Ai-Guided Phet Inquiry for Correcting Engineering Students’ Misconceptions in Electromagnetism
Keywords:
Electromagnetism, Misconceptions, Phet Simulations, Artificial Intelligence, Inquiry-Based Learning, Conceptual Change, Engineering Education, CSEM, Formative Feedback, Normalized GainAbstract
This paper proposes an instructional model for reducing engineering students’ misconceptions in electromagnetism through the coordinated use of PhET simulations, inquiry-based tasks, and artificial-intelligence-supported scaffolding. The study is motivated by the persistent difficulty students experience in interpreting electric field, potential, induction, vector direction, and charge interaction as coherent physical relationships rather than disconnected formulas. The article argues that conceptual change becomes more likely when three conditions are established simultaneously: visual access to invisible processes, structured opportunities for prediction and testing, and dialogic feedback that exposes faulty reasoning. Within the proposed model, PhET is treated as an interactive representational environment, while AI functions as a regulated pedagogical mediator that provides prompts, hint sequences, and misconception checks under teacher supervision. The methodological section outlines a quasi-experimental design using baseline diagnostics, CSEM-informed concept questions, reflective writing, and normalized gain analysis. The paper concludes that AI-guided PhET inquiry is especially promising for misconception repair because it links evidence, explanation, and revision in a single instructional cycle.
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