Tác động của năng lực đổi mới sáng tạo tới động lực nghiên cứu khoa học của sinh viên Việt Nam: Vai trò điều tiết của các công cụ tích hợp AI
Abstract
Innovative capacity (IC), defined as the ability to engage in divergent thinking, is a critical factor in enhancing the drive for learning and research motivation. Moreover, artificial intelligence (AI) tools promote exploration and creativity by providing significant support to students during the research process. This study examines the moderating role of AI in the relationship between IC and undergraduate research motivation, using the theoretical foundations of Expectancy-Value Theory and Self-Determination Theory. A quantitative analysis was performed using the partial least squares structural equation modeling (PLS-SEM) approach, based on primary data obtained from a survey of 923 university students. The results demonstrate that IC has a positive effect on students' research motivation, and this relationship is further strengthened with increased utilization of AI tools. Based on the analysis and findings, this study proposes several recommendations to advance IC and improve undergraduate research motivation.
Tóm tắt
Năng lực đổi mới sáng tạo (NLĐMST) biểu hiện qua khả năng suy nghĩ khác biệt, là yếu tố kích thích tinh thần học tập và nâng cao động lực nghiên cứu khoa học (NCKH). Bên cạnh đó, công cụ trí tuệ nhân tạo AI hỗ trợ sinh viên trong quá trình triển khai, góp phần thúc đẩy động lực khám phá và sáng tạo. Trong bài báo này, việc phân tích vai trò điều tiết của AI trong mối quan hệ giữa NLĐMST và động lực NCKH được tập trung thực hiện dựa trên cơ sở lý thuyết kỳ vọng - giá trị và thuyết tự quyết. Phân tích định lượng qua mô hình cấu trúc PLS-SEM dựa trên dữ liệu sơ cấp được thu thập từ 923 sinh viên tại các trường đại học. Kết quả cho thấy, NLĐMST tác động thuận chiều tới động lực NCKH của sinh viên và mối quan hệ này được tăng cường khi việc ứng dụng công cụ tích hợp AI càng nhiều. Dựa trên phân tích và kết quả nghiên cứu, một số khuyến nghị được đề xuất nhằm thúc đẩy NLĐMST và hoạt động NCKH.
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Tài liệu tham khảo
Bui, L. T., & Tran, L. M. (2022), Determinants of the Intention to Participate in Scientific Research of Students at Vietnam National University of Agriculture, Vietnam Journal of Agricultural Sciences, 20(11), 1550-1560, (in Vietnamese).
Cao, V. Q., Bach, A. N. H., & Nguyen, A. Q. (2019). Assessment of the moderating role of moderator variable in multivariate research model – illustrative case study: innovative work behaviour, yersin journal of science, 5, 3–15, (in Vietnamese).
Chen, C., Huang, J., & Hsiao, Y. (2010). Knowledge management and innovativeness. International Journal of Manpower, 31(8), 848–870. https://doi.org/10.1108/01437721011088548
Chen, X., Chen, I., Jiang, X., Li, X., & Gamble, J. H. (2024). Factors influencing innovation competence among children and adolescents in China – A multilevel, cross-cohort study. Heliyon, 10(12), e32640. https://doi.org/10.1016/j.heliyon.2024.e32640
Comrey, A. L. (1973). A first course in factor analysis. NY: Academic Press. New York.
Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Deemer, E. D., Martens, M. P., & Buboltz, W. C. (2010). Toward a tripartite model of research motivation: development and initial validation of the research motivation scale. Journal of Career Assessment, 18(3), 292–309. https://doi.org/10.1177/1069072710364794
Do, D. A. (2021), Factors influencing the innovation capacity of students in universities, Journal of Economics & Development, (286), 96–106, (in Vietnamese).
Eccles, J. (1983). Expectancies, values and academic behaviors, In J. T. Spence (Ed.). Achievement and achievement motives: Psychological and sociological approaches (pp. 75-146), San Francisco, CA: Free man.
