Not Too Early, Not All at Once: Design Tensions in AI-Mediated Self-Disclosure in Online Dating

Tsai, P., Zhang, T., Poh, E., Tang, A., and Chang, Y. (2026). Not Too Early, Not All at Once: Design Tensions in AI-Mediated Self-Disclosure in Online Dating. In Proceedings of the 2026 ACM Designing Interactive Systems Conference (DIS '26).

Abstract

Online dating relies on self-disclosure, yet initial conversations are fragile: users must navigate uncertainty around timing, boundaries, and reciprocity with little shared context. While advances in AI raise the possibility of mediating disclosure, how such support might reshape the experience of early-stage relational disclosure remains underexplored. We conducted 29 semi-structured interviews to examine how daters envision AI-mediated self-disclosure in online dating. Our findings surface recurring design tensions rather than simple opportunities or risks. Participants welcomed guidance that could pace disclosure, support reflection, and reduce social awkwardness, but stressed preserving agency and authorship. They valued interpretive assistance for sense-making of ambiguous partner cues, yet worried that algorithmic interpretation might foreclose gradual discovery. Participants also described relational buffering as face-saving, while cautioning that increased efficiency risks undermining reciprocity, surprise, and co-constructed intimacy. Together, these findings suggest that designing AI for intimate contexts requires attending to how support redistributes agency, interpretation, and participation over time, rather than treating disclosure as an optimization problem.

Materials

BibTeX

@inproceedings{tsai2026dating,
  abstract = {Online dating relies on self-disclosure, yet initial conversations are fragile: users must navigate uncertainty around timing, boundaries, and reciprocity with little shared context. While advances in AI raise the possibility of mediating disclosure, how such support might reshape the experience of early-stage relational disclosure remains underexplored. We conducted 29 semi-structured interviews to examine how daters envision AI-mediated self-disclosure in online dating. Our findings surface recurring design tensions rather than simple opportunities or risks. Participants welcomed guidance that could pace disclosure, support reflection, and reduce social awkwardness, but stressed preserving agency and authorship. They valued interpretive assistance for sense-making of ambiguous partner cues, yet worried that algorithmic interpretation might foreclose gradual discovery. Participants also described relational buffering as face-saving, while cautioning that increased efficiency risks undermining reciprocity, surprise, and co-constructed intimacy. Together, these findings suggest that designing AI for intimate contexts requires attending to how support redistributes agency, interpretation, and participation over time, rather than treating disclosure as an optimization problem.},
  note = {In press},
  type = {conference},
  publisher = {ACM},
  year = {2026},
  booktitle = {Proceedings of the 2026 ACM Designing Interactive Systems Conference (DIS '26)},
  title = {Not Too Early, Not All at Once: Design Tensions in AI-Mediated Self-Disclosure in Online Dating},
  author = {Tsai, Pei-Hua and Zhang, Tianyi and Poh, Emran and Tang, Anthony and Chang, Yung-Ju},
}