["European Journal of Education, Volume 61, Issue 3, September 2026. ", "\nABSTRACT\nThe rapid integration of generative artificial intelligence in higher education has intensified interest in understanding how students' motivational beliefs shape their engagement with AI‐supported learning. This study examines how “expectancy beliefs and task value” dimensions, namely “attainment, utility, interest, and cost”, influence Economics students' use of generative AI (GenAI) and how such use supports self‐regulated learning. Survey data were collected from 416 university Economics students in Ghana and analysed using “Partial Least Squares Structural Equation Modelling”. The findings show that expectancy, utility, interest, and cost significantly predict students' use of GenAI, whereas attainment value does not exert a significant effect. GenAI use, in turn, strongly predicts self‐regulated learning, indicating that sustained engagement with AI tools enhances students' capacity to plan, monitor, and regulate their learning processes. Mediation analysis further demonstrates that generative AI use transmits the effects of expectancy, utility, interest, and cost on self‐regulated learning, confirming its central role in translating motivational beliefs into effective learning behaviours. By integrating expectancy‐value theory with self‐regulated learning in an AI‐enabled context, this study advances understanding of the motivational foundations of responsible and purposeful AI use in higher education. The findings offer practical implications for curriculum design and instructional practices aimed at strengthening students' motivation and fostering autonomous, reflective, and productive engagement with GenAI tools.\n"]