Feeling Understood by Conversational AI: A Provisional Taxonomy and a Recognition–Disclosure Hypothesis
AI-generated conceptual working paper. Not peer-reviewed; no participant study or empirical validation is reported. Conversational AI can become the object of experiences that users describe in relational terms, including feeling understood, noticing a social orientation, or questioning the origin of a formulation. This paper specifies six candidate codes: The Blank Cursor, The Seen Feeling, Digital Confidant Drift, The Parasocial Slip, The Origin Doubt, and Still Here. A comparison with existing research identifies their proposed observational distinctions and temporal scales. Each code receives inclusion criteria, exclusions, and constructed examples. The analytic unit combines an interaction episode with a participant account; disclosure change additionally requires comparison across time. The source manuscript's six-phase cascade remains an unconfirmed background model. One narrower implication is developed for testing: whether experienced recognition precedes an increased tendency to direct personal reflection to AI. A prospective protocol separates interpretability, coding reliability, discriminant validity, and this temporal association. The contribution is a bounded descriptive specification and research hypothesis, rather than a validated scale or empirical account of a universal progression. AI-generated draft: This manuscript was generated and revised with AI from Andreas Ehstand's March 2026 source manuscript. The coding criteria and research protocol are proposals developed in this revision. No participant study was conducted for this paper, and no human substantive or editorial review of this version is claimed. Manuscript dated 15 September 2026. This edition, prepared on 21 September 2026, corrects an author initial in the bibliography. Examples are constructed; the taxonomy, hypothesis and validation protocol are proposals. No scientific priority, validated measurement scale or universal progression is claimed. This is a Zenodo working-paper deposit, not an arXiv or SSRN acceptance.
Authors
- Andreas Ehstand (ORCID: https://orcid.org/0009-0006-3773-7796)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-09-21
- DOI
- https://doi.org/10.5281/zenodo.22871354
- Primary Topic
- AI in Service Interactions
- Type
- article
- Field-Weighted Citation Impact
- 0.00