Volume 4, 2026 – Issue 2 
The AI Promise in Moroccan Public Schools: ELT Teachers Between Innovation and Constraint
Hajar Talouizet
1 * and Mohammed Moubtassime
2
CREDIF laboratory, Faculty of Letters and Human Sciences, Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fes, Morocco
* Corresponding author: hajar.talouizet@usmba.ac.ma
DOI: 10.5281/zenodo.21727949
Abstract
Artificial Intelligence (AI) is rapidly reshaping English Language Teaching (ELT) worldwide, prompting increasing attention to its integration within Moroccan public secondary education. Guided by Rogers’ Diffusion of Innovations Theory (2003), this study examines public high school teachers’ readiness for AI integration by exploring their adoption practices, perceptions, and the institutional barriers they encounter. A mixed-methods questionnaire, including both closed- and open-ended items, was administered to 97 public sector EFL teachers. Quantitative findings indicate a high level of grassroots engagement, with a large majority of respondents reporting active use of AI tools in their professional practice. In addition, most consider school-wide integration of AI to be feasible under current educational conditions. However, a Chi-square test of independence revealed a statistically significant association, suggesting a notable gap between usage and perceived effectiveness. While many teachers adopt AI in their preparatory routines, 54.6% report that it does not effectively address existing classroom challenges. Qualitative findings, interpreted through Rogers’ framework, point to structural constraints that limit full integration. These include infrastructural limitations, techno-pedagogical challenges, and concerns about pedagogical effectiveness. Participants also expressed concerns regarding students’ overreliance on AI, which they perceive as potentially undermining independent learning. The study concludes that although Moroccan EFL teachers demonstrate strong digital readiness and willingness to innovate, AI integration remains constrained by systemic and institutional limitations. Effective implementation requires strengthening infrastructure, providing localized pedagogical support, and aligning policy with classroom realities.
Keywords: ELT, artificial intelligence, Moroccan public schools, barriers, teacher perceptions
Published
2026/06/30
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Section
Research Papers
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References
Chen, L., Chen, P., & Lin, Z. (2017). Artificial Intelligence in Education: A Review. IEEE Access, 6. (Refers to AI-powered adaptive learning systems like Knewton).
Chen, X. (2018). China’s AI Education Experiment: Automatic Essay Correction in 60,000 Schools. MIT Technology Review. (Refers to the 8% budget allocation and 92% human-matching precision).
Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., & Wu, Y. (2023). Adaptive Learning Systems and Performance Optimization. Journal of Educational Computing Research.
Kohnke, L., & Zou, B. (2025). Digital literacies and AI integration in TESOL teacher education: Fostering critical TPACK frameworks. TESOL Quarterly, 59(1), 112–135.
Laanpere, M., et al. (2014). Digital Initiatives for Improving Learning Opportunities. International Journal of Educational Technology.
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.
Mayer-Schönberger, V., & Cukier, K. (2014). Learning with Big Data: The Future of Education. Houghton Mifflin Harcourt.
Montebello, M. (2017). AI-Injected e-Learning. Springer.
Nye, B. D. (2015). Intelligent Tutoring Systems by and for the Developing World: A Review of Trends and Approaches for Educational Technology. Educational Technology & Society, 18(3), 177-192.
Pack, A., & Maloney, J. (2024). Ethical and pedagogical considerations of generative AI and automated writing evaluation in language learning. TESOL Quarterly, 58(3), 745–768.
Perera, M., & Aboal, D. (2018). The Impact of the Mathematics Adaptive Platform (PAM) on Learning Outcomes. CEE-PR Reports.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit Spewer or the End of Traditional Assessments in Higher Education? Journal of Applied Learning and Teaching, 6(1).
Schittek Janda, M., Mattheos, N., Nattestad, A., Wagner, A., Nebel, D., Färbom, C., … & Attström, R. (2001). Simulation of Patient Encounters in Dental Education: Computer-Assisted Learning (CAL). European Journal of Dental Education.
Song, K. (2024). AI as a teaching partner: Co-creating language learning activities and navigating classroom realities. TESOL Journal, 15(2), e789.
Sunkel, G., & Trucco, D. (2012). The Integration of ICTs in Latin American Schools. ECLAC.
UNESCO. (2011). UNESCO ICT Competency Framework for Teachers.
UNESCO. (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development. (Refers to asynchronous discussion groups and the 2018/2019 Framework update).
Uskov, V. L., Bakken, J. P., Howlett, R. J., & Jain, L. C. (2019). Smart Education and e-Learning. Springer.
VanLehn, K., et al. (2005). The Behavior of Tutoring Systems. Educational Psychologist. (Refers to Intelligent Tutoring Systems simulating human interaction).
Zhang, K., & Aslan, A. B. (2021). AI Foundations for Personalizing Education: A Systematic Review. International Journal of Educational Technology in Higher Education.
About the authors
- Hajar Talouizet is a PhD student. Laboratoire : Cultures, Représentations, Education, Didactique et Ingénierie de Formation – CREDIF FLDM Fès.
hajar.talouizet@usmba.ac.ma
↩︎ - Mohammed Moubtassime is the Dean of the Faculty of Letters and Human Sciences at FLDM Fez, Morocco.
↩︎


