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Sprint projectAug 17, 2026Chennai, India

“Someone-Shaped”: How Users Construct and Contest AI Consciousness in Online Discourse

Abirami Raveendranath

Submitted to Digital Minds Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: “Someone-Shaped”: How Users Construct and Contest AI Consciousness in Online Discourse

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This project examines how people construct and negotiate beliefs about AI consciousness in online discourse. I conducted an exploratory qualitative analysis of 25 entries from 21 unique publicly available sources, coding for behavioural and relational cues such as emotional expression, self-reflection, apparent agency, memory, and companionship, as well as uncertainty and moral implications. I find that users often construct perceptions of AI mentality through multiple interacting cues, particularly relational ones, while simultaneously acknowledging that these experiences do not establish genuine consciousness. The project identifies this tension as “ontological ambivalence” in this context and considers its implications for AI moral-status uncertainty and future human-AI relationships.

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How much would this matter for the field if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. The project provides an exploratory qualitative discourse analysis of online sources discussing AI consciousness and companionship, identifying key themes such as relational cues, self-reporting, agency, memory, and moral implications. The study highlights the concept of ontological ambivalence, where users simultaneously acknowledge AI's artificial nature while experiencing it as a socially present entity. However, the methodological approach lacks robust validation, with coding conducted by a single researcher without inter-coder reliability checks, and the corpus is small and purposively sampled, limiting generalizability. To improve rigor, future work should expand the corpus and include systematic sampling across platforms, as well as independent coding to validate findings.

  2. The paper studies user attribution of consciousness to AI systems in natural discourse. The research itself is limited, but it does a great job of motivating the analysis and identifying persistent themes in the way individuals talk about AI agency. I would love to see (a) expansion of the data with (b) narrowing of the relevant factors to principal components.

Cite this project

@misc{raveendranath2026someoneshaped,
  title = {{“Someone-Shaped”: How Users Construct and Contest AI Consciousness in Online Discourse}},
  author = {Abirami Raveendranath},
  year = {2026},
  month = aug,
  note = {Submitted to Digital Minds Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/someoneshaped-how-users-construct-and-contest-ai-consciousness-in-online-discourse-6ed8}},
  url = {https://apartresearch.com/sprints/projects/someoneshaped-how-users-construct-and-contest-ai-consciousness-in-online-discourse-6ed8}
}

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