Eccles, J. S., & Wigfield, A. (1995). In the mind of the actor: The structure of adolescents' achievement task values and expectancy-related beliefs. Personality and Social Psychology Bulletin, 21(3), 215–225. https://doi.org/10.1177/0146167295213003
Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & Mac Iver, D. (1993). Development during adolescence: The impact of stage-environment fit on young adolescents' experiences in schools and in families. American Psychologist, 48(2), 90–101. https://doi.org/10.1037/0003-066X.48.2.90
Ergen, O., & Belcastro, K. D. (2019). Ai Driven Advanced Internet Of Things (Iotx2): The Future Seems Irreversibly Connected in Medicine, The anatolian journal of cardiology, 22, 15-17.
https://doi.org/10.14744/AnatolJCardiol.2019.73466
Ha, S. D., & Nong, M. T. N (2021). Determinants of students’ scientific research participation – a case study in university of finance – marketing, Journal of Finance - Marketing Research, 49, 13-24, (in Vietnamese). https://doi.org/10.52932/jfm.vi49.92
Hair, J. F., Jr, Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121. https://doi.org/10.1108/ebr-10-2013-0128
Hair, J. F., Risher, J. J., Sarstedt, M. & Ringle, C.M. (2019), "When to use and how to report the results of PLS-SEM", European Business Review, 31(1), 2-24.
https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling, Journal of the Academy of Marketing Science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8
Hock, M., & Ringle, C. M. (2010). Local strategic networks in the software industry: an empirical analysis of the value continuum. International Journal of Knowledge Management Studies, 4(2), 132.
https://doi.org/10.1504/ijkms.2010.030789
Ivanov, S. (2023). The dark side of artificial intelligence in higher education. Service Industries Journal, 43(15–16), 1055–1082. https://doi.org/10.1080/02642069.2023.2258799
Karampelas, A. (2021). Artificial Intelligence and Machine Learning in the STEAM classroom. Hellenic Journal of STEM Education, 1(2), 59–66. https://doi.org/10.51724/hjstemed.v1i2.13
Keinänen, M. M., & Kairisto-Mertanen, L. (2019), "Researching learning environments and students’ innovation competences", Education + Training, 61(1), 17-30.
https://doi.org/10.1108/ET-03-2018-0064
Lacson, E. E., & Dejos, E. A., Jr. (2022). Research Skills Scale for Senior High School Students: Development and Validation. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.6727946
Long, T., Zhang, D., Li, G., Taraif, B., Menon, S., Smith, K. S., Wang, S., Gero, K. I., & Chilton, L. B. (2023). Tweetorial Hooks: Generative AI tools to Motivate science on Social Media. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2305.12265
Oudeyer, P. (2017). Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning. Behavioral and Brain Sciences, 40. https://doi.org/10.1017/s0140525x17000243
Oxford Insights. (2004). Government AI Readiness Index 2024.
https://oxfordinsights.com/ai-readiness/ai-readiness-index/
Popenici, S. a. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1).
https://doi.org/10.1186/s41039-017-0062-8
Roger, B. (2006). Estimation and Sample Size Determination for Finite Populations (10th ed.). CD Rom Topics, Section 8.7, West Chester University of Pennsylvania.
Roni, S. M., Djajadikerta, H., & Ahmad, M. A. N. (2015). PLS-SEM Approach to Second-order Factor of Deviant Behaviour: In Constructing Perceived Behavioural Control. Procedia Economics and Finance, 28, 249-253. 7th International Conference On Financial Criminology, 13-14 April 2015 (pp. 249-253). Elsevier.
https://doi.org/10.1016/S2212-5671(15)01107-7
Ryan, R. M., & Connell, J. P. (1989). Perceived locus of causality and internalization: examining reasons for acting in two domains. Journal of personality and social psychology, 57(5), 749–761.
https://doi.org/10.1037//0022-3514.57.5.749
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68
Samid, G. (2021). Artificial intelligence assisted innovation. In Artificial intelligence.
https://doi.org/10.5772/intechopen.96112
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115.
https://doi.org/10.1016/j.jfbs.2014.01.002
The National Assembly of the Socialist Republic of Vietnam. (2013). Science and technology law (Number 29/2013/QH13) (in Vietnamese). https://vanban.chinhphu.vn/default.aspx?pageid=27160&docid=169383
Tremblay, M. A., Blanchard, C. M., Taylor, S., Pelletier, L. G., & Villeneuve, M. (2009). Work Extrinsic and Intrinsic Motivation Scale: Its value for organizational psychology research. Canadian Journal of Behavioural Science/Revue Canadienne Des Sciences Du Comportement, 41(4), 213–226.
https://doi.org/10.1037/a0015167
United Nations Educational, Scientific and Cultural Organization. (2023). Guidance for generative AI in education and research.
https://doi.org/10.54675/EWZM9535
Vo, N. T. M. (2023), A study on the factors influencing students' motivation to participate in scientific research. Journal of science and Technology, 21(4), 27–33, (in Vietnamese).
World Intellectual Property Organization. (2024). Global Innovation Index 2024.
https://www.wipo.int/web-publications/global-innovation-index-2024/en/