Dataset and Extended Data for: Measurement and Structural Modelling of Epistemic Regulation under Algorithmic Ambiguity in AI-Mediated Science Learning

This dataset supports the article “Measurement and Structural Modelling of Epistemic Regulation under Algorithmic Ambiguity in AI-Mediated Science Learning.” The dataset contains anonymized survey responses from 1,237 junior high school students who participated in a study examining Deepfake Learning Credibility Ambiguity (DLCA), Epistemic Vigilance (EV), AI Verification Competence (AVC), Authenticity Commitment (AC), and Authentic Knowledge Construction (AKC). No personally identifiable information is included. The dataset complies with ethical standards for research involving minors and institutional approval requirements. This dataset enables replication and validation of the structural equation modelling (PLS-SEM) analysis reported in the associated article. In addition to the underlying dataset, this record includes extended data, consisting of: Conceptual definitions and operational indicators of all constructs Research questionnaire instrument used in the study These materials are provided to enhance transparency, reproducibility, and methodological clarity.