Pedagogical models of multimodal automated responses for university students' learning in smart environments
Keywords:
pedagogical models, multimodal responses, artificial intelligence, smart environments, educationAbstract
Introduction: This article presents a systematic review of automated multimodal response pedagogical models in smart educational environments, aiming to identify the most relevant trends, advances, and challenges in the application of artificial intelligence (AI) to university education. Materials and methods: Twenty articles published between 2020 and 2025 were selected from various academic databases, and the technological and pedagogical approaches employed were analyzed. The results suggest that multimodal AI has great potential to improve learning interaction and personalization; however, challenges related to ethics, accessibility, and system adaptation to different educational contexts were identified. Results: The findings of this review provide critical insights for future research and practices in the implementation of AI in educational environments. Discussion: Automatic multimodal response pedagogical models represent a significant evolution in university education within smart environments. Their ability to personalize, emote, and interact across multiple sensory dimensions opens up new possibilities for more effective, inclusive, and human learning. However, their implementation requires a rigorous balance between technological innovation, educational ethics, and pedagogical foundation. Unlike classic models such as behaviorism or the competency-based approach, current models allow for real-time feedback tailored to the student's profile. Conclusions: The systematic review conducted shows that pedagogical models based on automated multimodal responses constitute a disruptive innovation in university learning, especially when implemented in smart environments. These models allow for richer, more responsive, and adaptive interaction between the system and the student, facilitating personalized, emotionally aware, and user-centered learning experiences.
References
Ahmed, Z., & Parsons, D. (2022). Enhancing adaptive learning through multimodal feedback in intelligent environments. Computers & Education: Artificial Intelligence, 3, 100072. https://doi.org/10.1016/j.caeai.2022.100072
Aleven, V., & Koedinger, K. R. (2002). An intelligent tutoring system for basic math. International Journal of Artificial Intelligence in Education, 13(1), 83–117.
Alkhatlan, A., & Kalita, J. (2018). Intelligent tutoring systems: A comprehensive historical survey with recent developments. arXiv preprint arXiv:1812.09628. https://arxiv.org/abs/1812.09628
Alonso, L. A., & Rodríguez, S. M. (2020). Evaluación y diagnóstico en sistemas de aprendizaje inteligentes. Tecnología en la Educación, 22(4), 415–428.
Bai, X., & Feng, S. (2021). AI-powered tutoring systems in language learning. International Journal of Educational Technology, 38(1), 78–94.
Baker, R. S. J. D. (2007). Modeling and understanding students’ learning: A review. User Modeling and User-Adapted Interaction, 17(3), 115–155.
Baltrušaitis, T., Ahuja, C., & Morency, L.-P. (2017). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443. https://doi.org/10.1109/TPAMI.2018.2798607
Campbell, M., McKenzie, J., & Sowden, A. (2020). Synthesis without meta-analysis (SWiM)
Carvajal Chávez, C. A. (2024). Inteligencia artificial como recurso didáctico en la educación superior. Una revisión sistemática. RECIMUNDO, 8(4), 51–65. https://doi.org/10.26820/recimundo/8.(4).diciembre.2024.51-65
Castro, J. M., & Sanz, J. (2019). Inteligencia artificial y aprendizaje adaptativo en plataformas educativas. Revista Latinoamericana de Tecnología Educativa, 18(3), 23–38.
Cedeño, V., López, R., & Carrión, D. (2023). Innovación educativa y retos curriculares en la era digital: Un estudio en universidades ecuatorianas. Revista Científica Educare, 27(1), 45–60. https://doi.org/10.15359/ree.27-1.3
Chen, Y., Liu, S., & Wang, X. (2023). Predictive learning analytics in smart education systems. Educational Technology Research and Development, 71(3), 465–482. https://doi.org/10.1007/s11423-023-10112-0
Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715–4729. https://doi.org/10.1016/j.eswa.2013.02.007
Clemente, J., Ramírez, J., & de Antonio, A. (2011). A proposal for student modeling based on ontologies and diagnosis rules. Expert Systems with Applications, 38(7), 8066–8078. https://doi.org/10.1016/j.eswa.2010.12.146
Conejo, R., Millán, E., Pérez, J., & Trella, M. (2001). Modelado del alumno: un enfoque bayesiano. Revista Iberoamericana de Inteligencia Artificial, 12, 50–58.
