Artificial intelligence and educational psychology: A framework for self-regulated learning, motivation, and student independence in higher education
Debates about artificial intelligence in higher education tend to focus on the capabilities of the tool and to neglect the psychology of the learner who uses it. This chapter argues that any defensible framework for AI-assisted learning must be built on what educational psychology knows about how people learn independently and well. It synthesises three established bodies of theory self-regulated learning and metacognition, the self-determination theory of motivation, and the concept of self-efficacy and reads them against emerging evidence on how generative AI affects learning processes. The central tension it identifies is that the same tool can either scaffold self-regulation or induce “metacognitive laziness,” depending on whether it prompts thinking or simply supplies answers; unscaffolded reliance risks offloading the very cognitive work in which learning consists, eroding self-regulation, motivation, and the sense of agency. From this analysis the chapter derives a framework in which AI support is designed to scaffold the forethought, performance, and self-reflection phases of self-regulated learning, to protect self-efficacy through productive struggle, and to support autonomy, competence, and relatedness rather than substitute for them. It applies the framework to African higher education, where scarce access to individual tutoring makes well-designed AI support genuinely valuable but where the risks of dependence, de-skilling, and inequity are correspondingly acute. The significance is a learner-centred rather than tool-centred account of AI in education, contributing to debates on pedagogy and on artificial intelligence and emerging technologies.