Towards AI-Supported Research Design in Education: Simulating Experimental Conditions in Humanities
DOI :
https://doi.org/10.63939/JAAS.2026-Vol9.N30.78-91Mots-clés :
Artificial Intelligence in Education, Experimental Design, Simulation-Based Research, Human Sciences.Résumé
Recent developments in artificial intelligence (AI) have introduced innovative possibilities for enhancing research methodology in education and the humanities. This study proposes an AI-based simulation model for educational experiments, where experimental conditions are virtually constructed and tested before being implemented in the real world. The main objective is to explore how AI tools can support researchers in designing more efficient, valid, and reliable experimental studies in EFL contexts. The proposed model replaces traditional pre-experimental preparation with a virtual experimental environment supported by multiple AI systems. First, the simulation of participants is achieved through agent-based modeling tools such as NetLogo, which allows the creation of virtual student profiles with varying cognitive, motivational, and emotional intelligence characteristics. These AI-generated agents replicate learner diversity, enabling researchers to observe potential behavioral patterns under different instructional conditions. Second, the simulation of teaching conditions and instructional interventions is supported by platforms such as AnyLogic, which enables the modeling of complex classroom environments and pedagogical scenarios. Through this system, different teaching strategies focusing on the development of emotional intelligence, such as emotion recognition tasks, reflective activities, and AI-assisted emotional awareness training, can be tested in a controlled virtual space, allowing researchers to manipulate variables and examine interaction patterns between learners and instructional designs. Third, the simulation of research outcomes and predictive analysis is conducted using machine learning and statistical environments such as Google Colab (Python), where predictive models estimate learner autonomy, engagement, and performance levels based on simulated data. These predictions help researchers anticipate potential results and refine their experimental design before conducting actual fieldwork. The integration of these tools creates a structured AI-supported simulation framework that enhances methodological decision-making, reduces experimental limitations, and improves research validity. The study concludes that AI-based simulation does not replace empirical experimentation but serves as a powerful preparatory stage that strengthens the design and rigor of educational research
Références
Aldrich, C. (2009). The complete guide to simulations and serious games: How the most valuable content will be created in the age beyond Gutenberg to Google. Pfeiffer.
Banks, J., Carson, J. S., Nelson, B. L., & Nicol, D. M. (2010). Discrete-event system simulation (5th ed.). Pearson.
Berry, D. M. (Ed.). (2012). Understanding the digital humanities. Palgrave Macmillan.
Blei, D. M., & Smyth, P. (2017). Science and data science. Proceedings of the National Academy of Sciences, 114(33), 8689–8692. https://doi.org/10.1073/pnas.1702076114
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Suppl. 3), 7280–7287. https://doi.org/10.1073/pnas.082080899
Borshchev, A. (2013). The big book of simulation modeling: Multimethod modeling with AnyLogic 6. AnyLogic North America.
Burdick, A., Drucker, J., Lunenfeld, P., Presner, T., & Schnapp, J. (2016). Digital humanities. MIT Press.
Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Denzin, N. K., & Lincoln, Y. S. (Eds.). (2018). The SAGE handbook of qualitative research (5th ed.). SAGE Publications.
Dörnyei, Z., & Ryan, S. (2015). The psychology of the language learner revisited. Routledge.
Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., ... & Li, Y. (2024). Large language models empowered agent-based modeling and simulation: A survey and perspectives. Humanities and Social Sciences Communications, 11(1), 1-24.
Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly Media.
Gilbert, N., & Troitzsch, K. G. (2005). Simulation for the social scientist (2nd ed.). Open University Press.
Gkonou, C., Dewaele, J.-M., & King, J. (Eds.). (2021). The emotional rollercoaster of language teaching. Multilingual Matters.
Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2), 337–355.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. https://doi.org/10.1007/s40593-021-00239-1
Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeiffer, F., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., ... Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Mayer, J. D., Caruso, D. R., & Salovey, P. (2016). The ability model of emotional intelligence: Principles and updates. Emotion Review, 8(4), 290–300. https://doi.org/10.1177/1754073916639667
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252–254). Association for Computing Machinery. https://doi.org/10.1145/2330601.2330661
Vulic, J., Jacobson, M. J., & Levin, J. A. (2024). Exploring education as a complex system: Computational educational research with multi-level agent-based modeling. Education Sciences, 14(5), 551.
Wilensky, U. (1999). NetLogo [Computer software]. Center for Connected Learning and Computer-Based Modeling, Northwestern University. http://ccl.northwestern.edu/netlogo/
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Publiée
Numéro
Rubrique
Licence
Merci de créditer les auteurs lors de toute citation : Revue des études Afro-Asiatiques (2026)
Ce travail est disponible sous licence Creative Commons Attribution - Pas d’Utilisation Commerciale 4.0 International.
