La ética de la inteligencia artificial en educación: ¿Amenaza u oportunidad?
DOI:
https://doi.org/10.15359/ree.28-S.20541Palabras clave:
DAFO, ética, inteligencia artificial, estudiantes, universidad, ODS 4, Educación de calidad, formación tecnológicaResumen
Introducción. En la actual era de avances tecnológicos, en el último bienio, el desarrollo de la inteligencia artificial ha experimentado un auge en su uso y en su desarrollo, tanto es así que se emplea para elaborar personas virtuales ficticias, con voz y expresiones faciales, elaboración automática de textos, etc. Es innegable el aporte que esta hace en la vida humana; pero, ¿dónde se establece el límite de su uso?, ¿es ético su uso? Objetivo. El presente trabajo se plantea con el objetivo de analizar los límites éticos del uso de la inteligencia artificial en el ámbito académico y educativo. Análisis. Para esta investigación, se ha empleado un enfoque mixto. En un primer lugar, se llevó a cabo una revisión sistemática de la producción científica sobre inteligencia artificial en la base de datos Web of Science. Posteriormente, empleando un análisis DAFO, de corte cualitativo, se analizan y se discuten los resultados. Resultados. Cabe destacar que el uso de la inteligencia artificial, productora de textos, es una ventaja y una herramienta útil para el estudio y uso de idiomas, ya que permite elaborar discursos en otras lenguas de forma casi instantánea. No obstante, existen debilidades como el riesgo de comisión de plagio en entornos educativos, universitarios y académicos. Conclusiones. Establecer normas y límites éticos sobre el uso de inteligencia artificial generativa resulta una base fundamental para sustentar su utilización.
Referencias
*Alexander-Izrailevich, N., Boris-Alexandrovich, N., Artem-Vladimirovich, E., Leonid-Grigoryevich, S., Dmitry-Olegovich, K., Dmitry-Georgievich, T., & Leonid-Moiseevich, R. (2023). The use of intelligent analysis (IA) in determining the tactics of treating patients with nephrolithiasis. Urologia Journal, 90(4), 663-669. https://doi.org/10.1177/03915603231162881
*Ali, M. J. & Djalilian, A. (2023). Readership Awareness Series – Paper 4: Chatbots and ChatGPT - Ethical Considerations in Scientific Publications. Seminars in Ophthalmology, 38(5), 1-2. https://doi.org/10.1080/08820538.2023.2193444
Aliaga, F. M., Gutiérrez-Braojos, C., & Fernández-Cano, A. (2018). Las revistas de investigación en educación: Análisis DAFO. Revista de Investigación Educativa, 36(2), 563-579. https://doi.org/10.6018/rie.36.2.312461
*Arif, T. B., Munaf, U., & Ul-Haque, I. (2023). The future of medical education and research: Is ChatGPT a blessing or blight in disguise? Medical Education Online, 28(1), 1-2. https://doi.org/10.1080/10872981.2023.2181052
*Bai, C., Yan, P., Piao, H., Pan, W., & Guo, J. (2023). Learning-Based Multi-UAV Flocking Control With Limited Visual Field and Instinctive Repulsion. IEEE Transactions on Cybernetics, 54(1) 1-14. https://doi.org/10.1109/TCYB.2023.3246985
*Barriga Fray, J.I; Barriga Fray, L.F; Barriga Fray, S.F. (2023). Aportes al desarrollo de habilidades cognitivas en la interacción didáctica de idiomas. Tecnologías: Inteligencia artificial. Bibliotecas. Anales de Investigacion,19(2), 1-11. http://revistas.bnjm.sld.cu/index.php/BAI/article/view/544
*Belda-Medina, J. & Calvo-Ferrer, J. R. (2022). Using Chatbots as AI Conversational Partners in Language Learning. Applied Sciences, 12(17), 1-16. https://doi.org/10.3390/app12178427
Belk, R. W., Belanche, D., & Flavián, C. (2023). Key concepts in artificial intelligence and technologies 4.0 in services. Service Business, 17(1), 1-9. https://doi.org/10.1007/s11628-023-00528-w
*Benmohammed, K., Valensi, P., Omri, N., Al Masry, Z., & Zerhouni, N. (2022). Metabolic syndrome screening in adolescents: New scores AI_METS based on artificial intelligence techniques. Nutrition, Metabolism and Cardiovascular Diseases, 32(12), 2890-2899. https://doi.org/10.1016/j.numecd.2022.08.007
