LegacySphere: Facilitating Intergenerational Communication Through Perspective-Taking and Storytelling in Embodied VR
Bibliographic record
Abstract
Intergenerational communication can enhance well-being and family cohesion, but stereotypes and low empathy can be barriers to achieving effective communication. VR perspective-taking is a potential approach that is known to enhance understanding and empathy toward others by allowing a user to take another’s viewpoint. In this study, we introduce LegacySphere, a novel VR perspective-taking experience leveraging the combination of embodiment, role-play, and storytelling. To explore LegacySphere’s design and impact, we conducted an observational study involving five dyads with a one-generation gap. We found that LegacySphere promotes empathetic and reflexive intergenerational dialogue. Specifically, avatar embodiment encourages what we term “relationship cushioning,” fostering a trustful, open environment for genuine communications. The blending of real and embodied identities prompts insightful questions, merging both perspectives. The experience also nurtures a sense of unity and stimulates reflections on aging. Our work highlights the potential of immersive technologies for enhancing empathetic intergenerational relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".