AI & XR Explorations to Support Social Interactions: Speculative Design for Ubiquitous Workplace Space by and for Neurodivergent Employees
Bibliographic record
Abstract
This study explores the potential of interdisciplinary theories and advanced technologies, such as augmented realities and artificial intelligence, to address the socio-professional integration challenges faced by neurodivergent individuals, particularly those on the autism spectrum. It investigates the design of personalized, functional spaces that integrate interconnected living environments and intelligent systems tailored to support communication needs. Using speculative design methodology, the research adopts an experiential framework to examine alternative solutions, starting with a central hypothesis and testing it through debates with researchers, experts, neurodivergent individuals, and knowledge users. The premise is rooted in the recognition that neurodivergent individuals encounter significant barriers to social interaction and communication, limiting their integration into professional and social environments. This project envisions leveraging Internet of Things devices and immersive extended reality systems to create accessible, personalized spaces where individuals can reflect, feel secure, and engage in meaningful interactions. These spaces aim to foster confidence, well-being, and adaptability while addressing the limitations of existing tools. Hypotheses are informed by frameworks addressing autistic experiences and studies on self-narratives, proposing innovative applications of AI and immersive technologies. A diegetic device illustrates an augmented reality environment allowing users to practice social scenarios, engage in interest-based interactions, and participate freely, while preserving the emotional safety of a personalized setting. The research highlights the potential for such environments to transform social and workplace adaptation, fostering inclusion and employability amongeurodivergent individuals. Results and discussions delve into emerging ideas, providing a foundation for advancing adaptive strategies that meet the specific needs of this population. By bridging theoretical and technological innovations, this work seeks to open new possibilities for integrating neurodivergent individuals into society while enhancing their quality of life through tailored, intelligent, and interactive solutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".