Artificial Intelligence in Science Education: Ethical Challenges and a Conceptual Framework for Responsible Integration
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AI in Education, Science Education, AI Ethics, Responsible AI##article.abstract##
Artificial intelligence (AI) is increasingly shaping educational practices through technologies such as intelligent tutoring systems, automated assessment, and generative language models. In science education, these tools are often associated with personalized learning and support for inquiry-based instruction. However, their integration also raises important ethical concerns that remain underexplored within the disciplinary context of science education. This paper presents a theoretical analysis of the ethical challenges associated with the use of AI in science teaching and learning. Drawing on literature from AI ethics, educational technology, and science education, the study examines how AI-mediated learning environments interact with the core aims of science education, including inquiry, evidence-based reasoning, and conceptual understanding. The analysis identifies key ethical concerns such as the epistemic reliability of AI-generated scientific explanations, risks of student overdependence, challenges to academic integrity, and issues of fairness, transparency, and accountability. Based on this analysis, the paper proposes a conceptual framework for the responsible integration of AI in science education. The framework emphasizes epistemic responsibility, pedagogical mediation, learner autonomy, transparency, and equity as guiding principles. It highlights the need to maintain human judgment and promote critical engagement with AI tools to ensure that they support rather than replace scientific inquiry and reasoning. The study contributes to the emerging discourse on ethically grounded and human-centered uses of AI in education by sitting ethical concerns within the specific context of science education.
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