Evolution of the concept of artificial intelligence in scientific literature: a systematic analysis
DOI:
https://doi.org/10.1344/der.2025.46.65-76Keywords:
Artificial Intelligence, Scientific production, Critical analysis, Bibliometric Analysis, Evolution of the termAbstract
The integration of Artificial Intelligence (AI) in education has sparked controversy due to the diversity of interpretations of its concept, its potential benefits, and the associated ethical concerns, highlighting the need for informed debate and careful implementation to optimize its impact on learning. This research systematically reviews, following the PRISMA protocol guidelines, the evolution of the concept of Artificial Intelligence (AI) in scientific production from 2017 to 2023 using the WOS and Scopus databases. A mixed methods approach was employed, consisting of bibliometric and content analysis. For the bibliometric analysis, data were processed in Bibliometrix based on variables: evolution and annual scientific production, Bradford's law, most relevant authors, scientific production by countries, keyword map, and global collaboration map. The results indicate that the term "Artificial Intelligence" is controversial. The bibliometric analysis reveals a steady growth in scientific production on AI from 2017 to 2023, with a peak in the last year. AI has been shown to have remarkable capabilities in specific tasks, such as voice recognition, image classification, and decision-making in complex situations.
References
Antoniou, G., & Van Harmelen, F. (2003). Web ontology language: OWL. In Handbook on Ontologies, 67–92. Springer. https://doi.org/10.1007/978-3-540-92673-3_4
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? En Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623. ACM. https://doi.org/10.1145/3442188.3445922
Binet, A., & Simon, T. (1916). The development of intelligence in children (The Binet-Simon Scale). (E. S. Kite, Trans.). Williams & Wilkins Co. https://doi.org/10.1037/11069-000
Bourla, A., Mouchabac, S., El Hage, W., & Ferreri, F. (2018). Artificial intelligence in psychiatry: Ethical concerns and future perspectives. Frontiers in Psychiatry, 9, 51. https://doi.org/10.3389/fpsyt.2018.00051
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. https://doi.org/10.48550/arXiv.2005.14165
Campina-López, A., Lorca-Marín, A. A., & De las Heras Pérez, M. A. (2024). Indagación, modelización y pensamiento computacional: Un análisis bibliométrico con el uso de Bibliometrix a través de Biblioshiny. Revista Eureka sobre Enseñanza y Divulgación de las Ciencias, 21(1), 1102. https://doi.org/10.25267/Rev_Eureka_ensen_divulg_cienc.2024.v21.i1.1102
Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383
Craik, K. (1943). The Nature of Explanation. Cambridge University Press, Cambridge.
De Vega, M. (1984). Introducción a la Psicología Cognitiva. Psicología Cognitiva. Alianza Editorial.
García-Peña, V. R., Mora-Marcillo, A. B., & Ávila-Ramírez, J. A. (2020). La inteligencia artificial en la educación. Dominio de las Ciencias, 6(3, Especial), 648-666. https://doi.org/10.23857/dc.v6i3.1421
Gardner, H. (2004). Frames of Mind: The Theory of Multiple Intelligences. Basic Books.
Gentile, M., Città, G., Perna, S., & Allegra, M. (2023). Do we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1161777
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
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
Joy C., S., & George, N. (2023). Advancements and challenges of artificial intelligence in education: A comprehensive review. International Journal of Creative Research Thoughts (IJCRT), 11(12), Article IJCRT2312896. ISSN: 2320-2882. Retrieved from https://ijcrt.org
Kandel, E. R., Schwartz, J. H., & Jessell, T. M. (1991). Principles of neural science. Prentice-Hall.
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253. https://doi.org/10.1017/S0140525X16001837
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
Marcus, G., y Davis, E. (2020). Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon.
Maturana, H. R. (1975). The organization of the living: A theory of the living organization. International Journal of Man-Machine Studies, 7(3), 313-332. https://doi.org/10.1016/S0020-7373(75)80015-0
Maturana, H. R. (2002). Ontology of Observing: The Biological Foundations of Self-Consciousness and the Physical Domain of Existence. Semantic Scholar.
McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1958). A proposal for the Dartmouth summer research project on artificial intelligence (Informe interno). Dartmouth College, Hannover, New Hampshire.
