Designing Age-Inclusive Interfaces: Emerging Mobile, Conversational, and Generative AI to Support Interactions across the Life Span
Bibliographic record
Abstract
We are concurrently witnessing two significant shifts: voice and chat-based conversational user interfaces (CUIs) are becoming ubiquitous (especially more recently due to advances in generative AI and LLMs - large language models), and older people are becoming a very large demographic group (and increasingly adopting of mobile technology on which such interfaces are present). However, despite the recent increase in research activity, age-relevant and inter/cross-generational aspects continue to be underrepresented in both research and commercial product design. Therefore, the overarching aim of this workshop is to increase the momentum for research within the space of hands-free, mobile, and conversational interfaces that centers on age-relevant and inter- and cross-generational interaction. For this, we plan to create an interdisciplinary space that brings together researchers, designers, practitioners, and users, to discuss and share challenges, principles, and strategies for designing such interfaces across the life span. We thus welcome contributions of empirical studies, theories, design, and evaluation of hands-free, mobile, and conversational interfaces designed with aging in mind (e.g. older adults or inter/cross-generational). We particularly encourage contributions focused on leveraging recent advances in generative AI or LLMs. Through this, we aim to grow the community of CUI researchers across disciplinary boundaries (human-computer interaction, voice and language technologies, geronto-technologies, information studies, etc.) that are engaged in the shared goal of ensuring that the aging dimension is appropriately incorporated in mobile / conversational interaction design research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".