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Record W4412489207 · doi:10.1080/00140139.2025.2511869

Empowering non-designers with tangible tools for divergent thinking

2025· article· en· W4412489207 on OpenAlexafffund
Annemarie Lesage, Simon Bourdeau, Béatrice Couturier Caron, Pierre‐Majorique Léger

Bibliographic record

VenueErgonomics · 2025
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsHEC MontréalOpmedic Group (Canada)École de Technologie SupérieureCanadian Institute for International Peace and Security
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAffordanceLiteral and figurative languageDesign thinkingProcess (computing)Perspective (graphical)Divergent thinkingHuman–computer interactionCognitionInterface (matter)Thinking processesInterface designPsychologyCreativityComputer scienceEngineeringMathematics educationSocial psychologyArtificial intelligenceLinguistics

Abstract

fetched live from OpenAlex

Design Thinking (DT) has emerged as a pivotal approach harnessed by professionals across domains beyond traditional design practices. To reap its benefits, designers rely on sketching, but non-designers hesitate to do so. This paper investigates an alternative tool to engage non-designers effectively in the design process. A comparative study was conducted, juxtaposing the use of pen & paper against tangible figurative toys, assessing the creative outcomes through Torrance's framework for creative thinking. 36 participants were tasked with producing two web interface designs using one or both tools, according to four different conditions. While pen & paper yielded a greater quantity of ideas, they fell short in generating a broader spectrum of idea categories or more original concepts. Using a tangible tool resulted in more elaborate proposals. Figurative tangibles appear to exhibit a greater affordance for divergent thinking compared to pen & paper, despite imposing a higher cognitive effort on participants.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.368
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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