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Attention to Non-Adopters

Paper · 2026 · Kaitlyn Zhou, Kristina Gligorić, Myra Cheng et al.

Summary

This position paper argues that chat-model research mostly learns from people who already use LLMs, through interaction logs, preference data and user studies. Most Americans still do not use these tools: by June 2025, two-thirds had never tried ChatGPT. Adoption is also uneven across rural, less educated and female groups. The authors warn that this adopter-centric loop could create a new digital divide and leave evaluation blind to many everyday tasks.

To support the claim, they ran two IRB-approved case studies:

  • Interviews with 23 U.S. non-adopters.
  • A Prolific survey comparing non-adopters with adopters.

Two findings stand out. First, many non-adopters are not opposed to AI. They want to use it but find learning new technology hard. Second, their priorities differ from adopters' and rarely appear in benchmarks. Examples include using healthcare portals, coordinating caregiving and getting information that fits their situation.

From these needs, the paper derives new benchmark ideas, such as sensemaking in unfamiliar, time-sensitive situations. It also recommends ways to include non-adopters in development. The broader goal is to measure how well models help people do things, not just what models can do alone.

Key points

  • Studies: Case Study 1 interviewed 23 non-adopters aged 20–67 across nine U.S. states. Case Study 2 surveyed 230 people on Prolific (136 non-adopters, 94 adopters). Participants were paid at least $15/hour.
  • Definitions: Non-adopters use chat models rarely or never; adopters use them several times a week or daily. People in between, using them a few times a month, were excluded.
  • Crowdworker skew: Only about 5% of U.S. online crowdworkers have never used chat models, compared with 66% of the general U.S. population. This suggests standard annotation pools miss non-adopters.
  • Proposed benchmark task: "Sensemaking in Unfamiliar, Time-Sensitive Contexts," such as preparing for upcoming surgery. It calls for multi-turn, tutor-like model behavior. Evaluation would cover user control, understanding and learning, not only factual accuracy.
  • Recommendations and limitations:
    • Recommendations: rebalance annotation and log data, use participatory design to build evaluations, and generate task ideas centered on non-adopters.
    • Limitations: the sample covers only English-speaking U.S. participants, and interview themes were coded using the OECD Survey of Adult Skills.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Stanford UniversityStanford · United States434 works · 314,419 citations🎓Johns Hopkins UniversityBaltimore · United States240 works · 96,487 citations🎓Cornell UniversityIthaca · United States120 works · 59,940 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Kaitlyn Zhou, Kristina Gligorić, Myra Cheng, Michelle S. Lam, Vyoma Raman, Boluwatife Aminu, Caeley Woo, Michael Brockman, Hannah Cha and Dan Jurafsky. 2026. Attention to Non-Adopters. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.67

@inproceedings{zhou2026attention,
  title = {{Attention to Non-Adopters}},
  author = {Kaitlyn Zhou, Kristina Gligorić, Myra Cheng, Michelle S. Lam, Vyoma Raman, Boluwatife Aminu, Caeley Woo, Michael Brockman, Hannah Cha and Dan Jurafsky},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.67},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.67}
}

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