EIREducational Innovation Research3029-18443029-1852WHIOCE PUBLISHING PTE. LTD.10.18063/EIR.v3i7.787ArticleVibe Learning: Cultivating Mathematical Modelling Literacy in High School: A Quasi-Experimental Mixed-Methods Studyhttps://artdesignp.com/journal/EIR/3/7/10.18063/EIR.v3i7.787WangZhihao,FanDechen,NiuTingting2025372025-08-26Vibe learning brings the idea-first ethos of vibe programming into classroom practice: learners set intentions and evaluation criteria while technology recedes. We report a quasi-experimental mixed-methods study in two parallel Grade 11 classes: an experimental class used an AI-augmented modelling sandbox with a Socratic micro-tutor across three scenarios, while a control class received traditional instruction. Data included baseline questionnaires, platform logs, post-task micro-surveys, and summative assessments. Vibe learning enabled full-cycle modelling within regular periods: completion time fell by 33%, first-attempt correctness rose, submission cycles dropped, and feedback latency shrank from minutes to seconds. Both classes reached similar concept mastery by end of the term, with larger self-efficacy gains in the experimental group. We outlined orchestration to balance efficiency with productive struggle and situate the design in pragmatism, social constructivism, phenomenology, hermeneutics, and virtue epistemology, showing a scalable, human-centered path to modelling literacy with transparent, computable evidence.AI tutor,Mathematical modelling literacy,Vibe learning

[1] Koedinger K, Corbett A, Perfetti C, 2012, The Knowledge-Learning-Instruction Framework: Bridging the Science-Practice Chasm to Enhance Robust Student Learning. Cognitive Science, 36(5): 757–798. [2] VanLehn K, 2011, The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist, 46(4): 197–221. [3] Shute V, 2008, Focus on Formative Feedback. Review of Educational Research, 78(1), 153–189. [4] Kirschner P, Sweller J, Clark R, 2006, Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist, 41(2): 75–86. [5] Sweller J, 2011, Cognitive Load Theory. Psychology of Learning and Motivation, 55. [6] Kasneci E, Sessler K, Küchemann S, et al., 2023, ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 2023(103): 102274. [7] Zawacki-Richter O, Marín V, Bond M, et al., 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(1): 39. [8] Graesser A, Person N, Magliano J, 2004, AutoTutor: A Tutor with Dialogue in Natural Language. Behavior Research Methods, Instruments, & Computers, 36(2): 180–192. [9] D’Mello S, Graesser A, 2012, AutoTutor and Affective AutoTutor: Learning by Talking with Cognitively and Emotionally Intelligent Computers that Talk Back. ACM Transactions on Interactive Intelligent Systems, 2(4): 1–39. [10] Mislevy R, Almond R, Lukas J, 2004, A Brief Introduction to Evidence-Centered Design. Tech Rep, 2004: 632. [11] Mislevy R, 2005, Evidence-Centered Assessment Design: Layers, Structures, and Terminology. PADI Tech Rep, 2005: 9. [12] Luckin R, Holmes W, Griffiths M, et al., 2016, Intelligence Unleashed: An Argument for AI in Education, Pearson, London. [13] Blum W, Galbraith P, Henn H, et al., 2007, Modelling and Applications in Mathematics Education: The 14th ICMI Study, Springer, New York. [14] Blum W, Ferri R, 2009, Mathematical Modelling: Can It be Taught and Learnt? Journal of Mathematical Modelling and Application, 1(1): 45–58. [15] Kaiser G, Blum W, Ferri R, et al., 2011, Trends in Teaching and Learning of Mathematical Modelling, Springer, The Netherlands. [16] Webster F, 1958, Traffic Signal Settings, Road Research Tech, Her Majesty’s Stationery Office, London. [17] Lighthill M, Whitham G, 1955, On Kinematic Waves II: A Theory of Traffic Flow on Long Crowded Roads. Proceedings of the Royal Society A, 229(1178): 317–345. [18] Richards P, 1956, Shock Waves on the Highway. Operations Research, 4(1): 42–51. [19] Bocci F, 2012, Whether or Not to Run in the Rain. European Journal of Physics, 33(5): 1321–1332. [20] Usher E, Pajares F, 2009, Sources of Self-Efficacy in Mathematics: A Validation Study. Contemporary Educational Psychology, 34(1): 89–101. [21] Betz N, Hackett G, 1983, The Relationship of Mathematics Self-Efficacy Expectations to the Selection of Science-Based College Majors. Journal of Vocational Behavior, 23(3): 329–345. [22] Black F, Scholes M, 1973, The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3): 637–654. [23] Merton R, 1973, Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, 4(1): 141–183. [24] Dewey J, 1938, Logic: The Theory of Inquiry, Henry Holt, New York. [25] Vygotsky L, 1978, Mind in Society: The Development of Higher Psychological Processes, Harvard Univ Press, Cambridge. [26] Gadamer H, 2004, Truth and Method, 2nd rev ed, Bloomsbury, London. [27] Aristotle, 1999, Nicomachean Ethics, 2nd ed, Hackett, Indianapolis.