The Epistemological Status of AI-Generated Knowledge in Science Education: A Theoretical Examination of Knowledge, Evidence, and Trust
TL;DR
The analysis argues that AI-generated outputs should not be positioned directly as scientific evidence or verified knowledge, but rather as provisional knowledge claims that require verification that require verification.
Abstract
The increasing integration of generative artificial intelligence (GenAI) systems into educational environments has brought about not only a technological transformation of teaching and learning processes but also fundamental epistemological questions concerning the sources of knowledge, its verification and justification, and the ways in which its reliability should be evaluated. This issue is particularly salient in science education, where a central learning objective is for students to relate scientific claims to evidence, recognize uncertainty, and critically examine how explanations are constructed. This study aims to theoretically examine the epistemological status of AI-generated outputs in science education through the concepts of scientific evidence, the Nature of Science (NOS), epistemic autonomy, epistemic agency, and human oversight. The study was not designed as an empirical document analysis or a systematic review; rather, it develops a conceptual synthesis drawing on current literature in education, science education, and artificial intelligence. The analysis argues that AI-generated outputs should not be positioned directly as scientific evidence or verified knowledge, but rather as provisional knowledge claims that require verification. To translate this epistemological positioning into pedagogical practice, the article proposes a seven-stage framework entitled the Epistemic Verification Cycle. Each stage of the cycle is theoretically justified, and an illustrative classroom implementation scenario is developed using photosynthesis as an example. In addition, AI hallucinations and the generation of fabricated references are analyzed in a separate section as structural characteristics of epistemic risk rather than as merely marginal system failures. Overall, the value of AI in science education depends less on its ability to generate correct answers rapidly than on how it is pedagogically designed to support students’ capacities for evidence evaluation, scientific reasoning, and epistemic responsibility.