GENERATİVE AI (GENAI) İN SCİENCE EDUCATİON AS AN INNOVATİVE PRACTİCE: A SYSTEMATİC REVİEW

Generative artificial intelligence (GenAI)—particularly large language model (LLM) tools—has rapidly entered educational practice and is beginning to reshape science teaching, learning, and assessment. Science education is a distinctive use case because it requires epistemic reliability: learners must justify claims with evidence, apply disciplinary constraints (e.g., units, conservation laws), and engage in inquiry practices. This chapter offers a PRISMA 2020–aligned systematic review of recent research on GenAI in science education, complemented with a policy and ecosystem analysis for India. Evidence from published systematic reviews and empirical studies suggests that GenAI can support explanation, scientific writing, formative feedback, and inquiry planning when embedded in well-designed tasks. However, risks persist: hallucinations and inaccuracies, bias, privacy concerns, and academic integrity threats, especially where institutional guidance is limited. In India, NEP 2020 and NCF-SE 2023 emphasize competency-based learning, technology integration, and scientific temper, while national digital infrastructure (DIKSHA and NDEAR) provides a scalable platform for teacher professional development and content delivery. This chapter synthesizes evidence into an India-ready implementation framework (S‑CIENTIFIC), proposes assessment redesign options, and provides classroom-ready prompt templates, rubrics, and a reproducible search strategy (databases and Boolean strings).