D’Mello, S. K., & Graesser, A. C. (2015). Autotutor and other conversational agents in educational settings. International Journal of Artificial Intelligence in Education, 25(1), 56–70.
De Bra, P., Aerts, A., Berden, B., De Lange, B., Rousseau, B., Santic, T., … Stash, N. (2003). AHA! The adaptive hypermedia architecture. In HYPERTEXT 03 Proceedings of the fourteenth ACM conference on hypertext and hypermedia (pp. 81–84). New York, New York, USA: ACM.
Despotović-Zrakić, M., Marković, A., Bogdanović, Z., Barać, D., & Ilića, J. (2012). Providing adaptivity in Moodle LMS courses. Educational Technology & Society, 15(1), 326–338.
Dorça, F. A., Lima, L. V., Fernandes, M. A., & Lopes, C. R. (2013). Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimental analysis. Expert Systems with Applications, 40(6), 2092–2101. https://doi.org/10.1016/j.eswa.2012.10.014
Duque, N. (2009). Modelo adaptativo multi-agente para la planificación y ejecución de cursos virtuales personalizados. Universidad Nacional de Colombia.
Esteves, Z., Cevallos, M., Herrera, M., & Muñoz, J. (2020). Tendencias en educación adaptativa y sistemas de tutoría inteligente. Revista de Investigación Educativa, 11(2), 79–102.
Gao, J., Galley, M., & Li, L. (2018). Neural approaches to conversational AI. Foundations and Trends® in Information Retrieval, 13(2–3), 127–298. https://doi.org/10.1561/1500000036
García-Peña, V. R., Mora-Marcillo, A. B., & Ávila-Ramírez, J. A. (2020). Impacto de los sistemas de tutoría inteligente. Una revisión sistemática. Edutec, Revista Electrónica de Tecnología Educativa, 72, 1–19. https://doi.org/10.21556/edutec.2020.72.3025
García-Peñalvo, F. J., Corell, A., Abella-García, V., & Grande, M. (2022). La retroalimentación automatizada en entornos de aprendizaje inteligentes. RIED. Revista Iberoamericana de Educación a Distancia, 25(2), 127–144. https://doi.org/10.5944/ried.25.2.30610
Gupta, P., & Verma, P. (2018). Designing intelligent tutoring systems for adaptive learning environments. Artificial Intelligence in Education, 13(1), 82–103.
Haller, S., Aldea, A., Seifert, C., & Strisciuglio, N. (2022). Survey on automated short answer grading with deep learning: from word embeddings to transformers. arXiv preprint arXiv:2204.03503. https://arxiv.org/abs/2204.03503
Higgins, J. P., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (2019). Cochrane handbook for systematic reviews of interventions (2nd ed.). Wiley-Blackwell.
Jermann, P., & Dillenbourg, P. (2015). Adaptive learning systems: A review of the state of the art. Education and Information Technologies, 20(2), 211–232.
Jiang, L., & Wang, Z. (2023). Innovations in multimodal feedback for adaptive learning. Educational Psychology Review, 35(3), 275–288.
Johnson, L., & Adams Becker, S. (2021). Trends in adaptive learning systems: An overview. Journal of Educational Technology, 42(6), 12–18.
Joubert, M., & Pahl, C. (2021). Framework for adaptive learning. Educational Technology & Society, 24(1), 110–122.
Kim, H., & Laird, D. (2021). Adaptive AI in modern learning systems. Journal of Artificial Intelligence in Education, 25(4), 158–172.
Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning science to the classroom. International Journal of Artificial Intelligence in Education, 16(3), 123–128.
Laine, T. H., & Vehviläinen, S. (2022). The future of AI in educational contexts. Computers in Education Journal, 31(4), 45–59.
Li, Y., Zhang, M., & Huang, J. (2021). Constructivist learning in smart classrooms: A systematic review. British Journal of Educational Technology, 52(5), 1858–1875. https://doi.org/10.1111/bjet.13112
Li, Z., & Gu, J. (2022). Review of multimodal learning analytics in adaptive systems. Journal of Educational Data Mining, 14(2), 67–81.
López, A., & Sánchez, P. (2020). Sistema de tutoría inteligente para estudiantes de matemáticas. Revista de Educación y Tecnología, 15(2), 144–157.
