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Use generative AI in two distinct phases: first create without it, then use it to challenge and extend your thinking. Compare not only the finished artifact, but also originality, usefulness, personal meaning, process choices, and how different participants’ work is from one another. This makes the exercise about creative range and judgment—not simply producing more, faster.
What this creativity exercise is designed to reveal
This is a recommended exercise sequence, not a validated curriculum or a protocol reproduced from one study. Its central question is: When does generative AI expand an individual’s creative possibilities, and when might reliance on its suggestions narrow what a group produces?
That question requires more than one definition of creativity. Treat these as separate outcomes:
- Originality: How unusual is the idea or artifact?
- Usefulness: Does it solve the prompt or communicate what it needs to communicate?
- Craft and quality: How coherent, technically effective, or well written is it?
- Enjoyment: Is the result engaging to its audience?
- Personal relevance: Can the creator recognize their own intent, experience, and choices?
- Collective diversity: Do participants produce meaningfully different work, or do their outputs converge?
Do not collapse these dimensions into one unsupported “creativity score.” A polished result can still feel generic, and an unconventional idea can be personally meaningful even when it needs revision.
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What the evidence says—and what it does not
AI ideas improved individual short stories while increasing similarity
In a 2024 Science Advances experiment, Anil R. Doshi and Oliver P. Hauser gave online participants access to GPT-4-generated story ideas or had them write without such assistance. Evaluators rated AI-assisted stories as more creative, better written, and more enjoyable, with especially large benefits for less creative writers in that experiment. However, the stories produced with AI ideas were more similar to one another: similarity to the generated idea was 5.2% higher when participants received one idea and 5.0% higher when they received five ideas, compared with the human-only condition.
The authors describe this tension as “an increase in individual creativity at the risk of losing collective novelty.” The result is causal evidence for that tested short-story setup, not a prediction about every model, medium, team, classroom, or form of art.
Productivity gains are not the same as creative gains
Shakked Noy and Whitney Zhang’s randomized 2023 Science experiment assigned 453 college-educated professionals incentivized, occupation-specific writing tasks. With ChatGPT, the average time taken decreased by 40% and output quality rose by 18% on those assigned tasks. Those measurements explain why AI is attractive at work, but they measure speed and task quality—not originality, personal agency, or diversity of ideas.
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Human and chatbot performance depends on the test
In a separate divergent-thinking comparison involving specific chatbots and one test, chatbot responses were stronger than the average human response, while the strongest human ideas matched or exceeded chatbot answers. That finding cannot support the broad claim that AI is “more creative than humans.” It shows why the task, comparison group, and scoring method matter.
Run the exercise in five stages
1. Create a human-only baseline
- Give everyone the same prompt. Choose a task that can produce several distinct responses, such as a short scene, product concept, campaign idea, visual brief, or opening paragraph.
- Set a short, fixed period and ask each participant to draft several alternatives or one small artifact without an AI tool.
- Have participants save the initial version unchanged. It is the baseline for later reflection, not a disposable warm-up.
A shared prompt makes comparison possible; a fixed time prevents the AI group from gaining an advantage simply by spending longer.
2. Ask AI for contrasting options
- After the baseline is saved, ask the model for a small set of alternatives, unexpected angles, or deliberately contrasting directions.
- Request options rather than a single “best” answer. For example: “Give me three approaches that differ in tone, audience, and underlying assumption. Do not write the final piece.”
- Save the exact prompt and response. Record the model and date if the group may repeat the activity, because results are model-specific and can change with system updates.
Contrasting options reduce the risk that the first plausible suggestion becomes an unexamined anchor. They also make it easier to see whether AI genuinely widened the search space.
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3. Make every human choice visible
- For each suggestion, mark it accept, reject, combine, or transform.
- Annotate the reason: What problem did it solve? What felt derivative, impractical, or unlike your intention?
- Label the contribution as originating in your baseline, supplied by AI, or emerging from the interaction between them.
