Sistemas Adaptativos Baseados em IA para Melhoria da Experiência de Aprendizado em Educação a Distância: Um Mapeamento Sistemático
Resumo
Este mapeamento sistemático analisa 20 estudos publicados entre 2020 e 2024 sobre sistemas adaptativos baseados em inteligência artificial aplicados à educação a distância. Os resultados destacam o uso de aprendizado de máquina, lógica fuzzy, modelagem de conhecimento e tecnologias emergentes para personalização da aprendizagem, adaptação dinâmica de conteúdo e avaliação contínua dos estudantes. Os estudos relatam maior engajamento e melhoria do desempenho acadêmico, embora persistam desafios relacionados à qualidade dos dados, privacidade e escalabilidade. As direções futuras incluem alinhamento pedagógico, governança de dados e ampliação da adoção dessas tecnologias.
Palavras-chave:
Sistemas Adaptativos, Inteligência Artificial, Educação a Distância
Referências
AL-Chalabi, H. K. M., Hussein, A. M. A. and Apoki, U. C. (2021). “An Adaptive Learning System Based on Learner's Knowledge Level”. In: 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–4. DOI: 10.1109/ECAI52376.2021.9515158.
Assiry, K. A. H. and Muniasamy, A. (2022). “Predicting Learning Styles Using Machine Learning Classifiers”. In: 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1–7. DOI: 10.1109/ICECET55527.2022.9872971.
Calò, T. and MacLellan, C. J. (2024). “Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces”. In: Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24), pp. 305–309. DOI: 10.1145/3657604.3664694.
Chergui, M., Chakir, A., Mansouri, H. and Sayouti, A. (2021). “Smart Pedagogical Knowledge Management Model for Higher Education”. In: Innovations in Smart Cities Applications, vol. 4, pp. 230–239. Springer. DOI: 10.1007/978-3-030-66840-2_18.
Chergui, M., Nagano, A. and Ammoumou, A. (2024). “Toward an Adaptive Learning System by Managing Pedagogical Knowledge in a Smart Way”. Multimedia Tools and Applications, 84, 27777–27793. DOI: 10.1007/s11042-024-20207-w.
Chen, Y., Ding, N., Zheng, H. T., Liu, Z., Sun, M. and Zhou, B. (2024). “Empowering Private Tutoring by Chaining Large Language Models”. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 354–364. DOI: 10.1145/3627673.3679665.
Eryılmaz, M. and Adabashi, A. (2020). “Development of an Intelligent Tutoring System Using Bayesian Networks and Fuzzy Logic for a Higher Student Academic Performance”. Applied Sciences, 10(19), 6638. DOI: 10.3390/app10196638.
Ezzaim, A., Dahbi, A., Haidine, A., Aqqal, A. (2024). “Development, Implementation, and Evaluation of a Machine Learning-Based Multi-Factor Adaptive E-Learning System”. IAENG International Journal of Computer Science, 51(9), 1250–1271.
Ghergulescu, I., Flynn, C., O'Sullivan, C., van Heck, I. and Slob, M. (2021). “A Conceptual Framework for Extending Domain Model of AI-enabled Adaptive Learning with Sub-skills Modelling”. In: Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021), vol. 1, pp. 116–123. DOI: 10.5220/0010451201160123.
Jamal, N. N., Jawawi, D. N. A., Hassan, R. and Kamil, R. M. I. (2020). “Adaptive Learning in Computing Education: A Systematic Mapping Study”. In: IOP Conference Series: Materials Science and Engineering, vol. 864, 012069. DOI: 10.1088/1757-899X/864/1/012069.
Kitchenham, B. and Charters, S. (2007). “Guidelines for Performing Systematic Literature Reviews in Software Engineering”. EBSE Technical Report, Version 2.3.
Kubsch, M., Czinczel, B., Lossjew, J., Wyrwich, T., Bednorz, D., Bernholt, S., Fiedler, D., Straß, S., Cress, U., Drachsler, H., Neumann, K. and Rummel, N. (2022). “Toward Learning Progression Analytics: Developing Learning Environments for the Automated Analysis of Learning Using Evidence-Centered Design”. Frontiers in Education, 7, 981910. DOI: 10.3389/feduc.2022.981910.
Liu, S., Zhang, X., Chen, W. and Zhang, W. (2021). “Construction of Intelligent Adaptive Learning Platform in Ubiquitous Environment”. In: Proceedings of the 2021 10th International Conference on Educational and Information Technology (ICEIT 2021), pp. 56–60. DOI: 10.1109/ICEIT51700.2021.9375613.
Mandal, S. and Naskar, S. K. (2021). “Classifying and Solving Arithmetic Math Word Problems—An Intelligent Math Solver”. IEEE Transactions on Learning Technologies, 14(1), 28–41. DOI: 10.1109/TLT.2021.3057805.
