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Thomas N. Jambor, Leibniz University Hannover, GermanyAbstract
This research paper examines the suitability 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, a generative AI tool is examined regarding the identification of preconceptions and measures within learning processes. Subsequently, the author undertakes a systematic evaluation of all suggestions. The results show that the AI tool identifies numerous potential preconceptions (up to 80), although some of them refer to the same underlying 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. 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 tool can be considered adequate. The amount of generated information may be 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 paper.
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Conference: PCE2026Stream: Learning Experiences
This paper is part of the PCE2026 Conference Proceedings (View)
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To cite this article:
Jambor T. (2026) Artificial Intelligence in the Classroom: Supporting Teachers in Addressing Electrical Engineering Preconceptions ISSN: 2758-0962 The Paris Conference on Education 2026: Official Conference Proceedings (pp. 773-786) https://doi.org/10.22492/issn.2758-0962.2026.58
To link to this article: https://doi.org/10.22492/issn.2758-0962.2026.58
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