Systematic Reviews of the Scientific Literature on Artificial Intelligence and Science Education using PRISMA Model

Abstract

Introduction: With the growing need for innovative teaching approaches, researchers are also increasingly seeking ways on how AI can solve problems in science education and enhance learner achievements.

Objectives: This systematic review investigates the incorporation of Artificial Intelligence (AI) into science education during the period 2015-2025.

Methods: It employs the PRISMA model to integrate evidence from recent scientific research, with an emphasis on how AI technologies advance teaching and learning through personalized learning environments, automated exams, and online labs.

Results: AI-based individualized learning systems evolve to fit individual student requirements, smart assessment instruments give immediate feedback, and virtual laboratories provide a secure place to experiment. The review also considers the possibilities and challenges that come with adopting AI, such as data privacy, the risk of overuse of AI diminishing one's ability to think critically, and the need for sufficient teacher training to efficiently incorporate AI instruments.

Conclusions: Combining results across different studies, the review endeavours to inform teachers, policymakers, and researchers regarding the use of AI to enhance science teaching and learning by confronting issues in science education and enhancing learning outcomes