Early PLT Student Perceptions of the Integration of Generative Artificial Intelligence in Legal Education

This article explores the reflections of Australian law students on the use and integration of Generative Artificial Intelligence (GenAI) in the practical legal training law curriculum. Participants were enrolled as students in the Graduate Diploma in Legal Practice at Queensland University of Technology (QUT) between April and November 2024 and engaged with several GenAI use cases embedded in their law subjects. Surveys were used to assess participants’ perceptions of the incorporation of GenAI into the subjects. The findings indicated that some participants had no prior GenAI experience, but the majority had at least a limited experience. Participants reported that all GenAI use cases improved their GenAI literacy and that they were interested in engaging with different AI tools and applications and wanted to learn how to prompt more effectively. While students’ understanding of GenAI capabilities improved, they remain cautious about using GenAI in their future legal practice, particularly for tasks such as legal research, feedback on a video recordings and written communication. Having engaged with GenAI in their studies, participants reported feeling better prepared for entry into a legal profession that is increasingly incorporating the use of GenAI. Implications from this study include an increased understanding of how best to embed GenAI in legal curriculum and assessment to ensure law students are provided with opportunities to explore the appropriate and responsible use of GenAI and to develop their AI literacy skills.

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Publication Details

Journal
Law Technology and Humans
Published
2025-11-02
DOI
https://doi.org/10.5204/lthj.4031
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

Early PLT Student Perceptions of the Integration of Generative Artificial Intelligence in Legal Education

Nicole Landy
Law Technology and Humans
Artificial Intelligence in Healthcare and Education
article

Early PLT Student Perceptions of the Integration of Generative Artificial Intelligence in Legal Education

Nicole Landy
article en

Abstract

This article explores the reflections of Australian law students on the use and integration of Generative Artificial Intelligence (GenAI) in the practical legal training law curriculum. Participants were enrolled as students in the Graduate Diploma in Legal Practice at Queensland University of Technology (QUT) between April and November 2024 and engaged with several GenAI use cases embedded in their law subjects. Surveys were used to assess participants’ perceptions of the incorporation of GenAI into the subjects. The findings indicated that some participants had no prior GenAI experience, but the majority had at least a limited experience. Participants reported that all GenAI use cases improved their GenAI literacy and that they were interested in engaging with different AI tools and applications and wanted to learn how to prompt more effectively. While students’ understanding of GenAI capabilities improved, they remain cautious about using GenAI in their future legal practice, particularly for tasks such as legal research, feedback on a video recordings and written communication. Having engaged with GenAI in their studies, participants reported feeling better prepared for entry into a legal profession that is increasingly incorporating the use of GenAI. Implications from this study include an increased understanding of how best to embed GenAI in legal curriculum and assessment to ensure law students are provided with opportunities to explore the appropriate and responsible use of GenAI and to develop their AI literacy skills.

Law Technology and Humans
Queensland University of Technology (AU)
Openalex Percentile: Top 76%
Artificial Intelligence in Healthcare and Education
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Early PLT Student Perceptions of the Integration of Generative Artificial Intelligence in Legal Education — Nicole Landy · Law Technology and Humans (2025) | TGRS Research Map | TGRS