Implementation of Artificial Intelligence to Enhance the Effectiveness of Adaptive Learning in Psychological Education in Schools

Sutikno Sutikno; Junita Sipahelut; Isep Djuanda

doi:10.55927/fjmr.v5i3.25

Authors

Sutikno Sutikno Universitas Negeri Jakarta
Junita Sipahelut Institut Agama Kristen Negeri (IAKN) Ambon
Isep Djuanda Universitas Islam Depok

DOI:

https://doi.org/10.55927/fjmr.v5i3.25

Keywords:

Artificial Intelligence, Adaptive Learning, Psychological Education, Emotional Regulation, Learning Effectiveness

Abstract

The advancement of Artificial Intelligence (AI) in education offers significant potential to address the limitations of conventional instructional approaches, which tend to be uniform and insufficiently responsive to students’ diverse learning characteristics, particularly in psychological education that requires both conceptual understanding and the development of emotional regulation skills. This study aims to examine the effectiveness of implementing AI-based adaptive learning in improving students’ comprehension of psychological concepts and their emotional regulation abilities at the senior high school level. The research employed a quasi-experimental approach with a pretest–posttest control group design involving 60 tenth-grade students from a public senior high school in Papua, divided into experimental and control groups. Data were collected through a psychological comprehension test, an emotional regulation scale, and a learning engagement questionnaire, and were analyzed using independent samples t-tests and effect size calculations. The findings indicate that students in the AI-based adaptive learning group demonstrated significantly greater improvement compared to those in the conventional learning group, both in cognitive achievement and emotional regulation, along with higher levels of learning engagement. These results suggest that integrating AI into psychological education effectively supports differentiated instruction and strengthens students’ emotional competencies.

References

Babbie, E., Wagner-Huang, W. E., & Zaino, J. (2022). Adventures in social research: Data analysis using IBM SPSS statistics. Sage Publications.

Balashov, E. (2022). Psychological well-being as cognitive-emotional component of student self-regulated learning. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 10(2), 101–109.

Banda, T. (2024). Unveiling Educational Frontiers: An Exploration of Innovative Pedagogical Approaches and Learning Outcomes in Remote Mountain Communities. Interdisciplinary Journal Papier Human Review, 5(4), 1–9.

Buckley, J. (2024). Conducting Power Analyses to Determine Sample Sizes in Quantitative Research: A Primer for Technology Education Researchers Using Common Statistical Tests. Journal of Technology Education, 35(2), 81–109.

Efklides, A., & Schwartz, B. L. (2024). Revisiting the metacognitive and affective model of self-regulated learning: Origins, development, and future directions. Educational Psychology Review, 36(2), 61.

Fan, F., Wang, S., Dinuer, M., Hemuti, M., Nie, X., & Yang, L. T. (2025). Enhancing Students’ Metacognition with Innovative IA-Based Metacognitive Reflective Learning Tool. IEEE Transactions on Learning Technologies.

Farhood, H., Nyden, M., Beheshti, A., & Muller, S. (2025). Artificial intelligence-based personalised learning in education: a systematic literature review. Discover Artificial Intelligence, 5(1), 331.

Gabr, M. A. M., Sleem, W. F., & El-Wkeel, N. S. (2025). Effect of virtual reality educational program on critical thinking disposition among nursing students in Egypt: a quasi-experimental pretest–posttest design. BMC Nursing, 24(1), 874.

Gheyssens, E., Coubergs, C., Griful-Freixenet, J., Engels, N., & Struyven, K. (2022). Differentiated instruction: the diversity of teachers’ philosophy and praxis to adapt teaching to students’ interests, readiness and learning profiles. International Journal of Inclusive Education, 26(14), 1383–1400.

Guo, Y., Xu, B., Lyu, B., & Chen, C. (2025). How social and emotional skills affect Chinese college student engagement in high-impact educational practices: a moderated mediation model. Studies in Higher Education, 50(1), 27–46.

Kennedy, I. (2022). Sample size determination in test-retest and Cronbach alpha reliability estimates. British Journal of Contemporary Education, 2(1), 17–29.

Laurence, P. G. (2025). Executive Function Beyond Minority World Contexts: A Call for Culturally Relevant Assessments in Historically Overlooked Populations in Majority World Countries. Mind, Brain, and Education.

Lynn, S., Carroll, A., Nankervis, K., & Antrobus, E. (2024). A systematic review of delivery modes in school‐based adolescent social–emotional learning programs—Current perspectives and future directions. Review of Education, 12(3), e70019.

Nieto Carracedo, A., Gómez-Iñiguez, C., Tamayo, L. A., & Igartua Perosanz, J. J. (2024). Emotional intelligence and academic achievement relationship: emotional well-being, motivation, and learning strategies as mediating factors. Psicología Educativa, 30(2), 67–74.

Prajapati, M. A. B. (2024). Artificially intelligent in education:“redefining learning in the 21st century.” Journal of Educational Technology, v1. Https://Doi. Org/10.5281/Zenodo, 12818287.

Ravichandran, K. (2024). Identifying learning difficulties at an early stage in education with the help of artificial intelligence models and predictive analytics. International Research Journal of Multidisciplinary Scope (IRJMS), 5(4), 1455–1461.

Sajja, R., Sermet, Y., Cwiertny, D., & Demir, I. (2025). Integrating AI and learning analytics for data-driven pedagogical decisions and personalized interventions in education. Technology, Knowledge and Learning, 1–31.

Saleem, S., Aziz, M. U., Iqbal, M. J., & Abbas, S. (2025). AI in education: Personalized learning systems and their impact on student performance and engagement. The Critical Review of Social Sciences Studies, 3(1), 2445–2459.

Sari, H. E., Tumanggor, B., & Efron, D. (2024). Improving educational outcomes through adaptive learning systems using AI. International Transactions on Artificial Intelligence, 3(1), 21–31.

Shengyao, Y., Xuefen, L., Jenatabadi, H. S., Samsudin, N., Chunchun, K., & Ishak, Z. (2024). Emotional intelligence impact on academic achievement and psychological well-being among university students: the mediating role of positive psychological characteristics. BMC Psychology, 12(1), 389.

Sibley, L., Fabian, A., Plicht, C., Pagano, L., Ehrhardt, N., Wellert, L., Bohl, T., & Lachner, A. (2025). Adaptive teaching with technology enhances lasting learning. Learning and Instruction, 99(10214), 1.

Tuomi, I. (2022). Artificial intelligence, 21st century competences, and socio‐emotional learning in education: More than high‐risk? European Journal of Education, 57(4), 601–619.

Velastegui, D., Pérez, M. L. R., & Garcés, L. F. S. (2023). Impact of Artificial Intelligence on learning behaviors and psychological well-being of college students. Salud, Ciencia y Tecnologia-Serie de Conferencias, 2, 343.

Villegas-Ch, W., García-Ortiz, J., & Sánchez-Viteri, S. (2024). Application of artificial intelligence in online education: Influence of student participation on academic retention in virtual courses. IEEE Access, 12, 73045–73065.

Wu, D., & Zhang, J. (2025). Generative artificial intelligence in secondary education: Applications and effects on students’ innovation skills and digital literacy. PLoS One, 20(5), e0323349.

Yang, C. (2025). Adapting teaching methods to accommodate diverse learning styles in education. Journal of Higher Education Research. Https://Doi. Org/10.32629/Jher. V5i6, 3382.