Evaluación la inteligencia artificial generativa en el contexto de la automática un análisis crítico
Contenido principal del artículo
- Antonio Javier Barragán+−
- Arturo Aquino+−
- Juan Manuel Enrique+−
- Francisca Segura+−
- Miguel Ángel Martínez+−
- José Manuel Andújar+−
Resumen
La reciente proliferación de las inteligencias artificiales (IAs), en particular las IAs generativas, está impulsando una necesidad de transformación en la educación universitaria. La habilidad de las IAs para generar contenido, redactar informes, resúmenes y solucionar problemas de diversa complejidad, debería inducir una revisión de muchos de los métodos de evaluación tradicionales; o al menos, un reconocimiento de la capacidad del estudiantado para emplear estas herramientas en la ejecución de sus tareas. Este artículo tiene como objetivo evaluar las competencias de las principales IAs disponibles en la actualidad para llevar a cabo tareas asociadas con la ingeniería de control, tanto teóricas como prácticas. Los resultados indican que las IAs actuales todavía no pueden resolver problemas de control de manera efectiva, y tienden a recurrir a soluciones estándar que no siempre son apropiadas; no obstante, muestran un rendimiento satisfactorio respecto de conocimientos teóricos generales.
Palabras clave:
Detalles del artículo
Citas
Bethencourt-Aguilar, A., Castellanos-Nieves, D., Sosa-Alonso, J. J., Area-Moreira, M., 2023. Use of generative adversarial networks (GANs) in educational technology research. Journal of New Approaches in Educational Research. DOI: 10.7821/naer.2023.1.1231
Divasón, J., de Pison, F. J. M., Romero, A., de Cabezón, E. S., 2023. Artificial intelligence models for assessing the evaluation process of complex student projects. IEEE Transactions on Learning Technologies 16, 694– DOI: 10.1109/TLT.2023.3246589
Google AI, 2024. Gemini.
Hemachandran, K., Verma, P., Pareek, P., Arora, N., Kumar, K. V. R., Ahanger, T., Pise, A., Ratna, R., 2022. Artificial intelligence: A universal virtual tool to augment tutoring in higher education. Computational Intelligence and Neuroscience 2022. DOI: 10.1155/2022/1410448
Jiayu, Y., 2023. Challenges and opportunities of generative artificial intelligence in higher education student educational management. Advances in Educational Technology and Psychology 7 (9). DOI: 10.23977/aetp.2023.070914
Khalil, M., Er, E., 2023. Will chatgpt get you caught? Rethinking of plagiarism detection. In: Zaphiris, P., Ioannou, A. (Eds.), Learning and Collaboration Technologies. Springer Nature Switzerland, Cham, pp. 475–487.
Likert, R., 1932. A technique for the measurement of attitudes. Archives of Psychology, 1–55.
Lopez-Carreño, J., Calvo-Lavado, C., Azpilcueta-Vasquez, M., Zárate-Pérez, E., 2022. Artificial-intelligence-based school assistant for detecting the behavior of university students. 2022 IEEE 2nd International Conference on Advanced Learning Technologies on Education & Research (ICALTER), 1–4. DOI: 10.1109/ICALTER57193.2022.9964801
Lund, B., Ting, W., 2023. Chatting about ChatGPT: How may AI and GPT impact academia and libraries? SSRN Electronic Journal. DOI: 10.2139/ssrn.4333415
Meta Platforms, Inc., 2022. Llama 3. URL: https://llama.meta.com
Microsoft Corporation, 2024. Copilot Pro. URL: https://copilot.microsoft.com
OpenAI, 2022. ChatGPT 3.5. URL: https://chat.openai.com
Perplexity AI, 2022. Llama 3. URL: www.perplexity.ai
Popenici, S. A. D., Kerr, S., 2017. Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning 12. DOI: 10.1186/s41039-017-0062-8
Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., González- Rodríguez, M., 2023. Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability. DOI: 10.3390/su151511524
Rybiński, K., Kopciuszewska, E., 2020. Will artificial intelligence revolutionise the student evaluation of teaching? A big data study of 1.6 million student reviews. Assessment & Evaluation in Higher Education 46, 1127– DOI: 10.1080/02602938.2020.1844866
Stone, C., 2023. Artificial intelligence in social work practice education. the potential use of generative ai for learning. The Journal of Practice Teaching and Learning. DOI: 10.1921/jpts.v20i3.2192
UNESCO, 2023. Guidance for generative AI in education and research. URL: https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
Walczak, K., Cellary, W., 2023. Challenges for higher education in the era of widespread access to generative AI. Economics and Business Review 9, –100. DOI: 10.18559/ebr.2023.2.743
Wang, J., Tan, Z., Zhou, F., Hu, Z., Fu, B., Wang, Y., 2023. Evaluation of the influence of artificial intelligence on college students’ learning based on group decision-making method. Journal of Artificial Intelligence Practice. DOI: 10.23977/jaip.2023.060805
Wang, S., 2022. The day the AGI was born. URL: https://www.latent.space/p/everything-we-know-about-chatgpt
Yang, C., Huan, S., Yang, Y., 2020. A practical teaching mode for colleges supported by artificial intelligence. Int. J. Emerg. Technol. Learn. 15, 195– DOI: 10.3991/ijet.v15i17.16737
Yeralan, S., Lee, L. A., 2023. Generative AI: Challenges to higher education. Sustainable Engineering and Innovation. DOI: 10.37868/sei.v5i2.id196
Yi, Y., 2021. Uspostavljanje koncepta UI pismenosti: Focusing on competence and purpose. JAHR 12 (2), 353–368. DOI: 10.21860/j.12.2.8
Yin, Y., 2021. Research on ideological and political evaluation model of university students based on data mining artificial intelligence technology. J. Intell. Fuzzy Syst. 40, 3689–3698. DOI: 10.3233/jifs-189403
Yu, H., Guo, Y., Jun. 2023. Generative artificial intelligence empowers educational reform: current status, issues, and prospects. Frontiers in Education DOI: 10.3389/feduc.2023.1183162
Zhang, E., Shi, W., 2021. The construction of university teachers’ scientific research performance evaluation system under artificial intelligence. 2021 World Automation Congress (WAC), 287–290. DOI: 10.23919/WAC50355.2021.9559607
Åström, K., Hägglund, T., Apr. 2000. Benchmark systems for pid control. IFAC Proceedings Volumes 33 (4), 165–166. DOI: 10.1016/s1474-6670(17)38238-1