Author(s): Mihály KOVÁCS, Zoltán MURÁNYI

Title: ENHANCING INQUIRY-BASED LEARNING WITH GENERATIVE AI: INSIGHTS FROM STUDENT INTERACTIONS

Source: P. Baka, D. Borbélyová, A. Csehiová, K. Józsa, K. Kéri, B. Kiss, O. Magirovskaya, Y. Orsovics, B. Pukánszky, Sz. Simon, P. Šenkár, A. Tóth-Bakos, A. Tóthné Litovkina, A. Varga (eds.): 17th International Scientific Conference of J. Selye University. Sessions of the Faculty of Education. Conference Proceedings

ISBN: 978-80-8122-539-0

DOI: https://doi.org/10.36007/5390.2026.262

Publisher: J. Selye University, Komárno, Slovakia

PY, pages: 2025, 262-269

Published on-line: 2026

Language: en

Abstract: This pilot study explores the potential of using generative artificial intelligence (AI) in chemistry education to support inquiry-based learning (IBL) and attempts to model AI-assisted chemistry research practice in the classroom. It fills a gap in the literature, which covers AI-supported IBL in other fields but not in chemistry. N=23 9th and 10th grade students used Mistral Le Chat to design an experiment to determine what causes cherry cake dough to turn green. The dialogues between the students and the AI and the students' exit cards were analysed, suggesting that AI can function as a supportive tool, e.g. in formulating hypotheses. Students found AI useful, e.g., for clear explanations and experiment design, although some reported that AI solved the problem for them. Based on these results, AI-supported IBL is a promising method in chemistry education, but further large-scale research is needed on how to use it effectively.

Keywords: Inquiry-Based Learning (IBL), Generative AI, Chemistry Education, Scaffolding, Student-AI interaction

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