Artificial Intelligence and New Pathways in Student Learning (In case of NUM students)
DOI:
https://doi.org/10.65168/Keywords:
Artificial Intelligence, Self-Regulated Learning, Intrinsic Goal Orientation, Metacognitive Self-Regulation, Effort RegulationAbstract
The quality of education provided by Colleges of Technology and Polytechnic Colleges plays a crucial role in supporting the sustainable economic development of Mongolia. The higher education system of Mongolia currently comprises four types of higher education institutions: universities, institutes, colleges, and KOSEN model Colleges of Technology.
Over the past decade, the Government of Mongolia has explored the possibility of introducing the engineering education model of KOSEN Colleges of Technology, which has demonstrated effective performance, into the country's Polytechnic Colleges. A comparative analysis of the learning outcomes of engineering programs offered by KOSEN model Colleges of Technology and similar engineering programs at Polytechnic Colleges is expected to provide valuable evidence for educational policy development and labor market research in Mongolia.
Colleges of Technology offer associate degree programs, whereas Polytechnic Colleges provide diploma-level professional education. Both types of institutions implement outcome-based curricula founded on the integration of education, research, and industry. This study evaluated and compared the learning outcomes of the Mechanical Engineering programs offered by Polytechnic Colleges and Japanese KOSEN model Colleges of Technology using performance-based assessment criteria with the participation of key stakeholders.
The study included three Polytechnic Colleges and two KOSEN model Colleges of Technology that offer comparable Mechanical Engineering programs. Structural Equation Modeling (SEM) was employed for data analysis using SPSS and Smart PLS software. The findings indicate that the selected factors have a significant influence on the learning outcomes of the Mechanical Engineering programs. Furthermore, the comparative analysis revealed notable differences in learning outcomes between the programs offered by Polytechnic Colleges and those offered by KOSEN model Colleges of Technology.
The results suggest that improving program learning outcomes requires clearly defining measurable learning outcomes, systematically assessing achievement at each stage of the educational process, and continuously improving the curriculum based on assessment results. Such practices are essential for enhancing the competencies of students enrolled in these engineering programs.
References
Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21(10). https://doi.org/10.1186/s41239-024-00444-7
Chan, C. K. Y. (2024). Is AI changing the rules of academic misconduct? An in-depth look at students' perceptions of "AI-giarism". Education and Information Technologies, 30, 8087–8108. https://doi.org/10.1007/s10639-024-13151-7
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). 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
Dalalah, D., & Dalalah, O. M. A. (2023). The false positives and false negatives of generative AI detection tools in education and academic research: The case of ChatGPT. The International Journal of Management Education, 21(2), 100822. https://doi.org/10.1016/j.ijme.2023.100822
Delcker, J., Heil, J., Ifenthaler, D., Seufert, S., & Spirgi, L. (2024). First-year students AI-competence as a predictor for intended and de facto use of AI-tools for supporting learning processes in higher education. International Journal of Educational Technology in Higher Education, 21(1), NA–NA. https://doi.org/10.1186/s41239-024-00452-7
Digital Education Council. (2024, August 2). Digital Education Council Global AI Student Survey 2024. Digitaleducationcouncil.com https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024
Duncan, T. G., & McKeachie, W. J. (2005). The making of the Motivated Strategies for Learning Questionnaire. Educational Psychologist, 40(2), 117–128. https://doi.org/10.1207/s15326985ep4002_6
Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2024). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544
Freeman, J. (2025). Student Generative AI Survey 2025. https://www.hepi.ac.uk/wp- content/uploads/2025/02/HEPI-Kortext-Student-Generative-AI-Survey-2025.pdf
Iván Miguel García-López, & Trujillo-Liñán, L. (2025). Correction: Ethical and regulatory challenges of Generative AI in education: a systematic review. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1681252.
Guerrero-Dib, J. G., Portales, L., & Heredia-Escorza, Y. (2020). Impact of academic integrity on workplace ethical behaviour. International Journal for Educational Integrity, 16(2). https://doi.org/10.1007/s40979-020-0051-3
Hayes, A. F. (2013). Mediation, moderation and conditional process analysis : a regression-based approach. The Guilford Press.
Xu, J. (2026). How generative AI enhances self-regulated learning in EFL learners: a chain mediation model of “intention to use” and “learning engagement.” Frontiers in Psychology, 17. https://doi.org/10.3389/fpsyg.2026.1808183
International Center for Academic Integrity. (2021). The fundamental values of academic integrity (3rd ed.). https://academicintegrity.org/resources/fundamental-values
Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023). Generative AI tools and assessment: Guidelines of the world's top-ranking universities. Computers and Education Open, 5, 100151. https://doi.org/10.1016/j.caeo.2023.100151
Morais, E., Santos, A. C., & Mouraz, A. (2025). Validation of the Portuguese short version of the Motivated Strategies for Learning Questionnaire (MSLQ-PTS). Frontiers in Education, 10, 1445548. https://doi.org/10.3389/feduc.2025.1445548
Pintrich, P. R. (1991). A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). https://files.eric.ed.gov/fulltext/ED338122.pdf
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students' academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Ussenova, G., Issayeva, G., Urazaliyeva, R., Karimova, G., & Mukhanova, M. (2025). Examining cognitive independence in the context of digital learning: A moderation analysis of student motivation, self-regulation skills, and cognitive engagement. Contemporary Educational Technology, 17(4), ep618. https://doi.org/10.30935/cedtech/17570
Theobald, M. (2021). Self-regulated learning training programs enhance university students' academic performance, self-regulated learning strategies, and motivation: A meta-analysis. Contemporary Educational Psychology, 66, 101976. https://doi.org/10.1016/j.cedpsych.2021.101976
Kangwa, D., Msafiri, M. M., & Fute, A. (2025). Balancing innovation and ethics: promote academic integrity through support and effective use of GenAI tools in higher education. AI and Ethics. https://doi.org/10.1007/s43681-025-00689-6
van Sonderen, E., Sanderman, R., & Coyne, J. C. (2013). Ineffectiveness of reverse wording of questionnaire items: Let's learn from cows in the rain. PLoS ONE, 8(7), e68967. https://doi.org/10.1371/journal.pone.0068967
Frank, A., Amenyeke, F. B., Rockson Ofori Amanfo, & Wussah, C. (2025). The Impact of the Usage of Generative AI on Academic Engagement of Students: A Case Study at a College of Education in Ghana. International Journal of Research and Innovation in Applied Science, 10(10), 847–858. https://doi.org/10.51584/IJRIAS.2025.1010000068
YUSUF, P., Pam, N., Nanpon, J., Bala Yakubu, Musa, N. N., & Samuel, G. I. (2026). The Impact of Generative AI Tutors on Metacognitive Monitoring and Self-Regulation in Higher Education: An Experimental Study. Zenodo. https://doi.org/10.5281/zenodo.19501204
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
Japheth Kodua Wiredu, Nelson Seidu Abuba, & Zakaria, H. (2024). Impact of Generative AI in Academic Integrity and Learning Outcomes: A Case Study in the Upper East Region. Asian Journal of Research in Computer Science, 17(7), 214–232. https://doi.org/10.9734/ajrcos/2024/v17i7491
Theoharakis, V., Mylonopoulos, N., & Papadopoulou, K. (2025). AI’s learning paradox: how business students’ engagement with AI amplifies moral disengagement-driven misconduct. Studies in Higher Education, 1–18. https://doi.org/10.1080/03075079.2025.2533365
