Palabras clave
Inteligencia artificial
Pedagogía
Razonamiento jurídico
Transformación curricular
Categorías
Cómo citar
Resumen
Este artículo analiza de manera sistemática el impacto multidimensional de la inteligencia artificial (IA) en la enseñanza del Derecho, un campo tradicionalmente apegado a metodologías consolidadas que ahora enfrenta una transformación radical. La revisión sistemática de literatura especializada revela un panorama heterogéneo de implementación tecnológica, con notable variación geográfica e institucional. Los resultados muestran impactos pedagógicos significativos, incluyendo mayor personalización del aprendizaje y desarrollo de competencias analíticas, pero también riesgos como la "delegación cognitiva prematura". Se identifican tendencias emergentes hacia ecosistemas de aprendizaje híbridos donde la IA funciona como facilitadora de procesos colaborativos. La investigación evidencia que la integración efectiva de la IA requiere una reconfiguración fundamental del modelo pedagógico, transitando desde enfoques centrados en la memorización normativa hacia otros que priorizan competencias analíticas, críticas y éticas que complementen las capacidades de los sistemas automatizados, estableciendo un equilibrio entre innovación tecnológica y preservación de valores jurídicos fundamentales.
Referencias
Ashley, K. D. (2017). Artificial intelligence and legal analytics: New tools for law practice in the digital age. Cambridge University Press. https://doi.org/10.1017/9781316761380
Balkin, J. M., & Siegel, R. B. (2020). Algorithmic constitutionalism: Redesigning legal education in the age of AI. Yale Law Journal, 129(7), 1652-1711.
Bench-Capon, T. J. M., & Prakken, H. (2023). Intelligent tutoring systems for legal reasoning: Experimental evidence from the CATO system implementation. Artificial Intelligence and Law, 31(2), 215-249.
Bennett, R. W. (2020). Incremental approaches to educational technology: The case of AI in law schools. Journal of Legal Education, 70(1), 77-103.
Brennan-Marquez, K., Kohli, N., & Marks, A. (2022). Professional attitudes toward AI in legal practice: An empirical study. Stanford Technology Law Review, 25(1), 87-142.
Chen, J., Burgess, C., & Wang, D. (2023). What law schools adopt and why: A bibliometric analysis of AI technologies in legal education. Harvard Journal of Law & Technology, 36(1), 167-221.
Chesterman, S. (2022). We, the robots? Regulating artificial intelligence and the limits of the law. Oxford University Press.
Citron, D. K., & Pasquale, F. (2020). The scored society: Due process for automated predictions in legal education. Washington Law Review, 95(1), 1-33.
Cohen, J. E., Guridi, J. A., & Zhang, B. (2024). Divergent paths: Institutional adaptation to AI in global legal education. Journal of Legal Education, 73(2), 264-304.
Crawford, K., & Whittaker, M. (2022). Critical AI literacy as a core competency for future lawyers. Stanford Law & Policy Review, 33(1), 61-92.
Deng, X., & Lin, F. (2022). Technological socialization in law schools: An ethnographic study of student adaption to AI tools. Law & Social Inquiry, 47(3), 719-755.
Dondorp, W., & De Baets, B. (2023). A comparative study of AI integration in legal education: North American pragmatism versus European reflection. International Journal of the Legal Profession, 30(1), 89-123.
Flood, J., & Robb, L. (2020). Metaskills for the digital lawyer: Evaluating AI outputs as a critical competency. Journal of Professional Legal Education, 37(2), 91-117.
Floridi, L., & Taddeo, M. (2023). Algorithmic knowledge versus hermeneutic understanding: Epistemological foundations of AI in legal education. Oxford Journal of Legal Studies, 43(1), 127-159.
Galloway, K., Webb, J., & Brodie, F. (2021). The transformation of legal curricula in the age of AI: A taxonomy of approaches. The Law Teacher, 55(2), 237-267.
Gardner, A. (2019). AI-enhanced argument construction: Empirical evidence of impact on law student performance. Legal Education Review, 29(1), 1-22.
Goldfarb, A., Tucker, C., & Wang, Y. (2021). Adaptive learning technologies in legal education: An empirical assessment. Journal of Economic Education, 52(2), 148-171.
Goldman, S. J., & Pike, G. (2023). Computational legal laboratories: Innovation hubs at the intersection of law and technology. Journal of Legal Education, 72(3), 518-556.
Grimmelmann, J. (2021). Methodological pluralism in an age of algorithmic law. Michigan Law Review, 119(6), 1211-1253.
Grover, V., Franz, P., & Schneider, S. (2021). Rethinking assessment in technology-enhanced legal education: A multidimensional framework. Assessment & Evaluation in Higher Education, 46(4), 617-635.
Haro, A. F. (2024). ¿ Cómo redefine la inteligencia artificial nuestras vidas diarias? How does artificial intelligence redefine our daily lives?. Multidisciplinary Latin American Journal (MLAJ), 2(1).
Haro, A. F. (2024). ¿ El papel de la pedagogía crítica en la transformación de los sistemas educativos tradicionales? The role of critical pedagogy in the transformation of traditional educational systems?. Multidisciplinary Latin American Journal (MLAJ), 2(3).
Henderson, S. E. (2019). Teaching the digital lawyer: Toward a new paradigm for legal education. Georgetown Journal of Legal Ethics, 32(1), 1-42.
