Protecting Statistical Reasoning from Cognitive Offloading: An AI-Resilient Framework for Malaysian Undergraduates

The utilisation of generative artificial intelligence (GenAI) by undergraduate students has exceeded that of institutional governance. Introductory statistics is particularly susceptible to this trend due to the easy delegation of its procedural tasks. It is possible for students to obtain accurate responses without engaging in the reasoning that the tasks were intended to foster. This conceptual paper examines the potential for unregulated GenAI reliance to erode the statistical reasoning of Malaysian bachelor's degree students and suggests strategies to safeguard this ability. The conceptual synthesis employed in the paper is structured. This study integrates the statistical thinking framework, cognitive load theory, cognitive offloading, self-efficacy theory, and evaluative judgement. It utilises sources from Scopus, Web of Science, ERIC, and Malaysian indexed databases, with a particular emphasis on Malaysian evidence and Malaysian Qualifications Agency guidance. Local cohorts approach statistics as a mandatory service course with documented anxiety, and national adoption is driven by perceived learning value rather than institutional facilitation, which exacerbates the concern in the Malaysian context. The primary mechanism by which GenAI use may impede three outcomes: the match between perceived and demonstrated competence, assessment validity, and statistical reasoning development is cognitive offloading. The paper suggests the AI-Resilient Statistical Reasoning (ARSR) framework in response, which is founded on three pillars: AI-transparent assessment, reasoning-first pedagogy, and calibrated AI use. It is accompanied by five testable propositions, each of which is linked to a research design, a level of analysis, and a pillar. The paper's contribution is not the individual constructs, which are well-established, but rather their statistical synthesis and conversion into falsifiable claims. Statistics educators, curriculum designers, and policymakers are provided with a structured foundation for utilising GenAI to enhance rather than replace statistical reasoning by the framework, which is in accordance with Sustainable Development Goal 4.

cognitive offloading, generative artificial intelligence, Malaysian higher education, SDG 4 (Quality Education), statistical reasoning

1. American Statistical Association. (2025). Guidelines for assessment and instruction in statistics education (GAISE) college report 2025 [Draft in development]. Retrieved July 21, 2026, from [Google Scholar] [Crossref]

2. https://amstat.quarto.pub/college-gaise [Google Scholar] [Crossref]

3. Asio, J. M. R. (2024). AI literacy, self-efficacy, and self-competence among college students: Variances and interrelationships among variables. Malaysian Online Journal of Educational Sciences, 12(3), 44–60. https://doi.org/10.22452/aldad.vol12no3.4 [Google Scholar] [Crossref]

4. Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman. [Google Scholar] [Crossref]

5. Bearman, M., & Ajjawi, R. (2023). Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(5), 1160–1173. [Google Scholar] [Crossref]

6. https://doi.org/10.1111/bjet.13337 [Google Scholar] [Crossref]

7. Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, Article 38. [Google Scholar] [Crossref]

8. https://doi.org/10.1186/s41239-023-00408-3 [Google Scholar] [Crossref]

9. Chan, C. K. Y., & Hu, W. (2023). Students' voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, Article 43. https://doi.org/10.1186/s41239-023-00411-8 [Google Scholar] [Crossref]

10. Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, Article 100197. [Google Scholar] [Crossref]

11. https://doi.org/10.1016/j.caeai.2023.100197 [Google Scholar] [Crossref]

12. Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148 [Google Scholar] [Crossref]

13. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, Article 22. [Google Scholar] [Crossref]

14. https://doi.org/10.1186/s41239-023-00392-8 [Google Scholar] [Crossref]

15. Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210, Article 104967. [Google Scholar] [Crossref]

16. https://doi.org/10.1016/j.compedu.2023.104967 [Google Scholar] [Crossref]

17. Dawson, P., Bearman, M., Dollinger, M., & Boud, D. (2024). Validity matters more than cheating. Assessment & Evaluation in Higher Education, 49(7), 1005–1016. [Google Scholar] [Crossref]

18. https://doi.org/10.1080/02602938.2024.2386662 [Google Scholar] [Crossref]

19. Dodeen, H., & Alharballeh, S. (2024). Predicting statistic anxiety by attitude toward statistics, statistics self-efficacy, achievement in statistics and academic procrastination among students of social sciences colleges. Journal of Applied Research in Higher Education, 17(4). https://doi.org/10.1108/JARHE-01-2024-0021 [Google Scholar] [Crossref]

20. delMas, R., Garfield, J., Ooms, A., & Chance, B. (2007). Assessing students' conceptual understanding after a first course in statistics. Statistics Education Research Journal, 6(2), 28–58. [Google Scholar] [Crossref]

21. Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. [Google Scholar] [Crossref]

22. https://doi.org/10.1111/bjet.13544 [Google Scholar] [Crossref]

23. Friedrich, A., Schreiter, S., Vogel, M., Becker-Genschow, S., Brünken, R., Kuhn, J., Lehmann, J., & Malone, S. (2024). What shapes statistical and data literacy research in K–12 STEM education? A systematic review of metrics and instructional strategies. International Journal of STEM Education, 11, Article 58. https://doi.org/10.1186/s40594-024-00517-z [Google Scholar] [Crossref]

24. Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2024). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction, 40(17), 4501–4520. https://doi.org/10.1080/10447318.2023.2226495 [Google Scholar] [Crossref]