Pavlik, P. I., Olney, A. M., Banker, A., Eglington, L. and Yarbro, J. (2020). “The Mobile Fact and Concept Textbook System (MoFaCTS)”. In: 21st International Conference on Artificial Intelligence in Education (AIED 2020), Second Workshop on Intelligent Textbooks. CEUR Workshop Proceedings.
Prihar, E., Haim, A., Sales, A. and Heffernan, N. (2022). “Automatic Interpretable Personalized Learning”. In: Proceedings of the Ninth ACM Conference on Learning @ Scale, pp. 1–11. DOI: 10.1145/3491140.3528267.
Qu, Y. and Ogunkunle, O. (2021). “Enhancing the Intelligence of the Adaptive Learning Software through an AI Assisted Data Analytics on Students Learning Attributes with Unequal Weight”. In: 2021 IEEE Frontiers in Education Conference (FIE), pp. 1–6. DOI: 10.1109/FIE49875.2021.9637387.
Rao, G. M. and Kumar, K. K. (2021). “Students Performance Prediction in Online Courses Using Machine Learning Algorithms”. United International Journal for Research & Technology, 2(11), 74–79.
Santhosh, J., Dengel, A. and Ishimaru, S. (2024). “Gaze-Driven Adaptive Learning System With ChatGPT-Generated Summaries”. IEEE Access, 12, 173714–173733. DOI: 10.1109/ACCESS.2024.3503059.
Singh, N., Gunjan, V. K. and Nasralla, M. M. (2022). “A Parametrized Comparative Analysis of Performance Between Proposed Adaptive and Personalized Tutoring System "Seis Tutor" With Existing Online Tutoring System”. IEEE Access, 10, 39376–39386. DOI: 10.1109/ACCESS.2022.3166261.
Szczepański, M. and Marciniak, J. (2023). “Application of a Fuzzy Controller in Adaptive E-Learning Content Used to Evaluate Student Activity”. Procedia Computer Science, 225, 2526–2535. DOI: 10.1016/j.procs.2023.10.244.
Troussas, C., Krouska, A., Mylonas, P. and Sgouropoulou, C. (2023). “Personalized Learner Assistance Through Dynamic Adaptation of Chatbot Using Fuzzy Logic Knowledge Modeling”. In: 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023), pp. 1–5. DOI: 10.1109/SMAP59435.2023.10255169.
Wang, S., Christensen, C., McBride, E. A., Kelly, H., Cui, W., Tong, R., Shear, L., Yarnall, L. and Feng, M. (2020). “Identifying Gaps in Use of and Research on Adaptive Learning Systems”. In: International Conference on Computer Supported Education. DOI: 10.5220/0009590701180124.
Assiry, K. A. H. and Muniasamy, A. (2022). “Predicting Learning Styles Using Machine Learning Classifiers”. In: 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET), pp. 1–7. DOI: 10.1109/ICECET55527.2022.9872971.
Calò, T. and MacLellan, C. J. (2024). “Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces”. In: Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24), pp. 305–309. DOI: 10.1145/3657604.3664694.
Chergui, M., Chakir, A., Mansouri, H. and Sayouti, A. (2021). “Smart Pedagogical Knowledge Management Model for Higher Education”. In: Innovations in Smart Cities Applications, vol. 4, pp. 230–239. Springer. DOI: 10.1007/978-3-030-66840-2_18.
Chergui, M., Nagano, A. and Ammoumou, A. (2024). “Toward an Adaptive Learning System by Managing Pedagogical Knowledge in a Smart Way”. Multimedia Tools and Applications, 84, 27777–27793. DOI: 10.1007/s11042-024-20207-w.
Chen, Y., Ding, N., Zheng, H. T., Liu, Z., Sun, M. and Zhou, B. (2024). “Empowering Private Tutoring by Chaining Large Language Models”. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 354–364. DOI: 10.1145/3627673.3679665.
Eryılmaz, M. and Adabashi, A. (2020). “Development of an Intelligent Tutoring System Using Bayesian Networks and Fuzzy Logic for a Higher Student Academic Performance”. Applied Sciences, 10(19), 6638. DOI: 10.3390/app10196638.
Ezzaim, A., Dahbi, A., Haidine, A., Aqqal, A. (2024). “Development, Implementation, and Evaluation of a Machine Learning-Based Multi-Factor Adaptive E-Learning System”. IAENG International Journal of Computer Science, 51(9), 1250–1271.
Ghergulescu, I., Flynn, C., O'Sullivan, C., van Heck, I. and Slob, M. (2021). “A Conceptual Framework for Extending Domain Model of AI-enabled Adaptive Learning with Sub-skills Modelling”. In: Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021), vol. 1, pp. 116–123. DOI: 10.5220/0010451201160123.
