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.
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6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
AI support during drafting decreases writing ownership more than during planning due to greater AI text and idea contributions, while improving essay quality.
Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
HAAS is an implemented framework using rule-based governance and contextual bandits to adapt human-AI task allocation, with empirical results showing tunable governance can improve manufacturing performance and reduce fatigue.
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.
citing papers explorer
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ChatGPT: Friend or Foe When Comprehending and Changing Unfamiliar Code
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.
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From Planning to Revision: How AI Writing Support at Different Stages Alters Ownership
AI support during drafting decreases writing ownership more than during planning due to greater AI text and idea contributions, while improving essay quality.
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Effects of Generative AI Errors on User Reliance Across Task Difficulty
Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
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HAAS: A Policy-Aware Framework for Adaptive Task Allocation Between Humans and Artificial Intelligence Systems
HAAS is an implemented framework using rule-based governance and contextual bandits to adapt human-AI task allocation, with empirical results showing tunable governance can improve manufacturing performance and reduce fatigue.
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Collaborative Agent Reasoning Engineering (CARE): A Three-Party Design Methodology for Systematically Engineering AI Agents with Subject Matter Experts, Developers, and Helper Agents
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.
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To Vibe Research or Not to Vibe Research? Generative AI in Qualitative Research
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.