Alternative Uses Task Designer

Purpose

This skill encodes expert methodological knowledge for designing Alternative Uses Task (AUT) experiments — the most widely used measure of divergent thinking in creativity research. It provides domain-specific parameter recommendations for stimulus selection, timing, condition design (including AI-augmented variants), online implementation, and quality control. A general-purpose programmer would not know the standard objects, timing constraints, scoring dimensions, or the critical design choices that determine whether an AUT experiment yields valid creativity data.

When to Use This Skill

Designing a study measuring divergent thinking or creative ideation
Setting up an AUT experiment with AI-assisted conditions (e.g., ChatGPT, web search)
Choosing appropriate objects, timing, and instructions for an AUT
Adapting the AUT for online administration (MTurk, Prolific, Qualtrics)
Planning attention checks and exclusion criteria for creativity studies

Research Planning Protocol

Before executing the domain-specific steps below, you MUST:

State the research question — What specific question is this AUT study addressing?
Justify the method choice — Why AUT (not RAT, CAT, or other creativity tasks)? What alternatives were considered?
Declare expected outcomes — What pattern of results would support vs. refute the hypothesis?
Note assumptions and limitations — What does AUT assume about creativity? Where could it mislead?
Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

AUT Overview

The Alternative Uses Task (Guilford, 1967) asks participants to generate as many unusual uses as possible for a common everyday object within a fixed time limit. It is the standard measure of divergent thinking — the ability to generate multiple, varied, and novel ideas.

Core Parameters

Parameter	Default	Source
Time limit	5 minutes per object	Lee & Chung, 2024; Reiter-Palmon et al., 2019
Number of objects	1-3 per session	Silvia et al., 2008
Response format	Open-ended text, one use per line	Reiter-Palmon et al., 2019
Instructions emphasis	"unusual, creative, uncommon" uses	Guilford, 1967; Wallach & Kogan, 1965

Standard Objects

Objects should be concrete, familiar, and have many conventional uses so that departing from typical uses requires genuine creative thinking.

Object	Commonly Used In	Source
Brick	Most widely validated	Guilford, 1967
Paperclip	Classic Guilford item	Guilford, 1967
Newspaper	Used in Lee & Chung, 2024	Lee & Chung, 2024
Cardboard box	Common alternative	Silvia et al., 2008
Tin can	Common alternative	Wallach & Kogan, 1965
Shoe	Frequently used	Reiter-Palmon et al., 2019

Avoid: Objects that are already unusual (e.g., "kaleidoscope") or that have very few conventional uses (e.g., "toothpick"). The task requires a clear baseline of common uses to depart from.

Condition Design

Standard Conditions (Creativity Research)

Is the study examining AI's impact on creativity?
|
+-- YES --> Include at minimum:
| 1. AI-assisted condition (e.g., ChatGPT access)
| 2. No-assistance control
| 3. [Recommended] Web search control (Lee & Chung, 2024, Exp 2A/2B)
|
+-- NO --> Standard AUT with:
 1. Experimental manipulation (priming, mood, instructions)
 2. Control condition (neutral or baseline)

AI-Augmented Design (Lee & Chung, 2024)

For studying AI's impact on creativity:

Condition	Participant Instructions	Implementation
ChatGPT	"You may use ChatGPT to assist you"	Embed ChatGPT in new browser tab; record interaction logs
Web Search	"You may use web search to assist you"	Allow Google/Bing access; record search queries
No Assistance	"Complete the task on your own"	Disable external tool access

Critical design decisions:

Between-subjects assignment to conditions (Lee & Chung, 2024) — avoids carryover effects
Random assignment via survey platform (Qualtrics randomizer)
Cover story: Frame as "idea generation study," not "creativity study" to reduce demand characteristics
Manipulation check: Ask participants whether they used the assigned tool

Online Implementation

Platform Specifications

Parameter	Recommendation	Source
Platform	Qualtrics (survey) + MTurk/Prolific (recruitment)	Lee & Chung, 2024
Sample size per condition	100-200 for between-subjects AUT	Lee & Chung, 2024 (N=256 in Exp 2B)
Compensation	Prolific minimum + bonus for completion	Lee & Chung, 2024
Estimated duration	15-25 minutes total session	Lee & Chung, 2024

Attention and Quality Checks

Attention check questions — Embed 1-2 instructed-response items (e.g., "Please select 'Strongly Agree' for this item") (Oppenheimer et al., 2009)
Seriousness check — Post-task: "Did you take this study seriously?" (Lee & Chung, 2024)
Gibberish detection — Flag responses that are incoherent or clearly auto-generated
Minimum response threshold — Exclude participants with <2 responses (indicates disengagement)
Duplicate detection — Check for repeated responses within a participant
Bot detection — reCAPTCHA or honeypot fields; check completion time (exclude if <3 minutes)