Jamal, N. N., Jawawi, D. N. A., Hassan, R. and Kamil, R. M. I. (2020). “Adaptive Learning in Computing Education: A Systematic Mapping Study”. In: IOP Conference Series: Materials Science and Engineering, vol. 864, 012069. DOI: 10.1088/1757-899X/864/1/012069.
Kitchenham, B. and Charters, S. (2007). “Guidelines for Performing Systematic Literature Reviews in Software Engineering”. EBSE Technical Report, Version 2.3.
Kubsch, M., Czinczel, B., Lossjew, J., Wyrwich, T., Bednorz, D., Bernholt, S., Fiedler, D., Straß, S., Cress, U., Drachsler, H., Neumann, K. and Rummel, N. (2022). “Toward Learning Progression Analytics: Developing Learning Environments for the Automated Analysis of Learning Using Evidence-Centered Design”. Frontiers in Education, 7, 981910. DOI: 10.3389/feduc.2022.981910.
Liu, S., Zhang, X., Chen, W. and Zhang, W. (2021). “Construction of Intelligent Adaptive Learning Platform in Ubiquitous Environment”. In: Proceedings of the 2021 10th International Conference on Educational and Information Technology (ICEIT 2021), pp. 56–60. DOI: 10.1109/ICEIT51700.2021.9375613.
Mandal, S. and Naskar, S. K. (2021). “Classifying and Solving Arithmetic Math Word Problems—An Intelligent Math Solver”. IEEE Transactions on Learning Technologies, 14(1), 28–41. DOI: 10.1109/TLT.2021.3057805.
Pavlik, P. I., Olney, A. M., Banker, A., Eglington, L. and Yarbro, J. (2020). “The Mobile Fact and Concept Textbook System (MoFaCTS)”. In: 21st International Conference on Artificial Intelligence in Education (AIED 2020), Second Workshop on Intelligent Textbooks. CEUR Workshop Proceedings.
Prihar, E., Haim, A., Sales, A. and Heffernan, N. (2022). “Automatic Interpretable Personalized Learning”. In: Proceedings of the Ninth ACM Conference on Learning @ Scale, pp. 1–11. DOI: 10.1145/3491140.3528267.
Qu, Y. and Ogunkunle, O. (2021). “Enhancing the Intelligence of the Adaptive Learning Software through an AI Assisted Data Analytics on Students Learning Attributes with Unequal Weight”. In: 2021 IEEE Frontiers in Education Conference (FIE), pp. 1–6. DOI: 10.1109/FIE49875.2021.9637387.
Rao, G. M. and Kumar, K. K. (2021). “Students Performance Prediction in Online Courses Using Machine Learning Algorithms”. United International Journal for Research & Technology, 2(11), 74–79.
Santhosh, J., Dengel, A. and Ishimaru, S. (2024). “Gaze-Driven Adaptive Learning System With ChatGPT-Generated Summaries”. IEEE Access, 12, 173714–173733. DOI: 10.1109/ACCESS.2024.3503059.
Singh, N., Gunjan, V. K. and Nasralla, M. M. (2022). “A Parametrized Comparative Analysis of Performance Between Proposed Adaptive and Personalized Tutoring System "Seis Tutor" With Existing Online Tutoring System”. IEEE Access, 10, 39376–39386. DOI: 10.1109/ACCESS.2022.3166261.
Szczepański, M. and Marciniak, J. (2023). “Application of a Fuzzy Controller in Adaptive E-Learning Content Used to Evaluate Student Activity”. Procedia Computer Science, 225, 2526–2535. DOI: 10.1016/j.procs.2023.10.244.
Troussas, C., Krouska, A., Mylonas, P. and Sgouropoulou, C. (2023). “Personalized Learner Assistance Through Dynamic Adaptation of Chatbot Using Fuzzy Logic Knowledge Modeling”. In: 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023), pp. 1–5. DOI: 10.1109/SMAP59435.2023.10255169.
Wang, S., Christensen, C., McBride, E. A., Kelly, H., Cui, W., Tong, R., Shear, L., Yarnall, L. and Feng, M. (2020). “Identifying Gaps in Use of and Research on Adaptive Learning Systems”. In: International Conference on Computer Supported Education. DOI: 10.5220/0009590701180124.
Publicado
13/10/2026
Como Citar
GRION, Renan; MORAES, Laura O..
Sistemas Adaptativos Baseados em IA para Melhoria da Experiência de Aprendizado em Educação a Distância: Um Mapeamento Sistemático. In: ARTIGOS DE PESQUISA - CONFERÊNCIA LATINO-AMERICANA DA TECNOLOGIAS DE APRENDIZAGEM (LACLO), 21. , 2026, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
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p. 171-186.
