Alternative Uses Task Designer

1. State the research question — What specific question is this AUT study addressing?
2. Justify the method choice — Why AUT (not RAT, CAT, or other creativity tasks)? What alternatives were considered?
3. Declare expected outcomes — What pattern of results would support vs. refute the hypothesis?
4. Note assumptions and limitations — What does AUT assume about creativity? Where could it mislead?
5. 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

Standard Objects

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

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:

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

Attention and Quality Checks

1. Attention check questions — Embed 1-2 instructed-response items (e.g., "Please select 'Strongly Agree' for this item") (Oppenheimer et al., 2009)
2. Seriousness check — Post-task: "Did you take this study seriously?" (Lee & Chung, 2024)
3. Gibberish detection — Flag responses that are incoherent or clearly auto-generated
4. Minimum response threshold — Exclude participants with <2 responses (indicates disengagement)
5. Duplicate detection — Check for repeated responses within a participant
6. 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

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

1. 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).

2. 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).

3. 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).

4. 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).

5. 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).

6. 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):

• Object(s) used and rationale for selection
• Time limit per object
• Exact wording of instructions (verbatim or cited)
• Condition descriptions and assignment method (random, counterbalanced)
• Sample size per condition with power justification
• Platform and recruitment source (MTurk, Prolific, lab)
• Attention check and exclusion criteria with exclusion counts
• For AI conditions: AI model and version, access method, interaction logging
• Scoring method used (fluency, flexibility, originality, semantic distance) — see divergent-thinking-scoring skill
• Inter-rater reliability for subjective scores (ICC or Cohen's kappa)
• Pre-registration status and link

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.