Speaker
Description
This study highlights the increasing use of technology in teaching and learning, particularly in improving student understanding of complex concepts in electricity. It investigates the effectiveness of integrating PHET simulations with embedded ChatGPT support on student learning outcomes using a pretest–post-test design. The students interacted with the simulation while receiving real-time guidance, feedback, and explanations from ChatGPT, which enhanced engagement, supported inquiry-based learning, and strengthened conceptual understanding. Data were collected from a cohort of students assessed across five sections of a structured and validated questionnaire. Descriptive and inferential statistical analyses were conducted to evaluate changes in student performance before and after the intervention. The results showed a clear and consistent improvement in post-test scores compared to pretest scores, indicating the effectiveness of the learning approach. A paired samples t-test confirmed that the gains were statistically significant, while Cohen’s d indicated a moderate to strong effect size, demonstrating meaningful practical impact. Although improvements were observed across all sections, some variations suggest that certain electricity concepts remain challenging. Overall, the findings demonstrate the strong potential of combining interactive simulations with artificial intelligence to enhance learning outcomes in electricity education.
| Apply for student award at which level: | None |
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