Artificial Experience (AX) Design: The Social Future of User Experience
Bibliographic record
Abstract
Since the 1980s, user experience (UX) has been a critical, human-centric movement to improve the design and usability of technology. UX frameworks, such as Garrett’s Five Planes, prescribe considerations for the strategy, scope, structure, skeleton, and surface of a screen-based interaction. However, recent advances in conversational AI are beginning to move us away from screens and such frameworks now fall short on two key elements of modern AI: semiotics and social. Semiotics, the meanings we derive from a broad set of signs, have expanded in the age of AI and robotics to include a collection of humanlike nonverbal cues. Social acknowledges that these humanlike technologies are no longer lifeless tools, but have begun to occupy a new ontological space in our minds, somewhere between hammer and human. We need a discussion about these additional elements and how they contribute to the emerging idea of Artificial Experience (AX): the complex interactions we have and relationships we form with modern conversational AI and social robots. Our technologies have always been social by being extensions of ourselves. Modern AI is yet another social extension, however, it is unique in being the first generation of technologies to which we also assign such vast social agency. UX tells us how to design these technologies to be good tools, however, this is not enough for the age of AI. We need AX to show us how to design these technologies to be good collaborators.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".