The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index; 4 of the 9 sources its claims cite were checked for support.
Generative artificial intelligence enhances creativity but reduces the diversity of novel content
Open accessAbstract
Creativity is core to being human. Generative artificial intelligence (GenAI) holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on GenAI ideas. We study the causal impact of GenAI on the production of a creative output in an online experimental study where some writers are could obtain ideas for a story from a GenAI platform. Access to GenAI ideas causes an increase in the writer’s creativity with stories being evaluated as better written and more enjoyable, especially among less creative writers. However, GenAI-enabled stories are more similar to each other than stories by humans alone. Our results have implications for researchers, policy-makers and practitioners interested in bolstering creativity, but point to potential downstream consequences from over-reliance.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
In a preregistered online experiment, writers given generative AI story ideas produced stories rated as more creative, better written, and more enjoyable, especially less inherently creative writers. However, AI-assisted stories were more similar to one another and to the AI ideas, suggesting individual gains at the cost of collective novelty.
Key findings
- Access to generative AI increased third-party evaluator ratings of novelty and usefulness: one AI idea raised novelty by 5.4% (b = 0.207, P = 0.021) and usefulness by 3.7% (b = 0.185, P = 0.039); five AI ideas raised novelty by 8.1% (b = 0.311, P < 0.001) and usefulness by 9.0% (b = 0.453, P < 0.001) versus Human-only.
- Benefits were concentrated among less inherently creative writers: low-DAT writers with five AI ideas improved novelty by 10.7%, usefulness by 11.5%, how well written by up to 26.6%, enjoyment by up to 22.6%, and boringness decreased by up to 15.2%; high-DAT writers saw little effect.
- AI-assisted stories were more similar to other stories in the same condition (one idea: b = 0.871, P < 0.001; five ideas: b = 0.718, P = 0.003) and more similar to the AI ideas (5.2% and 5.0% increases, respectively).
- Writers’ self-assessments showed no statistically significant differences in novelty or usefulness across conditions; evaluators imposed an ownership penalty of at least 25% on writers who received AI ideas.
Methods
- Preregistered two-phase online experiment: N = 293 writers on Prolific were randomized to Human-only, Human with one GenAI idea, or Human with five GenAI ideas; they wrote an eight-sentence story for a teenage/young adult audience, with GPT-4 providing three-sentence story ideas in AI conditions.
- Writers completed the divergent association task (DAT) before writing; DAT mean was 77.24, SD = 6.48, computed for 291 of 293 writers.
- N = 600 separate evaluators rated six randomly assigned stories each, yielding 3,519 evaluations; outcomes were analyzed with OLS regressions using robust standard errors, clustered at evaluator level for evaluator outcomes, plus cosine similarity of OpenAI embeddings.
Limitations
- The paper states the creative task was constrained in length (eight sentences), medium (writing), and output type (short story), with no interactiveness with the LLM or variation in prompts, limiting generalizability.
- The paper states the sample was relatively “typical” study participants, not professional writers or unusually creative individuals, and that the usefulness construct was adapted to this context.
- Evident from the text: writers could opt into receiving AI ideas rather than being forced to use them, though the authors argue intention-to-treat analysis and high uptake (88.4%) support causal interpretation; no financial incentives for creativity were introduced.
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Claims the paper makes
4 of 9 cited sources checked · 3 supported · 0 flagged · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“First, having access to generative AI effectively equalizes the evaluations of stories, removing any disadvantage or advantage based on the writers’ inherent creativity (25).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
What the sources say
- Could not verifycites [25] H Wang, 2023, Scientific discovery in the age of artificial intelligence, Nature sourceExperimental — accuracy not yet measured
The Abstract describes a task for measuring verbal creativity, and the Discussion > Strengths describes its automatic scoring; neither addresses AI-assisted story writing or story evaluations. Searches for generative AI, story writing, and equalized ratings found no passage supporting the claimed effect. (LLM, conf=0.76)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Rests on an assumption: GPT-4 is representative of generative AI more broadly.
