ARTIFICIAL INTELLIGENCE AND EDUCATIONAL TECHNOLOGIES: AN EMOTIONAL APPROACH TO ADAPTIVE LEARNING
DOI:
https://doi.org/10.37472/2617-3107-2024-7-11Keywords:
emotional approach, artificial intelligence, adaptive learning, emotional intelligence, personalization, educational technologies, convolutional neural networks, recurrent neural networks, emotion analysis, ethicsAbstract
Modern education requires innovative approaches that consider both cognitive and emotional aspects of learning. This paper presents the concept of an adaptive educational platform that utilizes artificial intelligence technologies for analysing students’ emotional states and integrating an emotional approach into the educational process. The platform is based on advanced machine learning methods, including convolutional and recurrent neural networks, as well as ensemble learning algorithms. Special attention is paid to data protection and ethics, emphasizing developers’ responsibility to all participants in the educational process. The author concluded that for further development it is necessary to strengthen interdisciplinary collaboration between artificial intelligence experts and educational researchers, enhance educators’ competencies in artificial intelligence and educational technologies, and develop ethical standards governing data use. The implementation of the described technological and methodological solutions will enable the development of a functional educational platform prototype and conduct comprehensive most effective use of digital tools and platforms.
References
Запотічна, Р. (2024). AI-generated content for language learning. У Актуальні проблеми навчання іноземних мов для спеціальних цілей: збірник наукових статей (pp. 46–54). ЛьвДУВС. https://dspace.lvduvs.edu.ua/handle/1234567890/8333
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–17. https://jedm.educationaldatamining.org/index.php/JEDM/article/download/8/2
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (pp. 149–159). http://proceedings.mlr.press/v81/binns18a/binns18a.pdf
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://link.springer.com/content/pdf/10.1023/a:1010933404324.pdf
Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education, 13(2–4), 156–169. https://www.academia.edu/download/73493243/ijwltt_20preface_2041.pdf
Dede, C. (2009). Immersive interfaces for engagement and learning. Science, 323(5910), 66–69. https://projects.iq.harvard.edu/files/rivercityproject/files/dede_immersive_interfaces.pdf
Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta‐analysis of school‐based universal interventions. Child Development, 82(1), 405–432. https://library.bsl.org.au/jspui/bitstream/1/3563/1/Impact%20of%20enhancing%20students’%20social%20and%20emotional%20learning.pdf
Dweck, C. S. (2006). Mindset: The new psychology of success. Random House. https://www.keithdwalker.ca/s/EBS-Mindset-The-New-Psychology-of-Success.pdf
Fredrickson, B. L. (2002). Positive emotions. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 120–134). Oxford University Press.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://projecteuclid.org/journals/annals-of-statistics/volume-29/issue-5/Greedy-function-approximation-A-gradient-boosting-machine/10.1214/aos/1013203451.pdf
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. http://www.e-hir.org/upload/pdf/hir-22-351.pdf
Hernández-Leo, D., Martinez-Maldonado, R., Pardo, A., Muñoz-Cristóbal, J. A., & Rodríguez-Triana, M. J. (2019). Analytics for learning design: A layered framework and tools. British Journal of Educational Technology, 50(1), 139–152. https://research.monash.edu/files/286050347/285809957_oa.pdf
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://blog.xpgreat.com/file/lstm.pdf
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics, 6(2), 27–52. https://files.eric.ed.gov/fulltext/ED618924.pdf
Immordino-Yang, M. H., & Damasio, A. (2007). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, Brain, and Education, 1(1), 3–10. https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1751-228X.2007.00004.x
Jones, S. M., & Kahn, J. (2017). The evidence base for how we learn: Supporting students’ social, emotional, and academic development. Aspen Institute. https://files.eric.ed.gov/fulltext/ED577039.pdf
