Implementation of Artificial Intelligence in Enhancing Metacognitive Interaction in Mathematics Learning
Keywords:
Artificial Intelligence (AI), Metacognition, Mathematics Education, Adaptive Learning, Personalized LearningAbstract
This study investigates the implementation of Artificial Intelligence in enhancing metacognitive interaction in Mathematics education. Metacognition, which encompasses awareness and regulation of one's cognitive processes, plays a crucial role in effective problem solving. By leveraging AI-driven adaptive learning systems, this research will develop personalized educational experiences tailored to student's individual needs, thereby fostering deeper engagement and understanding of mathematical concepts. This case study, involving 8 students and one teacher, explores the use of interactive chat prompt, particularly ChatGPT, that provides real- time guidance, encouraging students to reflect on their problem-solving strategies and thought processes. The effectiveness of the AI Platform is assessed through a series of interventions designed to enhance metacognitive awareness can improve learning outcomes in mathematics. The Result can indicate that the integration of AI not only assists students in recognizing their cognitive strengths and weaknesses but also supports them in employing effective problem-solving strategies. By enhancing metacognitive interactions, this approach prepares students for complex mathematical challenges and contributes to innovative, technology-base educational methodologies.
References
[1] Baker, R. S. J. d., & Inventado, P. S. Educational data mining and learning analytics. In J. A. Larusson & P. Arctic (Eds.), Learning, education and data mining, Springer, 2014, pp. 213–228.
[2] Belland, B. R. A systematic review of the empirical literature on the use of scaffolding in STEM education. International Journal of STEM Education, 4(1), 9, 2017 pp109-123. https://doi.org/10.1186/s40594-017-0072-2
[3] Brown, A. L. Metacognition, executive control, and school learning. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 65–116). Lawrence Erlbaum Associates, 1987.
[4] Dziuban, C. D., Moskal, P. D., & Hartman, J. L. Blended learning: Medium or method? Education and Information Technologies, 23(3), 2018, pp.1039–1059. https://doi.org/10.1007/s10639-018-9762-7
[5] Flavell, J. H. Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 1979, pp. 906–911. https://doi.org/10.1037/0003-066X.34.10.906
[6] Gee, J. P. What video games have to teach us about learning and literacy. Computers in Human Behavior, 22(1), 2007, pp. 31–314. https://doi.org/10.1016/j.chb.2005.08.014
[7] Hattie, J. Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge, 2009
[8] Heffernan, N. T., & Heffernan, C. L. Study Cycle: A new approach to studying mathematics. Studies in Higher Education, 39(1), 2014, pp. 1–10. https://doi.org/10.1080/03075079.2015.1078258
[9] Kerr, J., & Cormack, M. The potential of AI for personalized learning in mathematics education. Journal of Mathematics Education Science and Technology, 15(4), 2020, pp. 115–125.
[10] Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. Intelligence unleashed: An argument for AI in education. Pearson Education, 2016.
[11] Murray, P. Learning in the age of AI: A teacher’s guide. International Journal of Artificial Intelligence in Education, 29(3), 2019, pp. 373–397. https://doi.org/10.1007/s40593-018-00164-3
[12] Russell, S. J., & Norvig, P. Artificial intelligence: A modern approach (3rd ed.). Pearson Education, 2016.
[13] Schraw, G. Promoting general metacognitive awareness. In R. F. Flippo & D. C. Caverly (Eds.), Issues in assessment, instruction, and evaluation, Learning and study strategies, 1998 pp. 3–16
[14] Schraw, G. Assessing metacognitive awareness. Contemporary Educational Psychology, 25(1), 2001, 113–137. https://doi.org/10.1006/ceps.2000.1040
[15] Tamim, R. M., et al. The impact of technology on K–12 student learning: A meta-analysis. Computers & Education, 57(1), 2011, pp. 314–328. https://doi.org/10.1016/j.compedu.2010.10.003
[16] Van Lehn, K. The relative effectiveness of human tutoring, Intelligent Tutoring Systems, and other tutoring systems. American Educational Research Journal, 48(3), 2011, pp. 535–570. https://doi.org/10.3102/0002831211410293
[17] Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications.
[18] Creswell, J. W., & Poth, C. N. Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications, 2018
[19] Denzin, N. K. The Research Act: A theoretical introduction to sociological methods. Routledge, 2017
[20] Denzin, N. K., & Lincoln, Y. S. The SAGE handbook of qualitative research (5th ed.). SAGE Publications, 2018
[21] Geertz, C. The interpretation of cultures: Selected essays. Basic Books, 1993
[22] Lincoln, Y. S., & Guba, E. G. Naturalistic inquiry. SAGE Publications, 1985
[23] Merriam, S. B., & Tisdell, E. J. Qualitative research: A guide to design and implementation (4th ed.), Jossey-Bass, 2016.
[24] Saldaña, J. The coding manual for qualitative researchers (4th ed.). SAGE Publications, 2021
[25] Yin, R. K. Case study research and applications: Design and methods (6th ed.). SAGE Publications, 2018.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Yao Tung Khoe
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