*Cao, F., Lei, M., Lin, S., & Xiang, M. (2022). Application of Artificial Intelligence-Based Big Data AI Technology in Physical Education Reform. Mobile Information Systems, Article 4017151, , 1-12. https://doi.org/10.1155/2022/4017151
*Carvalho, I. & Ivanov, S. (2023). ChatGPT for tourism: Applications, benefits and risks. Tourism Review, 79(2), 290-303. https://doi.org/10.1108/TR-02-2023-0088
*Castro Nascimento, C. M., & Pimentel, A. S. (2023). Do Large Language Models Understand Chemistry? A Conversation with ChatGPT. Journal of Chemical Information and Modeling, 63(6), 1649-1655. https://doi.org/10.1021/acs.jcim.3c00285
*Chen, C. (2023). Influence of Employees’ Intention to Adopt AI Applications and Big Data Analytical Capability on Operational Performance in the High-Tech Firms. Journal of the Knowledge Economy, 15, 3946–3974. https://doi.org/10.1007/s13132-023-01293-x
*Choi, E. P. H., Lee, J. J., Ho, M.-H., Kwok, J. Y. Y., & Lok, K. Y. W. (2023). Chatting or cheating? The impacts of ChatGPT and other artificial intelligence language models on nurse education. Nurse Education Today, 125, Article 105796. https://doi.org/10.1016/j.nedt.2023.105796
*Clancy, R., Bode, I., & Zhu, Q. (2023). The need for and nature of a normative, cultural psychology of weaponized AI (artificial intelligence). Ethics and Information Technology, 25(1), Article 6. https://doi.org/10.1007/s10676-023-09680-3
*Cooper, G. (2023). Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. Journal of Science Education and Technology, 32, 444–452. https://doi.org/10.1007/s10956-023-10039-y
*Cugurullo, F. & Acheampong, R. A. (2023). Fear of AI: An inquiry into the adoption of autonomous cars in spite of fear, and a theoretical framework for the study of artificial intelligence technology acceptance. AI & SOCIETY, 39, 1569–1584. https://doi.org/10.1007/s00146-022-01598-6
*Curtis, N. (2023). To ChatGPT or not to ChatGPT? The Impact of Artificial Intelligence on Academic Publishing. Pediatric Infectious Disease Journal, 42(4), 275-275. https://doi.org/10.1097/INF.0000000000003852
D’Avella, S., Avizzano, C. A., & Tripicchio, P. (2023). ROS-Industrial based robotic cell for Industry 4.0: Eye-in-hand stereo camera and visual servoing for flexible, fast, and accurate picking and hooking in the production line. Robotics and Computer-Integrated Manufacturing, 80, Article 102453. https://doi.org/10.1016/j.rcim.2022.102453
*Do, H. M., Spear, L. G., Nikpanah, M., Mirmomen, S. M., Machado, L. B., Toscano, A. P., Turkbey, B., Bagheri, M. H., Gulley, J. L., & Folio, L. R. (2020). Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence. Academic Radiology, 27(1), 96-105. https://doi.org/10.1016/j.acra.2019.09.014
*Emenike, M. E. & Emenike, B. U. (2023). Was This Title Generated by ChatGPT? Considerations for Artificial Intelligence Text-Generation Software Programs for Chemists and Chemistry Educators. Journal of Chemical Education, 100(4), 1413-1418. https://doi.org/10.1021/acs.jchemed.3c00063
*Fant, C. & Adelman, D. S. (2022). Building Community Resilience in Support of Public Health Emergency Preparedness with Big Data and AI. CIN: Computers, Informatics, Nursing, 40(12), 836-840. https://doi.org/10.1097/CIN.0000000000000869
*Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3) 1-15. https://doi.org/10.1080/14703297.2023.2195846
*Füßl, A., Nissen, V., & Heringklee, S. H. (2023). Knowledge Graph-Based Explainable Artificial Intelligence for Business Process Analysis. International Journal of Semantic Computing, 17(2), 1-25. https://doi.org/10.1142/S1793351X23600024
George-Reyes, C. E., López-Caudana, E. O., Ramírez-Montoya, M. S., & Ruiz-Ramírez, J. A. (2023). Pensamiento computacional basado en realidad virtual y razonamiento complejo: Caso de estudio secuencial. Revista de Educación a Distancia (RED), 23(73), 1-25. https://doi.org/10.6018/red.540841
