Artificial Intelligence (AI) Driven Dual-Loop Adaptive Learning in Economics Education: Evidence from a Quasi-Experimental Study
Keywords:
Adaptive Learning, Artificial Intelligence, Differentiated Learning, Economics Education, Metacognition
Abstract
Artificial Intelligence-based Dual-Loop Adaptive Learning model designed to support differentiated economics learning in senior high schools. The model responds to a major limitation of conventional adaptive learning systems, which often adjust content and task difficulty without diagnosing students’ learning strategies, misconceptions, or recurring error patterns. The proposed model integrates automatic content adaptation with reflective feedback to help students reconsider their assumptions, regulate their learning strategies, and strengthen metacognitive awareness. This study employed a Research and Development orientation combined with a quasi-experimental pretest-posttest control group design. Model feasibility was examined through expert validation, while its effectiveness was analyzed through comparative statistical procedures and AI classification performance metrics. The findings indicate that the model achieved a high level of validity and produced stronger improvement in cognitive learning outcomes, intrinsic motivation, and metacognition compared with conventional instruction. The AI component also demonstrated adequate capability in classifying student error patterns to support reflective feedback. These results suggest that the Dual-Loop Adaptive Learning model can function not only as a content personalization system but also as a pedagogical intervention that promotes reflective, differentiated, and data-informed economics learning. The model offers practical value for teachers in diagnosing learner needs, designing targeted interventions, and supporting more adaptive classroom decision-making.
References
B, S., Riski Wulandari, S., Arifin, I., & Prusdianto, P. (2025). An Analysis of Artificial Intelligence Integration Among Students: Usage, Impacts, and Educational Implications. EDUKASIA Jurnal Pendidikan Dan Pembelajaran, 6(2), 803–818. https://doi.org/10.62775/edukasia.v6i2.1546
Bo, N. S. W. (2024). OECD Digital Education Outlook. In Hungarian Educational Research Journal (Vol. 15, Number 2). https://akjournals.com/view/journals/063/15/2/article-p284.xml
Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Dalila, A. A., Rahmah, S., Liliawati, W., & Kaniawati, I. (2022). Effect of Differentiated Learning in Problem Based Learning on Cognitive Learning Outcomes of High School Students. Jurnal Penelitian Pendidikan IPA, 8(4), 2116–2122. https://doi.org/10.29303/jppipa.v8i4.1839
Dario Lombardi, L. T. F. M. and A. M. (2025). Higher Education Learning Methodologies and Technologies Online (Vol. 2468). https://link.springer.com/10.1007/978-3-031-93999-0
Dino, M. J. S., Arellano, Y. B., Evangelista, A. G., Esteban, W. C., Feliciano, C. M. G., Quizana, V. C., Sevilla, P. S., Trillana, R. S. C., Malacas, M. K. G., Ortiz, C. G. E., Vital, J. C. T., Vasquez, F. A., & Agustin, P. D. E. (2025). Utilization of Artificial Intelligence in Reducing the Incidence of Medication Error: A Bibliometric Analysis. Health Science Reports, 8(8). https://doi.org/10.1002/hsr2.71067
