Identifying Five Archetypes of Interaction Design Professionals and Their Universal Design Expertise
Bibliographic record
Abstract
Abstract Systems and services based on Information and Communications Technology (ICT) are now prevalent in our daily lives. Digital transformations have been, and are still being, initiated across private and public sectors. As such, the consequences of digital exclusion are severe and may block access to key aspects of modern life, such as education, employment, consumerism and health services. In order to combat this, regions and countries such as the USA, Canada, EU and Scandinavia have all legislated universal design (UD) in relation to ICT, in order to ensure as many citizens as possible have the opportunity to access and use digital information and services. However, there has been limited research into how higher educational programs address legislated accessibility responsibilities. This paper looks into the discipline of interaction design (IxD). IxD is the design domain focused on ‘how human beings relate to other human beings through the mediating influence of products’ (Buchanan, R. (2001) Designing research and the new learning. Des. Issues, 17, 3–23). The study presents an analysis of Norwegian higher educational programs within IxD. Based on document analysis, we map the skillsets the study programs state to deliver and investigate to what degree UD expertise is included. Our findings indicate the study programs do not deliver adequate training in UD, in order to fulfill the professional responsibilities related to ICT accessibility. From our findings, we extrapolate five ‘archetypes’ of interaction designers. These personas-like analytical constructs hold slightly different characteristics. For each of the five, we propose UD expertise fitting key skillsets. We hope our contributions are useful both for the higher education sector and the industry and will contribute to raised awareness of UD skills so they can educate interaction designers in their different industry roles with required competences. RESEARCH HIGHLIGHTS We indicate the current content of interaction design (IxD) programs in higher education and document the lacking focus on universal design (UD). We identify five different archetypes of interaction designers being educated in such programs. We describe key skillsets and strengths for each archetype. We propose UD expertise for the (IxD) profession and link UD expertise to archetype skillsets to emphasize relevance.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".