When AI does more of the thinking, do ideas get worse? Four experiments found a workflow effect
Across four randomized online experiments with 728 participants, high-assistance or high-delegation workflows reduced reported planning and checking. In one 176-person experiment, blind experts scored proposals 3.95 versus 4.78 out of seven—but the workflows differed in more than delegation alone.
The short version
- What happened
- Four randomized online experiments recruited 150, 162, 176 and 240 valid participants—728 in total—to complete short product, service, public-space or employee-benefit proposal tasks.
- Who it affects
- For managers, teachers and professional teams, the study supports testing workflows that require a human problem frame, alternatives and a final evidence check before accepting fluent AI output—not banning AI or assuming every use weakens thinking.
- What remains unproven
- Four separate experiments did not estimate the full proposed pathway in one sample
Keep in mind
Four separate experiments did not estimate the full proposed pathway in one sample
Online Credamo samples and short simulated innovation tasks
Compound workflow manipulation changed response detail, effort instructions and responsibility
Metacognitive disengagement and offloading were self-reported
Expert ratings were reliable but partly subjective
No workplace productivity, delivered-project, longitudinal or skill-retention outcomes
AI literacy was measured, not experimentally trained
Source analysis · 1 linked source. Primary analysed source date: 28 October 2026.
Read the full report · 4 min The evidence, methods, implications and limitations
More complete AI help changed how participants approached the task
A new peer-reviewed study tested a concern familiar to many knowledge workers: when an AI supplies a nearly complete solution, do people stop planning, checking and reflecting? Across four separate randomized online experiments, 728 participants used researcher-configured AI assistants for short innovation tasks. High-assistance groups received solution-oriented responses; low-assistance groups received brief factual help and were expected to develop the core proposal themselves. The researchers measured what they call task-level metacognitive disengagement with a new nine-item self-report scale covering reduced planning, weaker monitoring and absent reflection.
The first experiment, with 150 participants designing a smart-home proposal, found higher disengagement in the high-assistance group, a standardized difference of d=0.67. A 162-person service-design experiment linked that difference partly to reported cognitive offloading. These results concern an immediate task response, not a permanent loss of skill. The paper validates the scale across the four samples, but it does not follow participants to see whether the effect persists or accumulates.[1]
Blind expert ratings found a quality gap, with an important causal limit
The clearest performance test involved 176 participants designing a public-space renewal proposal. Three experts in urban planning or community design, blind to the assigned condition, rated novelty, usefulness and developability on seven-point scales. High-delegation proposals averaged 3.95, against 4.78 for low delegation; d=0.75. The high-delegation group also reported more cognitive offloading and disengagement. A fourth, 240-person experiment found the negative indirect association was stronger for a more complex employee-benefits task and weaker among people reporting higher AI literacy.
The comparison should be read as a workflow effect, not proof that AI assistance by itself makes ideas worse. High-delegation participants received more detailed responses, were encouraged to rely on the tool and held less responsibility for core content. The low-delegation group received only factual support. Those bundled differences prevent the study from isolating delegation alone. All tasks were short online simulations, expert scores remain partly subjective, and no field team, delivered project, productivity measure or later skill test was observed.[1]
The distinction that matters
What changed between the two workflows
The experiment compared packages of instructions and AI support, not simply AI present versus AI absent.
High delegation
Detailed AI responses and encouragement to offload core work
Lower expert-rated proposal quality in Experiment 3
Low delegation
Factual AI help while the participant developed core content
Higher average novelty, usefulness and developability scores
Unresolved
No long-term or workplace follow-up
Skill retention and real project outcomes remain unknown