Inteligencia Artificial en la Educación en Biología: Una Revisión Sistemática de las Tendencias de Investigación, las Aplicaciones y las Direcciones Futuras (2020–2026)
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A pesar de la creciente integración de la inteligencia artificial (IA) en la educación, la investigación sobre su uso en la educación biológica sigue siendo fragmentada, lo que dificulta obtener una comprensión integral de las tendencias y aplicaciones actuales. Este estudio examina de manera sistemática las tendencias de investigación, las características metodológicas, las aplicaciones de la IA y los principales hallazgos en el ámbito de la educación biológica. Se llevó a cabo una revisión sistemática de la literatura siguiendo las directrices PRISMA. Se identificaron un total de 27 estudios empíricos publicados entre 2020 y 2026 mediante búsquedas en bases de datos, los cuales fueron analizados utilizando métodos de análisis descriptivo y análisis de contenido. Los resultados muestran un aumento notable en el número de publicaciones, especialmente a partir de 2023, siendo los estudiantes universitarios el grupo de participantes más estudiado. ChatGPT y otras tecnologías de inteligencia artificial generativa emergieron como las herramientas de IA predominantes. En términos generales, la IA contribuye al rendimiento académico, al desarrollo de habilidades cognitivas, al aprendizaje personalizado, a la evaluación y a la motivación de los estudiantes. Sin embargo, también se identificaron importantes limitaciones, entre ellas problemas de fiabilidad, preocupaciones éticas y un apoyo limitado para el desarrollo de habilidades de pensamiento de orden superior. Esta revisión ofrece una síntesis integral de las tendencias actuales de investigación, las aplicaciones de la IA, los enfoques metodológicos y las lagunas existentes en la investigación, y propone orientaciones para futuras investigaciones en el ámbito de la educación biológica.
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Referencias
Al-Muqbil, N. (2024). The Impact of an Artificial Intelligence Application-Based Training Program on Developing Sustainable Thinking among High School Biology Teachers. Educational Sciences: Theory and Practice, 24(1), 201-215. https://n9.cl/167q2
Arf, C. (1959). Can a Machine Think and How Can It Think? PublisherStudios. https://n9.cl/mlb6w
Ariely, M., Nazaretsky, T., & Alexandron, G. (2023). Machine Learning and Hebrew NLP for Automated Assessment of Open-Ended Questions in Biology. International Journal of Artificial Intelligence in Education, 33. https://doi.org/10.1007/s40593-021-00283-x
Baidoo-anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500.
Baroud, S., El Alaoui, M., Idrissi, R., Boucetta, N., Ghariz, G., & Seghir, H. (2025). Improving Biology Learning in High School with ChatGPT: A Case Study in Morocco. Eurasia Journal of Mathematics, Science and Technology Education, 21(7). https://doi.org/10.29333/ejmste/16606
Bender, E., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). https://doi.org/10.1145/3442188.3445922
Bond, M., Bedenlier, S., Marin, V., & Handel, M. (2023). Emergency Remote Teaching in Higher Education: Mapping the First Global Online Semester. International Journal of Educational Technology in Higher Education, 18. https://doi.org/10.1186/s41239-021-00282-x
Bruner, J. (1997). The Culture of Education. Harvard University Press. https://n9.cl/7v345
Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
Creswell, J. W., & Plano-Clark, V. (2007). Designing and Conducting Mixed Methods Research. Sage. https://n9.cl/udxy1
Crowther, G. J., Funk, M. D., Hennessey, K. M., & Lawrence, M. M. (2025). Frontier Model Chatbots Can Help Instructors Create, Improve, and Use Learning Objectives. Advances in Physiology Education, 49(1), 219-229. https://doi.org/10.1152/advan.00159.2024
Crowther, G., Sankar, U., Knight, L., Myers, D., Patton, K., Jenkins, L., & Knight, T. (2023). Chatbot Responses Suggest that Hypothetical Biology Questions Are Harder than Realistic Ones. Journal of Microbiology and Biology Education, 24(3). https://doi.org/10.1128/jmbe.00153-23
Czinczel, B., Furtak, E., Fiedler, D., & Harms, U. (2025). Emerging Patterns of Understanding: Tracing Patterns in Students' Conceptions of Evolution Using Machine Learning. Evolution: Education and Outreach, 18(1). https://doi.org/10.1186/s12052-025-00226-3
