Developers using AI showed the same core problem-solving behaviors as those without but differed in how they became stuck and recovered, with AI helping or hindering in specific cases.
and Lifshitz-Assaf, Hila and Kellogg, Katherine C
7 Pith papers cite this work. Polarity classification is still indexing.
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SynBench benchmarks DP text generators across nine datasets and uses a new MIA to show that public pre-training on portions of private data overestimates synthetic text quality and breaks DP privacy bounds.
HAAS combines governance rules with contextual bandits to adaptively allocate tasks across a five-mode autonomy spectrum, showing that moderate governance improves manufacturing outcomes and that no single setting dominates.
Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
CARE is a structured, artifact-driven methodology using SMEs, developers, and LLM helpers to engineer LLM agents, demonstrated in a scientific use case to improve development efficiency and complex query performance.
Generative AI suitability in qualitative research depends primarily on the approach (small-q positivist/post-positivist or Big Q non-positivist) along with skills, ethics, and personal preferences.