O IMPACTO DAS REDES NEURAIS NAS POLÍTICAS E PRÁTICAS DO PROCESSO EDUCACIONAL PARA FORMAÇÃO DE ESPECIALISTAS MODERNOS

Daria Semenova, Mikhail Borodin, Natalia Shafazhinskaya, Irina Vaslavskaya, Vitalii Vasyukov

Abstract


Objetivo: Analisar as oportunidades e riscos associados ao uso de redes neurais (NNs) no ensino superior, com foco em sua aplicação no aprendizado, gestão administrativa e personalização de experiências educacionais.

Método: O estudo utilizou pesquisa documental e um levantamento com 40 especialistas. Os critérios de seleção incluíram publicações relevantes sobre o tema. As respostas dos especialistas foram analisadas usando o coeficiente de concordância de Kendall para avaliar as oportunidades e riscos das NNs.

Resultados:

  • Oportunidades:
  1. Assistência no aprendizado, como personalização e feedback.
  2. Apoio em tarefas administrativas, como avaliação e monitoramento de alunos.
  3. Análise de grandes conjuntos de dados para gestão institucional.
Riscos:
  1. Consequências imprevistas dos processos de aprendizado automatizados.
  2. Aplicação de teorias inadequadas.
  3. Algoritmos que atribuem tarefas impróprias aos estudantes.

Conclusão: As NNs oferecem grande potencial para melhorar a educação, especialmente ao liberar educadores de tarefas rotineiras. No entanto, sua implementação deve ser guiada por ética e transparência, priorizando o papel complementar dos professores e abordando os riscos identificados para evitar impactos negativos no aprendizado.


Keywords


Redes neurais artificiais; Ensino superior; Aprendizado; Estudantes; Professores

References


Bakunova, O. M., Kalitenya, I. L., Bakunov, A. M., Paluyko, A. F., Antonov, E. D., & Grechko, I. S. (2018). The use of neural networks in education. Web of Scholar, 1(19): 8–10.

Ben-David, S., Hrubes, P., Moran, S., Shpilka, A., & Yehudayoff, A. (2019). Learnability can be undecidable. Nature Machine Intelligence, 7: 44–48.

Bosov, A. V. (2022). Application of self-organizing neural networks to the formation of individualized learning paths. Informatics and Its Applications, 16(3): 7–15.

Castrillón, O., Sarache, W., & Ruiz, S. (2020). Prediction of academic performance using artificial intelligence techniques. From Univ, 13: 93–102.

Cazarez, R. L. U., & Martin, C. L. (2018). Neural networks for predicting student performance in online education. IEEE Latin America Transactions, 16(7): 2053–2060.

Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1): 28–47.

Denning, P. J., & Denning, D. E. (2020). Dilemmas of artificial intelligence. CACM, 63(3): 22–24.

Fiore, U. (2019). Neural networks in the educational sector: Challenges and opportunities. Balkan Region Conference on Engineering and Business Education, 1: 332–337.

Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4): 681–694.

Furman, J., & Seamans, R. (2019). AI and the economy. Innovation Policy and the Economy, 19: 161–191.

Kooli, C. (2023). Chatbots in education and research: A critical examination of ethical implications and solutions. Sustainability, 15(7): 5614. https://doi.org/10.3390/su15075614

Kazachenok, V. V. (2020). Application of neural networks in education. Informatics and Education, 2(311): 41–47.

Khabibullin, I. R., Azovtseva, O. V., & Gareev, A. D. (2023). Relevance of neural networks in educational purposes. Young Scientist, 13(460): 176–178.

Okuda, T., & Shoda, S. (2018). AI-based chatbot service for financial industry. Fujitsu Scientific and Technical Journal, 54(2): 4–8.

Okewu, E., Adewole, P., Misra, S., Maskeliunas, R., & Damasevicius, R. (2021). Artificial neural networks for educational data mining in higher education: A systematic literature review. Applied Artificial Intelligence, 35(13): 983–1021.

Perrotta, C., & Selwyn, N. (2020). Deep learning goes to school: Toward a relational understanding of AI in education. Learning, Media and Technology, 45: 251–269.

Pesonen, J. A. (2021). 'Are you OK?' Students' trust in a chatbot providing support opportunities. In Learning and Collaboration Technologies: Games and Virtual Environments for Learning: 8th International Conference (pp. 199–215).

Przegalinska, A., Ciechanowski, L., Stroz, A., Gloor, P., & Mazurek, G. (2019). In bot we trust: A new methodology of chatbot performance measures. Business Horizons, 62(6): 785–797.

Repkina, N. G. (2016). Prediction of educational success for technical students using artificial neural networks. Almanac of World Science, 5-1(8): 92–95.

Rincon-Flores, E. G., López-Camacho, E., Mena, J., & López, O. O. (2020). Predicting academic performance with artificial intelligence (AI), a new tool for teachers and students. In 2020 IEEE Global Engineering Education Conference (EDUCON) (pp. 1049–1054). Porto, Portugal.

Shamsutdinova, T. M. (2022). Problems and prospects of using neural networks in education. Open Education, 26(6): 4–10.

Stojnić, A. (2015). Digital anthropomorphism: Performers, avatars, and chat-bots. Performance Research, 20(2): 70–77.

Valko, N., & Osadchyi, V. (2020). Education individualization by means of artificial neural networks. The International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters, 10021. https://doi.org/10.1051/e3sconf/202016610021




DOI: http://dx.doi.org/10.21902/Revrima.v3i45.7487

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Brazilian Journal of Law and International Relations e-ISSN: 2316-2880

Rua Chile, 1678, Rebouças, Curitiba/PR (Brazil). CEP 80.220-181