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Record W4387370339 · doi:10.21606/iasdr.2023.409

Unlocking creative potential: idea generation training for design students

2023· article· en· W4387370339 on OpenAlexaff
Wonjoon Chung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsCarleton University
Fundersnot available
KeywordsIdeationDesign thinkingCreativityNoveltyComputer scienceProcess (computing)Creative problem-solvingDivergent thinkingSerendipityConvergent thinkingLateral thinkingBrainstormingThinking processesModular designMathematics educationCognitive sciencePsychologyCreative thinkingHuman–computer interactionArtificial intelligenceEpistemologySocial psychology

Abstract

fetched live from OpenAlex

For most designers, the ability to generate creative and compelling ideas is an essential skill. Achieving creativity often requires breaking away from traditional thinking and exploring new and unconventional ideas, thinking beyond the limits of conventional thought processes. While the most unconventional and wild ideas that emerge from this exploration are often the ones that lead to innovative and creative design solutions later stage, nurturing the early seed ideas into viable design solutions requires more than just exploration. It also requires a thorough understanding of creative logical thinking principles, such as abductive reasoning and bisociation, as well as experiencing them through practice. In addition, it is also crucial to generate the early wild ideas into a reasonably acceptable design concept that may exist at the intersection between novelty and acceptability where MAYA (Most Advanced Yet Acceptable) stage is located. By using forced connection with randomly selected stimuli, this paper proposes a pedagogical technique or a drill, called Random Ideation, which enables students to experience those principles and practice them through a group workshop. Its board game-like process asks participants to develop a design scenario by employing the given conditions with a user, environment, and activity. We hope this method could serve as a drill for design students to practice thinking outside the box and cultivate early ideas into plausible solutions.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.185
GPT teacher head0.382
Teacher spread0.197 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2023
Admission routes1
Has abstractyes

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