Palabras clave
enseñanza superior
competencias
inteligencia artificial
análisis de redes
Cómo citar
Resumen
El reciente lanzamiento de ChatGPT para uso público ha avanzado a pasos agigantados, lo que se refleja en la proliferación de herramientas basadas en Inteligencia Artificial, entre ellas el ChatGPT que comenzó en su versión GPT3 y actualmente se encuentra en su versión GPT4o. ¿Cuáles son los desafíos que enfrenta la educación superior en este escenario? Este estudio tuvo como objetivo examinar la evolución del conocimiento sobre ChatGpt en los primeros seis meses de acceso abierto y sus implicaciones para la estructura de aprendizaje (qué se privilegia en la enseñanza, cómo se enseñan estos conceptos y qué deben hacer quienes enseñan) en las instituciones de educación superior. Utilizando técnicas de minería de texto y PNL, examinamos la evolución de la conceptualización de ChatGPT generada en Wikipedia, analizando y modelando las agrupaciones, la evolución de la conceptualización y los usos en la investigación en Educación Superior a través del análisis de redes. Los resultados revelaron que; 1) las descripciones textuales de ChatGPT han sido principalmente técnicas dirigidas a usuarios con formación profesional. Es decir, los términos empleados son comprensibles para los hablantes nativos de inglés con al menos once años de educación formal o la educación formal estándar de un estudiante universitario de primer año. 2) La investigación se centra en gran medida en las preocupaciones relacionadas con el uso de ChatGPT, con pocos estudios que exploren sus usos positivos en la educación. Se necesita más investigación para explorar las aplicaciones prácticas de ChatGPT en beneficio tanto de los estudiantes como de los profesores.
Referencias
Ahmed, K. (2024). A Catalog of Generative AI Tools for Faculty Advancement. 2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET), 1-8. https://doi.org/10.1109/ITHET61869.2024.10837663
Alhanai, T., Ghassemi, M., & Glass, J. (2018). Detecting Depression with Audio/Text Sequence Modeling of Interviews. Interspeech 2018, 1716-1720. https://doi.org/10.21437/Interspeech.2018-2522
Aria, M., & Cuccurullo, C. (2017). An R-Tool for Comprehensive Science Mapping Analysis. Bibliometrix. In Journal of Informetrics, 11 (4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
Bail, C. A. (2016). Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media. Proceedings of the National Academy of Sciences of the United States of America, 113 (42), 11823-11828. https://doi.org/10.1073/pnas.1607151113
Batchelor, J. (2023). Just another clickbait title: A corpus-driven investigation of negative attitudes toward science on Reddit. Public Understanding of Science, 32 (5), 580-595. https://doi.org/10.1177/09636625221146453
Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018). quanteda: An R package for the quantitative analysis of textual data. Journal of Open Source Software, 3 (30), 774. https://doi.org/10.21105/joss.00774
Biswas, S. S. (2023). Role of Chat GPT in Public Health. Annals of Biomedical Engineering, 51 (5), 868-869. https://doi.org/10.1007/s10439-023-03172-7
Castillo-González, W., Lepez, C. O., & Bonardi, M. C. (2022). [Chat GPT: A promising tool for academic editing]. Data & Metadata, 1, 23. https://doi.org/10.56294/dm202223
Chan, C., & Tsi, L. (2024). Will generative AI replace teachers in higher education? A study of teacher and student perceptions. Studies in Educational Evaluation, 83. https://doi.org/10.1016/j.stueduc.2024.101395
Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023). Time to Revisit Existing Student’s Performance Evaluation Approach in Higher Education Sector in a New Era of ChatGPT — A Case Study. Cogent Education, 10 (1), 2210461. https://doi.org/10.1080/2331186X.2023.2210461
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory fiel. Journal of Informetricsl, 5, 146-166. https://doi.org/10.1016/j.joi.2010.10.002
Correa, J. C., García-Chitiva, M. P., & García-Vargas, G. R. (2018). A Text Mining Approach to the Text Difficulty of Latin American Peace Agreement. Latin American Journal of Psychology 50 (1). https://doi.org/10.14349/rlp.2018.v50.n1.6
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20 (1), 22. https://doi.org/10.1186/s41239-023-00392-8
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network researc. InterJournal, Complex Systems, 1695 (5), 1-9.
