Effects of generative artificial intelligence on students' learning motivation: evidence from a meta-analysis

Abstract

The use of generative artificial intelligence (GenAI) in the education setting has expanded quickly. GenAI may enhance learning motivation by providing immediate feedback, adaptive support, and multimodal resources, yet empirical findings remain inconsistent. To address the controversy of current studies, a meta-analysis was conducted to examine the overall effect of GenAI on students' learning motivation and test whether effects differed across study contexts. A random-effects meta-analysis synthesized 56 effect sizes from 42 controlled experimental and quasi-experimental studies involving 6,059 participants. Moderator analyses assessed variation by educational level, cultural context, subject domain, and device type. The random-effects model showed a positive average effect of GenAI on students' learning motivation (Hedges' g = 0.764). However, heterogeneity was very high ( I 2 = 93.7%), indicating substantial variation across study contexts. Exploratory moderator analyses suggested that effects were larger in higher education than in primary/secondary education, larger in collectivistic than individualistic contexts, larger in language learning than in other subjects, and larger when GenAI was used on mobile devices (MD) rather than computers. The observed subgroup differences should be interpreted cautiously because these moderators represented study-level characteristics rather than experimentally manipulated conditions. GenAI-supported learning is associated with higher learning motivation on average; however, the substantial heterogeneity suggests that its motivational benefits are not uniform across educational contexts. Future research should identify the instructional, learner, and contextual conditions under which GenAI can most effectively support students' motivation.