Duan, S., Zhang, H., Liu, L., Lin, Y., Zhao, F., Chen, P., Cao, S., Zhou, K., Gao, C., Liu, Z., Shi, Q., Lee, C., & Wu, J. (2024). A comprehensive review on triboelectric sensors and AI-integrated systems. Materials Today, 80(November), 450–480. https://doi.org/10.1016/j.mattod.2024.08.013
Feriyadi, D., Yani T, A., & Tiur Maria, H. (2025). A deep learning-based adaptive e-module in mathematics: Design and evaluation for visual communication design vocational students. Al-Jabar : Jurnal Pendidikan Matematika, 16(2), 483–498. https://doi.org/10.24042/ajpm.v16i1.26718
Fernhaber, S. A., & Zou, H. (2022). Advancing societal grand challenge research at the interface of entrepreneurship and international business: A review and research agenda. Journal of Business Venturing, 37(5). https://doi.org/10.1016/j.jbusvent.2022.106233
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis Eighth Edition. www.cengage.com/highered
Hamilton, A. (2024). Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education. Cureus, 16(5). https://doi.org/10.7759/cureus.59747
Hasibuan, N. K., Yeli, S., & Zaitun, Z. (2025). The Influence of Differentiated Learning Methods on Student Motivation and Learning Outcomes. Jurnal Inovatif Manajemen Pendidikan Islam, 4(1), 29–40. https://doi.org/10.38073/jimpi.v4i1.1909
Hu, R., Bao, Z., Lin, Z., & Lv, K. (2024). The Innovative Construction of Provinces, Regional Artificial Intelligence Development, and the Resilience of Regional Innovation Ecosystems: Quasi-Natural Experiments Based on Spatial Difference-in-Differences Models and Double Machine Learning. Sustainability (Switzerland), 16(18). https://doi.org/10.3390/su16188251
Hu, S. (2024). The Effect of Artificial Intelligence-Assisted Personalized Learning on Student Learning Outcomes: A Meta-Analysis Based on 31 Empirical Research Papers. Science Insights Education Frontiers, 24(1), 3873–3894. https://doi.org/10.15354/sief.24.re395
Juliastuti, D., Alexandrina, E., Sana, E., Muti, R. N., & Cesna, G. P. (2025). Integrating Artificial Intelligence for Academically Challenged Students Education and Health. International Transactions on Artificial Intelligence (ITALIC), 4(1), 13–24. https://doi.org/10.33050/italic.v4i1.949
Lestari, F., Alim, J. A., & Noviyanti, M. (2024). Implementation of Differentiated Learning to Enhance Elementary School Students’ Mathematical Critical and Creative Thinking Skills. International Journal of Elementary Education, 8(1), 178–187. https://doi.org/10.23887/ijee.v8i1.64295
Manurung, I. D. (2025). Integration of Artificial Intelligence in Adaptive Learning: Opportunities and Challenges for Teachers in the Era of Education 5.0. MSJ : Majority Science Journal, 3(4), 73–83. https://doi.org/10.61942/msj.v3i4.481
Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 57(4), 632–645. https://doi.org/10.1111/ejed.12527
Moreira, L. L., Pinto, S. S., Costa, L., & Araújo, N. (2025). Evaluating digital transformation in small and medium enterprises using the Alkire-Foster method. Heliyon, 11(2), e41838. https://doi.org/10.1016/j.heliyon.2025.e41838
Muhammad Afifi Rahman, Satunggale Kurniawan, Y. S. (2025). Pengembangan Media Pembelajaran Adaptif. Alfihris: Jurnal Pendidikan Dan Pembelajaran, 3(2), 5–11.
Nabila, N. A., & Rachman, I. F. (2025). Peran Ai Dalam Mendeteksi Dan Mengintervensi Keterlambatan Belajar Siswa di Pendidikan Dasar. Jurnal Kesehatan Dan Kebidanan Nusantara, 3(2), 36–43.
Putranti, N. A., Nur, M., & Maksum, R. (2024). Implementation of Differentiated Learning Methods in Increasing Student Learning Motivation at SMPN 23 Surakarta. Jurnal Kependidikan, 13(1), 947-952. https://jurnaldidaktika.org
Romero-Rodríguez, J. M., Martínez-Menéndez, A., Alonso-García, S., & Victoria-Maldonado, J. J. (2024). The reality of the gamification methodology in Primary Education: A systematic review. International Journal of Educational Research, 128(July). https://doi.org/10.1016/j.ijer.2024.102481
Șerban, M., Toader, C., & Covache-Busuioc, R. A. (2025). CRISPR and Artificial Intelligence in Neuroregeneration: Closed-Loop Strategies for Precision Medicine, Spinal Cord Repair, and Adaptive Neuro-Oncology. International Journal of Molecular Sciences, 26(19), 1–52. https://doi.org/10.3390/ijms26199409
Umali, J. N. D. (2024). Artificial Intelligence Technology Management of Teachers,Learners Motivation and Challenges Encountered. International Journal of Multidisciplinary and Current Educational Research (IJMCER) , 6(2), 821–880. www.ijmcer.com
Zhang, L., Qu, J., & Zhang, Z. (2024). The Impact of Personalized Learning Driven by Artificial Intelligence on STEM Education in Primary and Secondary Schools. International Journal of New Developments in Education, 6(10), 29–34. https://doi.org/10.25236/ijnde.2024.061005
Bo, N. S. W. (2024). OECD Digital Education Outlook. In Hungarian Educational Research Journal (Vol. 15, Number 2). https://akjournals.com/view/journals/063/15/2/article-p284.xml
Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Dalila, A. A., Rahmah, S., Liliawati, W., & Kaniawati, I. (2022). Effect of Differentiated Learning in Problem Based Learning on Cognitive Learning Outcomes of High School Students. Jurnal Penelitian Pendidikan IPA, 8(4), 2116–2122. https://doi.org/10.29303/jppipa.v8i4.1839
Dario Lombardi, L. T. F. M. and A. M. (2025). Higher Education Learning Methodologies and Technologies Online (Vol. 2468). https://link.springer.com/10.1007/978-3-031-93999-0
Dino, M. J. S., Arellano, Y. B., Evangelista, A. G., Esteban, W. C., Feliciano, C. M. G., Quizana, V. C., Sevilla, P. S., Trillana, R. S. C., Malacas, M. K. G., Ortiz, C. G. E., Vital, J. C. T., Vasquez, F. A., & Agustin, P. D. E. (2025). Utilization of Artificial Intelligence in Reducing the Incidence of Medication Error: A Bibliometric Analysis. Health Science Reports, 8(8). https://doi.org/10.1002/hsr2.71067
Duan, S., Zhang, H., Liu, L., Lin, Y., Zhao, F., Chen, P., Cao, S., Zhou, K., Gao, C., Liu, Z., Shi, Q., Lee, C., & Wu, J. (2024). A comprehensive review on triboelectric sensors and AI-integrated systems. Materials Today, 80(November), 450–480. https://doi.org/10.1016/j.mattod.2024.08.013
Feriyadi, D., Yani T, A., & Tiur Maria, H. (2025). A deep learning-based adaptive e-module in mathematics: Design and evaluation for visual communication design vocational students. Al-Jabar : Jurnal Pendidikan Matematika, 16(2), 483–498. https://doi.org/10.24042/ajpm.v16i1.26718
Fernhaber, S. A., & Zou, H. (2022). Advancing societal grand challenge research at the interface of entrepreneurship and international business: A review and research agenda. Journal of Business Venturing, 37(5). https://doi.org/10.1016/j.jbusvent.2022.106233
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis Eighth Edition. www.cengage.com/highered
Hamilton, A. (2024). Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education. Cureus, 16(5). https://doi.org/10.7759/cureus.59747
Hasibuan, N. K., Yeli, S., & Zaitun, Z. (2025). The Influence of Differentiated Learning Methods on Student Motivation and Learning Outcomes. Jurnal Inovatif Manajemen Pendidikan Islam, 4(1), 29–40. https://doi.org/10.38073/jimpi.v4i1.1909
