Pith. sign in

REVIEW 2 cited by

Scientists' Perspectives on the Potential for Generative AI in their Fields

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2304.01420 v1 pith:4XJCYHNM submitted 2023-04-04 cs.CY cs.AIcs.HC

classification cs.CYcs.AIcs.HC
keywords generativeincludingmodelsscientificeducationliferangescientists
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Generative AI models, including large language models and multimodal models that include text and other media, are on the cusp of transforming many aspects of modern life, including entertainment, education, civic life, the arts, and a range of professions. There is potential for Generative AI to have a substantive impact on the methods and pace of discovery for a range of scientific disciplines. We interviewed twenty scientists from a range of fields (including the physical, life, and social sciences) to gain insight into whether or how Generative AI technologies might add value to the practice of their respective disciplines, including not only ways in which AI might accelerate scientific discovery (i.e., research), but also other aspects of their profession, including the education of future scholars and the communication of scientific findings. In addition to identifying opportunities for Generative AI to augment scientists' current practices, we also asked participants to reflect on concerns about AI. These findings can help guide the responsible development of models and interfaces for scientific education, inquiry, and communication.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Synthetic Heuristic Evaluation: A Comparison between AI- and Human-Powered Usability Evaluation

    cs.HC 2025-07 reject novelty 6.0 of 10

    An LLM prompted to conduct heuristic evaluation reported more usability issues on two apps than five human experts, but the ground truth included the LLM's own findings.

  2. Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning

    cs.HC 2025-06 conditional novelty 6.0 of 10

    Most adults in a German Prolific sample report using large language models for informal learning, with four distinct learner profiles emerging from their usage patterns.

Pith tools