Keywords
collaborative learning outcomes
group projects
How to Cite
Abstract
The purpose of this study is to analyze the effects that AI-integrated tools have on collaborative learning outcomes within higher education in Vietnam. Based on collaborative learning theory, it observes the impacts of AI platforms regarding student engagement, communication efficiency, and perceived learning outcomes in group projects. 86 undergraduate students from three universities participated in a quasi-experimental between-groups design. The experimental group used some AI-supported platforms (e.g., ChatGPT, Notion AI, Google Docs AI), while the control group used traditional means (i.e., email, face-to-face meetings, standard Google Docs). The project lasted six weeks; both groups performed under exactly the same tasks and assessment criteria with corresponding deadlines. A mixed-method approach was adopted for data collection. Quantitative data were collected by a post-project survey analyzed with Statistical Tests plus some quality insight from semi-structured interviews involving 12 students and 4 instructors. Results indicate that the treatment group displayed significantly higher levels of engagement and communication effectiveness. Qualitative data further underscores improved management of brainstorming and reduced work pressure. It, therefore, brings to the limelight the potentiality of AI tools in managing cognitive and behavioral challenges within a collaborative setup. This information can be useful for developing technologically enhanced teaching methods in blended and online learning environments.
References
Barkley, E. F., Major, C. H., & Cross, K. P. (2014). Collaborative learning techniques: A handbook for college faculty. John Wiley & Sons. https://download.e-bookshelf.de/download/0002/5216/40/L-G-0002521640-0003712114.pdf
Binns, R., Van Kleek, M., Veale, M., Lyngs, U., Zhao, J., & Shadbolt, N. (2018, April). 'It's Reducing a Human Being to a Percentage' Perceptions of Justice in Algorithmic Decisions. In Proceedings of the 2018 Chi conference on human factors in computing systems (pp. 1-14). https://doi.org/10.1145/3173574.3173951
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002
Crawford, K., & Paglen, T. (2021). Excavating AI: The politics of images in machine learning training sets. Ai & Society, 36(4), 1105-1116. https://doi.org/10.1007/s00146-021-01162-8
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
Dillenbourg, P. (1999). What do you mean by collaborative learning? Collaborative-learning: Cognitive and Computational Approaches., 1-19. https://doi.org/10.1016/S0360-1315(00)00011-7
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press. https://doi.org/10.5204/lthj.v1i0.1386.
Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical inquiry in a text-based environment: Computer conferencing in higher education. The internet and higher education, 2(2-3), 87-105. https://doi.org/10.1016/S1096-7516(00)00016-6
Gillies, R. M. (2016). Cooperative learning: Review of research and practice. Australian Journal of Teacher Education (Online), 41(3), 39-54. https://doi.org/10.14221/ajte.2016v41n3.3
Hadwin, A. F., Järvelä, S., & Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. Handbook of self-regulation of learning and performance, 30, 65-84. https://www.researchgate.net/publication/385592933_Self-regulated_co-regulated_and_socially_shared_regulation_of_learning_in_collaborative_learning_environments
Hew, K. F., Huang, W., Du, J., & Jia, C. (2023). Using chatbots to support student goal setting and social presence in fully online activities: Learner engagement and perceptions. Journal of Computing in Higher Education, 35(1), 40-68. https://doi.org/10.1007/s12528-022-09338-x
Hofstede, G. (2001). Culture's consequences: Comparing values, behaviors, institutions and organizations across nations. International Educational and Professional. https://doi.org/10.1016/S0005-7967(02)00184-5
Holmes, W., & Porayska-Pomsta, K. (2023). The ethics of artificial intelligence in education. Lontoo: Routledge, 621-653. https://hrcak.srce.hr/en/file/439501
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign. https://www.researchgate.net/publication/332180327_Artificial_Intelligence_in_Education_Promise_and_Implications_for_Teaching_and_Learning
Hrastinski, S. (2009). A theory of online learning as online participation. Computers & Education, 52(1), 78-82. https://doi.org/10.1016/j.compedu.2008.06.009
Huang, A. Y., Lu, O. H., & Yang, S. J. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684
Huang, Y., Aleven, V., McLaughlin, E., Koedinger, K. (2020). A General Multi-Method Approach to Design-Loop Adaptivity in Intelligent Tutoring Systems. In: Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12164. Springer, Cham. https://doi.org/10.1007/978-3-030-52240-7_23