*Ghioni, R., Taddeo, M., & Floridi, L. (2023). Open source intelligence and AI: A systematic review of the GELSI literature. AI & SOCIETY, 39, 1827–1842 https://doi.org/10.1007/s00146-023-01628-x
*Guerrero, L. E., Castillo, L. F., Arango-López, J., & Moreira, F. (2023). A systematic review of integrated information theory: A perspective from artificial intelligence and the cognitive sciences. Neural Computing and Applications. https://doi.org/10.1007/s00521-023-08328-z
*Hinton, M. & Wagemans, J. H. M. (2023). How persuasive is AI-generated argumentation? An analysis of the quality of an argumentative text produced by the GPT-3 AI text generator. Argument & Computation, 14(1), 59-74. https://doi.org/10.3233/AAC-210026
Hitch, D. (2023). Artificial Intelligence Augmented Qualitative Analysis: The Way of the Future? Qualitative Health Research, 34(7), 595-606. https://doi.org/10.1177/10497323231217392
*Hitsuwari, J., Ueda, Y., Yun, W., & Nomura, M. (2023). Does human–AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry. Computers in Human Behavior, 139, 1-10. https://doi.org/10.1016/j.chb.2022.107502
*Ikhlasse, H., Benjamin, D., Vincent, C., & Hicham, M. (2022). Recent implications towards sustainable and energy efficient AI and big data implementations in cloud-fog systems: A newsworthy inquiry. Journal of King Saud University - Computer and Information Sciences, 34(10), 8867-8887. https://doi.org/10.1016/j.jksuci.2021.11.002
*Jia, F., Sun, D., Ma, Q., & Looi, C.-K. (2022). Developing an AI-Based Learning System for L2 Learners’ Authentic and Ubiquitous Learning in English Language. Sustainability, 14(23), 1-18. https://doi.org/10.3390/su142315527
Jo, H. & Park, D.-H. (2023). AI in the Workplace: Examining the Effects of ChatGPT on Information Support and Knowledge Acquisition. International Journal of Human–Computer Interaction, 20(23), 8091-8106. https://doi.org/10.1080/10447318.2023.2278283
Kahambing, J. G. (2023). ChatGPT, ‘polypsychic’ artificial intelligence, and psychiatry in museums. Asian Journal of Psychiatry, 83, 1-2. https://doi.org/10.1016/j.ajp.2023.103548
*Ketchum, A. & Morena, N. (2022). AI, Big Data, and surveillance zines as forms of community healthcare. Feminist Media Studies, 23(8), 1-17. https://doi.org/10.1080/14680777.2022.2149598
*Kevorkova, Z. A., Zhukova, G. S., Antonova, O., Dolbik-Vorobey, T. A., & Petrov, A. M. (2020). Organizational Prerequisites for Transition to IFRS in the Artificial Intelligence Context. The bulletin, 2(384), 128-136. https://doi.org/10.32014/2020.2518-1467.51
*King, M. R. (2023). A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education. Cellular and Molecular Bioengineering, 16(1), 1-2. https://doi.org/10.1007/s12195-022-00754-8
Kirshner, S. N. (2024). GPT and CLT: The impact of ChatGPT’s level of abstraction on consumer recommendations. Journal of Retailing and Consumer Services, 76, 1-13. https://doi.org/10.1016/j.jretconser.2023.103580
*Kjell, O. N. E., Sikström, S., Kjell, K., & Schwartz, H. A. (2022). Natural language analyzed with AI-based transformers predict traditional subjective well-being measures approaching the theoretical upper limits in accuracy. Scientific Reports, 12(1), 1-9. https://doi.org/10.1038/s41598-022-07520-w
*Kleebayoon, A. & Wiwanitkit, V. (2023). Artificial Intelligence, Chatbots, Plagiarism and Basic Honesty: Comment. Cellular and Molecular Bioengineering, 16(2), 173-174. https://doi.org/10.1007/s12195-023-00759-x
*Kreuzen, H., Dull, D., De Rover, V., & Span, R. (2023). Can AI Powered Speech-to-Text and Text-to-Speech techniques limit the interviewer bias in sensory and consumer research? Food Quality and Preference, 107, 1-8. https://doi.org/10.1016/j.foodqual.2023.104828
*Lee, H. (2023). The rise of ChatGPT: Exploring its potential in medical education. Anatomical Sciences Education, 17, 926–931. https://doi.org/10.1002/ase.2270
*Lee, J. H., Shin, D., & Noh, W. (2023). Artificial Intelligence-Based Content Generator Technology for Young English-as-a-Foreign-Language Learners’ Reading Enjoyment. RELC Journal, 54(2), 508-516. https://doi.org/10.1177/00336882231165060