25. GAISE College Report ASA Revision Committee. (2016). Guidelines for assessment and instruction in statistics education (GAISE) college report 2016. American Statistical Association. https://www.amstat.org/education/gaise [Google Scholar] [Crossref]

26. Gal, I. (2002). Adults’ statistical literacy: Meanings, components, responsibilities. International Statistical Review, 70(1), 1–25. https://doi.org/10.2307/1403713 [Google Scholar] [Crossref]

27. Garfield, J., & Ben-Zvi, D. (2008). Developing students’ statistical reasoning: Connecting research and teaching practice. Springer. https://doi.org/10.1007/978-1-4020-8383-9 [Google Scholar] [Crossref]

28. Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), Article 6. https://doi.org/10.3390/soc15010006 [Google Scholar] [Crossref]

29. Gok, S., & Goldstone, R. L. (2024). How do students reason about statistical sampling with computer simulations? An integrative review from a grounded cognition perspective. Cognitive Research: Principles and Implications, 9, Article 33. https://doi.org/10.1186/s41235-024-00561-x [Google Scholar] [Crossref]

30. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage. [Google Scholar] [Crossref]

31. Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10(1–2), 18–26. https://doi.org/10.1007/s13162-020-00161-0 [Google Scholar] [Crossref]

32. Jie, A. L. X., & Kamrozzaman, N. A. (2024). The challenges higher education students face in using artificial intelligence (AI) against their learning experiences. Open Journal of Social Sciences, 12(10), 362–377. https://doi.org/10.4236/jss.2024.1210025 [Google Scholar] [Crossref]

33. Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274 [Google Scholar] [Crossref]

34. Koga, S. (2022). Characteristics of statistical literacy skills from the perspective of critical thinking. Teaching Statistics, 44(2), 59–67. https://doi.org/10.1111/test.12302 [Google Scholar] [Crossref]

35. Lodge, J. M., Thompson, K., & Corrin, L. (2023). Mapping out a research agenda for generative artificial intelligence in tertiary education. Australasian Journal of Educational Technology, 39(1), 1–8. [Google Scholar] [Crossref]

36. https://doi.org/10.14742/ajet.8695 [Google Scholar] [Crossref]

37. Maat, S. M., Mohd, N., Che Ahmad, C. N., & Puteh, M. (2022). Statistical anxiety and achievement among university students. TEM Journal, 11(1), 420–426. https://doi.org/10.18421/TEM111-53 [Google Scholar] [Crossref]

38. Malaysian Qualifications Agency. (2023). Advisory note no. 2/2023: The use of generative artificial intelligence technology in higher education. MQA. [Google Scholar] [Crossref]

39. Mishra, P., Warr, M., & Islam, R. (2023). TPACK in the age of ChatGPT and generative AI. Journal of Digital Learning in Teacher Education, 39(4), 235–251. https://doi.org/10.1080/21532974.2023.2247480 [Google Scholar] [Crossref]

40. Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., Lyden, S., Neal, P., & Sandison, C. (2023). ChatGPT versus engineering education assessment: A multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education, 48(4), 559–614. [Google Scholar] [Crossref]

41. https://doi.org/10.1080/03043797.2023.2213169 [Google Scholar] [Crossref]

42. Onwuegbuzie, A. J., & Wilson, V. A. (2003). Statistics anxiety: Nature, etiology, antecedents, effects, and treatments—a comprehensive review of the literature. Teaching in Higher Education, 8(2), 195–209. https://doi.org/10.1080/1356251032000052447 [Google Scholar] [Crossref]

43. Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. [Google Scholar] [Crossref]

44. https://doi.org/10.1016/j.tics.2016.07.002 [Google Scholar] [Crossref]

45. Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1), 342–363. [Google Scholar] [Crossref]

46. https://doi.org/10.37074/jalt.2023.6.1.9 [Google Scholar] [Crossref]

47. Saravanan, T., & Kamrozzaman, N. A. (2025). Artificial intelligence in primary education: Teacher perceptions, instructional decisions, and integration challenges in urban Malaysia. International Journal of Research and Innovation in Social Science, 9(9), 9393–9406. [Google Scholar] [Crossref]

48. https://doi.org/10.47772/IJRISS.2025.909000772 [Google Scholar] [Crossref]

49. Sabbag, A., Zieffler, A., & Ng, C. (2025). Can we distinguish statistical literacy and statistical reasoning? Statistics Education Research Journal, 24(1). https://doi.org/10.52041/serj.v24i1.587 [Google Scholar] [Crossref]

50. Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1), 31–40. https://doi.org/10.37074/jalt.2023.6.1.17 [Google Scholar] [Crossref]

51. Sweller, J. (2011). Cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), Psychology of learning and motivation (Vol. 55, pp. 37–76). Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002-8 [Google Scholar] [Crossref]

52. Trassi, A. P., Leonard, S. J., Rodrigues, L. D., Rodas, J. A., & Santos, F. H. (2022). Mediating factors of statistics anxiety in university students: A systematic review and meta-analysis. Annals of the New York Academy of Sciences, 1512(1), 76–97. https://doi.org/10.1111/nyas.14746 [Google Scholar] [Crossref]

53. Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223–265. https://doi.org/10.1111/j.1751-5823.1999.tb00442.x [Google Scholar] [Crossref]

54. Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370 [Google Scholar] [Crossref]

55. Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart Learning Environments, 11, Article 28. [Google Scholar] [Crossref]

56. https://doi.org/10.1186/s40561-024-00316-7 [Google Scholar] [Crossref]

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