Dave, T., Athaluri, S. A., & Singh, S. (2023). ChatGPT in medicine: An overview of its applications, advantages, limitations prospects, and ethical considerations. Frontiers in Artificial Intelligence, 6, 1169595. https://doi.org/10.3389/frai.2023.1169595
Dormann, C. F., Fründ, J., Blüthgen, N., & Gruber, B. (2009). Indices, Graphs and Null Models: Analyzing Bipartite Ecological Networks. The Open Ecology Journal, 2, 7-24.
Edwards, M. L., & Ziegler, C. (2022). Examining science communication on Reddit: From an “Assembled” to a “Disassembling” approach. Public Understanding of Science, 31 (4), 473-488. https://doi.org/10.1177/09636625211057231
Elcoro, M., Diller, J. W., & Correa, J. C. (2023). Promoting Reciprocal Relations across Subfields of Behavior Analysis via Collaborations. Perspectives on Behavior Science, 46 (3-4), 431-446. https://doi.org/10.1007/s40614-023-00386-x
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv preprint arXiv:2303.10130.
Emsley, R. (2023). ChatGPT: These are not hallucinations – they’re fabrications and falsifications. Schizophrenia, 9 (1), 52, s41537-023-00379-4. https://doi.org/10.1038/s41537-023-00379-4
Estrada, E. (2012). The structure of complex networks: Theory and applications. Oxford University Press, USA.
Frank, M. C. (2023). Baby steps in evaluating the capacities of large language models. Nature Reviews Psychology, 2 (8), 451-452. https://doi.org/10.1038/s44159-023-00211-x
Fuchs, K., & Aguilos, V. (2023). Integrating Artificial Intelligence in Higher Education: Empirical Insights from Students about Using ChatGPT. International Journal of Information and Education Technology, 13 (9), 1365-1371. https://doi.org/10.18178/ijiet.2023.13.9.1939
Furlan, R., & Travieso, G. (2015). Analyzing complex networks through correlations in centrality measurements. Journal of Statistical Mechanics: Theory and Experiment, 2015 (5), P05030. https://doi.org/10.1088/1742-5468/2015/05/P05030
Garcia-Chitiva, M. del P., & Correa, J. C. (2023). Soft skills centrality in graduate studies offerings. Studies in Higher Education, 49 (6)1-26. https://doi.org/10.1080/03075079.2023.2254799
Goretzko, D., & Israel, L. S. F. (2022). Pitfalls of Machine Learning-Based Personnel Selection: Fairness, Transparency, and Data Quality. Journal of Personnel Psychology, 21 (1), 37-47. https://doi.org/10.1027/1866-5888/a000287
Hassani, H., & Silva, E. S. (2023). The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field. Big Data and Cognitive Computing, 7 (2), 62. https://doi.org/10.3390/bdcc7020062
Hill-Yardin, E. L., Hutchinson, M. R., Laycock, R., & Spencer, S. J. (2023). A Chat(GPT) about the future of scientific publishing. Brain, Behavior, and Immunity, 110, 152-154. https://doi.org/10.1016/j.bbi.2023.02.022
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A Fast Learning Algorithm for Deep Belief Nets. Neural Computation, 18 (7), 1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
Hmoud, M., Swaity, H., Hamad, N., Karram, O., & Daher, W. (2024). Higher Education Students’ Task Motivation in the Generative Artificial Intelligence Context: The Case of ChatGPT. Information, 15 (1), 33. https://doi.org/10.3390/info15010033
Hubner, A. Y., & Bond, R. (2022). I am a scientist . . . Ask Me Anything: Examining differences between male and female scientists participating in a Reddit AMA session. Public Understanding of Science, 31 (4), 458-472. https://doi.org/10.1177/09636625211048775
Hunter, R. J., & Shannon, J. H. (2023). How a Simple Tort Claim is Transformed into an Exposition of the Implications of Artificial Intelligence on the American Legal System. Advances in Social Sciences Research Journal, 10 (7). https://doi.org/10.14738/assrj.107.15075
Kapugama, K. D. C. G., Lorensuhewa, S. A. S., & Kalyani, M. A. L. (2016). Enhancing Wikipedia search results using Text Mining. 2016 Sixteenth International Conference on Advances in ICT for Emerging Regions (ICTer), 168-175. https://doi.org/10.1109/ICTER.2016.7829915
Lavoie, J. R., & Daim, T. (2019). Technology Transfer: A Literature Review. En T. Daim, M. Dabić, N. Başoğlu, J. R. Lavoie, & B. J. Galli (Eds.), R&D Management in the Knowledge Era: Challenges of Emerging Technologies (pp. 421-438). Springer International Publishing. https://doi.org/10.1007/978-3-030-15409-7_17
Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P. N., Hellmann, S., Morsey, M., Van Kleef, P., Auer, S., & Bizer, C. (2015). DBpedia – A large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web, 6 (2), 167-195. https://doi.org/10.3233/SW-140134
Lerner, J., & Tirole, J. (2005). The Economics of Technology Sharing: Open Source and Beyond. Journal of Economic Perspectives, 19 (2), 99-120. https://doi.org/10.1257/0895330054048678
Lindgren, B.-M., Lundman, B., & Graneheim, U. H. (2020). Abstraction and interpretation during the qualitative content analysis process. International Journal of Nursing Studies, 108, 103632. https://doi.org/10.1016/j.ijnurstu.2020.103632
Ludwig, D. (2014). Extended cognition in science communication. Public Understanding of Science, 23 (8), 982-995. https://doi.org/10.1177/0963662513476798
Luke, D. (2015). A User’s Guide to Network Analysis in R. Springer.
