correlational survey design to investigate these dynamics across selected federal and state universities in Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe states. A total of 500 undergraduate students enrolled in Educational Psychology courses were selected through stratified random sampling. Data were collected using a structured questionnaire comprising the Academic Motivation Scale (AMS) and a Perceived Usefulness of AI Scale adapted for educational settings. Descriptive statistics (means, standard deviations) were used to profile respondents’ levels of academic motivation and perceived usefulness of AI, while Pearson’s correlation coefficient and multiple regression analysis tested the strength and direction of relationships between variables. Findings revealed that undergraduates generally reported moderate to high levels of academic motivation and positive perceptions of AI’s usefulness in learning Educational Psychology. A significant, positive correlation was observed between academic motivation and perceived usefulness of AI (r = .56, p < .01), indicating that more academically motivated students tend to perceive AI tools as more beneficial for their learning. Further, intrinsic motivation emerged as the strongest predictor of perceived AI usefulness, compared with extrinsic and amotivation dimensions. Regression results suggested that motivation accounted for a meaningful proportion of the variance in perceived usefulness scores (R² = .32, F(3, 496) = 78.45, p < .001). The study underscores the interconnectedness of motivational factors and technology perceptions in tertiary education. It highlights that fostering intrinsic motivation may enhance students’ receptivity to AI-enhanced instructional approaches. Practical implications include the integration of AI literacy components into curricula, faculty development on motivational strategies, and targeted interventions to align AI applications with learners’ needs and goals. The study recommends further longitudinal and experimental research to validate causal pathways and explore contextual moderators such as gender, academic discipline, and digital access disparities in the Northeast Nigerian higher education landscape.