Dao, X., & Le, N. (2023). LLMs performance on Vietnamese high school biology examination. International Journal of Modern Education and Computer Science, 15(6), 14-30. https://doi.org/10.5815/ijmecs.2023.06.02
De Jong, T., Linn, M., & Zacharia, Z. (2013). Physical and Virtual Laboratories in Science and Engineering Education. Science, 340(6130), 305-308. https://doi.org/10.1126/science.1230579
Denyer, D., & Tranfield, D. (2009). Producing a Systematic Review. In D. Buchanan and A. Bryman (eds.), The Sage Handbook of Organizational Research Methods (pp. 671-689). Sage. https://n9.cl/k7sc7
Dogru, M. S., & Faulconer, E. (2026). ChatGPT as a Virtual Laboratory Teaching Assistant in Undergraduate Biology. Research in Science Education, 56, 379-399. https://doi.org/10.1007/s11165-025-10271-z
Gonzalez, V., Prendes, M., & Roig, R. (2021). Artificial Intelligence for Student Assessment: A Systematic Review. Applied Sciences, 11(12). https://doi.org/10.3390/app11125467
Grossmann, L., Koberstein-Schwarz, M., Kruger, D., & Krell, M. (2025). Lesson Planning with ChatGPT for Inquiry-Based Biology Instruction: A(I) Roll of the Dice? International Journal of Science Education. https://doi.org/10.1080/09500693.2025.2567509
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R., & Kumar, P. (2021). Artificial Intelligence to Deep Learning: Machine Intelligence Approach for Drug Discovery. Molecular Diversity, 25(3), 1315-1360. https://doi.org/10.1007/s11030-021-10217-3
Habibah, L., Ibrohim, I., & Susilo, H. (2025). The Effect of AI-Assisted Problem-Oriented Project-Based Learning on Students' Critical Thinking and Communication Skills. JPBI. Jurnal Pendidikan Biologi Indonesia, 11(2), 656-668. https://doi.org/10.22219/jpbi.v11i2.40667
Hamizi, M., & Mokmin, N. (2026). Enhancing Secondary Biology Learning through Guided and Problem-Based Virtual Reality: Effects on Interest, Concentration, and Performance. Education and Information Technologies, 31, 755-785. https://doi.org/10.1007/s10639-025-13840-x
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign. https://n9.cl/fhfzz
Holmes, W., Persson, J., Chounta, I., Wasson, B., & Dimitrova, V. (2022). Artificial Intelligence and Education: A Critical View through the Lens of Human Rights, Democracy, and the Rule of Law. Council of Europe. https://n9.cl/k18tp
Huang, Y., Kleespies, M., & Shiang-Yao, L. (2025). Perspectives on Artificial Intelligence in Precision and Genomic Medicine among Biology Students in Taiwan and Germany. Biochemistry and Molecular Biology Education, 53(4), 389-399. https://doi.org/10.1002/bmb.21905
Hubbard, K. (2024). Plant Biology Education: A Competency-Based Vision for the Future. Plants, People, Planet, 6(4), 780-790. https://doi.org/10.1002/ppp3.10503
Jones, E., Newman, J., Kim, B., & Fogle, E. (2025). AI-Generated "Slop" in Online Biomedical Science Educational Videos: Mixed Methods Study of Prevalence, Characteristics, and Hazards to Learners and Teachers. JMIR Medical Education, 11. https://doi.org/10.2196/80084
Kasneci, E., Sessler, K., Kuchemann, S., Bannert, M., Dementieva, D., Fischer, F., & Kasneci, G. (2023). ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 103. https://doi.org/10.1016/j.lindif.2023.102274
Koc-Januchta, M. M., Schonborn, K. J., Roehrig, C., Chaudhri, V. K., Tibell, L. A., & Heller, H. C. (2022). "Connecting Concepts Helps Put Main Ideas Together": Cognitive Load and Usability in Learning Biology with an AI-Enriched Textbook. International Journal of Educational Technology in Higher Education, 19(1), 11. https://doi.org/10.1186/s41239-021-00317-3
Koc-Januchta, M., Schonborn, K., Tibell, L., Chaudhri, V., & Heller, H. (2020). Engaging with Biology by Asking Questions: Investigating Students' Interaction and Learning with an Artificial Intelligence-Enriched Textbook. Journal of Educational Computing Research, 58(6), 1190-1224. https://doi.org/10.1177/0735633120921581
Lee, K., Lee, M., & Lee, S. (2024). Figure-Based Approach in Creating ChatGPT-4o-Resistant Multiple-Choice Questions for Introductory Biology Courses: An Instructional Guide. International Journal of Technology in Education and Science, 8(4), 689-709. https://doi.org/10.46328/ijtes.589
Litavczova, F., & Annus, N. (2025). Biology School Education in the Digital Era. Information Technologies and Learning Tools, 110(6), 80-99. https://doi.org/10.33407/itlt.v110i6.6357
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson. https://n9.cl/nft8e
