Dynamic relationships between Human Intelligence and Artificial Intelligence in academic research
DOI:
https://doi.org/10.5195/biblios.2024.1227Keywords:
Academic research, Artificial intelligence, Levels of knowledge, Human intelligence, Technological developments, Technological limitsAbstract
Objective. Determine which research activities must continue to be assumed by human researchers because they represent a limitation to the current developments of Artificial Intelligence for the context of current higher education.
Method. To validate this hypothesis, a hermeneutic-critical research approach is adopted, which seeks to interpret advances in previous research from contextualized questions. To develop this research approach, qualitative methods are used, which, although they do not rule out the use of numbers, generate the validation of the hypothesis from non-numerical analysis. For this reason, the documentary review of new knowledge products classified as scientific by the medium in which they have been published is adopted as an information collection strategy.
Results. The knowledge of Human Intelligence was organized into four levels for the management of research data: first, declarative; second, procedural; third, schematic; and strategic fourth. Within the framework of this classification, the present and expected developments of Artificial Intelligence were interpreted, which have six types: reactive, short-term memory, autonomous, theory of mind, general and superintelligence. Currently, only the first three types of artificial intelligence have been developed, which correspond to the first two levels of human knowledge. Therefore, it is possible to determine that Artificial Intelligence has the possibility of assuming the research activities of the first two levels and human intelligence refers to the last two levels.
Conclusions. Academic research must accept the dynamic coexistence between Human Intelligence and Artificial Intelligence, considering that the former has the possibility of using the latter as support in the generation of new knowledge. While Artificial Intelligence can make review products, Human Intelligence has the possibility of making reflection and proposal products to ensure advances in knowledge. Consequently, the academic community must prioritize the publication of exclusive products of human activity, both in its editorial processes and in strategies to measure the quality of higher education institutions.
References
Balcero, A., Gabalán, J. & Vasquez, F. (2022). Sistema de aseguramiento de la calidad de la educación superior: Falencias y propuesta de mejora. Praxis & Saber, 13(34), 39-57. https://doi.org/10.19053/22160159.v13.n34.2022.14084
Bolaño, M., & Duarte, N. (2024). Una revisión sistemática del uso de la inteligencia artificial en la educación. Revista Colombiana de Cirugía, 39(1), 51-63. https://doi.org/10.30944/20117582.2365
Castro, M. (2019). Ambientes de aprendizaje. Sophia, 15(2), 40-54. https://doi.org/10.18634/sophiaj.15v.2i.827
Davis, E. (2015). Ethical guidelines for a superintelligence. Artificial Intelligence, 220,121-124. https://doi.org/10.1016/j.artint.2014.12.003.
Díaz, J. (2021). Aprendizaje automático y aprendizaje profundo. Ingeniare. Revista Chilena de Ingeniería, 29(2), 180-181. https://dx.doi.org/10.4067/S0718-33052021000200180
Difabio, H., & Álvarez, G. (2022). Las conclusiones de la tesis doctoral en educación: Sus movimientos y pasos retóricos. Areté, Revista Digital del Doctorado en Educación, 8(16), 11-36. https://doi.org/10.55560/arete.2022.16.8.1
Duan, Y., Edwards, J., & Dwivedi, Y. (2019). Artificial intelligence for decision making in the era of big data: Evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Espinoza, E. (2018). El problema de investigación. Conrado, 14(64), 22-32. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1990-86442018000400022&lng=es&tlng=es
García, J., & Sánchez, P. (2020). Diseño teórico de la investigación: Instrucciones metodológicas para el desarrollo de propuestas y proyectos de investigación científica. Información Tecnológica, 31(6), 159-170. https://dx.doi.org/10.4067/S0718-07642020000600159
Goertzel, B. (2014). Artificial general intelligence: Concept, state of the art, and future prospects. Journal of Artificial General Intelligence, 8(1), 1-22. https://doi.org/10.2478/jagi-2014-0001
Gómez, J., & Calvache, J. (2018). El artículo de reflexión. Colombian Journal of Anestesiology, 46(1), 1-2. https://doi.org/10.1097/cj9.0000000000000037
Jarrahi, M., Askay, D., Eshraghi, A., & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99. https://doi.org/10.1016/j.bushor.2022.03.002
Kuusi, O., & Heinonen, S. (2022). Scenarios from artificial narrow intelligence to artificial general intelligence: Reviewing the results of the international work/technology 2050 study. World Futures Review, 14(1), 65-79. https://doi.org/10.1177/19467567221101
Leal, F. (2017). ¿Qué función cumple la argumentación en la metodología de la investigación en ciencias sociales? Espiral (Guadalajara), 24(70), 9-49. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-05652017000300009&lng=es&tlng=es
Llanos, A. (2022). Metodología de la investigación interdisciplinaria: Fundamentos y proyecciones. Quipukamayoc, 30(64), 63-76. https://dx.doi.org/10.15381/quipu.v30i64.24314
Magni, D., Del Gaudio, G., Papa, A., & Della Corte, V. (2024). Digital humanism and artificial intelligence: The role of emotions beyond the human–machine interaction in society 5.0. Journal of Management History, 30(2), 195-218. https://doi.org/10.1108/JMH-12-2022-0084
Mainzer, K. (2020). From natural and artificial intelligence to superintelligence? Technik im Fokus, 1, 183-241. https://doi.org/10.1007/978-3-662-59717-0_10
