Transforming Science Learning Materials in the Era of Artificial Intelligence

2026-02-08

AI Summary

This study addresses the limitations of traditional science learning materials, which often fail to meet demands for personalization, authenticity, and accessibility or reflect contemporary scientific practices. It proposes an integrated framework that systematically explores six key applications of artificial intelligence in science education: integration into authentic scientific practices, adaptive instruction, interactive simulations, multimodal content generation, enhanced accessibility, and support for co-creation between teachers and students. Leveraging generative AI, adaptive algorithms, and interactive simulation technologies, the framework enables learning experiences closely aligned with real-world scientific inquiry, supporting dynamic modeling, real-time data interaction, and culturally responsive resource development. The work underscores the synergy among scientific rigor, inclusivity, and student agency in AI-driven innovation while highlighting critical ethical challenges such as algorithmic bias and data privacy, advocating for responsible AI deployment in educational contexts.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Applications
• Intelligent Robots: Embodied AI

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The integration of artificial intelligence (AI) into science education is transforming the design and function of learning materials, offering new affordances for personalization, authenticity, and accessibility. This chapter examines how AI technologies are transforming science learning materials across six interrelated domains: 1) integrating AI into scientific practice, 2) enabling adaptive and personalized instruction, 3) facilitating interactive simulations, 4) generating multimodal content, 5) enhancing accessibility for diverse learners, and 6) promoting co-creation through AI-supported content development. These advancements enable learning materials to more accurately reflect contemporary scientific practice, catering to the diverse needs of students. For instance, AI support can enable students to engage in dynamic simulations, interact with real-time data, and explore science concepts through multimodal representations. Educators are increasingly collaborating with generative AI tools to develop timely and culturally responsive instructional resources. However, these innovations also raise critical ethical and pedagogical concerns, including issues of algorithmic bias, data privacy, transparency, and the need for human oversight. To ensure equitable and meaningful science learning, we emphasize the importance of designing AI-supported materials with careful attention to scientific integrity, inclusivity, and student agency. This chapter advocates for a responsible, ethical, and reflective approach to leveraging AI in science education, framing it as a catalyst for innovation while upholding core educational values.

Problem

• artificial intelligence
• science education
• learning materials
• ethical concerns
• personalized instruction

Innovation

• adaptive instruction
• interactive simulations
• multimodal content generation
• AI-supported co-creation
• inclusive science education