Makransky, G., & Petersen, G. (2019). Immersive Virtual Reality and Learning: A Meta-Analysis. Educational Psychology Review, 31(2), 343-372. https://n9.cl/830qc
Miles, M., & Huberman, A. (1994). Qualitative Data Analysis: An Expanded Sourcebook. Sage. https://n9.cl/ka2vl
Millen, J. (2025). Using Generative AI for Interview Simulations to Enhance Student Research Skills in Biology Education. Journal of Microbiology and Biology Education, 26(2). https://doi.org/10.1128/jmbe.00122-25
Mishra, P., & Koehler, M. (2006). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Record, 108(6), 1017-1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
National Research Council (2012). A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. National Academies Press. https://n9.cl/sivgdf
Ossowska, A., Kusiak, A., & Swietlik, D. (2022). Artificial Intelligence in Dentistry: Narrative Review. International Journal of Environmental Research and Public Health, 19(6). https://doi.org/10.3390/ijerph19063449
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial Intelligence in Online Higher Education: A Systematic Review of Empirical Research from 2011 to 2020. Education and Information Technologies, 27, 7893-7925. https://doi.org/10.1007/s10639-022-10925-9
Page, M., McKenzie, J., Bossuyt, P., Boutron, I., Hoffmann, T., Mulrow, C., Shamseer, L., Tetzlaff, J., Akl, E., Brennan, S., Chou, R., Glanville, J., Grimshaw, J., Hrobjartsson, A., Lalu, M., Li, T., Loder, E., Mayo-Wilson, E., McDonald, S., McGuinness, L., Stewart, L., Thomas, J., Tricco, A., Welch, V., Whiting, P., & Moher, D. (2021). The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. International Journal of Surgery, 88. https://doi.org/10.1016/j.ijsu.2021.105906.
Rai, M., Ngaw, M., & Nannas, N. (2025). Artificial Intelligence Performance in Introductory Biology: Passing Grades but Poor Performance at High Cognitive Complexity. Education Sciences, 15(10). https://doi.org/10.3390/educsci15101400
Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. John Wiley & Sons. https://n9.cl/rnsot
Sripathi, K., Moscarella, R., Steele, M., Yoho, R., You, H., Prevost, L., Urban-Lurain, M., Merrill, J., & Haudek, K. (2024). Machine Learning Mixed Methods Text Analysis: An Illustration from Automated Scoring Models of Student Writing in Biology Education. Journal of Mixed Methods Research, 18(1), 48-70. https://doi.org/10.1177/15586898231153946
Stanja, J., Dannemann, S., Krugel, J., & Hoppe, A. (2025). Investigating Evidence-Oriented Generation of Synthetic Text Data with a Generative Large Language Model in Science Education. International Journal of Science Education. https://doi.org/10.1080/09500693.2025.2538834
Tendrita, M., & Hidayati, U. (2025). AI-Based Mind Mapping in Project-Based Learning: Impact on Students' Collaboration Skills. JPBI. Jurnal Pendidikan Biologi Indonesia, 11(1), 370-377. https://doi.org/10.22219/jpbi.v11i1.37831
Thanh, D., Dinh, V., & Van, H. (2025). Exploring AI Integration in Biology Self-Learning: A TAM-Based Analysis among High School Students in Ho Chi Minh City. Eurasia Journal of Mathematics, Science and Technology Education, 21(10). https://doi.org/10.29333/ejmste/17158
Tobler, S., & Kohler, K. (2025). Towards Personalized Education in Life Sciences: Tailoring Instruction to Students' Prior Knowledge and Interest through Machine Learning. Trends in Higher Education, 4(4). https://doi.org/10.3390/higheredu4040068
Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books. https://n9.cl/fttktl
Wang, S., & Notteboom, T. (2014). The Adoption of Liquefied Natural Gas as a Ship Fuel: A Systematic Review of Perspectives and Challenges. Transport Reviews, 34(6), 749-774. https://doi.org/10.1080/01441647.2014.981884
Yang, H., Xie, D., Wei, P., Ge, J., & Li, Y. (2025). A Case Study of ChatGPT-Assisted Building of a Microbiome-Based Machine Learning Model for Biologists. Journal of Microbiology and Biology Education, 26(3). https://doi.org/10.1128/jmbe.00082-25
Yin, J., Goh, T., & Hu, Y. (2024). Interactions with Educational Chatbots: The Impact of Induced Emotions and Students' Learning Motivation. International Journal of Educational Technology in Higher Education, 21. https://doi.org/10.1186/s41239-024-00480-3
Zawacki-Richter, O., Marin, V., Bond, M., & Gouverneur, F. (2019). Systematic Review of Research on Artificial Intelligence Applications in Higher Education. International Journal of Educational Technology in Higher Education, 16. https://doi.org/10.1186/s41239-019-0171-0