Evaluating Prototype Fidelity: Impacts on Cognitive Workload and Mental-Model Alignment in Flight-Booking Interfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early-stage prototyping is essential for translating user requirements to functional design concepts. However, empirical evidence that investigates how prototype fidelity impacts cognitive workload and mental-model alignment within multi-step tasks is limited. This study examines three levels of fidelity (low-fidelity paper sketches, medium-fidelity clickable wireframes and high-fidelity static HTML pages) in a multi-step flight-booking scenario. Participants in this study included 25 undergraduate students who completed the moderate-complexity task workflow using a counterbalanced within-subjects design. The NASA-TLX was used to measure perceived cognitive workload and mental-model alignment was evaluated using a self-report Likert-scale questionnaire. Thus, the following results demonstrated a significant reduction in cognitive workload and increase in mental-model alignment with increase in fidelity. Further task performance analysis indicated that completion times were faster for medium-fidelity wireframes than for other conditions. Therefore, these findings present empirical guidance for selection of prototype fidelity which further demonstrates that medium-fidelity wireframes provide considerable cognitive and usability benefits along with reduced resources.
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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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.000 | 0.002 |
| 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 it