- Could not verify
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Rests on an assumption: GPT-4 is representative of generative AI more broadly.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers.”
- Also stated in: another passage · plain
“However, the gains from writing more creative stories benefit some more than others: Less creative writers experience greater uplifts for their stories, seeing increases of 10 to 11% for creativity and of 22 to 26% for how enjoyable and well written the story is.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > Abstract
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Rests on an assumption: GPT-4 is representative of generative AI more broadly.
- Also stated in: another passage · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers.”
- Also stated in: another passage · plain
“we find that stories written by writers with access to generative AI ideas are more enjoyable (Human with one GenAI idea condition: b = 0.216, P = 0.028; Human with five GenAI ideas condition: b = 0.375, P < 0.001, see table S6)”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Rests on an assumption: GPT-4 is representative of generative AI more broadly.
- Also stated in: another passage · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, generative AI–enabled stories are more similar to each other than stories by humans alone.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Rests on an assumption: GPT-4 is representative of generative AI more broadly.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find that access to generative AI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers.”
- Also stated in: another passage · plain
“Relative to Human-only stories, when the writer had access to up to five generative AI ideas, the stories are considered to be better written (b = 0.372, P < 0.001)”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Rests on an assumption: GPT-4 is representative of generative AI more broadly.
- Also stated in: another passage · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The usefulness of stories from writers with access to one generative AI idea is 3.7% (b = 0.185, P = 0.039) higher than that of writers with no generative AI access.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > RESULTS > Baseline versus generative AI conditions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The empirical evidence shows that nearly 9 of 10 people in the generative AI conditions choose to receive at least one generative AI when offered”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We asked participants in our study to self-assess the creativity of their stories, but find that they generally do not self-assess accurately.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We observe a similar result among high-DAT writers for how well the story was written, how enjoyable, and, conversely, how boring it is: Having access to generative AI does not affect high-DAT writers’ already good performance on these outcomes.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > RESULTS > Heterogeneity by inherent creativity
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Probably not a substantial claim. 35
- It may state two separate claims as one. 23
- The quote only partly states this claim. 1
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Creativity is typically assessed across two dimensions: novelty and usefulness (11, 12).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [11] W Kenower, 2020, The Cold Open: Facing the Blank Page. Writer's Diget source
- Supportedcites [12] S Harvey, 2022, Toward a meta-theory of creativity forms: How novelty and usefulness shape creativity source
The 'Meta-dimensions for Reviewing the Creativity Literature' section describes a consensus on novelty and usefulness while noting that their definitions and measurements vary. The 'Distal relationship between novelty and usefulness' section also describes them as two dimensions contributing to judgments of creativity. The supplied passages do not give page numbers. (LLM, conf=0.92)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“That generative AI particularly benefited less able writers is paralleled in recent studies focusing on other domains in which generative AI has been shown to help less productive workers (4, 5).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
What the sources say
- Not checked — no full text of the sourcecites [4] S Noy, 2023, Experimental evidence on the productivity effects of generative artificial intelligence, Science source
- Supportedcites [5] E Brynjolfsson, 2023, Generative AI at work, National Bureau of Economic Research source
The Abstract reports that AI assistance increased customer support agents’ productivity, with the greatest gains among novice and lower-skilled workers. This supports the cited comparison with less able writers in another domain. (LLM, conf=0.94)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
Every source the paper cites for this claim was checked and supports it.
In the paper
“We do so among a sample of relatively “typical” study participants often used in academic studies (which comes with limitations on population representativeness) (41)—that is, we do not study professional writers or unusually creative individuals.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
What the sources say
- Supportedcites [41] #* (-1"",#(&%)'(#"'# source
Section 1.1 describes online research participants as a convenience sample, supporting the cited caveat about population representativeness. Section 1.2 identifies Prolific as a subject pool used in academic research. The statement that this particular study did not recruit professional writers or unusually creative people is the citing authors’ own description, not a finding attributed to this source. (LLM, conf=0.84)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We find that generative AI assistance increases both the novelty and usefulness of stories (results are discussed in section S4).”
- Also stated in: another passage · plain
“We find that having access to generative AI causally increases the average novelty and usefulness—two frequently studied dimensions of creativity—relative to human writers on their own.”
§ Results > Baseline versus generative AI conditions
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Rests on an assumption: GPT-4 is representative of generative AI more broadly.
- Also stated in: another passage · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Most evaluators also indicated that disclosure of the use of AI or the underlying text from AI should be part of publications that used such tools.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Again, writers’ self-assessments of their own stories show no statistically significant differences in the story characteristics across conditions (see table S7).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > RESULTS > Exploratory analyses: Emotional characteristics
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Stories in the Human with five GenAI ideas condition are, however, not evaluated as more funny than those in the Human-only condition; if anything, the coefficient is negative but not statistically significant (b = −0.106, P = 0.115).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > RESULTS > Exploratory analyses: Emotional characteristics
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Across all conditions, high-DAT writers’ stories are evaluated relatively highly, in terms of both novelty and usefulness, and providing them with access to generative AI does not affect their high evaluations.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > RESULTS > Heterogeneity by inherent creativity
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, however, most evaluators found the use of AI in writing stories to be ethical and still a “creative act.””
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“most evaluators indicated that the content creators, on which the models were based, should be compensated.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To put these values in context, consider that in the Human-only condition, the similarity scores span a range of 8.10 points; therefore, the increase in similarity from having access to one or five generative AI ideas represents 10.7% and 8.9% of the total range, respectively.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > RESULTS > Similarity of stories
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“with our results showing that generative AI professionalizes the writing but reduces the variance in creative outputs”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Participants were randomly assigned to one of three conditions: Human-only, Human with one GenAI idea condition, and Human with five GenAI ideas (see table S1 for balance across conditions).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > INTRODUCTION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results point to an increase in individual creativity at the risk of losing collective novelty.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Rests on an assumption: GPT-4 is representative of generative AI more broadly.
- Rests on an assumption: Collective novelty is valuable.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This dynamic resembles a social dilemma: With generative AI, writers are individually better off, but collectively a narrower scope of novel content is produced.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Rests on an assumption: GPT-4 is representative of generative AI more broadly.
- Rests on an assumption: Collective novelty is valuable.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“little is known about generative AI’s potential impact on a fundamental human behavior: the ability of humans to be creative.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > INTRODUCTION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, the emergence of generative artificial intelligence (AI) technologies—such as large language models (LLMs) as used in our study—is challenging several long-standing assumptions about the uniqueness and superiority of human-generated content (2).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > INTRODUCTION
What the sources say
- Not checked — no full text of the sourcecites [2] Z Epstein, Investigators of Human Creativity (2023a). Art and the science of generative AI, Science source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“see table S1 for balance across conditions”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > INTRODUCTION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While these results point to an increase in individual creativity, there is risk of losing collective novelty.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The number of evaluations per story varied because of random assignment of stories to evaluators: One story received 9 reviews, 9 stories received 10 reviews, 61 stories received 11 reviews, 141 stories received 12 reviews, 77 stories received 13 reviews, and 4 stories received 14 reviews.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > MATERIALS AND METHODS > Evaluator study
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“our results suggest that despite the enhancement effect that generative AI had on individual creativity, there may be a cautionary note if generative AI were adopted more widely for creative tasks.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The order in which the stories were presented for review was randomized across evaluators.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > MATERIALS AND METHODS > Evaluator study
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We designed our study such that endogenous decisions by the writer are minimized, but not fully eliminated.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The study was preregistered at AsPredicted.org (ID 136723); a copy of the preregistration is included in section S9.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > MATERIALS AND METHODS > Preregistration and ethics approval
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“both of which may increase the effectiveness and magnitude of the impact of generative AI on creativity.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“When given the option to call upon generative AI more than once in the Human with five GenAI ideas condition, participants did so on average 2.55 times, with 24.5% requesting the maximum of five generative AI ideas.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > RESULTS > Baseline versus generative AI conditions
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If individual writers find out that their generative AI-inspired writing is evaluated as more creative, they have an incentive to use generative AI more in the future, but by doing so, the collective novelty of stories may be reduced further.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Informed consent was obtained for both the writer study and the evaluator study.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > MATERIALS AND METHODS > Preregistration and ethics approval
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we did not study or vary the myriad of motivating factors that encourage creativity in the real world.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All stories were evaluated by multiple evaluators on novelty, usefulness, and several emotional characteristics, which comprise key outcome variables related to our main research question (see section S1).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > INTRODUCTION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These results indicate support for the use of generative AI in creative outputs, with important potential limits on ownership or credit and requirements for disclosure.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These constraints limit the generalizability and conclusions we can draw from this study.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Participants were randomized into one of three experimental conditions: Human-only, Human with one GenAI idea, and Human with five GenAI ideas.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > MATERIALS AND METHODS > Writer study and experimental conditions
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the 100 writers in the Human with one GenAI idea condition, 82 opted to generate one, while 93 of 98 writers in the Human with five GenAI ideas condition did so.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > RESULTS > Baseline versus generative AI conditions
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“There were a total of 3519 evaluations of 293 stories made by 600 evaluators.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > MATERIALS AND METHODS > Evaluator study
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cronbach’s α for the novelty and usefulness indices was 0.92 and 0.89, respectively.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > MATERIALS AND METHODS > Outcome variables
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Our choice of the experimental design offers a fairly stringent test to measure the causal impact of generative AI on creativity (34).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
What the sources say
- Not checked — no full text of the sourcecites [34] If a human creator (author) uses AI in part of the writing of a story, the AI-generated content should be accessible alongside the final story source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Evaluators read six randomly selected stories without being informed about writers being randomly assigned to access generative AI in some conditions (or not).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > INTRODUCTION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We do not allow writers to customize the call to the generative AI engine, nor do we allow for repeated interactions between writers and generative AI”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“our study has limitations in that the creative task is constrained in its length (i.e., eight sentences), medium (i.e., writing), and type of output (i.e., short story), and there is no interactiveness with the LLM or variation in prompts.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In our study, we randomly assigned writers to one of the generative AI conditions to mitigate selection bias.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This paper aims to provide an initial answer to these questions through a preregistered, two-phase experimental online study on written creative output”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > INTRODUCTION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“if the publishing (and self-publishing) industry were to embrace more generative AI-inspired stories, our findings suggest that the produced stories would become less unique in aggregate and more similar to each other.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The study was approved by the ethics boards at the UCL School of Management (ID UCLSOM-2023-002) and the University of Exeter (ID 1642263).”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > MATERIALS AND METHODS > Preregistration and ethics approval
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The overall effect is a more novel and even more useful story that is well written and enjoyable.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: Writers in the Human-only condition did not use generative AI.
- Rests on an assumption: GPT-4 is representative of generative AI more broadly.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cronbach’s α for the ownership index was 0.92.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > MATERIALS AND METHODS > Outcome variables
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Constraining the task in such a way may constrain the extent to which participants are able to express their creativity and may not generalize to other less-constrained creativity tasks.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > DISCUSSION
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Two writers failed to properly submit seven valid words; thus, the DAT score was successfully computed for 291 of 293 writers.”
§ Generative AI enhances individual creativity but reduces the collective diversity of novel content > RESULTS > Heterogeneity by inherent creativity
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Writers in the Human-only condition did not use generative AI.
“In our Human-only baseline condition, writers were assigned the task with no mention of or access to generative AI.”
The paper relies on this without support.
GPT-4 is representative of generative AI more broadly.
“we gave writers the option to call upon a generative AI platform (OpenAI’s GPT-4 LLM) to provide a three-sentence starting idea to inspire their own story writing.”
The paper relies on this without support.
Collective novelty is valuable.
“These results point to an increase in individual creativity at the risk of losing collective novelty.”
The paper relies on this without support.
Allowing writers to opt into receiving generative AI ideas ensures that writers are invested in and receptive to what generative AI produces.
“We do this to ensure that writers are invested in, and receptive to, what generative AI produces.”
The paper relies on this without support.
Evaluators in the evaluator study represent “regular” readers.
“Participants were not selected on the basis of prior experience in the publishing industry, but represent “regular” readers.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source doesn’t support the claim as written.
In the citing paper
“First, prior studies compared essays written entirely by students and those generated entirely by ChatGPT 7-8,14,15 . However, this may not accurately reflect how most students interact with AI chatbots in reality.”
From Style, sentiment, and quality of undergraduate writing in the AI era: A cross-sectional and longitudinal analysis of 4,820 authentic empirical reports (2025) · cites this paper as [8]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The most widely accepted framework evaluates innovation across two primary dimensions: novelty and usefulness (Amabile, 1982; Doshi and Hauser, 2024; Harvey and Berry, 2022).”
From AI-assisted design synthesis and human creativity in engineering education (2026) · cites this paper as Doshi and Hauser, 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Hence, simply by means of the training data of GenAI, the generated ideas may also be less diverse than those of humans [16].”
From Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Equipped with a vast knowledge base and divergent thinking, AI can generate unexpected ideas and thus evoke a stronger sense of novelty (Doshi and Hauser, 2024; Tigre Moura et al., 2023)”
From From humans to AI: understanding why AI is perceived as the preferred co-creation partner (2025) · cites this paper as Doshi and Hauser, 2024
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Because LLMs are trained on large corpora of existing scientific text, including published papers and public records of funded projects, they may tend to produce high-probability, discipline-typical language.”
From The rise of large language models and the direction and impact of US federal research funding (2026) · cites this paper as [36]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“When used in proposal preparation, such tools may enhance fluency and alignment with prevailing norms while reducing rhetorical and topical variance, making proposed projects more closely resemble those that have recently succeeded (23, 26, 36).”
From The rise of large language models and the direction and impact of US federal research funding (2026) · cites this paper as [36]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“creativity is usually evaluated by the novelty and usefulness of the outputs, often externally by reviewers rather than self-assessed by creators [23, 25].”
From Decoupling of Usefulness and Novelty: Evaluating the Impact of Generative AI on Design Outputs and Novice Designers' Creative Thinking (2026) · cites this paper as [25]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Reliance on a few dominant generative models such as Nano Banana or Midjourney may inadvertently homogenize design outcomes [25], limiting stylistic diversity and narrowing the exploration of design solution space.”
From Decoupling of Usefulness and Novelty: Evaluating the Impact of Generative AI on Design Outputs and Novice Designers' Creative Thinking (2026) · cites this paper as [25]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Emerging evidence shows measurable shifts in human writing styles, creative ideation, and divergent thinking following the widespread adoption of systems like ChatGPT [46, 6, 7, 24, 44, 79].”
From Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond) (2025) · cites this paper as [24]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We operationalized group-level homogeneity using the most common approach from prior research: the cosine similarity of each idea to the centroid embedding of the other ideas in the same condition for the same object [4].”
From The Role of Human Agency in Human-AI Co-Creativity (2026) · cites this paper as [4]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“which broadens forms of expression, strengthens creativity [4,5], and supports higher-order and critical thinking [6,7].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [4]
The cited source says what the claim attributes to it.
In the citing paper
“For example, several studies find benefits from GenAI in creative tasks, but these findings are typically limited to specific creative tasks (e.g., story writing, ideating business models) [16, 39, 64, 72].”
From Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis (2025) · cites this paper as [16]
The cited source says what the claim attributes to it.
In the citing paper
“This finding strengthens the impression that collaboration with LLMs might improve individual performance while showing detrimental effects on group-level [16].”
From Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis (2025) · cites this paper as [16]
The cited source says what the claim attributes to it.
In the citing paper
“These benefits also imply a shift in individual creative potential: as GenAI can augment human creativity, especially lower innate creative capabilities might be compensated [16].”
From Generative AI and Creativity: A Systematic Literature Review and Meta-Analysis (2025) · cites this paper as [16]
The cited source says what the claim attributes to it.
In the citing paper
“Other studies have found evidence of a homogenization effect (i.e., less semantically distinct outputs) from LLM assistance across a variety of GenAI text and image generation tools [8, 12, 31, 99, 139].”
From The Impact of Generative AI on Creativity in Software Development: A Research Agenda (2025) · cites this paper as [31]
The cited source says what the claim attributes to it.
In the citing paper
“However, Doshi and Hauser (2024) show that generative AI elevates not only the novelty of ideas but also their usefulness.”
From From humans to AI: understanding why AI is perceived as the preferred co-creation partner (2025) · cites this paper as Doshi and Hauser, 2024
The cited source says what the claim attributes to it.
In the citing paper
“Moreover, most existing studies on AI's impact on creativity have focused on textual outputs using text-to-text models, like writing essays [75], ctional stories [28], or imaginative and experimental scenarios [9, 10].”
From Creativity in the Age of AI: Evaluating the Impact of Generative AI on Design Outputs and Designers' Creative Thinking (2024) · cites this paper as [28]
The cited source says what the claim attributes to it.
In the citing paper
“While some studies suggest that generative AI can enhance individual creativity by providing inspiration and facilitating exploration [41, 67], others have found that it may reduce conceptual diversity at a collective level [28].”
From Creativity in the Age of AI: Evaluating the Impact of Generative AI on Design Outputs and Designers' Creative Thinking (2024) · cites this paper as [28]
The cited source says what the claim attributes to it.
In the citing paper
“Existing research has shown that LLM usage can lead to the homogenization of ideas within groups, where participants tend to converge on similar outcomes when using AIgenerated suggestions [2,19,45].”
From Human Creativity in the Age of LLMs: Randomized Experiments on Divergent and Convergent Thinking (2025) · cites this paper as [19]
The cited source says what the claim attributes to it.
In the citing paper
“We also saw recurring patterns such as very bright colors and complex compositions, hinting at a converging “AI style,” which echoes concerns that generative AI homogenizes outputs [99] and reduces diversity at a collective level [25].”
From Decoupling of Usefulness and Novelty: Evaluating the Impact of Generative AI on Design Outputs and Novice Designers' Creative Thinking (2026) · cites this paper as [25]
The cited source says what the claim attributes to it.
In the citing paper
“Consistent with 7 and 8 , our analyses demonstrated a growing divergence between post- and pre-ChatGPT reports, with the former increasingly exhibiting lexical characteristics typical of GPTrewritten texts.”
From Style, sentiment, and quality of undergraduate writing in the AI era: A cross-sectional and longitudinal analysis of 4,820 authentic empirical reports (2025) · cites this paper as [8]
The cited source says what the claim attributes to it.
In the citing paper
“People who use AI tend to produce more similar outputs compared to their counterparts who do not—an effect that has been shown in a range of contexts involving open-ended output, such as product design [1, 18] and short-story writing [4].”
From The Role of Human Agency in Human-AI Co-Creativity (2026) · cites this paper as [4]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Furthermore, two corpus-based studies 7,8 compared student-written and ChatGPT-generated essays across a range of linguistic features, such as lexical diversity and syntactic complexity. Both studies reported significant differences, with ChatGPT-generated texts containing more linguistic features associated with greater formality.”
From Style, sentiment, and quality of undergraduate writing in the AI era: A cross-sectional and longitudinal analysis of 4,820 authentic empirical reports (2025) · cites this paper as [8]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“However, unlike 7 and 8 , we found no compelling evidence of a sustained shift on the syntactic level.”
From Style, sentiment, and quality of undergraduate writing in the AI era: A cross-sectional and longitudinal analysis of 4,820 authentic empirical reports (2025) · cites this paper as [8]
Experimental — accuracy not yet measuredIn the citing paper
“While such self-directed engagement has potential benefits, as suggested by recent articles, such as expanding access to new forms of expression and augmenting creativity [16,18]”
From Child-AI Co-Creation: A Review of the Current Research Landscape and a Proposal for Six Design Considerations (2025) · cites this paper as [16]
In the citing paper
“Specifically, a great deal of empirical research that has sought to test the creative capabilities of LLMs have not used “expert” evaluators (e.g., domain experts evaluate creative outcomes). Instead, this body of work has made use of “lay” evaluations of creativity (laypeople evaluate creative outcomes) to substantiate claims of creative generative AI7,8,15,16.”
From Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence (2024) · cites this paper as [15]
In the citing paper
“Finally, of the limited body of work that has researched the creativity of human–AI dyads (e.g.15), most lack the internal validity of controlled and robustly designed experiments.”
From Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence (2024) · cites this paper as [15]
In the citing paper
“Moreover, placing people in the role of an editor can invoke an anchoring effect, where ‘editors’ are highly influenced by the default they are presented with and make insufficient edits as a consequence15.”
From Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence (2024) · cites this paper as [15]
In the citing paper
“When occupying the role of an editor, the presentation of a default body of text for editing restricts autonomy because people are unconsciously influenced by default effects15,46.”
From Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence (2024) · cites this paper as [15]
Citations
Cites
Hardin, G. (1968) · Science · doi:10.1126/science.162.3859.1243 · Cited by 25,167
Prolific.ac—A subject pool for online experiments
Palan, S., & Schitter, C. (2018) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2017.12.004 · Cited by 3,450
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023) · arXiv · doi:10.48550/arxiv.2303.10130 · Cited by 2,330
Social psychology of creativity: A consensual assessment technique.
Amabile, T. M. (1982) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.43.5.997 · Cited by 2,278
Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence
Noy, S., & Zhang, W. (2023) · doi:10.2139/ssrn.4375283 · Cited by 2,157
Experimental evidence on the productivity effects of generative artificial intelligence
Noy, S., & Zhang, W. (2023) · Science · doi:10.1126/science.adh2586 · Cited by 2,150 · cited 2×
“That generative AI particularly benefited less able writers is paralleled in recent studies focusing on other domains in which generative AI has been shown to help less productive workers ( 4 , 5 ).”
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823
Brynjolfsson, E., Li, D., & Raymond, L. (2023) · National Bureau of Economic Research · doi:10.3386/w31161 · Cited by 995 · cited 1×
“…( 3 ), increase quality and efficiency of production of typical white-collar work ( 4 ), promote productivity in customer support relations ( 5 , 6 ), speed up programming tasks ( 7 ), and enhance persuasion messaging ( 8 ), little is known about generative AI’s potential impact on a…”
When and How Artificial Intelligence Augments Employee Creativity
Jia, N., Luo, X., Zheng, F., & Liao, C. (2023) · Academy of Management Journal · doi:10.5465/amj.2022.0426 · Cited by 820
Art and the science of generative AI
Epstein, Z., Hertzmann, A., the Investigators of Human Creativity, Akten, M., Farid, H., Fjeld, J., Frank, M. R., Groh, M., Herman, L., Leach, N., et al. (2023) · Science · doi:10.1126/science.adh4451 · Cited by 788
Flagged first, then by citations
Research works printed but not linked: 18
- W Kenower, 2020, The Cold Open: Facing the Blank Page. Writer's Diget
- K Girotra, 2023, Ideas are Dimes a Dozen: Large Language Models for Idea Generation in Innovation
- If a human creator (author) uses AI in part of the writing of a story, the AI-generated content should be accessible alongside the final story
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- + '##"'& ; #)(#!'"&), Writer study screenshots Condition: Human only Condition: Human with 1 AI idea (before story has been generated) Condition: Human with 1 AI idea (after story has been generated) Condition: Human with 5 AI ideas (before story has been generated) Condition: Human with 5 AI ideas (after story has been generated) Only shown in condition: Human only Only shown in condition: Human only and answered "yes" on previous page Only shown in condition: Human with 1 AI idea Only shown in condition: Human with 5 AI ideas (before rating any story) Only shown in condition: Human with 5 AI ideas (after rating a story) Section 6. Evaluator study screenshot Example of a story from the Human Only condition: the writer said they did not use an outside AI tool Example of a story from the Human Only condition: the writer said they did use an outside AI tool Example of a story from the Human with 1 or 5 AI idea(s) condition but the author did not access the GenAI idea Example of a story from the Human
Cited by
Sorted by citations
Items 901–967 of 967
Use and Effects of LLMs in Peer Review: A Randomized Experiment and Survey at ICML 2026
Kim, S. S. Y., Deng, W. H., Vaughan, J. W., Su, B., Su, W., Agarwal, A., Li, S., Jaggi, M., Goldstein, D. G., Shah, N. B., et al. (2026) · arXiv · doi:10.48550/arxiv.2609.19420
Experimental Evidence on the Learning Impact of Generative AI
Contractor, Z., & Reyes, G. (2026) · arXiv · doi:10.48550/arxiv.2607.08849
Bullich, V. (2025) · Interfaces numériques · doi:10.25965/interfaces-numeriques.5521
Guided Creativity: AI Intermediation for Enhancing Originality and Quality in Visual DesignPreprint
Wu, X., Aridor, G., & Timoshenko, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5371575
Development and Validation of the Generative Ai Engagement ScalePreprint
Zhang, D.-W., Tan, J. Y., Chew, Y. Y., Hew, L., & Choo, J. Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5401033
Zhang, Y., Yao, F., Liu, T., Wang, X., & Zhang, Q. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5482423
The Impact of Digital Technologies on Corporate Decision-Making : A Scoping Review
Nakri, A., & REGRAGUI, Y. (2025) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa65407.2025.11166540
Generative AI Technology and Creativity in Education
Karal, Y. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7579-3.ch005
When LLMs meet creativity: rethinking GFL writing through Human–LLM co-creation
Guo, X. (2026) · Innovation in Language Learning and Teaching · doi:10.1080/17501229.2026.2678315
Mobile anime character generation using artificial intelligence and interactive creative pathways
Gao, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01801-3
López-Nores, M., Antoniou, A., Kamel, M. M., Dahroug, A., & Lykourentzou, I. (2026) · AI · doi:10.3390/ai7100393
Cross-Border Tensions in Creative Service Delivery on Global Professional Digital Platforms
Nemkova, E., Bartsch, F., & Tóth, Z. (2026) · Journal of International Marketing · doi:10.1177/1069031x261490582
Scaling Language Models Show Diminishing Returns in Divergent Creativity
Zhuo, Y., Song, T., Du, H., & Peng, K. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14100244
Wang, W., Zhao, H., Li, X., Wu, S., & Pan, Y. (2026) · IEEE Access · doi:10.1109/access.2026.3740513
Collaborative Speculation with AI Support: Observations from the “Futures Studio”
Mok, L., Li, J., Huynh, B., Van, M. P., & Paredes, P. E. (2026) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3785651.3831556
Made and Real: Neomythism and the Life of Myth Beyond Its Makers
de Lune, R. (2026) · Global Philosophy · doi:10.1007/s10516-026-09804-6
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Suat, O. K. A. Y. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1533.c6201
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Metadata
- Authors
- Doshi, A., & Hauser, O.
- Affiliations
- University College London (GB)
- University of Exeter (GB)
- Corresponding author
- Doshi, Anil
- Hauser, Oliver
- Venue
- Science Advances
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 10
- Issue
- 28
- Pages
- eadn5290-eadn5290
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC11244532
- OpenAlex
- W4400578758
- Semantic Scholar
- 5409f3fa0d9e1a7ffea029557a5871a25b3b1b7e
- Cited by
- 1,350
- References
- 42
- Citations by year
- 2026: 843 · 2025: 409 · 2024: 90 · 2023: 8
- FWCI
- 377.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Science Advances (publishedVersion, cc-by)
- SSRN Electronic Journal (submittedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- Open Research Exeter (University of Exeter) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Harvard Business School
- University of Exeter
- University of Oxford
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Creativity in Education and Neuroscience
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- generative AI, large language models, creativity, short stories, story ideas, individual creativity, novelty, social dilemmas
- MeSH terms
- Artificial Intelligence, Creativity, Humans, Language, Writing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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