Kashyap, R., Samuel, Y., & Friedman, L. W. (2024). Artificial intelligence education & governance-human enhancive, culturally sensitive and personally adaptive HAI. Frontiers in Artificial Intelligence, 4, 1443386. https://www.frontiersin.org/articles/10.3389/frai.2024.1443386/full
Micheal, S., & Marjadi, B. (2023). Gamified innovations to teach social determinants of health in medical school. BMC Medical Education, 23(1), 1–8. https://doi.org/10.1186/s12909-023-04523-7
Molnar, C. (2020). Interpretable machine learning: A guide for making black box models explainable. Leanpub. https://www.academia.edu/download/103712558/Christoph_Molnar_Interpretable_Machine_Learning_lulu.com_20210426_.pdf
Osadchyi, V. V., Varina, H. B., Osadcha, K. P., Kovalova, O. V., Voloshyna, V. V., Sysoiev, O. V., & Shyshkina, M. P. (2021). The use of augmented reality technologies in the development of emotional intelligence of future specialists of socionomic professions under the conditions of adaptive learning. In AREdu 2021: 4th International Workshop on Augmented Reality in Education (Vol. 2898, pp. 269–293). CEUR Workshop Proceedings. https://elibrary.kdpu.edu.ua/bitstream/123456789/4633/1/paper15.pdf
Pan, Y., Magosi, M., & Yan, Z. (2021). Artificial intelligence in higher education: A bibliometrics analysis. The SNU Journal of Education Research, 30(2), 65–84. https://s-space.snu.ac.kr/bitstream/10371/211562/1/4.ArtificialIntelligenceinHigherEducation_ABibliometricsAnalysis.pdf
Paul, E. S., Harding, E. J., & Mendl, M. (2005). Measuring emotional processes in animals: The utility of a cognitive approach. Neuroscience and Biobehavioral Reviews, 29, 469–491. https://doi.org/10.1016/j.neubiorev.2005.01.002
Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achievement emotions and academic performance: Longitudinal models of reciprocal effects. Child Development, 88(5), 1653–1670. https://centaur.reading.ac.uk/65981/3/CD%202015-229%20Pekrun%20et%20al.%20Emotion%20Achievement%20Prepublication%20Manuscript%20June%202016.pdf
Pereira, P. A., & de Lima, D. L. (2023). The use of technology and innovation for teaching mathematics: A systematic review. International Journal of Instruction, 16(1), 959–976. https://doi.org/10.29333/iji.2023.16153a
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://link.springer.com/content/pdf/10.1007/s40593-016-0110-3.pdf
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://arxiv.org/pdf/2402.07956
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122117/
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://www.mzkperformance.com/s/SDTandintmotive-1.pdf
Salovey, P., Mayer, J. D., & Caruso, D. (2002). Emotional intelligence. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 159–171). https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470939338#page=455
Santos-Guevara, B. N., & Rincon-Flores, E. G. (2023). Elevate your learning: Unveiling students’ emotions in a gamified matrix modeling class. Education Sciences, 13(2), 135. https://doi.org/10.3390/educsci13020135
Stanton, A. L., Kirk, S. B., Cameron, C. L., & Danoff-Burg, S. (2000). Coping through emotional approach: Scale construction and validation. Journal of Personality and Social Psychology, 78(6), 1150–1169. https://www.academia.edu/download/25438131/stanton2000.pdf
Stracqualursi, L., & Agati, P. (2023). Twitter users’ perceptions of AI-based e-learning technologies: A sentiment analysis approach. Computers and Education: Artificial Intelligence, 4, 100107. https://doi.org/10.1016/j.caeai.2023.100107
Tyng, C. M., Amin, H. U., Saad, M. N., & Malik, A. S. (2017). The influences of emotion on learning and memory. Frontiers in Psychology, 8, 1454. https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2017.01454/full
Weissberg, R. P., Durlak, J. A., Domitrovich, C. E., & Gullotta, T. P. (2015). Social and emotional learning: Past, present, and future. In J. A. Durlak, C. E. Domitrovich, R. P. Weissberg, & T. P. Gullotta (Eds.), Handbook of social and emotional learning: Research and practice (pp. 3–19). https://psycnet.apa.org/record/2015-24776-001
Zapotichna, R. (2024). AI-generated content for language learning. In Aktualni problemy navchannia inozemnykh mov dlia spetsialnykh tsilei: zbirnyk naukovykh statei (pp. 46–54). LvDUVS. https://dspace.lvduvs.edu.ua/handle/1234567890/8333
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://link.springer.com/content/pdf/10.1186/s41239-019-0171-0.pdf







