The Impact of Artificial Intelligence on Flipped Classroom Learning: A Quasi-Experimental Study in Energy Education
Asmae AHALLI, Mohamed HALIM, Abdelmajid TAHIRI, Nouha ADADI
Abstract. This study utilized a quasi-experimental design to assess whether using AI within a flipped structure created added value for physics education focused specifically on energy through the support of personalized resources and activities. In all, sixty second-year baccalaureate students participated in this study divided into two sections of thirty students each. The experimental section, designated as the intelligent learning environment, provided students with a wide array of personalized resources and activities, whereas control section of students used a traditional structure of a flipped approach without any AI implementation. The results of this research demonstrated that students within the AI-supported experimental section had substantially higher levels of growth in their ability to conceptualize energy (p < 0.001, d = 0.92), while at the same time having a substantially higher level of energy literacy (p < 0.01). Additionally, students demonstrated a higher degree of involvement during both their preparatory and classroom time, as compared to those students in the control section. Overall, by customizing learning pathways using AI, it appears there is considerable evidence that this method can strengthen the impact of flipped learning when teaching complex science subjects.
Keywords
Flipped Classroom, Artificial Intelligence, Personalization, Energy Education, Learning, Quasi-Experimental Design
Published online 4/25/2026, 7 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Asmae AHALLI, Mohamed HALIM, Abdelmajid TAHIRI, Nouha ADADI, The Impact of Artificial Intelligence on Flipped Classroom Learning: A Quasi-Experimental Study in Energy Education, Materials Research Proceedings, Vol. 64, pp 1154-1160, 2026
DOI: https://doi.org/10.21741/9781644904091-142
The article was published as article 142 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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