AI-Driven Learning: Mediating and Moderating Dynamics in Self-Regulated Learning.

Melda Mahniza(1*), Resti Elma Sari(2), Puji Hujria Suci(3), Indra Saputra(4), Elviza Yeni Putri(5),

(1) Universitas Negeri Padang
(2) Universitas Negeri Padang
(3) Universitas Negeri Padang
(4) Universitas Negeri Padang
(5) Universitas Negeri Padang
(*) Corresponding Author




DOI: https://doi.org/10.26858/est.v10i3.68254

Abstract


The rapid integration of artificial intelligence (AI) in education has transformed how students learn, particularly in fostering self-regulated learning (SRL). However, understanding the mechanisms and conditions under which AI adoption influences SRL remains underexplored. This study investigates the roles of achievement goals, cognitive load, personalized learning, students' adaptability, and AI competence in shaping SRL within an AI-enhanced educational framework. The research employs Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach to analyze direct, mediating, and moderating effects while accounting for demographic controls such as age, gender, internet access, and environment. The findings reveal a complex interplay of factors. Direct effect testing showed that five hypothesized relationships, including the influence of achievement goals, cognitive load, personalized learning, and students’ adaptability on SRL, were unsupported. Mediation analysis confirmed that AI adoption significantly mediates the effects of achievement goals, cognitive load, and personalized learning on SRL, emphasizing the role of technology acceptance in enhancing learning autonomy. Moderation analysis identified that AI competence strengthens the relationship between achievement goals and SRL but does not moderate other interactions, such as those involving AI adoption or cognitive load. These results underscore the nuanced dynamics between cognitive, technological, and motivational factors in AI-enhanced learning. The study contributes to the growing literature on AI-driven education by highlighting the pivotal role of mediating variables like AI adoption and the limited yet strategic influence of AI competence. Future research should explore broader contextual and pedagogical factors to optimize the integration of AI tools in fostering self-regulated learning

Keywords


Artificial Intelligence (AI) in Education; Self-Regulated Learning (SRL); AI Adoption; Achievement Goals; AI Competence.

Full Text:

PDF

References


Ames, C. (1992). Classrooms: Goals, Structures, and Student Motivation. Journal of Educational Psychology, 84(3), 261–271. https://doi.org/10.1037/0022-0663.84.3.261

Ameen, N., Wang, J., & Zhang, Y. (2022). The Impact of Digital Competence on Self-Regulated Learning in Technology-Integrated Learning Environments. Computers & Education, 179, 104403. https://doi.org/10.1016/j.compedu.2022.104403

Artino, A. R., La Rochelle, J. S., & Durning, S. J. (2019). Second-Order Factor Structure of the Motivated Strategies for Learning Questionnaire. Educational and Psychological Measurement, 79(3), 532–556. https://doi.org/10.1177/0013164418794307

Baker, R. S. J. D., Corbett, A. T., & Koedinger, K. R. (2010). Adapting to Individual Differences in Intelligent Tutoring Systems. International Journal of Artificial Intelligence in Education, 20(2), 187–224.

Bower, M., Howe, C., McCredie, N., Robinson, J., & Grover, D. (2019). Augmented Reality in Education: Cases, Places, and Potentials. Educational Media International, 56(1), 1–15. https://doi.org/10.1080/09523987.2019.1598989

Catrambone, R. (1998). Training Complex Problem-Solving Skills: A Comparison of Methods. Journal of Experimental Psychology: Applied, 4(4), 335–355. https://doi.org/10.1037/1076-898X.4.4.335

Chen, C. M., & Huang, S. H. (2014). Learning in a Ubiquitous Environment: An Adaptive Context-Aware Learning System. Computers & Education, 72, 108–120. https://doi.org/10.1016/j.compedu.2013.10.002

Cheng, B. H., Liao, H. L., & Tsai, C. C. (2020). Students' Adaptability to AI in Education: The Role of Attitudes and Technological Readiness. Computers in Human Behavior, 110, 106398. https://doi.org/10.1016/j.chb.2020.106398

Dabbagh, N., & Kitsantas, A. (2012). Supporting Self-Regulated Learning in Online Environments. The International Review of Research in Open and Distributed Learning, 13(1), 3–25. https://doi.org/10.19173/irrodl.v13i1.977

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Elliot, A. J., & McGregor, H. A. (2001). A 2 × 2 Achievement Goal Framework. Journal of Personality and Social Psychology, 80(3), 501–519. https://doi.org/10.1037/0022-3514.80.3.501

Heffernan, N., Heffernan, C., & Lin, L. (2016). The Effects of Personalized Learning in an AI-Powered Adaptive Learning System. Journal of Educational Technology Systems, 45(2), 213–225. https://doi.org/10.1177/0047239516659267

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign.

Hsieh, P. A., Sullivan, J. R., & Guerra, N. (2012). Personalized Learning and Students' Academic Performance: The Role of Self-Regulation. Journal of Educational Psychology, 104(3), 742–752. https://doi.org/10.1037/a0028184

Hwang, G. J., Wu, P. H., & Chen, C. H. (2016). Research Trends in Mobile and Ubiquitous Learning: A Review of Journal Publications from 2000 to 2015. British Journal of Educational Technology, 47(4), 661–683. https://doi.org/10.1111/bjet.12450

Joo, Y. J., Park, S. H., & Lim, D. H. (2013). The Effects of Adaptability and Learning Strategies on Self-Regulated Learning. Educational Psychology, 33(3), 259–274. https://doi.org/10.1080/01443410.2012.694866

Kerr, C. L., & McKendrick, J. D. (2020). The Role of Technology in Student Engagement and Learning. Computers in Human Behavior, 104, 106164. https://doi.org/10.1016/j.chb.2019.106164

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson Education.

Moreno, R., & Mayer, R. E. (2007). Multimedia Learning. Cambridge University Press.

Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive Load Theory and Instructional Design. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1

Pintrich, P. R. (2004). A Conceptual Framework for Assessing Motivation and Self-Regulated Learning in College Students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x

Roll, I., & Winne, P. H. (2015). Understanding, Evaluating, and Supporting Self-Regulated Learning Using Learning Analytics. Journal of Learning Analytics, 2(1), 7–12. https://doi.org/10.18608/jla.2015.21.2

Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4

Walkington, C. A. (2013). Using Adaptive Learning Technologies to Personalize Instruction to Students’ Interests. Journal of Educational Psychology, 105(4), 932–945. https://doi.org/10.1037/a0031882

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

Zimmerman, B. J. (2013). From Cognitive Modeling to Self-Regulation: A Social Cognitive Career Path. Educational Psychologist, 48(3), 135–147. https://doi.org/10.1080/00461520.2013.794676

Zawacki-Richter, O., Bäcker, A. L., & Vogt, S. (2019). The Impact of Artificial Intelligence on Learning and Education. Educational Technology & Society, 22(2), 52–67.


Article Metrics

Abstract view : 171 times | PDF view : 2 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Melda Mahniza, Resti Elma Sari, Puji Hujria Suci

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Editorial Office

Journal of Educational Science and Technology
Graduate Program Universitas Negeri Makassar

   

address icon red

 Jl Bonto Langkasa Gunungsari Baru Makassar, 90222 Kampus PPs UNM Makassar Gedung AD Ruang 406 Lt 4, Indonesia  
  jurnalestunm@gmail.com | est.journal@unm.ac.id 
  https://ojs.unm.ac.id/JEST/index 
   085299898201 (WA) 
 

EST Index by: