AI Chatbots in LMS: A Pedagogical Review of Cognitive, Constructivist, and Adaptive Principles
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The sudden growth of technology has profoundly shifted various sectors, notably education, where Artificial Intelligence (AI) chatbots are revolutionizing Learning Management Systems (LMS). LMSs are pivotal in the management of educational materials and engagements between educators and students. Traditional LMSs often encounter obstacles like limited interactivity and static content, which impact student engagement and overall effectiveness. AI chatbots can tackle these challenges by providing real-time, adaptable support, thereby enriching the educational process. This study explores the integration of these chatbots in LMS through the lens of three pedagogical principles: Cognitive Load Theory (CLT), Constructivist Learning Theory, and Adaptive Learning Theory. CLT strives to regulate cognitive load to enhance learning efficiency, with chatbots simplifying content and offering instant feedback. Constructivist Learning Theory advocates for active, contextual learning through interaction, a principle supported by AI chatbots engaging learners in conversations and problem-solving activities. Adaptive Learning Theory emphasizes the personalization of educational experiences, a goal achieved by AI chatbots tailoring content and adjusting to student performance in real time. This study presents AI chatbots' alignment with pedagogical principles, revealing their potential to enhance LMS environments and improve student engagement, comprehension, and achievements.
Abkemeier, M. (2020). Cognitive Load Theory. Encyclopedia of Education and Information Technologies. https://doi.org/10.1007/springerreference_226235
AbuSahyon, A. S. E., Alzyoud, A., Alshorman, O., & Al-Absi, B. (2023). AI-driven Technology and Chatbots as Tools for Enhancing English Language Learning in the Context of Second Language Acquisition: A Review Study. International Journal of Membrane Science and Technology, 10(1), 1209–1223. https://doi.org/10.15379/ijmst.v10i1.2829
Baskara, Fx. R. (2023). Chatbots and Flipped Learning: Enhancing Student Engagement and Learning Outcomes through Personalised Support and Collaboration. IJORER : International Journal of Recent Educational Research, 4(2), 223–238. https://doi.org/10.46245/ijorer.v4i2.331
Cao, C. C., Ding, Z., Lin, J., & Hopfgartner, F. (2023). AI Chatbots as Multi-Role Pedagogical Agents: Transforming Engagement in CS Education. https://doi.org/10.48550/ARXIV.2308.03992
Chanda, R., & Prabhu, S. (2023). Secured Framework for Banking Chatbots using AI, ML and NLP. 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), 60–65. https://doi.org/10.1109/ICICCS56967.2023.10142289
Chuang, S. (2021). The Applications of Constructivist Learning Theory and Social Learning Theory on Adult Continuous Development. Performance Improvement, 60(3), 6–14. https://doi.org/10.1002/pfi.21963
Clark, C., & Kimmons, R. (2023). Cognitive Load Theory. EdTechnica. https://doi.org/10.59668/371.12980
Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216
Grubaugh, S., Levitt, G., & Deever, D. (2023). Harnessing AI to Power Constructivist Learning: An Evolution in Educational Methodologies. EIKI Journal of Effective Teaching Methods, 1(3). https://doi.org/10.59652/jetm.v1i3.43
Jain, V., Singh, I., Syed, M., Mondal, S., & Ranjan Palai, D. (2024). Enhancing Educational Interactions: A Comprehensive Review of AI Chatbots in Learning Environments. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1–5. https://doi.org/10.1109/ICRITO61523.2024.10522392
Liu, L., Jiang, H., He, P., Chen, W., Liu, X., Gao, J., & Han, J. (2019). On the Variance of the Adaptive Learning Rate and Beyond. https://doi.org/10.48550/ARXIV.1908.03265
Lopez, S. (2024). Impact of Cognitive Load Theory on the Effectiveness of Microlearning Modules. European Journal of Education and Pedagogy, 5(2), 29–35. https://doi.org/10.24018/ejedu.2024.5.2.799
Medintsev, V. A. (2021). Methodology for implementing the personal approach in adaptive learning systems. Yaroslavl Pedagogical Bulletin, 1(118), 151–161. https://doi.org/10.20323/1813-145X-2021-1-118-151-161
Pasindu Gayashan, S. P., & Samarasinghe, S. (2024). Factors Affecting the Intention to Use AI-Based Chatbots in Learning. 2024 International Research Conference on Smart Computing and Systems Engineering (SCSE), 1–9. https://doi.org/10.1109/SCSE61872.2024.10550537
S, Mr. R., Vs, A., H, A., & R, M. (2023). E-Learning Management System with AI Assistance. International Journal for Research in Applied Science and Engineering Technology, 11(11), 1233–1238. https://doi.org/10.22214/ijraset.2023.56730
Sajja, R., Sermet, Y., Cikmaz, M., Cwiertny, D., & Demir, I. (2023). Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2309.10892
Segovia-García, N. (2024). Optimizing Student Support. A Review of the Use of AI Chatbots in Higher Education. European Public & Social Innovation Review, 9, 1–20. https://doi.org/10.31637/epsir-2024-324
Van Rossem, S., Soenen, T., Tavernier, W., Colle, D., Pickavet, M., & Demeester, P. (2020). Adaptive & Learning-aware Orchestration of Content Delivery Services. 2020 6th IEEE Conference on Network Softwarization (NetSoft), 77–84. https://doi.org/10.1109/NetSoft48620.2020.9165475
Wang, G. (2023). Natural Language Analysis of Korean Texts of AI-based Chatbots and Exploration of Korean Education Utilization: Focusing on ChatGPT and New-Bing. The Korean Society of Culture and Convergence, 45(5), 1–17. https://doi.org/10.33645/cnc.2023.05.45.05.01
Wasfy, N. F., Abed, R. A. R., Gouda, E. M., Ghaly, M. S., & El-Wazir, Y. M. (2021). EFFECTIVENESS OF INSTRUCTIONAL DESIGN FRAMEWORK BASED ON COGNITIVE LOAD THEORY FOR CLINICAL SKILLS TRAINING. Advanced Education, 8(18), 102–108. https://doi.org/10.20535/2410-8286.225686
Zhao, Y. (2023). The Impact of Cognitive Load Theory on Online Learning Outcomes for Adolescent Students. Journal of Education, Humanities and Social Sciences, 18, 50–55. https://doi.org/10.54097/ehss.v18i.10946
Zharmukhanbetov, S., & Singh, C. P. (2023). Enhancing Flipped Classroom Engagement and Personalized Learning Through AI-Powered Adaptive Content Delivery. 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS), 1411–1416. https://doi.org/10.1109/ICTACS59847.2023.10389841
武星妍. (2023). An Overview of Constructivist Learning Theory. Advances in Social Sciences, 12(11), 6645–6651. https://doi.org/10.12677/ASS.2023.1211908