Martin, J. (2018). Calidad educativa en la educación superior colombiana: Una aproximación teórica. Sophia, 14(2), 4-14. https://doi.org/10.18634/sophiaj.14v.2i.799
Matthews, G., Hancock, P., Lin, J., Panganiban, A., Reinerman, L., Szalma, J., & Wohleber, R. (2021). Evolution and revolution: Personality research for the coming world of robots, artificial intelligence, and autonomous systems. Personality and Individual Differences, 169, 1-13. https://doi.org/10.1016/j.paid.2020.109969
Minati, G. (2020). Sistemas cognitivos complejos y su inconsciente: Conjeturas inspiradas relacionadas con la inteligencia artificial. Future Internet, 12(12), 1-24. https://doi.org/10.3390/fi12120213
Muthukrishnan, N., Maleki, F., Ovens, K., Reinhold, C., Forghani, B., & Forghani, R. (2020). Brief history of artificial intelligence. Neuroimaging Clinics of North America, 30(4), 393-399. https://doi.org/10.1016/j.nic.2020.07.004
Ocaña, Y., Valenzuela, L., & Garro, L. (2019). Inteligencia artificial y sus implicaciones en la educación superior. Propósitos y Representaciones, 7(2), 536-568. https://dx.doi.org/10.20511/pyr2019.v7n2.274
Perilla, J. (2023). Los niveles del conocimiento para el diseño curricular de las facultades de derecho. Revista Pedagogía Universitaria y Didáctica del Derecho, 10(1), 71–90. https://doi.org/10.5354/0719-5885.2023.69799
Perilla, J. (2024a). Posibilidades para vincular la inteligencia artificial en la etapa previa de los contratos estatales en Colombia. Revista Eurolatinoamericana de Derecho Administrativo, 11(2), 1-30. https://doi.org/10.14409/redoeda.v11i2.13879
Perilla, J. (2024b). Posibilidades de gestión de conflictos mediante la inteligencia artificial en los sistemas de administración de justicia latinoamericanos. Revista Oficial del Poder Judicial, 16(22), 449-473. https://doi.org/10.35292/ropj.v16i22.1025
Perilla, J. (2024c). La inteligencia artificial en las facultades de derecho a través de generadores de respuestas automáticas. Revista Pedagogía Universitaria y Didáctica del Derecho, 11(2), 55-70. https://doi.org/10.5354/0719-5885.2024.72696
Perilla, J. (2024d). Lineamientos para implementar la inteligencia artificial. European Journal of Privacy Law & Technologies, 2024(1), 42-58. https://doi.org/10.57230/EJPLT241JSAPG
Perilla, J. (2024e). O processo por meio da inteligência artificial. Revista Brasileira de Direito Processual Penal, 10(2). https://doi.org/10.22197/rbdpp.v10i2.988
Piña, L. (2024). Nuevos enfoques investigativos ante la inteligencia artificial. Revista Arbitrada Interdisciplinaria – Koinonía, 9(17), 2-3. https://doi.org/10.35381/rkv9i17.3231
Ramírez, A. (2009). La teoría del conocimiento en investigación científica: Una visión actual. Anales de la Facultad de Medicina, 70(3), 217-224. http://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S1025-55832009000300011&lng=es&tlng=es
Rodríguez, R. (2018). Los modelos de aprendizaje de Kolb, Honey y Mumford: Implicaciones para la educación en ciencias. Sophia, 14(1), 51-64. https://doi.org/10.18634/sophiaj.14v.1i.698
Saranya, A., & Subhashini, R. (2023). A systematic review of explainable artificial intelligence models and applications: Recent developments and future trends. Decision Analytics Journal, 7, 1-14. https://doi.org/10.1016/j.dajour.2023.100230
Signorelli, C. (2018). Can computers become conscious and overcome humans? Frontiers in Robotics and AI, 5, 121, 1-20. https://doi.org/10.3389/frobt.2018.00121
Totschnig, W. (2020). Fully autonomous AI. Science and Engineering Ethics, 26(5), 2473-2485. https://doi.org/10.1007/s11948-020-00243-z
Troncoso, M., Dueñas, Y., & Verdecia, E. (2023). Inteligencia artificial y educación: Nuevas relaciones en un mundo interconectado. Revista Estudios del Desarrollo Social: Cuba y América Latina, 11(2), 1-20. http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S2308-01322023000200014&lng=es&tlng=es
Villegas, M., González, F., & Núñez, R. (2008). Línea de investigación conocimiento e investigación (LICOIN). Paradígma, 29(2), 223-236. http://ve.scielo.org/scielo.php?script=sci_arttext&pid=S1011-22512008000200012&lng=es&tlng=es
Zhang, B., Zhu, J., & Su, H. (2023). Toward the third generation artificial intelligence. Science China Information Sciences, 66(2), 1-23. https://doi.org/10.1007/s11432-021-3449-x
Zhang, J., Hilpert, B., Broekens, J., & Jokinen, J. (2024, May 11-16). Simulating emotions with an integrated computational model of appraisal and reinforcement learning [Conference session]. CHI '24: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, United States. (703, pp. 1–12). https://doi.org/10.1145/3613904.3641908
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Juan Sebastián Alejandro Perilla-Granados
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.
- Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
- The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons Attribution 4.0 International License or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
- Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
- The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post online a prepublication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work. Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
- Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
- The Author represents and warrants that:
- the Work is the Author’s original work;
- the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
- the Work is not pending review or under consideration by another publisher;
- the Work has not previously been published;
- the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
- the Work contains no libel, invasion of privacy, or other unlawful matter.
- The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.
Revised 7/16/2018. Revision Description: Removed outdated link.
