mlsanityData to 7 Oct 2026

To LLM, or Not to LLM: How Designers and Developers Navigate LLMs as Tools or Teammates

arXiv 2604.15344 ↗First posted 15 Mar 2026cs.HC cs.AI cs.IRcited by 3

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Abstract (arXiv)

Large language models (LLMs) are increasingly integrated into design and development workflows, yet decisions about their use are rarely binary or purely technical. We report findings from a constructivist grounded theory study based on interviews with 33 designers and developers across three large technology organisations. Rather than evaluating LLMs solely by capability, participants reasoned about the role an LLM could occupy within a workflow and how that role would interact with existing structures of responsibility and organisational accountability. When LLMs were framed as tools under clear human control, their use was typically acceptable and could be integrated within existing governance structures. When framed as teammates with shared or ambiguous agency, practitioners expressed hesitation, particularly when responsibility for outcomes could not be clearly justified. At the same time, participants also described productive teammate configurations in which LLMs supported collaborative reasoning while remaining embedded within explicit oversight structures. We identify tool and teammate framings as recurring ways in which designers and developers position LLMs relative to human work and present an analytic rubric describing how role framing shapes decision authority, accountability ownership, oversight strategies, and organisational acceptability. By foregrounding design-time reasoning, this work reframes To LLM or Not to LLM as a sociotechnical positioning problem that emerges during system design rather than during post-deployment evaluation.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 24 references that are in the map. First posted on arXiv 15 Mar 2026.

Builds on

4 of 24 references.

References · 4

  1. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  2. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 251
  3. 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
  4. 2019Problem Formulation and Fairnesscited by 85

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025LLMCode: Evaluating and Enhancing Researcher-AI Alignment in Qualitative Analysis ↗
  2. 2025Are We Aligned? A Preliminary Investigation of the Alignment of Responsible AI Values between LLMs and Human Judgment
  3. 2026Cognitive Biases in LLM-Assisted Software Development
  4. 2025DesignerlyLoop: Forming Design Intent through Curated Reasoning for Human-LLM Alignment
  5. 2026Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use
  6. 2025Decentralising LLM Alignment: A Case for Context, Pluralism, and Participation
  7. 2024Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models ↗
  8. 2026Distorted Perspectives of LLM-Simulated Preferences: Can AI Mislead Design? ↗