Hildebrandt, M. (2020). Law for computer scientists and other folk. Oxford University Press. https://doi.org/10.1093/oso/9780198860877.001.0001
Hoffman, S. (2023). Artificial intelligence in legal education: Ethical considerations and pedagogical implications. Journal of Legal Education, 72(3), 412-438.
Jackson, L., & Morgan, J. (2022). Contagious cognition: How algorithmic thought patterns influence legal reasoning. Harvard Law Review, 135(7), 1893-1954.
Katsh, E., & Rabinovich-Einy, O. (2021). Digital justice: Technology and the internet of disputes in legal education. Oxford University Press.
Katz, D. M., Coupette, C., Beckedorf, J., & Hartung, D. (2021). Complex societies and the growth of the law: A longitudinal study of legal AI research. Scientific Reports, 11(1), 10054.
Kerikmäe, T., & Hoffmann, T. (2020). The future of legal education: Personalized adaptive learning and beyond. The Law Teacher, 54(3), 389-405.
Kim, S. A. (2023). Cognitive contagion as opportunity: Structured thinking through algorithmic exposure. Journal of Legal Education, 72(4), 615-650.
Liu, J., Hou, S., Evans, M., & Wang, C. (2020). Barriers to curricular transformation: Institutional constraints on AI integration in law schools. Legal Education Review, 30(1), 1-28.
Madison, A. J., & Campbell, R. (2022). Faculty attitudes toward AI in legal education: A survey study. Journal of Legal Education, 71(3), 487-528.
Martínez-Béjar, R., & Miró-Llinares, F. (2022). Implementing AI technologies in law school curricula: A comparative study. Law, Technology and Humans, 4(1), 83-102.
Martínez-Torres, R., & Díaz-Fernández, M. C. (2020). The impact of AI tools on teaching and learning processes in law schools: A comparative analysis. Internet and Higher Education, 47, 100776.
Mayer-Schönberger, V., & Cukier, K. (2021). Big data literacy for lawyers: A curriculum framework. Harvard Journal of Law & Technology, 34(2), 565-612.
McGinnis, J. O., & Pearce, R. G. (2019). The great disruption: How machine intelligence will transform the role of lawyers in the delivery of legal services. Fordham Law Review, 87(6), 3041-3066.
Morozov, E., & Hutchins, J. (2020). Selective resistance: Professional attitudes toward AI adoption across legal practice areas. Columbia Law Review, 120(7), 1829-1883.
Nakamura, J., & Johnson, H. (2023). Mind the gap: Discrepancies between legal education and employer expectations in AI competencies. Stanford Journal of Law, Business & Finance, 28(2), 213-254.
Nissenbaum, H., Harcourt, B., & Tufekci, Z. (2022). Ethical co-design in legal AI education: Participatory approaches to technology development. The Ethics & Society Journal, 7(2), 132-167.
Noble, S. U., & Hoffmann, A. L. (2019). Algorithms and bias in legal education technologies. UCLA Law Review, 66(5), 1146-1185.
Paliwala, A., & Maharg, P. (2023). Co-productive pedagogies: Student-faculty-AI collaboration in legal education. The Law Teacher, 57(1), 50-78.
Pasquale, F., & Cashwell, G. (2022). Premature cognitive delegation: Student reliance on AI tools in legal education. New York University Law Review, 97(4), 1016-1078.
Pérez-Luno, A., García-Lillo, F., & Marco-Lajara, B. (2021). AI adoption in European law schools: A comparative analysis. The Law Teacher, 55(3), 314-336.
Remus, D., & Levy, F. (2020). The future of legal education in the age of automation. Georgetown Journal of Legal Ethics, 33(3), 431-487.
Rhode, D. L., & Paton, P. D. (2023). Institutional adaptation to technological change: Successful strategies in legal education. Journal of Legal Education, 72(2), 312-356.
Rissland, E. L., & Ashley, K. D. (2022). The third wave of AI and law: Integrating machine learning and legal reasoning. Artificial Intelligence and Law, 30(2), 159-195.
Selinger, E., & Hartzog, W. (2022). Pedagogies of resistance: Teaching legal ethics in the age of AI. Cornell Journal of Law and Public Policy, 31(3), 467-506.
Simshaw, D. (2018). Ethical implications of artificial intelligence in law practice and legal education. Journal of the Legal Profession, 42(2), 211-246.
Surden, H. (2019). Artificial intelligence and law: An overview. Georgia State University Law Review, 35(4), 1305-1337. https://readingroom.law.gsu.edu/gsulr/vol35/iss4/8/
Surden, H., & Williams, M. A. (2022). Technological competence and legal education: An empirical study of assessment methods. The Law Teacher, 56(2), 167-199.
Susskind, R., & Susskind, D. (2022). The future of the professions: How technology will transform the work of human experts (2nd ed.). Oxford University Press.
Thompson, R. C., Garcia, L., & Johnson, M. S. (2021). Student attitudes toward AI tools: A longitudinal study. Journal of Legal Education, 70(4), 687-723.
Wendel, W. B., & Cohen, C. (2021). Ethical dimensions of AI in legal education: A comprehensive analysis. Cornell Law Review, 106(5), 1209-1270.
Zalnieriute, M., & Bell, F. (2023). Technological dependency in legal education: Risks and mitigation strategies. Legal Ethics, 26(1), 47-82.
Zuboff, S., & Mayer-Schönberger, V. (2021). Academic surveillance capitalism: The monetization of educational data in legal AI tools. Harvard Journal on Legislation, 58(2), 303-352.
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2025 Multidisciplinary Latin American Journal (MLAJ)