Mc Laughlin, G. H. (1969). SMOG Grading-a New Readability Formula. Journal of Reading, 12 (8), 639-646.
McGowan, A., Gui, Y., Dobbs, M., Shuster, S., Cotter, M., Selloni, A., Goodman, M., Srivastava, A., Cecchi, G. A., & Corcoran, C. M. (2023). ChatGPT and Bard exhibit spontaneous citation fabrication during psychiatry literature search. Psychiatry Research, 326, 115334. https://doi.org/10.1016/j.psychres.2023.115334
Minsky, M., & Papert, S. A. (2017). Perceptrons: An Introduction to Computational Geometry. The MIT Press. https://doi.org/10.7551/mitpress/11301.001.0001
Mitchell, M., & Krakauer, D. C. (2023). The debate over understanding in AI’s large language models. Proceedings of the National Academy of Sciences, 120 (13), e2215907120. https://doi.org/10.1073/pnas.2215907120
Mukherjee, S., Romero, D. M., Jones, B., & Uzzi, B. (2017). The nearly universal link between the age of past knowledge and tomorrow’s breakthroughs in science and technology: The hotspot. Science Advances, 3 (4), e1601315. https://doi.org/10.1126/sciadv.1601315
Oldham, S., Fulcher, B., Parkes, L., Arnatkevic̆iūtė, A., Suo, C., & Fornito, A. (2019). Consistency and differences between centrality measures across distinct classes of networks. PLOS ONE, 14 (7), e0220061. https://doi.org/10.1371/journal.pone.0220061
OpenAI. (2023). GPT-4 Technical Report. https://doi.org/10.48550/ARXIV.2303.08774
Perkowitz, S. (2023). Does ChatGPT know physics? Physics World, 36 (6), 68-68. https://doi.org/10.1088/2058-7058/36/06/34
Poehlmann, K., Helm, R., Mauroner, O., & Auburger, J. (2020). Corporate spin-offs’ success factors: Management lessons from a comparative empirical analysis with research-based spin-offs. Review of Managerial Science, 15 (6), Article 6. https://doi.org/10.1007/s11846-020-00402-3
Qasem, F. (2023). ChatGPT in scientific and academic research: Future fears and reassurances. Library Hi Tech News, 40 (3), 30-32. https://doi.org/10.1108/LHTN-03-2023-0043
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. https://openai.com/research/better-language-models
Rejeb, A., Rejeb, K., Appolloni, A., Treiblmaier, H., & Iranmanesh, M. (2024). Exploring the impact of ChatGPT on education: A web mining and machine learning approach. The International Journal of Management Education, 22 (1), 100932. https://doi.org/10.1016/j.ijme.2024.100932
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65 (6), 386-408. https://doi.org/10.1037/h0042519
Rosenblatt, F. (1962). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books. https://apps.dtic.mil/sti/citations/tr/AD0256582
Roszkowski, M., & Włodarczyk, B. (2022). COVID-19 and the social organization of knowledge in Wikipedia: A study of social representations. Journal of Documentation, 78 (2), 242-263. https://doi.org/10.1108/JD-01-2021-0006
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323 (6088), Article 6088. https://doi.org/10.1038/323533a0
Salifu, I., Arthur, F., Arkorful, V., Abam Nortey, S., & Solomon Osei-Yaw, R. (2024). Economics students’ behavioural intention and usage of ChatGPT in Higher Education: A hybrid structural equation modelling-artificial neural network approach. Cogent Social Sciences, 10 (1), 2300177. https://doi.org/10.1080/23311886.2023.2300177
Sanderson, K. (2023). GPT-4 is here: What scientists think. Nature, 615 (7954), 773-773. https://doi.org/10.1038/d41586-023-00816-5
Segev, E., & Sharon, A. J. (2017). Temporal patterns of scientific information-seeking on Google and Wikipedia. Public Understanding of Science, 26 (8), 969-985. https://doi.org/10.1177/0963662516648565
Selwyn, N., & Gorard, S. (2016). Students’ use of Wikipedia as an academic resource—Patterns of use and perceptions of usefulness. The Internet and Higher Education, 28, 28-34. https://doi.org/10.1016/j.iheduc.2015.08.004
Silge, J., & Robinson, D. (2016). tidytext: Text Mining and Analysis Using Tidy Data Principles in R. The Journal of Open Source Software, 1 (3), 37. https://doi.org/10.21105/joss.00037
Singh, H., Tayarani-Najaran, M.-H., & Yaqoob, M. (2023). Exploring Computer Science Students’ Perception of ChatGPT in Higher Education: A Descriptive and Correlation Study. Education Sciences, 13 (9), 924. https://doi.org/10.3390/educsci13090924
Smil, V. (2023). Invention and Innovation: A Brief History of Hype and Failure. The MIT Press. https://mitpress.mit.edu/9780262551014/invention-and-innovation/
Stark, P. B. (2018). Before reproducibility must come preproducibility. Nature, 557 (7707), 613-613. https://doi.org/10.1038/d41586-018-05256-0
Stokel-Walker, C. (2022). AI bot ChatGPT writes smart essays—Should professors worry? Nature. https://doi.org/10.1038/d41586-022-04397-7
Stokel-Walker, C., & Van Noorden, R. (2023). What ChatGPT and generative AI mean for science. Nature, 614 (7947), 214-216. https://doi.org/10.1038/d41586-023-00340-6
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6 (1). https://doi.org/10.37074/jalt.2023.6.1.17
Tang, Y., Tran, C., Li, X., Chen, P.-J., Goyal, N., Chaudhary, V., Gu, J., & Fan, A. (2020). Multilingual Translation with Extensible Multilingual Pretraining and Finetuning. preprint arXiv. http://arxiv.org/abs/2008.00401
Thiel, P. A., & Masters, B. (2014). Zero to one: Notes on startups, or how to build the future (First edition). Crown Business.
Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379 (6630), 313-313. https://doi.org/10.1126/science.adg7879
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., … Scialom, T. (2023). Llama 2: Open Foundation and Fine-Tuned Chat Models. prerprint. arXiv. http://arxiv.org/abs/2307.09288
Tsai, M.-L., Ong, C. W., & Chen, C.-L. (2023). Exploring the use of large language models (LLMs) in chemical engineering education: Building core course problem models with Chat-GPT. Education for Chemical Engineers, 44, 71-95. https://doi.org/10.1016/j.ece.2023.05.001
Turing, A. M. (1950). Computing Machinery and Intelligence. Mind: A Quarterly Review of Psychology and Philosophy, 59 (239), 433-460.
Van Dis, E. A. M., Bollen, J., Zuidema, W., Van Rooij, R., & Bockting, C. L. (2023). ChatGPT: Five priorities for research. Nature, 614 (7947), 224-226. https://doi.org/10.1038/d41586-023-00288-7
Van Noorden, R., & Perkel, J. M. (2023). AI and science: What 1,600 researchers think. Nature, 621 (7980), 672-675. https://doi.org/10.1038/d41586-023-02980-0
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. preprint arXiv. https://doi.org/10.48550/arXiv.1706.03762
Waltzer, T., Pilegard, C., & Heyman, G. D. (2024). Can you spot the bot? Identifying AI-generated writing in college essays. International Journal for Educational Integrity, 20 (1), 11. https://doi.org/10.1007/s40979-024-00158-3
Wang, P., & Li, X. (2020). Assessing the quality of information on wikipedia: A deep‐learning approach. Journal of the Association for Information Science and Technology, 71(1), 16-28. https://doi.org/10.1002/asi.24210
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science: Import, tidy, transform, visualize, and model data (2nd edition). O’Reilly.
Youyou, W., Kosinski, M., & Stillwell, D. (2015). Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 112 (4), 1036-1040. https://doi.org/10.1073/pnas.1418680112
Zhai, X. (2022). ChatGPT User Experience: Implications for Education. Social Science Research Network. https://doi.org/10.2139/ssrn.4312418
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