PERC 2024 Abstract Detail Page
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| Abstract Title: | Enhancing the Learning Science and Engineering with Electronic Spreadsheets Cycle with Generative Artificial Intelligence: A case of study |
|---|---|
| Abstract Type: | Contributed Poster Presentation |
| Abstract: | This research presents the results of implementing the Learning Science and Engineering with Electronic Spreadsheets Cycle (LSEESC) methodology, with the added support of generative artificial intelligence (AI) to enhance learning across various physics topics for engineering programs. This goal has been pursued through diverse modalities (virtual, hybrid, and classroom) and the assessment was conducted using the FMCE and FCI tests. Valuable insights into conceptual gains, concentration factors, and the Rasch index, enabling us to discern patterns and trends in student learning pre- and post-implementation. However, it's crucial to note that AI exhibits significant limitations when solving abstract physics problems, with an efficiency rate of approximately 42%. This finding underscores the need for caution when utilizing AI in academic contexts, as students may become overly reliant and confused with the description that AI can present to them, potentially leading to misconceptions in the physics learning process. |
| Session Time: | Poster Session 2 |
| Poster Number: | B92 |
Author/Organizer Information | |
| Primary Contact: |
Erika Cervantes Juárez Unidad Profesional Interdisciplinaria de Ingeniería Campus Guanajuato IPN León de los Aldama, Non U.S. 37544 Phone: 2211357253 |
| Co-Author(s) and Co-Presenter(s) |
PhD Daniel Sánchez Guzmán, Unidad Profesional Interdisciplinaria de Ingeniería Campus Guanajuato IPN |
