Perceived AI feedback quality and mathematics learning outcomes: the mediating role of AI dependence among Ghanaian university students

Abstract

Artificial Intelligence (AI)-generated feedback is increasingly transforming mathematics education by providing timely, personalized, and adaptive learning support. However, limited evidence exists on the behavioural mechanisms through which students’ perceptions of AI-generated feedback influence mathematics learning outcomes, particularly in Sub-Saharan Africa. This study examined the direct and indirect relationships among Perceived AI Feedback Quality (PAQ), AI Dependence (AID), and Mathematics Learning Outcomes (MLO) among 360 undergraduate students in Ghana. A quantitative cross-sectional survey design was adopted, and data were analysed using Structural Equation Modelling (SEM) with IBM SPSS Statistics 23 and AMOS 23. The measurement model demonstrated satisfactory reliability and validity. The structural model showed that PAQ significantly predicted AID ( β  = 0.673, p  < .001) and MLO ( β  = 0.449, p  < .001), while AID also significantly predicted MLO ( β  = 0.441, p  < .001). Bootstrapping analysis further revealed that AID partially mediated the relationship between PAQ and MLO ( β  = 0.314, p  < .001). The findings indicate that students who perceive AI-generated feedback as accurate, relevant, and useful are more likely to depend on AI, and this dependence is in turn associated with stronger perceived mathematics learning outcomes. The study extends Feedback Theory and Social Cognitive Theory by identifying AI Dependence as a key behavioural mechanism linking AI feedback quality to mathematics learning. The findings provide practical implications for educators, instructional designers, and policymakers seeking to promote the effective and responsible integration of AI technologies into mathematics education.