Hu, R., Bao, Z., Lin, Z., & Lv, K. (2024). The Innovative Construction of Provinces, Regional Artificial Intelligence Development, and the Resilience of Regional Innovation Ecosystems: Quasi-Natural Experiments Based on Spatial Difference-in-Differences Models and Double Machine Learning. Sustainability (Switzerland), 16(18). https://doi.org/10.3390/su16188251
Hu, S. (2024). The Effect of Artificial Intelligence-Assisted Personalized Learning on Student Learning Outcomes: A Meta-Analysis Based on 31 Empirical Research Papers. Science Insights Education Frontiers, 24(1), 3873–3894. https://doi.org/10.15354/sief.24.re395
Juliastuti, D., Alexandrina, E., Sana, E., Muti, R. N., & Cesna, G. P. (2025). Integrating Artificial Intelligence for Academically Challenged Students Education and Health. International Transactions on Artificial Intelligence (ITALIC), 4(1), 13–24. https://doi.org/10.33050/italic.v4i1.949
Lestari, F., Alim, J. A., & Noviyanti, M. (2024). Implementation of Differentiated Learning to Enhance Elementary School Students’ Mathematical Critical and Creative Thinking Skills. International Journal of Elementary Education, 8(1), 178–187. https://doi.org/10.23887/ijee.v8i1.64295
Manurung, I. D. (2025). Integration of Artificial Intelligence in Adaptive Learning: Opportunities and Challenges for Teachers in the Era of Education 5.0. MSJ : Majority Science Journal, 3(4), 73–83. https://doi.org/10.61942/msj.v3i4.481
Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 57(4), 632–645. https://doi.org/10.1111/ejed.12527
Moreira, L. L., Pinto, S. S., Costa, L., & Araújo, N. (2025). Evaluating digital transformation in small and medium enterprises using the Alkire-Foster method. Heliyon, 11(2), e41838. https://doi.org/10.1016/j.heliyon.2025.e41838
Muhammad Afifi Rahman, Satunggale Kurniawan, Y. S. (2025). Pengembangan Media Pembelajaran Adaptif. Alfihris: Jurnal Pendidikan Dan Pembelajaran, 3(2), 5–11.
Nabila, N. A., & Rachman, I. F. (2025). Peran Ai Dalam Mendeteksi Dan Mengintervensi Keterlambatan Belajar Siswa di Pendidikan Dasar. Jurnal Kesehatan Dan Kebidanan Nusantara, 3(2), 36–43.
Putranti, N. A., Nur, M., & Maksum, R. (2024). Implementation of Differentiated Learning Methods in Increasing Student Learning Motivation at SMPN 23 Surakarta. Jurnal Kependidikan, 13(1), 947-952. https://jurnaldidaktika.org
Romero-Rodríguez, J. M., Martínez-Menéndez, A., Alonso-García, S., & Victoria-Maldonado, J. J. (2024). The reality of the gamification methodology in Primary Education: A systematic review. International Journal of Educational Research, 128(July). https://doi.org/10.1016/j.ijer.2024.102481
Șerban, M., Toader, C., & Covache-Busuioc, R. A. (2025). CRISPR and Artificial Intelligence in Neuroregeneration: Closed-Loop Strategies for Precision Medicine, Spinal Cord Repair, and Adaptive Neuro-Oncology. International Journal of Molecular Sciences, 26(19), 1–52. https://doi.org/10.3390/ijms26199409
Umali, J. N. D. (2024). Artificial Intelligence Technology Management of Teachers,Learners Motivation and Challenges Encountered. International Journal of Multidisciplinary and Current Educational Research (IJMCER) , 6(2), 821–880. www.ijmcer.com
Zhang, L., Qu, J., & Zhang, Z. (2024). The Impact of Personalized Learning Driven by Artificial Intelligence on STEM Education in Primary and Secondary Schools. International Journal of New Developments in Education, 6(10), 29–34. https://doi.org/10.25236/ijnde.2024.061005
Published
2026-07-20
How to Cite
Syarifuddin, S., Nurdin, N., Saripuddin, S., & Andi Aris Mattunruang. (2026). Artificial Intelligence (AI) Driven Dual-Loop Adaptive Learning in Economics Education: Evidence from a Quasi-Experimental Study. International Journal of Education, Vocational and Social Science , 5(03), 5767-5779. https://doi.org/10.63922/ijevss.v5i03.5417
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