- Produce a revised artifact while preserving the baseline and the annotation record.
The point is not to punish AI use. It is to expose judgment, authorship, and the moments when a suggestion changed the direction of the work.
4. Compare outcomes on separate axes
Use a simple rubric with independent ratings, such as 1–5, and require a short explanation for each rating. If possible, have people who do not know which version used AI provide the craft and usefulness ratings.
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|---|---|---|
| Individual quality | How effectively does this artifact meet the prompt? | Reader or peer rating with a reason |
| Originality | What is unexpected or distinctive about it? | Specific element that departs from familiar solutions |
| Usefulness | Does the unusual element actually work? | Fit to audience, constraints, and purpose |
| Personal agency | Can the creator explain the intent and key choices? | Baseline, annotations, and reflection |
| Collective diversity | How different are participants’ final ideas from one another? | Theme, structure, wording, or concept overlap across outputs |
| Process | Did AI help overcome a block or anchor the creator? | What changed after each suggestion and what was rejected |
Keep the human-only and AI-assisted versions side by side. A group may discover that the revised pieces are individually stronger while sharing the same premise, structure, or language.
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5. Reflect before discussing results
Ask participants to answer these questions privately, then discuss patterns:
- Which AI suggestion changed your direction most?
- Which suggestion did you reject, and what made it wrong for your goal?
- What would you probably have produced without seeing the suggestion?
- Which part comes from your own experience or intention?
- Did the tool expand your range, or pull you toward a familiar pattern?
- Did the final artifact become more effective without becoming more personally meaningful?
How to interpret the results
If the AI version scores higher individually
That may indicate better structure, wording, or problem fit. It does not establish that the participant became more original, nor that the improvement would transfer to another medium or model. Inspect the annotations to see whether AI supplied a useful prompt, a technical refinement, or most of the concept.
If the AI versions resemble one another
Similarity is a group-level finding, not proof that every participant lost creativity. Look for convergence in premises, metaphors, plot turns, visual compositions, or phrasing. Then ask whether the shared elements came from the model’s suggestions or from the original prompt and common cultural patterns.
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If the human-only versions are stronger
Check whether the prompt, time limit, model instructions, or participants’ unfamiliarity with prompting constrained the AI phase. A weak result is still useful if it reveals that suggestions were distracting, too generic, or poorly matched to the creator’s intent.
If ratings disagree
Do not force consensus. A story can be more enjoyable but less distinctive; a product idea can be highly original but impractical. Report the dimensions separately and preserve the reasons behind each rating.
Design choices that protect creative agency
- Preserve the baseline: Never overwrite the human-only version.
- Use options, not answers: Ask for contrasting directions and constraints rather than finished work.
- Require transformation: Participants should explain what they changed and why.
- Vary the prompt: Repeat with different domains before drawing a general conclusion.
- Inspect the group: Compare outputs across people, not only each person’s before-and-after result.
- Separate evaluation from self-report: Personal meaning and agency are important, but they are not interchangeable with an external quality rating.
Important limits
The short-story experiment tested an LLM idea-generation intervention and evaluated stories in a controlled online setting. It does not settle the effects of AI on visual art, music, teamwork, long-form writing, or classroom learning. The Noy and Zhang study tested professional writing tasks and productivity-related outcomes, not creativity. The divergent-thinking comparison covered one test and specific chatbots. Tested GPT-4 behavior and other model results should not be treated as benchmarks for every current generative-AI product.
Participants’ ratings in this activity are reflective learning material, not a controlled experiment. The exercise can help a group notice trade-offs and make its process visible; it cannot by itself prove a causal effect of AI on creativity.
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Generative AI is most useful for creativity when people retain a human starting point, demand contrasting options, and exercise visible judgment. Evaluate individual quality and collective diversity separately: AI may help one person make a stronger artifact while nudging a whole group toward similar ideas.
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