Lepratte, L. & Yoguel, G. (2023). Artefacts, routines, and co-production: A pioneering case of artificial intelligence-based health services in Argentina. Industry and Innovation, 30(9), 1-23. https://doi.org/10.1080/13662716.2023.2194241
Levantino, F. P. (2023). Generative and AI-powered oracles: “What will they say about you?” Computer Law & Security Review, 51, 1-6. https://doi.org/10.1016/j.clsr.2023.105898
*Li, X., Ye, P., Li, J., Liu, Z., Cao, L., & Wang, F.-Y. (2022). From Features Engineering to Scenarios Engineering for Trustworthy AI: I&I, C&C, and V&V. IEEE Intelligent Systems, 37(4), 18-26. https://doi.org/10.1109/MIS.2022.3197950
*Liu, R., Shu, X., Li, P., Xu, Y., Yeung, P., & Lee, R. (2021). AI-Based Language Chatbot 2.0 – The Design and Implementation of English Language Concept Learning Agent App. En W. Jia, Y. Tang, R. S. T. Lee, M. Herzog, H. Zhang, T. Hao, & T. Wang (Eds.), Emerging Technologies for Education (Vol. 13089, pp. 25-35). Springer International Publishing. https://doi.org/10.1007/978-3-030-92836-0_3
*Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74, 570–581. https://doi.org/10.1002/asi.24750
*Lyu, H., Zhang, Y., Wang, C., Long, S., & Guo, S. (2023). Federated learning privacy incentives: Reverse auctions and negotiations. CAAI Transactions on Intelligence Technology, 8, 1538–1557. https://doi.org/10.1049/cit2.12190
*Mageira, K., Pittou, D., Papasalouros, A., Kotis, K., Zangogianni, P., & Daradoumis, A. (2022). Educational AI Chatbots for Content and Language Integrated Learning. Applied Sciences, 12(7), 1-16. https://doi.org/10.3390/app12073239
*Maicher, K. R., Stiff, A., Scholl, M., White, M., Fosler-Lussier, E., Schuler, W., Serai, P., Sunder, V., Forrestal, H., Mendella, L., Adib, M., Bratton, C., Lee, K., & Danforth, D. R. (2023). Artificial intelligence in virtual standardized patients: Combining natural language understanding and rule based dialogue management to improve conversational fidelity. Medical Teacher, 45(3), 279-285. https://doi.org/10.1080/0142159X.2022.2130216
*McFarland, T. (2022). Reconciling trust and control in the military use of artificial intelligence. International Journal of Law and Information Technology, 30(4), 472-483. https://doi.org/10.1093/ijlit/eaad008
*Mehta, S., Gibson, M., Avila, J., Villagran, C., Fernandez, F., Niklitschek, S., Vera, F., Rocuant, R., Cardenas, G., Frauenfelder, A., Vieira, D., Merchant, S., Vijayan, Y., Tamayo, C. J., & Pinos, D. (2020). Reconfiguring traditional EKG interpretation with artificial intelligence – a reliable, time-saving alternative? European Heart Journal, 41(Supplement_2), 3498-3498. https://doi.org/10.1093/ehjci/ehaa946.3498
*Mitra, A., Bera, B., Das, A. K., Jamal, S. S., & You, I. (2023). Impact on blockchain-based AI/ML-enabled big data analytics for Cognitive Internet of Things environment. Computer Communications, 197, 173-185. https://doi.org/10.1016/j.comcom.2022.10.010
Niu, B. & Mvondo, G. F. N. (2024). I Am ChatGPT, the ultimate AI Chatbot! Investigating the determinants of users’ loyalty and ethical usage concerns of ChatGPT. Journal of Retailing and Consumer Services, 76, 1-12. https://doi.org/10.1016/j.jretconser.2023.103562
OpenAI. (2023). ChatGPT (versión 3.5) [Modelo de lenguaje]. https://openai.com/
*Ouyang, F., Wu, M., Zhang, L., Xu, W., Zheng, L., & Cukurova, M. (2023). Making strides towards AI-supported regulation of learning in collaborative knowledge construction. Computers in Human Behavior, 142, 1-11. https://doi.org/10.1016/j.chb.2023.107650
*Paul, J., Ueno, A., & Dennis, C. (2023). ChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda. International Journal of Consumer Studies, 47, 1213–1225. https://doi.org/10.1111/ijcs.12928
