THE IMPLEMENTATION OF CHAT BOT AI TO ENHANCE METACOGNITIVE INTERACTION IN INTEGRAL CALCULUS: A CASE STUDY ON THE METHOD OF INTEGRATION

Συγγραφείς

  • Khoe Yao Tung Universitas Pelita Harapan

DOI:

https://doi.org/10.19166/johme.v9i2.10424

Λέξεις-κλειδιά:

artificial intelligence, metacognitive interaction, Adaptive Learning Technologies, Integral Calculus Instruction, Problem Solving Strategies

Περίληψη

This study investigates the integration of Artificial Intelligence (AI) technologies, with a particular emphasis on AI-based chatbots, to enhance metacognitive interactions in the learning of integral calculus, specifically within the domain of integration methods. By cultivating learners’ capacities for planning, monitoring, and evaluating their problem-solving strategies, AI-driven interventions act as catalysts for the development of metacognitive awareness in advanced mathematics instruction. Metacognition, which entails both the awareness and regulation of one’s cognitive processes, constitutes a critical determinant of effective problem-solving proficiency. Employing a case study design, this research explores the utilization of interactive AI chat prompts to deliver real-time guidance, thereby encouraging students to engage in reflective evaluation of their strategies and cognitive approaches. The efficacy of the AI platform is assessed through targeted interventions aimed at fostering metacognitive engagement and enhancing learning outcomes. The findings demonstrate that AI integration significantly supports students in identifying their cognitive strengths and limitations, while promoting the deployment of effective, adaptive problem-solving strategies. By reinforcing metacognitive interaction, this approach equips learners with the cognitive flexibility necessary to navigate complex mathematical tasks, simultaneously contributing to the progression of innovative, technology-enhanced pedagogical practices. Furthermore, the study highlights the role of AI-facilitated personalized learning and adaptive learning environments in supporting differentiated instruction tailored to individual learner profiles.

Αναφορές

Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. Learning Analytics, 213–228. https://doi.org/10.1007/978-1-4614-3305-7_4

Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. Thousand Oaks: SAGE Publications.

Brown, A. L. (1987). Metacognition, executive control, and school learning. Metacognition, Motivation, and Understanding, 65–116. Hillsdale: Lawrence Erlbaum Associates.

Cambridge Assessment International Education. (2022). Cambridge international AS & A level further mathematics: Further pure mathematics 1. Retrieved from https://www.cambridgeinternational.org

Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 1–18. https://doi.org/10.1016/j.caeai.2020.100002

Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Thousand Oaks: SAGE Publications.

Denzin, N. K. (2017). The research act: A theoretical introduction to sociological methods. London, UK: Taylor and Francis.

Denzin, N. K., & Lincoln, Y. S. (2018). The SAGE handbook of qualitative research (5th ed.). Thousand Oaks: SAGE Publications.

Dziuban, C. D., Moskal, P. D., & Hartman, J. L. (2018). Blended learning: Medium or method? Education and Information Technologies, 23(3), 1039–1059. https://doi.org/10.1007/s10639-018-9762-7

Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906

Gee, J. P. (2007). What video games have to teach us about learning and literacy. Computers in Human Behavior, 22(1), 31–314. https://doi.org/10.1016/j.chb.2005.08.014

Geertz, C. (1973). The interpretation of cultures: Selected essays. New York, NY: Basic Books.

Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London, UK: Taylor and Francis.

Heffernan, N. T., & Heffernan, C. L. (2014). Study cycle: A new approach to studying mathematics. Studies in Higher Education, 39(1), 1–10. https://doi.org/10.1080/03075079.2015.1078258

Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education, 161, 104–135. https://doi.org/10.1016/j.compedu.2020.104120

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Thousand Oaks: SAGE Publications.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Retrieved from https://www.pearson.com/us/higher-education/product/Luckin-Intelligence-Unleashed-An-Argument-for-AI-in-Education-Report-2016/9780134150666.html

Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation (4th ed.). San Francisco, CA: John Wiley & Sons.

Murray, P. (2019). Learning in the age of AI: A teacher’s guide. International Journal of Artificial Intelligence in Education, 29(3), 373–397. https://doi.org/10.1007/s40593-018-00164-3

Opesemowo, O. A. G., & Adewuyi, H. O. (2024). A systematic review of artificial intelligence in mathematics education: The emergence of 4IR. Eurasia Journal of Mathematics, Science and Technology Education, 20(7), 1–20. https://doi.org/10.29333/ejmste/14762

Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach (3rd ed.). Harlow, UK: Pearson Education.

Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). Thousand Oaks: SAGE Publications.

Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science, 26, 113-125. https://doi.org/10.1023/a:1003044231033

Schraw, G. (2001). Assessing metacognitive awareness. Contemporary Educational Psychology, 25(1), 113–137. https://doi.org/10.1006/ceps.2000.1040

Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475. https://doi.org/10.1006/ceps.1994.1033.

Tamim, R. M., et al. (2011). The impact of technology on K–12 student learning: A meta-analysis. Computers & Education, 57(1), 314–328. https://doi.org/10.1016/j.compedu.2010.10.003

Thi-Nga, H. (2024). Metacognition in mathematics education: From academic chronicle to future research scenario. Eurasia Journal of Mathematics, Science and Technology Education, 20(4), 1–15. https://doi.org/10.29333/ejmste/14381

Van Lehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. American Educational Research Journal, 48(3), 535–570. https://doi.org/10.3102/0002831211410293

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Thousand Oaks: SAGE Publications.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

Λήψεις

Δημοσιευμένα

2025-12-03

Πώς να δημιουργήσετε Αναφορές

Tung, K. Y. (2025). THE IMPLEMENTATION OF CHAT BOT AI TO ENHANCE METACOGNITIVE INTERACTION IN INTEGRAL CALCULUS: A CASE STUDY ON THE METHOD OF INTEGRATION. JOHME: Journal of Holistic Mathematics Education, 9(2), 257–271. https://doi.org/10.19166/johme.v9i2.10424

Τεύχος

Ενότητα

Research in Mathematics Education