This study reframes generative AI not as an automation tool for analysis but as a collaborative partner for cognitive stimulation, and proposes PromptWeave, a prompt-design methodology intended to expand, deepen, and transform an analyst’s reasoning.
Abstract
Research at the intersection of human factors analysis and large language models (LLMs) has grown rapidly in recent years; however, much of this work emphasizes automation and efficiency, evaluating success primarily through model-centric metrics. In contrast, this study reframes generative AI not as an automation tool for analysis but as a collaborative partner for cognitive stimulation, and proposes PromptWeave, a prompt-design methodology intended to expand, deepen, and transform an analyst’s reasoning. We applied PromptWeave to industrial accident cases and conducted a quantitative evaluation using human-centered KPIs. The results indicate consistently high performance across all KPIs, supporting the utility of PromptWeave as a reproducible collaboration protocol executable on an LLM platform.
A student survey study is presented that examines perceptions of LLM output understanding, validation effort, trust and the perceived usefulness of vibe modeling across several AI-assisted development scenarios to inform future studies for trustworthy and explainable AI-based software engineering via vibe modeling.
Shalini Chakraborty, M. Mittermaier, Judith Michael· arXiv.org· 0 citations
Research on AI-assisted programming has concentrated on the gulf of execution -- how users write successful prompts. We report a candidate phenomenon, an integration bottleneck, that lies in Norman's gulf of evaluation: a repair-relevant contribution reaches the user and fails to become actionable at the point of recei...
It is concluded that GenAI, when implemented with clear guardrails and institutional integrity, has the potential to elevate advancement from an operational function to a strategic, mission-aligned partner in shaping the future of higher education.
Liz Hawkins· Journal of Education Advance...· 0 citations
Large language models and foundation models are increasingly embedded in reasoning systems that plan, invoke tools, use memory, gather evidence, and iteratively refine their outputs. The second KDD Day on AI Reasoning brings together researchers and practitioners from academia and industry to examine how these systems...
Jun Huan, James Caverlee, Lei Li et al.· Proceedings of the 32nd ACM...· 0 citations
A neuro-symbolic framework combining LLM-driven elicitation constrained by a rule-based reasoner fed by an ontology-compliant knowledge graph is proposed and results indicate that the framework reliably prevents hallucinations from propagating into formal specifications.
Diego Ferreira, Rakshit Mittal, Lucas Lima et al.· International Conference on...· 0 citations
This exploratory study aims to investigate how humans and CLLMs can collaborate as peers through vibe coding, an approach that integrates principles from prompt engineering, agile design, and human-AI co-creation to enhance collaboration.
Moritz Mock, Barbara Russo· arXiv.org· 1 citation
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