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115-133. https://doi.org/10.1007/BF02478259
Minsky, M. (1985). The Society of Mind. Simon & Schuster.
Mira, J. (2005). On the physical formal and semantic frontiers between human knowing and machine knowing. En R. Moreno-Díaz, F. Pichler, & A. Quesada Arencibia (Eds.), Computer Aided Systems Theory, EUROCAST 2005, LNCS 3643, 1–8. Springer. https://doi.org/10.1007/11556985_1
Mira, J. (2008). Inteligencia artificial: Un enfoque neuromimético. Thomson-Paraninfo.
Mira, J. M. (2008). Symbols versus connections: 50 years of artificial intelligence. Neurocomputing, 71, 671–680. https://doi.org/10.1016/j.neucom.2007.06.009
Mira, J., & Delgado, A. E. (1987). Some comments on the anthropocentric viewpoint in the neurocybernetic methodology. En Proceedings of the Seventh International Congress on Cybernetics and Systems 2, 891–895.
Mira, J., & Delgado, A. E. (1995). Aspectos metodológicos en IA. En Aspectos básicos de la inteligencia artificial, 53–87. Sanz y Torres.
Mira, J., & Delgado, A. E. (2003). Where is knowledge in robotics? Some methodological issues on symbolic and connectionist perspectives of AI. En Ch. Zhou, D. Maravall, & Da. Rua (Eds.), Autonomous Robotic Systems, Physical, 3–34. Springer.
Mira, J., & Delgado, A. E. (2004). From modeling with words to computing with numbers. En Proceedings of the IEEE 4th International Conference on Intelligent Systems Design and Application (ISDA-2004). Plenary Lecture.
Mira, J., & Delgado, A. E. (2006). On how the computational paradigm can help us to model and interpret the neural function. Natural Computing, Springer, Netherlands. https://doi.org/10.1007/s11047-006-9008-6
Newell, A. (1992). SOAR as a unified theory of cognition: Issues and explanations. Behavioral and Brain Sciences, 15(3), 464-492.
Newell, A., & Simon, H. A. (1955). The Logic Theorist—An appraisal. En Proceedings of the Western Joint Computer Conference.
Newell, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113-126. https://doi.org/10.1145/360018.360022
Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development (Working Papers on Education Policy, No. 7 [17], Documento ED-2019/WS/8). UNESCO.
Pfeifer, R., & Scheier, C. (1999). Understanding intelligence. MIT Press.
Rosenblatt, F. (1957). 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
Rosenblueth, A., Wiener, N., & Bigelow, J. (1943). Behavior, purpose and teleology. Philosophy of Science, 10(1), 18-24. https://doi.org/10.1086/286788
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. En D. E. Rumelhart, J. L. McClelland, & PDP Research Group (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations, 318–362. MIT Press.
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
Shapiro, S. C. (Ed.). (1990). Encyclopedia of Artificial Intelligence (2ª ed., Vols. 1 y 2). Wiley.
Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press.
Tozsin, A., Ucmak, H., Soyturk, S., Aydin, A., Gozen, A. S., Al Fahim, M., Güven, S., & Ahmed, K. (2024). The role of artificial intelligence in medical education: A systematic review. Surgical Innovation, 31(4), 415-423. https://doi.org/10.1177/15533506241248239
Turing, A. M. (1936). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 42(2), 230-265. https://doi.org/10.1112/plms/s2-42.1.230
UGRO. (2023). Conferencia Inteligencia Artificial en Educación: Oportunidades y Desafíos para el Aula del siglo XXI. Ponentes: Fernando Trujillo, David Álvarez.
Varela, F. J. (1979). Principles of Biological Autonomy. North-Holland.
Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. MIT Press.
Winograd, T., & Flores, F. (1986). Understanding computers and cognition: A new foundation for design. Ablex Publishing Corporation.
Yamins, D. L., & DiCarlo, J. J. (2016). Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience, 19(3), 356-365. https://doi.org/10.1038/nn.4244
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Copyright (c) 2025 Lizbeth Labañino Palmeiro, Antonio Alejandro Lorca Marin, Maria de los Angeles De las Heras Perez, Alejandro Carlos Campina López
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