López, E., & Ochoa, L. (2017). Estrategias de enseñanza adaptativa con inteligencia artificial en plataformas educativas. Revista de Innovación Educativa, 34(1), 72–85.
Malmberg, L. E., & Dehlin, E. (2020). Personalized learning pathways in modern learning systems. Interactive Learning Environments, 28(6), 643–655.
Martínez-Caro, E., Cegarra-Navarro, J. G., & Alfonso-Ruiz, F. J. (2021). Digital technologies and pedagogical transformation: A theoretical framework. Education and Information Technologies, 26, 1879–1896. https://doi.org/10.1007/s10639-020-10362-6
Medeiros, G., & Ferreira, S. (2019). Personalized learning systems with multimodal interaction. Educational Research Review, 17, 92–104.
Mitrovic, A. (2013). Cognitive tutors: From logic programming to machine learning. Expert Systems with Applications, 40(11), 4574–4584.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2015). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLOS Med, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Nguyen, T., Do, P., & Tran, L. (2024). Gamified AI systems in higher education: An empirical investigation. Computers & Education, 207, 104613. https://doi.org/10.1016/j.compedu.2024.104613
Ning, W., & Liu, Y. (2018). Affective computing in educational settings: A review of affective states and their impact. Educational Psychology, 35(4), 12–27.
Nye, B. D., & Graesser, A. C. (2020). Advances in intelligent tutoring systems. Journal of Educational Psychology, 112(6), 1219–1234.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. J Clin Epidemiol, 134, 103-112. https://doi.org/10.1016/j.jclinepi.2021.02.003
Page, M. J., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLOS Medicine, 18(3), Article e1003583. https://doi.org/10.1371/journal.pmed.1003583
Palomo-Díaz, M. E., & Sanchis-Roca, A. (2017). Adaptive learning systems based on machine learning algorithms. Computational Intelligence, 33(2), 132–149.
Pérez, J., & Trella, M. (2019). Introducción a los sistemas de tutoría inteligente y su evolución. Revista de Investigación Educativa, 10(2), 50–66.
Rojas Vera, R. A., Fabre Cavanna, J. E., Rojas Bajaña, R. A., Rizzo Fabre, L. G. y Abad Bautista, L. (2024). Uso de la inteligencia artificial en estudiantes. Maestro y Sociedad, 21(4), 2003-2012. https://maestroysociedad.uo.edu.cu
Santos, M., & García, A. (2020). Revisión de sistemas de aprendizaje personalizado basados en inteligencia artificial. Revista Latinoamericana de Educación, 19(2), 75–88.
Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and Instruction, 16(4), 475–522.
Sim, M., & Kang, Y. (2020). Multimodal interfaces in intelligent learning environments. Journal of Educational Technology, 39(4), 29–40.
Sterne, J. A., Savović, J., Page, M. J., Elbers, R. G., Blencowe, N. S., Boutron, I., ... & Higgins, J. P. (2019). RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ, 366, l4898. https://doi.org/10.1136/bmj.l4898
Suero Tejada, E. E. y Gallur Santorun, S. (2024). Las TIC en el aula de Educación Superior. Revisión de literatura. Maestro y Sociedad, 21(2), 876-886. https://maestroysociedad.uo.edu.cu
Tharp, M., & Chen, H. (2016). Adaptive systems in learning environments. Educational Technology Research and Development, 64(1), 23–41.
Zhao, F., & Liang, M. (2021). Integrating AI and multimodal learning for adaptive education. Journal of Adaptive Systems, 32(1), 65–78.
Zhao, Y., & Zhai, X. (2021). The role of artificial intelligence in personalized learning systems: A systematic review. Computers & Education, 163, 104128.
Zhou, X., & Lu, B. (2022). Development of multimodal interfaces for intelligent tutoring systems. Educational Technology Research and Development, 70(2), 111–124.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Cruz Maribel Galarza Ramírez, Monica Patricia Acurio Acurio, Juan Antonio Vera Zapata, Cesar Efren Vivero Quintero
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This journal provides immediate open access to its content, based on the principle that offering the public free access to research helps a greater global exchange of knowledge. Each author is responsible for the content of each of their articles.
Universidad de Oriente 