Pelly, M., Fatehi, F., Liew, D., & Verdejo-Garcia, A. (2023). Artificial intelligence for secondary prevention of myocardial infarction: A qualitative study of patient and health professional perspectives. International Journal of Medical Informatics, 173, 1-8. https://doi.org/10.1016/j.ijmedinf.2023.105041
*Peres, M. L., Sotelino, E. D., & Mesquita, L. C. (2023). Intelligent element: Coupling Green function approach and artificial intelligence to reduce discretization effort. International Journal for Numerical and Analytical Methods in Geomechanics, 47(6), 1051-1072. https://doi.org/10.1002/nag.3505
*Prikshat, V., Malik, A., & Budhwar, P. (2023). AI-augmented HRM: Antecedents, assimilation and multilevel consequences. Human Resource Management Review, 33(1), 1-18. https://doi.org/10.1016/j.hrmr.2021.100860
*Rees, N., Holding, K., & Sujan, M. (2023). Information governance as a socio-technical process in the development of trustworthy healthcare AI. Frontiers in Computer Science, 5, 1-6. https://doi.org/10.3389/fcomp.2023.1134818
*Richter, K.-F. & Scheider, S. (2023). Current topics and challenges in geoAI. KI - Künstliche Intelligenz, 37, 11–16. https://doi.org/10.1007/s13218-022-00796-0
Ruan, H., Wei, Z., Shang, W., Wang, X., & He, H. (2023). Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging. Applied Energy, 336, 1-11. https://doi.org/10.1016/j.apenergy.2023.120751
*Sadok, H., Sakka, F., & El Maknouzi, M. E. H. (2022). Artificial intelligence and bank credit analysis: A review. Cogent Economics & Finance, 10(1), 1-12. https://doi.org/10.1080/23322039.2021.2023262
*Sargazi-Moghadam, T., Darejeh, A., Delaramifar, M., & Mashayekh, S. (2023). Toward an artificial intelligence-based decision framework for developing adaptive e-learning systems to impact learners’ emotions. Interactive Learning Environments, 32(7), 1-21. https://doi.org/10.1080/10494820.2023.2188398
*Siripurapu, S., Darimireddy, N. K., Chehri, A., Sridhar, B., & Paramkusam, A. V. (2023). Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review-Part-I (AI, Big Data, Block Chain, Open-Source Technologies, and Cloud Computing). Electronics, 12(3), Article 750. https://doi.org/10.3390/electronics12030750
*Skavronskaya, L., Hadinejad, A., & Cotterell, D. (2023). Reversing the threat of artificial intelligence to opportunity: A discussion of ChatGPT in tourism education. Journal of Teaching in Travel & Tourism, 23(2) 1-6. https://doi.org/10.1080/15313220.2023.2196658
Su, J. & Yang, W. (2023). Powerful or mediocre? Kindergarten teachers’ perspectives on using ChatGPT in early childhood education. Interactive Learning Environments, 1-13. https://doi.org/10.1080/10494820.2023.2266490
Titus, L. M. (2024). Does ChatGPT have semantic understanding? A problem with the statistics-of-occurrence strategy. Cognitive Systems Research, 83, 1-13. https://doi.org/10.1016/j.cogsys.2023.101174
*Tong, Y. & Zhang, L. (2023). Discovering the next decade’s synthetic biology research trends with ChatGPT. Synthetic and Systems Biotechnology, 8(2), 220-223. https://doi.org/10.1016/j.synbio.2023.02.004
*Ullah, F., Bajahzar, A., Aldabbas, H., Farhan, M., Naeem, H., Bukhari, S. S., & Malik, K. (2019). An E-Assessment Methodology Based on Artificial Intelligence Techniques to Determine Students’ Language Quality and Programming Assignments’ Plagiarism. Intelligent Automation and Soft Computing, 26(1), 169-180. https://doi.org/10.31209/2019.100000138
*Vartiainen, H. & Tedre, M. (2023). Using artificial intelligence in craft education: Crafting with text-to-image generative models. Digital Creativity, 34(1), 1-21. https://doi.org/10.1080/14626268.2023.2174557
*Vujicic, N. (2022). The EU copyright exceptions for text and data mining: (Un)suitable model solution for the development of artificial intelligence? Australian Intellectual Property Journal, 33(2), 92–106. https://search.informit.org/doi/10.3316/agispt.20230103081160
*Waisberg, E., Ong, J., Masalkhi, M., Zaman, N., Kamran, S. A., Sarker, P., Lee, A. G., & Tavakkoli, A. (2023). Text-to-image artificial intelligence to aid clinicians in perceiving unique neuro-ophthalmic visual phenomena. Irish Journal of Medical Science, 192, 3139–3142. https://doi.org/10.1007/s11845-023-03315-8
