Presentation Schedule
Artificial Intelligence in the Classroom: Supporting Teachers in Addressing Electrical Engineering Preconception (109449)
Session Chair: Joosung Lee
This presentation will be live-streamed via Zoom (Online Access)
Friday, 19 June 2026 14:45
Session: Session 3
Room: Live-Stream Room 1
Presentation Type:Live-Stream Presentation
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This research paper provides insight into the usability of AI tools for teaching the fundamentals of electrical engineering. The research question addressed in this paper focuses on the quality of AI-suggestions to counteract the preconceptions (misconceptions) in courses for first-year students. To address this question, three generative AI tools are examined regarding the identification of preconceptions and measures within learning processes. Subsequently, the author undertakes a systematic evaluation of all suggestions by employing criteria catalogue, such as subject-specific relevance.
The results indicate that the AIs suggest numerous potential preconceptions (up to 80), although some of them can be traced back to the same preconception. For instance, the preconceptions "energy and current are the same thing" and "more current automatically means more energy consumption" are equivalent. Furthermore, in the second instance, the AI "unconsciously" introduces a preconception that focuses on energy consumption rather than conversion. Unfortunately, the AI makes such statements in the suggested materials for learners. Moreover, Gemini employs water analogies to facilitate the comprehension of electrical engineering concepts. This can engender further preconceptions. In planning learning-teaching arrangements, AI tools have shown effectiveness in engaging learners by counteracting preconceptions through experimentation and simulations.
In summary, despite their limitations, the results of the AI tools can be considered adequate. The amount of generated information can prove overwhelming for inexperienced teachers, who may encounter difficulties in identifying the key preconceptions. Consequently, the deliberate integration of AI tools into training programs for (future) teachers is imperative, a point also addressed in this article.
Authors:
Thomas N. Jambor, Leibniz University Hannover, Germany
About the Presenter(s)
Dr. Thomas Jambor is a private lecturer at Leibniz Universität Hannover.
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