Exclusion Criteria (Lee & Chung, 2024)

Failed attention check: exclude
Self-reported not taking study seriously: exclude
Completion time <3 minutes or >60 minutes: flag for review
Fewer than 2 responses on AUT: exclude
Non-native speakers (if language fluency is critical): exclude or control for

Additional Measures

Baseline Creativity

Measure	Items	Duration	What It Captures	Source
RAT (Remote Associates Test)	15 items	~5 min	Convergent thinking	Mednick, 1962; Lee & Chung, 2024
Creative Achievement Questionnaire	10 domains	~5 min	Real-world creative accomplishment	Carson et al., 2005
Creative Self-Efficacy Scale	3 items, 5-point Likert	<1 min	Belief in own creative ability	Tierney & Farmer, 2002

Mediators / Moderators (Lee & Chung, 2024)

Creative self-efficacy — 3-item scale (Tierney & Farmer, 2002): "I have confidence in my ability to solve problems creatively," "I feel that I am good at generating novel ideas," "I have a knack for further developing the ideas of others." 5-point Likert (1 = strongly disagree to 5 = strongly agree)
Task engagement — Self-report items on effort and involvement
AI reliance — Whether and how extensively participants used the AI tool

Common Pitfalls

Using "creative" in instructions without care: Telling participants to "be creative" changes the scoring profile — it increases originality but may decrease fluency. Decide a priori and keep consistent across conditions (Nusbaum et al., 2014).
Confounding fluency with originality: Participants who generate more ideas statistically have a higher chance of producing rare ideas. Either control for fluency when analyzing originality, or use ratio-based measures (Silvia et al., 2008).
Not controlling for AI-generated text: In AI-augmented conditions, participants may copy-paste AI outputs. Record interaction logs and code whether responses are self-generated, AI-assisted, or directly copied (Lee & Chung, 2024).
Ignoring the web search control: Comparing ChatGPT only to no-assistance confounds AI-specific effects with general information access effects. Include a web search condition as active control (Lee & Chung, 2024, Exp 2A/2B).
Insufficient sample size for between-subjects: AUT effect sizes for condition differences are typically small-to-medium (d ≈ 0.3-0.5). Plan for N ≥ 100 per condition (Lee & Chung, 2024).
Administering multiple objects sequentially without counterbalancing: Practice effects and fatigue can confound results. Counterbalance object order across participants (Reiter-Palmon et al., 2019).

Minimum Reporting Checklist

Based on Lee & Chung (2024) and Reiter-Palmon et al. (2019):

References

Carson, S. H., Peterson, J. B., & Higgins, D. M. (2005). Reliability, validity, and factor structure of the Creative Achievement Questionnaire. Creativity Research Journal, 17(1), 37-50.
Guilford, J. P. (1967). The nature of human intelligence. McGraw-Hill.
Lee, B. C., & Chung, J. (2024). An empirical investigation of the impact of ChatGPT on creativity. Nature Human Behaviour. https://doi.org/10.1038/s41562-024-01953-1
Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review, 69(3), 220-232.
Nusbaum, E. C., Silvia, P. J., & Beaty, R. E. (2014). Ready, set, create: What instructing people to "be creative" reveals about the meaning and mechanisms of divergent thinking. Psychology of Aesthetics, Creativity, and the Arts, 8(4), 423-432.
Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009). Instructional manipulation checks. Journal of Experimental Social Psychology, 45(4), 867-872.
Reiter-Palmon, R., Forthmann, B., & Barbot, B. (2019). Scoring divergent thinking tests: A review and systematic framework. Psychology of Aesthetics, Creativity, and the Arts, 13(2), 144-152.
Silvia, P. J., Winterstein, B. P., Willse, J. T., et al. (2008). Assessing creativity with divergent thinking tasks: Exploring the reliability and validity of new subjective scoring methods. Psychology of Aesthetics, Creativity, and the Arts, 2(2), 68-85.
Tierney, P., & Farmer, S. M. (2002). Creative self-efficacy: Its potential antecedents and relationship to creative performance. Academy of Management Journal, 45(6), 1137-1148.
Wallach, M. A., & Kogan, N. (1965). Modes of thinking in young children. Holt, Rinehart and Winston.

See references/ for detailed instruction templates and object selection guide.