*Wang, X., Liu, Q., Pang, H., Tan, S. C., Lei, J., Wallace, M. P., & Li, L. (2023). What matters in AI-supported learning: A study of human-AI interactions in language learning using cluster analysis and epistemic network analysis. Computers & Education, 194, 1-17. https://doi.org/10.1016/j.compedu.2022.104703
*Wang, X., Pang, H., Wallace, M. P., Wang, Q., & Chen, W. (2022). Learners’ perceived AI presences in AI-supported language learning: A study of AI as a humanized agent from community of inquiry. Computer Assisted Language Learning, 37(4), 814-840. https://doi.org/10.1080/09588221.2022.2056203
*Wollowski, M. (2023). Using ChatGPT to produce code for a typical college‐level assignment. AI Magazine, 44(1), 129-130. https://doi.org/10.1002/aaai.12086
Workman, J. & Mark, H. (2023). Artificial Intelligence in Analytical Spectroscopy, Part I: Basic Concepts and Discussion. Spectroscopy, 37(4), 13-22. https://doi.org/10.56530/spectroscopy.og4284z8
*Wu, J.-S., Chien, T.-H., Chien, L.-R., & Yang, C.-Y. (2021). Using Artificial Intelligence to Predict Class Loyalty and Plagiarism in Students in an Online Blended Programming Course during the COVID-19 Pandemic. Electronics, 10(18), 1-20. https://doi.org/10.3390/electronics10182203
*Yadava, O. P. (2023). ChatGPT—a foe or an ally? Indian Journal of Thoracic and Cardiovascular Surgery, 39, 217–221. https://doi.org/10.1007/s12055-023-01507-6
*Yang, T. & Ying, Y. (2023). AUC Maximization in the Era of Big Data and AI: A Survey. ACM Computing Surveys, 55(8), 1-37. https://doi.org/10.1145/3554729
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2023). Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study. International Journal of Human–Computer Interaction, 40(24), 8703-8715. https://doi.org/10.1080/10447318.2023.2288730
*Yu, L. & Yu, Z. (2023). Qualitative and quantitative analyses of artificial intelligence ethics in education using VOSviewer and CitNetExplorer. Frontiers in Psychology, 14, 1-14. https://doi.org/10.3389/fpsyg.2023.1061778
*Zandiyeh, A., Behroyan, I., Noori, M. M., & Babanezhad, M. (2023). Ant colony optimisation and fuzzy system for prediction of computational data of fluid flow in a bubble column reactor. Journal of Experimental & Theoretical Artificial Intelligence, 1-15. https://doi.org/10.1080/0952813X.2023.2183270
Zhang, P. & Tur, G. (2023). A systematic review of ChatGPT use in K‐12 education. European Journal of Education, 59(2), 1-22. https://doi.org/10.1111/ejed.12599
*Zhou, M. (2022). Editorial: Evolution from AI, IoT and Big Data Analytics to Metaverse. IEEE/CAA Journal of Automatica Sinica, 9(12), 2041-2042. https://doi.org/10.1109/JAS.2022.106100
*Zou, B., Guan, X., Shao, Y., & Chen, P. (2023). Supporting Speaking Practice by Social Network-Based Interaction in Artificial Intelligence (AI)-Assisted Language Learning. Sustainability, 15(4), 1-19. https://doi.org/10.3390/su15042872
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2. Derechos de reutilización: La UNA les concede a los AUTORES(AS) el derecho de reutilizar para cualquier propósito, entre ellos el autoarchivo, y poder publicar en internet o cualquier sitio electrónico la versión final aprobada y publicada (post print) del artículo, siempre y cuando se realice sin fines de lucro, no genere obra derivada sin previa autorización y respete las fuentes de autoría.
3. La postulación y posible publicación del artículo en la Revista Electrónica Educare se regirá por sus políticas editoriales, la normativa institucional de la Universidad Nacional y la legislación de la República de Costa Rica. Adicionalmente, cualquier eventual diferencia de criterio o disputa futura se dirimirá de acuerdo con los mecanismos de Resolución Alterna de Conflictos y la Jurisdicción Costarricense.
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