Abstract
Background: Artificial intelligence (AI) is increasingly integrated into science education, offering opportunities to support instruction, learning, and assessment. However, evidence of AI’s instructional benefits does not necessarily demonstrate its effectiveness in addressing barriers to inclusive participation in scientific learning.
Objectives: This study critically synthesizes evidence on AI applications in science education, examines the extent to which existing research supports claims of inclusive educational practice and outcomes, and develops an evidence-informed conceptual framework for evaluating AI-supported inclusion.
Methods: A critical, evidence-informed conceptual synthesis was conducted using selected empirical and conceptual literature on AI-supported science education, inclusive pedagogy, assessment, and responsible AI. Sources were examined for their educational contexts, AI functions, reported findings, inclusion-related evidence, and methodological limitations.
Results: The synthesis identified applications of AI in scientific dialogue, adaptive scaffolding, multimodal representation, feedback, and assessment. Although selected studies reported instructional benefits, direct evidence of reduced learning barriers, equitable epistemic participation, and inclusive outcomes remained limited.
Conclusions: AISIF offers a discipline-sensitive conceptual basis for evaluating whether AI-supported adaptations address learner-specific barriers while preserving meaningful participation in scientific reasoning.
First Page
365
Last Page
390
Recommended Citation
Suprihatiningrum, J.
(2026).
Artificial Intelligence in Science Education: Toward an Evidence-Informed Framework for Inclusive Teaching, Learning, and Assessment.
Jurnal Pendidikan dan Pembelajaran, 33(2), 365-390.
DOI: https://doi.org/https://doi.org/10.17977/2580-2313.1170
Available at:
https://citeus.um.ac.id/jpp/vol33/iss2/13
Manuscript clarification as migration from previous OJS
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