arXiv:2603.04982v3 Announce Type: replace-cross Abstract: Can targeted user training unlock the productive potential of generative artificial intelligence in professional settings? We study this question using a randomized experiment in which 164 law students completed an issue-spotting examination under one of three conditions: no GenAI access, optional access to a large language model (LLM), or LLM access with a brief training intervention. Untrained LLM access proved counterproductive: relative to participants without any LLM access, untrained users wrote significantly shorter answers, comm
The proliferation of generative AI tools necessitates understanding optimal integration strategies in professional workflows.
This research provides empirical evidence that effective AI adoption requires targeted training, directly impacting productivity and tool utility.
The assumption that simply providing LLM access enhances productivity is challenged; structured training is identified as a critical success factor.
- · AI training providers
- · Organizations investing in structured AI education
- · Legal sector (with proper AI integration)
- · Organizations implementing GenAI without training
- · Users relying on self-guided GenAI adoption
- · Ineffective GenAI platforms
Professional services firms will increasingly integrate and mandate GenAI training for their employees.
New specialized training programs and certifications for 'AI-assisted' professional roles will emerge.
Productivity metrics and compensation structures within professions will begin to differentiate between 'AI-native' and 'AI-naive' workflows.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI