FamilyDittos: Reimagining Intergenerational Interaction through Mimetic Agents
Bibliographic record
Abstract
Technology-mediated communication tools are widely used to maintain connections between older adults and their remote family members. However, challenges often arise due to differences in their life rhythms and communication preferences, such as time zones, daily routines, or preferred platforms. To address these challenges, we propose using Ditto, a mimetic embodied agent, in video-call-like interactions between an older adult and a remote family member. When direct interaction is difficult, FamilyDitto can represent either party, providing a strong social presence and a personalized experience to the other person. To explore the potential of Ditto in supporting intergenerational communication, we conducted seven co-design workshops (n=27) with older adults and younger family members. Our thematic analysis reveals Ditto's potential roles as both a temporal bridge and an emotional proxy, identifies the tension between faithful and idealized representations, and emphasizes the importance of personalization to support unique family dynamics. We distill our findings into implications for designing mediated semi-synchronous communication, using idealized representation responsibly, and addressing the asymmetric motivations and comfort levels with Ditto across roles and ages. This study provides a foundation for mimetic AI technologies that enhance, rather than replace, human connection in remote intergenerational relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".