Janssen, J., Erkens, G., Kirschner, P. A. (2011). Group awareness tools: It’s what you do with it that matters. Computers in human behavior, 27(3), 1046-1058. https://doi.org/10.1016/j.chb.2010.06.002
Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational researcher, 38(5), 365-379. https://doi.org/10.3102/0013189X09339057
Laal, M., & Ghodsi, S. M. (2012). Benefits of collaborative learning. Procedia - Social and Behavioral Sciences, 31, 486-490. https://doi.org/10.1016/j.sbspro.2011.12.091
Lin, H., & Chen, Q. (2024). Artificial intelligence (AI)-integrated educational applications and college students’ creativity and academic emotions: students and teachers’ perceptions and attitudes. BMC psychology, 12(1), 487. https://doi.org/10.1186/s40359-024-01979-0
Luckin, R. (2018). Machine Learning and Human Intelligence. The future of education for the 21st century. UCL institute of education press. https://discovery.ucl.ac.uk/id/eprint/10178695/1/Machine%20Learning%20and%20Human%20Intelligence.pdf
Malmberg, J., Järvelä, S., Järvenoja, H., & Panadero, E. (2015). Promoting socially shared regulation of learning in CSCL: Progress of socially shared regulation among high-and low-performing groups. Computers in human behavior, 52, 562-572. https://doi.org/10.1016/j.chb.2015.03.082
McNeil, S. G., Robin, B. R., & Miller, R. M. (2000). Facilitating interaction, communication and collaboration in online courses. Computers & Geosciences, 26(6), 699-708. https://doi.org/10.1016/S0098-3004(99)00106-5
Nguyen-Anh, T., Nguyen, A. T., Tran-Phuong, C., & Nguyen-Thi-Phuong, A. (2023). Digital transformation in higher education from online learning perspective: a comparative study of Singapore and Vietnam. Policy Futures in Education, 21(4), 335-354. https://doi.org/10.1177/14782103221124181
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. In Algorithms of oppression. New York university press. https://doi.org/10.18574/nyu/9781479833641.001.0001
Oakley, B., Felder, R. M., Brent, R., & Elhajj, I. (2004). Turning student groups into effective teams. Journal of Student-Centered Learning, 2(1), 9-34.
Pham, H., Tran, Q. N., La, G. L., Doan, H. M., & Vu, T. D. (2021). Readiness for digital transformation of higher education in the Covid-19 context: The dataset of Vietnam's students. Data in brief, 39, 107482. https://doi.org/10.1016/j.dib.2021.107482
Puliti, I. (2019). Combining synchronous and asynchronous communication in a transnational e-language learning environment: An analysis of teachers’ perspective. Italian Studies in Southern Africa/Studi d'Italianistica nell'Africa Australe, 32(2), 245-274.
Reeve, J. (2012). A self-determination theory perspective on student engagement. In S. L. Christenson et al. (Eds.), Handbook of research on student engagement (pp. 149-172). Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-2018-7_7
Roseth, C. J., Johnson, D. W., & Johnson, R. T. (2008). Promoting early adolescents' achievement and peer relationships: the effects of cooperative, competitive, and individualistic goal structures. Psychological bulletin, 134(2), 223.
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation. American Psychologist, 55(1), 68-78. https://doi.org/10.1037/0003-066X.55.1.68
Selwyn, N. (2007). The use of computer technology in university teaching and learning: a critical perspective. Journal of computer assisted learning, 23(2), 83-94. https://doi.org/10.1111/j.1365-2729.2006.00204.x
Slavin, R. E. (1995). Cooperative learning: Theory, research, and practice (2nd ed.). Allyn & Bacon. https://www.scirp.org/reference/referencespapers?referenceid=713321
Slavin, R. E., Hurley, E. A., & Chamberlain, A. (2003). Cooperative learning and achievement: Theory and research. Handbook of psychology: Educational psychology, 7, 177-198.
Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 409-426). Cambridge University Press. https://www.researchgate.net/publication/200773374_Computer-supported_Collaborative_Learning_An_Historical_Perspective
Strijbos, J. W., & Fischer, F. (2007). Methodological challenges for collaborative learning research. Learning and Instruction, 17(4), 389-393. https://doi.org/10.1016/j.learninstruc.2007.03.004
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (Vol. 86). Harvard University Press.
Williamson, B. (2021). Digital policy sociology: Software and science in data-intensive precision education. Critical Studies in Education, 62(3), 354-370. https://doi.org/10.1080/17508487.2019.1691030
Williamson, B., Macgilchrist, F., & Potter, J. (2021). Covid-19 controversies and critical research in digital education. Learning, Media and Technology, 46(2), 117-127. https://doi.org/10.1080/17439884.2021.1922437
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (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), 1-27. https://doi.org/10.1186/s41239-019-0171-0
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