Downloads

https://doi.org/10.62836/jissh.v3i1.0001
Liang, X., Wei, D., Zhang, X., Zhang, L., & Chen, H. (2026). Measurement, Divergence, and Cultivation Paths of Mathematical Literacy Among Higher Vocational Students Empowered by Generative AI. Journal of Integrated Social Sciences and Humanities, 3(1), 0001. https://doi.org/10.62836/jissh.v3i1.0001
Volume 3, Issue 1, 2026

Measurement, Divergence, and Cultivation Paths of Mathematical Literacy Among Higher Vocational Students Empowered by Generative AI

With the explosive development of Generative Artificial Intelligence (GenAI), the mathematical teaching paradigm in higher vocational education is undergoing an unprecedented restructuring. As the cornerstone of core competencies, mathematical literacy exhibits new characterizing dimensions in an AI-empowered environment. Based on the characteristics of mathematics as a discipline, this study constructs the “ABCE” analytical framework encompassing Affective Experience, Behavioral Engagement, Cognitive Criticism, and Ethical Awareness. Through an empirical investigation of 1,086 students from a high-level applied university in China, utilizing correlation, ANOVA, and cluster analysis, the findings indicate that students demonstrate an imbalanced profile of “high affective identification, moderate ethical awareness, and low behavioral efficacy.” Mathematical foundation and technology acceptance are the core drivers of literacy divergence, and a significant contradiction exists between “cognitive inertia” and “tool dependency.” Based on these findings, four typical learner profiles are identified, and a stratified pedagogical intervention strategy focusing on the deep integration of “Human-AI-Teacher” is proposed to provide theoretical support and empirical evidence for the reform of mathematical evaluation in vocational education.

Generative Artificial Intelligence (GenAI) higher vocational education mathematical literacy human-AI collaboration ABCE framework

References

  1. Long D, Magerko B. What Is AI literacy? Competencies and Design Consideration. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 25–30 April 2020; pp. 1–16.
  2. Ng DTK, Leung JKL, Chu KWS, et al. AI Literacy: Definition, Teaching, Evaluation and Ethical Issues. Proceedings of the Association for Information Science and Technology 2021; 58(1): 504–509.
  3. Ng DTK, Wu W, Leung JKL, et al. Design and Validation of the AI Literacy Questionnaire: The Affective, Behavioural, Cognitive and Ethical Approach. British Journal of Educational Technology 2024; 55(3): 1082–1104.
  4. Zhou X, Li Y, Chai CS, et al. Defining, Enhancing, and Assessing Artificial Intelligence Literacy and Competency in K-12 Education from a Systematic Review. Interactive Learning Environments 2025; 1–23.
  5. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 1989; 13: 319–340.
  6. Deci EL, Ryan RM. Intrinsic Motivation and Self-Determination in Human Behavior; Springer Science & Business Media: New York, NY, USA, 2013.
  7. Pekrun R. The Control-Value Theory of Achievement Emotions: Assumptions, Corollaries, and Implications for Educational Research and Practice. Educational Psychology Review 2006; 18(4): 315–341.
  8. Bloom BS. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives; Longman: New York, NY, USA, 2001.
  9. Flavell JH. Metacognition and Cognitive Monitoring: A New Area of Cognitive–Developmental Inquiry. American Psychologist 1979; 34(10): 906.

Supporting Agencies

  1. Funding: This research was funded by Guangdong Province Undergraduate Teaching Quality and Teaching Reform Construction Project (Higher Education Teaching Reform Project: 2024-30-884). Quality Engineering Project of Shenzhen Polytechnic University (General Project: 1005-0452). Smart Course Project of Guangdong University of Petrochemical Technology: 2024-59. Projects of Talents Recruitment of GDUPT:2020rc039.