Leveraging AI-driven assessment tools to transform Science Education for future-ready learning

Abstract

Artificial Intelligence (AI) is transforming education at a rapid rate, with greater possibility to restructure science teaching and learning. Although AI-based science assessment is gaining popularity globally, its usage in African edu cation systems remains relatively new. The current paper will discuss how AI-based assessment tools can bypass the limitations of traditional evaluation systems and ensure more quality science education for future readiness. A systematic analysis of empirical studies published during the years 2014-2023 on pre-generative AI and generative AI uses in science education was conducted. The analysis reviewed evidence regarding adaptive assessment tech nology, virtual labs, intelligent tutoring systems, and learning analytics. Results indicate that AI-based evaluation provides real-time feedback, enables individualized learning, and makes data-driven assessment possible for stu dents and instructors. AI-based technologies address problems such as large class sizes, resource shortages, and inequalities in assessment processes. While obstacles such as digital illiteracy, infrastructure, and ethical issues around the use of data persist. The study concludes that the integration of AI-based assessment tools offers a para digm shift in science education, moving testing from traditional models to more dynamic, inclusive, and forward looking models. Strategic investment, digital capacity development, and policy support are recommended to sup port effective and ethical application of the technologies among African universities.