AI IN LEGAL STUDIES: NAVIGATING THE PROSPECTS AND HURDLES FOR
LAW FACULTY IN HIGHER EDUCATION
Julio A. Alvarado-Vélez
Universidad Nacional de Chimborazo (UNACH)
Riobamba, Ecuador
Autor
para correspondencia: julio2alvarado@gmail.com
Recibido: 26/11/2024
Aceptado: 18/04/2025 Publicado: 07/07/2025
ABSTRACT
This paper examines the
influence of Artificial Intelligence (AI) on legal education, focusing on its
advantages as well as the ethical and pedagogical challenges it introduces in
the university training of future legal professionals. The aim was to evaluate
how AI can reshape legal education without compromising the ethical and
pedagogical integrity of the learning process. Using a qualitative methodology
with a documentary design, a content analysis of various educational AI tools
was performed, assessing elements like personalized learning, accessibility,
automated feedback, and usability. Findings suggest that AI enables personalized
learning and optimizes real-time feedback and assessment; however, it also
presents risks such as algorithmic bias and restricted accessibility.
Furthermore, AI use may alter classroom dynamics and reduce direct engagement
with professors, potentially affecting students’ ethical growth. In summary,
while AI offers considerable potential for legal education, its implementation
requires active oversight and a strong ethical framework to ensure inclusive
and equitable education, maintaining quality and pedagogical standards in legal
learning.
Keywords: artificial intelligence, legal education, legal
profession.
IA EN LOS ESTUDIOS DE DERECHO: NAVEGANDO
POR LAS OPORTUNIDADES Y OBSTÁCULOS PARA EL PROFESORADO EN LA EDUCACIÓN SUPERIOR
RESUMEN
Este
artículo examina la influencia de la Inteligencia Artificial (IA) en la
educación jurídica, enfocándose en sus ventajas y en los desafíos éticos y
pedagógicos que introduce en la formación universitaria de futuros
profesionales del derecho. El objetivo fue evaluar cómo la IA puede transformar
la educación jurídica sin comprometer la integridad ética y pedagógica del
proceso de aprendizaje. Utilizando la metodología cualitativa con un diseño
documental, se realizó un análisis de contenido de diversas herramientas
educativas de IA, evaluando elementos como el aprendizaje personalizado, la
accesibilidad, la retroalimentación automatizada y la usabilidad. Los hallazgos
sugieren que la IA permite un aprendizaje personalizado y optimiza la
retroalimentación y la evaluación en tiempo real; sin embargo, también presenta
riesgos como el sesgo algorítmico y la accesibilidad limitada. Además, el uso
de la IA puede alterar la dinámica en el aula y reducir la interacción directa
con los profesores, lo que podría afectar el desarrollo ético de los
estudiantes. Se concluye que aunque la IA ofrece un potencial considerable para
la educación jurídica, su implementación requiere supervisión activa y un
sólido marco ético para asegurar una educación inclusiva y equitativa,
manteniendo los estándares de calidad y valores pedagógicos en el aprendizaje
del derecho.
Palabras clave: inteligencia artificial,
enseñanza jurídica, profesión jurídica.
1. INTRODUCTION
Artificial Intelligence (AI) has profoundly
transformed multiple sectors of society, and education is no exception (An et al., 2024).
In the field of legal education, AI represents a highly potential tool that can
innovate teaching methodologies, facilitate learning, and promote more
equitable access to legal training. As technologies advance, educational
environments face growing pressure to adapt and leverage these tools and law
teaching in universities is not exempt from this shift (Tzirides et al., 2024).
For law professors, the integration of AI offers
opportunities to enhance teaching quality, broaden educational reach, and
better prepare students for an increasingly technological professional
environment (Stöhr et al., 2024).
However, the use of AI also raises various ethical, pedagogical, and
methodological challenges, requiring a thorough reflection on how these
technologies can and should be utilized in the law classroom (Fu & Weng, 2024).
Legal education currently faces specific challenges
that AI could help address. On one hand, the growing volume of legal
information and the complexity of modern jurisprudence demand teaching methods
that prepare students not only to handle large amounts of information but also
to develop analytical and critical reasoning skills (Doğan et al., 2024).
On the other hand, traditional legal education has been criticized for its
rigidity and reliance on theoretical, memorization-focused methods, often
neglecting practical skills and personalized learning (Grimes, 2020).
In this context, AI can offer innovative solutions, from tools that allow for
personalized student learning to programs that facilitate real-case analysis or
simulate complex legal situations.
AI use in legal education can manifest in various
forms, adapting to the specific needs of law professors and students.
Personalized learning platforms, which use algorithms to adjust content and
learning pace to individual student needs, are one of the most prominent
applications. These tools can assist professors in identifying areas of
difficulty among their students and providing targeted pedagogical solutions (Hashmi & Bal, 2024).
Furthermore, AI systems can support teaching by automating repetitive tasks,
such as grading and managing scores, enabling professors to dedicate more time
to teaching and personalized student support (Parker et al., 2024).
However, using AI in law education is not without
challenges and risks. One primary issue is the potential depersonalization of
education, where the focus on algorithms and technology may reduce the role of
human interaction, an essential part of training future lawyers (Alexander et al., 2024).
Legal education goes beyond technical knowledge transmission; it includes an
ethical, critical, and practical dimension that can only be conveyed through
direct, personal interactions between professors and students. This aspect is
crucial since lawyers need not only legal knowledge but also communication
skills, professional ethics, and a deep understanding of the law’s role in
society. Excessive reliance on AI tools could ultimately limit the development
of these skills in law students.
Furthermore, AI use in education raises significant
ethical questions (Kajiwara & Kawabata, 2024).
AI operates on algorithms and datasets that, while technically advanced, are
not free from bias (Vetter et al., 2024).
Personalized AI-driven learning, for instance, can generate inequalities if
algorithms fail to consider contextual differences adequately or if the data
used to train the system contain biases. For the legal field, where fairness
and justice are fundamental values, any form of bias in education is
particularly problematic. Therefore, implementing AI in law teaching requires
not only a technical focus but also continuous, careful evaluation of potential
biases, ensuring that these tools do not reproduce or amplify existing
educational inequalities.
The effectiveness of AI in legal education also
heavily depends on faculty training and adaptability. Law professors, largely
accustomed to traditional teaching methods, may encounter difficulties
integrating these new technologies into their daily practice (Onwuachi-Willig, 2023).
Faculty training in AI tools and institutional support for their adoption are
key factors influencing the success of this transformation (Cantatore, 2019).
At the same time, professors must recognize these tools' limitations and
understand that AI is a complement, not a substitute, for human teaching (Pahi et al., 2024).
Implementing AI in the law classroom requires a balance that allows for
technology's advantages without compromising pedagogical quality or the
teacher-student relationship.
Amid this context of opportunities and challenges,
it is essential to analyze how AI can be optimally used in law education. This
article seeks to address AI’s utility and potential for transforming
university-level legal education, as well as the difficulties and ethical
dilemmas it poses. Through a review of current tools and a critical analysis of
their implications, this study aims to provide a clear and balanced perspective
on AI’s application in the law classroom, contributing to a deeper understanding
of this technology and its impact on training future jurists.
2. METHODOLOGY
To address the
established objective, the methodology used in this study was qualitative with
a documentary design, allowing for an in-depth analysis of the impact of
artificial intelligence tools on law teaching for university professors. This
methodological approach sought to provide a detailed understanding of the
pedagogical and ethical aspects associated with AI use in legal education,
focusing on how these tools can support learning and to what extent their
implementation might challenge certain fundamental educational principles.
Data collection was
conducted through a detailed content analysis of documentary sources, which
included academic and scientific studies, case reports, and technical
descriptions of educational AI tools currently used in universities. This
process was supplemented by gathering supporting materials from major
educational technology providers and AI developers that offer adaptive and
personalized platforms in legal education. In addition, best practice guides
for AI use in education and ethical policies published by educational
institutions and international organizations were reviewed.
Perplexity AI, You.com,
and Google Bard were selected for this study. The methodological selection of
these AI tools is based on convenience sampling and a non-probabilistic
approach, suitable in contexts where sample comprehensiveness is neither
feasible nor necessary (Sexton, 2022; Zickar & Keith, 2023). By opting for this type of purposive sampling, the
study prioritized tools that exhibit specific characteristics aligned with the
research objectives, including adaptability, pedagogical support, and available
evidence of their effectiveness in higher education.
Furthermore, the choice
of these support tools in legal education is based on their capacity to offer
personalized and adaptive assistance through natural language models, which
enhance the understanding and analysis of complex topics. It is noteworthy that
these tools promote equity in access to advanced learning resources without
incurring additional costs. Their selection is further justified by their
effectiveness in processing large volumes of legal information, ability to
provide reliable references, and adaptability to the academic context, thus
enabling a personalized educational experience.
In parallel, content
analysis was applied to the specific AI tools previously selected, considering
relevant functionalities such as personalization algorithms, automated feedback
systems, and accessibility and adaptability options. This analysis involved a
review of interfaces, customization capabilities, and data security—critical
aspects to ensure that AI use in law classrooms does not compromise education
quality or equitable access.
Categories were
structured based on the research objectives for content analysis, including
personalization, automated feedback, usability, accessibility, and algorithmic
biases. The initial documentary review facilitated the creation of a conceptual
framework that guided the analysis of each educational AI tool. These
categories allowed for an in-depth examination of how each AI feature
influenced students’ educational experience and the pedagogical work of law
professors.
The analysis also focused
on identifying whether the algorithms exhibited biases in learning
personalization, which could impact educational equity. Each tool’s
adaptability to different learning styles was assessed, considering the
diversity of students in the classroom. This was crucial to understand whether
the platforms adhered to the principle of pedagogical justice and whether their
use equally contributed to the development of competencies in all students.
Finally, the data
obtained were interpreted through a qualitative analysis based on hermeneutic
techniques, contextualizing the results within a theoretical and ethical
framework. Each finding was evaluated against the reviewed literature, enabling
a critical interpretation of the impact of AI tools in legal education.
Grounded theory was employed to identify emerging patterns and key themes in
the results, building a theoretical structure that provided a reflective
analysis of the benefits and challenges of AI in law teaching.
3.
RESULTS
3.1 Brief characterization of AI tools
adaptable to the academic legal context
Perplexity AI is a free, AI-assisted search tool that
allows users to ask complex questions and receive detailed answers with
references to reliable sources (Daungsupawong
& Wiwanitkit, 2024). In the
legal field, it can be useful for students and professors researching case law,
legal articles, and specific doctrines, providing a personalized learning
experience.
You.com is an AI-powered search engine offering a free
interactive chat assistant (Tisman
& Seetharam, 2023). Law
students and scholars can use this tool to obtain answers to legal questions,
research case law, and receive writing assistance, adapting to the user’s
needs.
Google Bard, Google’s free conversational AI, enables
users to make complex inquiries and receive structured responses (Daraqel
et al., 2024). It can
support legal education by answering questions about legal concepts, offering
examples and references, and facilitating access to up-to-date legal
information.
3.2 Personalization of learning and
accessibility
One of the most notable findings from the content
analysis of artificial intelligence tools applied to law teaching was the
capacity for personalized learning they offer. The tools analyzed use
algorithms that adjust content and teaching pace according to each student’s
progress and needs. This personalization capability allows students to receive
an education more tailored to their skills and knowledge, promoting a more
inclusive and efficient learning experience.
Personalized learning in the legal context represents
a significant advancement in legal pedagogy, as it addresses the heterogeneity
in students’ preparation levels and learning styles. However, this adaptability
presents certain ethical and pedagogical risks. The algorithms’ ability to
personalize learning heavily depends on the quality and breadth of the data
with which they have been trained (Shoaib
et al., 2024). This
creates a risk of algorithmic bias, where students with characteristics
differing from those in the dataset may receive less effective learning
experiences. Additionally, although AI can enhance educational efficiency,
there is concern that individualized learning might reduce the collective and
collaborative dimension essential in lawyer training, by minimizing
opportunities for group discussion and shared learning (Lokare
& Jadhav, 2024).
3.3 Automation of feedback and
assessment
Another key finding was the automation of feedback and
assessment. The AI tools analyzed could provide instant feedback on students’
responses, using automated systems to assess knowledge, analyze answers, and
correct common errors in legal reasoning. This enabled continuous, real-time
evaluation that helped students identify their strengths and areas for
improvement promptly.
While instant feedback offers considerable advantages,
the use of AI for automatic assessment in the field of law presents significant
limitations. Legal education not only involves learning rules and procedures
but also the development of critical argumentation skills, ethics, and the
contextualization of specific cases (Fest
et al., 2022), which
are challenging to capture and assess through algorithms. Automated assessment,
while useful for technical or regulatory knowledge, may be insufficient for
evaluating the quality of arguments or the understanding of complex ethical and
social principles underlying the law (Battelli,
2020).
Additionally, reliance on automated feedback may lead students to depend
excessively on these systems, potentially reducing their capacity to develop
autonomous critical judgment, an essential aspect in the training of future
lawyers (Zhai
et al., 2024).
3.4 Usability and accessibility of tools
The analysis of AI tool interfaces revealed that,
overall, the platforms were intuitive and easy to navigate, facilitating use by
both students and professors. However, some accessibility barriers were
identified, particularly for students with visual or hearing disabilities or
those with limited access to high-quality technological devices. Although the
tools studied included certain accessibility features, such as automatic
captions and contrast adjustment options, their implementation was not always optimal
for all users.
Accessibility is a fundamental pillar of inclusive
education and should be a priority in any educational AI tool (Summers
et al., 2024). While
AI platforms offer intuitive usability that facilitates access to information (Yue
Yim, 2024), their implementation still faces challenges in
ensuring equity in access to legal education. The lack of complete
accessibility not only limits the learning opportunities for students with
disabilities but also contradicts the principles of justice and equity that law
promotes. It is essential for AI tools to include comprehensive accessibility
features to ensure that all students can equally benefit from their pedagogical
advantages. Moreover, reliance on high-performance technological devices presents
an additional barrier for students from diverse socioeconomic backgrounds,
potentially increasing inequalities in access to quality legal education (Lavalle,
2020).
3.5 Algorithmic bias and educational
equity
Another significant finding was the presence of
algorithmic biases in AI tools. These biases were identified in the way
algorithms interpreted responses and in the learning recommendations they
provided. The biases stemmed from the datasets used to train the tools, which
did not always reflect the diversity of students in terms of skills, cultural
background, or socioeconomic context. This could result in a less effective
learning experience for certain student groups.
The identification of algorithmic biases in AI tools
raises a critical concern about equity and justice in legal education. The
presence of biases in algorithms can reinforce existing inequalities and reduce
opportunities for effective learning for students from diverse backgrounds (Suresh,
2023).
In the field of law, where equity is a core value, any bias in education could
have significant repercussions on the training of professionals. To mitigate
these biases, it is essential for AI developers to use more inclusive and
representative datasets and to implement regular audits to detect and correct
potential biases in algorithms. Additionally, the use of AI in education should
be accompanied by a pedagogical approach that acknowledges the limitations of
algorithms and offsets any lack of equity with additional support (Lee
et al., 2024).
3.6 Impact on the teaching role and
legal pedagogy
Finally, the analysis revealed that AI usage has a
profound impact on the teacher's role and on legal pedagogy in general. Law
professors who use AI tools can focus on higher-value tasks, such as
individualized mentoring and developing students’ practical skills, as AI takes
on repetitive tasks or assessment duties. However, the implementation of AI
also shifts classroom dynamics, as students may become overly reliant on
technology, reducing their direct interaction with professors.
The transformation of the teaching role presents both
opportunities and challenges in legal education. AI allows educators to
concentrate their time and effort on areas where their expertise is
irreplaceable, such as developing critical thinking and ethical skills in
students (Walter,
2024).
However, technological reliance could diminish the human and ethical dimension
of education, which is crucial for the comprehensive training of lawyers (Zhao
et al., 2024). Direct
interaction with instructors enables students to understand not only the
technical framework of the law but also its social, ethical, and cultural
dimensions. Therefore, the integration of AI in legal education must strike a
balance, allowing educators to utilize technology without it replacing the
personal interaction and ethical guidance they provide.
4. CONCLUSIONS
This study demonstrated
the transformative potential of artificial intelligence in legal education,
highlighting how its use can personalize learning, improve feedback efficiency,
and redefine the role of instructors in the university setting. However, the
results also underscore significant ethical and pedagogical challenges that
must be addressed for this implementation to be truly inclusive and equitable.
Personalization, though beneficial, poses risks of algorithmic bias that could
affect the quality of education for certain student groups, limiting equity in
access to legal training. Additionally, while automated feedback and assessment
are valuable for technical learning, they fall short in developing critical and
ethical skills essential in the legal field.
Furthermore, the lack of
full accessibility in some tools and the potential shift in teacher-student
dynamics highlight the need to implement AI in a way that respects human
interaction and maintains the ethical dimension of legal education. AI should
be complemented by active oversight from instructors, who, as formative guides,
play an irreplaceable role in teaching values and practical skills. Thus, the
study concludes that to maximize AI's potential in legal education, its
application must be accompanied by a critical and regulated approach that
ensures justice and pedagogical quality for the benefit of all students.
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