“We Talk About the Risks, What About the Gains?” Large Language Models and Learning in Mathematics, Statistics, and Computing

Abstract

Large language models are increasingly present in higher education worldwide, yet debates in many institutions still concentrate mainly on fear, integrity concerns, and possible harm to student learning. This paper is a conceptual and argumentative contribution grounded in an extensive review of recent international and African literature on mathematics, statistics, and computational learning. It aims to clarify how large language models can strengthen quantitative learning when supported by responsible policy, ethical guidance, and sound pedagogy. The reviewed literature indicates that these systems can support stepwise reasoning in mathematics, deepen statistical understanding, enhance programming competence, strengthen writing and reporting skills, and improve student motivation, especially where traditional feedback is limited. The paper argues that meaningful benefit depends on AI literacy, prompting competence, guided use, verification practices, and strong human oversight. It further contends that African higher education requires curriculum reform, assessment redesign, and institutional readiness to integrate AI tools responsibly, while also attending to access, equity, and infrastructural realities. The paper concludes that large language models should not only be viewed as threats. When planned carefully, they can support learning quality, widen participation, and strengthen competence in quantitative disciplines that are critical for development in Nigeria and across Africa.