An Intelligent-Adaptive Learning Model to Reduce Novice Students' Cognitive Load in AI-Assisted Code Debugging
DOI:
https://doi.org/10.59890/ijetr.v4i3.11Keywords:
Adaptive Learning, Cognitive Load Theory, Artificial Intelligence in Education, Code Debugging, Python Programming, Vocational MechatronicsAbstract
Novice students in vocational mechatronics education frequently experience high cognitive load during programming code debugging. This challenge often leads to mental frustration and passive reliance on modern Generative Artificial Intelligence (AI) tools. This study develops and evaluates an Intelligent-Adaptive Learning Model esigned to provide structured pedagogical scaffolding during Python code debugging. Utilizing a Research and Development (R&D) methodology within the ADDIE framework, the model was implemented across eight practical lab sessions involving 21 novice students. Data were collected via post-activity quantitative questionnaires (5-point Likert scale), qualitative reflection sheets, and interaction matrix logs. The findings indicate that logic errors (66.7%) and indentation issues (42.9%) are the primary sources of extraneous cognitive load. Adaptive prompting guidance effectively mitigated passive copy-paste habits (mean = 2.33) while significantly boosting independent debugging self-efficacy (mean = 3.62). Furthermore, 76.2% of students engaged in autonomous troubleshooting prior to consulting AI, and 85.7% achieved complete daily task completion. Psychologically, 76.2% experienced cognitive relief and conceptual clarity, while 23.8% exhibited heightened intrinsic motivation. The model successfully achieved Technology Readiness Level (TRL) 4, demonstrating that pedagogically integrated AI scaffolding effectively reduces extraneous cognitive load and fosters autonomous problem-solving in vocational technology education.
References
Branch, R. M. (2009). Instructional Design: The ADDIE Approach. Springer Science & Business Media. https://doi.org/ 10.1007/978-0-387-09506-6
Denny, P., Luxton-Reilly, A., Leinonen, J., Hellas, A., & Prather, J. (2024). Computing education in the era of generative AI.Communications of the ACM, 67(2), 56-67. https://doi.org/10.1145/3624724
Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in Psychology, 52, 139-183. https://doi.org/10.1016/S0166-4115(08)62386-9
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., & Fischer, F. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Mayer, R. E. (2021). Multimedia Learning (3rd ed.). Cambridge University Press. https://doi.org/10.1017/9781316941164
Prather, J., Reeves, B., Denny, P., Becker, B. A., Leinonen, J., & Luxton-Reilly, A. (2023). "It's weird that it knows what I'm thinking": The impact of AI code completion tools on novice programmers. ACM Transactions on Computer-Human Interaction, 31(4), 1-31. https://doi.org/10.1145/3617367
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295-312. https://doi.org/10.1016/0959-4752(94)90003-5
Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press.
