Training for Technology: Adoption and Productive Use of Generative AI in Legal Analysis
#generative AI #legal analysis #training #adoption #productivity #technology #law
📌 Key Takeaways
- Generative AI is being integrated into legal analysis workflows to enhance efficiency and accuracy.
- Specialized training is required for legal professionals to effectively adopt and utilize generative AI tools.
- The adoption of AI in legal analysis aims to reduce manual research time and improve case preparation.
- Productive use of generative AI in law involves understanding its capabilities and limitations to avoid errors.
📖 Full Retelling
arXiv:2603.04982v1 Announce Type: cross
Abstract: Can targeted user training unlock the productive potential of generative artificial intelligence (GenAI) in professional settings? We investigate this question using a randomized study involving 164 law students completing an issue-spotting examination. Participants were assigned to one of three conditions: no GenAI access, optional access to a large language model (LLM), or optional access accompanied by an approximately ten-minute training int
🏷️ Themes
AI Adoption, Legal Technology
Entity Intersection Graph
No entity connections available yet for this article.
Original Source
--> Computer Science > Computers and Society arXiv:2603.04982 [Submitted on 5 Mar 2026] Title: Training for Technology: Adoption and Productive Use of Generative AI in Legal Analysis Authors: Benjamin M. Chen , Hong Bao View a PDF of the paper titled Training for Technology: Adoption and Productive Use of Generative AI in Legal Analysis, by Benjamin M. Chen and Hong Bao View PDF Abstract: Can targeted user training unlock the productive potential of generative artificial intelligence in professional settings? We investigate this question using a randomized study involving 164 law students completing an issue-spotting examination. Participants were assigned to one of three conditions: no GenAI access, optional access to a large language model , or optional access accompanied by an approximately ten-minute training intervention. Training significantly increased LLM adoption--the usage rate rose from 26% to 41%--and improved examination performance. Students with trained access scored 0.27 grade points higher than those with untrained access 0.027), equivalent to roughly one-third of a letter grade. By contrast, access to an LLM without training did not improve performance and was associated with shorter answers relative to no access. Using principal stratification, we decompose the overall effect into adoption and effectiveness channels. Point estimates are consistent with training operating primarily by expanding the scope of GenAI use rather than by enhancing effectiveness among existing users, though confidence intervals are wide. Overall, our findings provide evidence that complementary investments in user training are critical for realizing GenAI productivity gains in knowledge-intensive fields where concerns about reliability may inhibit adoption. Subjects: Computers and Society (cs.CY) ; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2603.04982 [cs.CY] (or arXiv:2603.04982v1 [cs.CY] for this version) https://doi.org/10.48550/...
Read full article at source