Abstract:

The integration of Artificial Intelligence (AI) in science education has seen substantial growth, notably in AI-based instructional tools, AI-enabled learning environments, and AI-supported assessment and feedback. These developments address educational challenges related to students' understanding of complex scientific concepts. However, there is a significant gap in comprehensive research on the effective application of these AI tools to enhance student learning outcomes. Furthermore, despite the increasing use of AI in educational settings, there is limited comprehensive empirical evidence on its effectiveness in improving student engagement and learning outcomes. This is partly due to limited partnerships between academic institutions and corporations that lead the development of AI tools. As a result, the design and implementation of AI tools in science education often overlook critical pedagogical considerations. Therefore, research is urgently needed to fill these gaps by providing evidence-based insights into the best practices for integrating AI into science curricula.

In light of the above, the research aim of this study was to provide a comprehensive overview of the current literature on AI-based instructional tools, AI-enabled learning environments, and AI-supported assessment and feedback in science education. The central research question is: What are the current trends and implications of integrating AI in science education? This study employed a qualitative systematic literature review methodology, analyzing purposively selected research papers published from 2013 to 2022. The sample included 102 peer-reviewed articles on AI integration in school and university science education. Data collection involved a systematic review of these articles, followed by thematic analysis to identify critical trends and outcomes.

The findings reveal that AI-based instructional tools are increasingly used to offer personalized and adaptive learning experiences. AI-enabled learning environments create immersive and interactive settings that enhance student engagement and motivation. Additionally, AI-supported assessment and feedback mechanisms provide timely and constructive evaluations, supporting formative and summative assessments. These tools significantly impact science education by promoting critical thinking, fostering self-directed learning, and improving learning experiences.

The implications of these findings suggest that AI has the potential to transform science education by providing tailored educational experiences, engaging students more deeply, and offering efficient assessment and feedback processes. However, the successful integration of AI requires addressing pedagogical challenges and ensuring that educators are adequately trained to use these technologies effectively. This study underscores the transformative potential of AI in science education, highlighting the need for continued research to optimize the use of AI tools and overcome existing challenges. The insights gained from this review can guide educators, researchers, and policymakers in leveraging AI to enhance science teaching and learning practices.

Keywords:

Artificial Intelligence, Science education, systematic review.