Passive, copy-based use of AI erodes workers' confidence, ownership and sense of meaningful work — and some harms persist after returning to manual tasks; by contrast, active, draft-first collaboration with AI preserves worker agency and avoids these psychological costs.
Artificial intelligence (AI) promises major productivity gains, but it also raises fundamental questions about how technology can reshape people's relationship to their work. Historical debates over industrialization warned that technological change could undermine people's connection to work and sense of meaning. Similar concerns now surround AI, where the key issue may not be whether AI is used, but how it is used. Across a pre-registered experiment (N = 269) and a follow-up survey (N = 270), we examine how different modes of AI use affect the confidence individuals have in completing work without AI assistance (self-efficacy), their sense of ownership over task output, and the meaning they perceive in their work. Participants completed occupation-specific writing tasks under one of three conditions: no AI use, passive AI use (copying AI-generated content), or active collaboration (drafting first and then using AI to refin). We find that passive use undermined self-efficacy, psychological ownership, and work meaningfulness, with declines in efficacy and meaningfulness persisting even when participants returned to manual work. In contrast, collaborative AI use preserved psychological connection to the task, producing outcomes comparable to independent work. Although passive use initially boosted enjoyment and satisfaction, these benefits reversed once participants resumed manual work. A complementary real-world survey mirrored these patterns across tasks beyond writing. Together, these findings show that the psychological consequences of AI use hinge on how it is integrated into human workflows, underscoring that strategies promoting active, collaborative use may help capture AI's productivity benefits while preserving human workers' agency, competence, and connection to their work.
Summary
Main Finding
Relying passively on AI to generate work (copying/pasting AI output) reduced workers’ AI-independent self-efficacy, psychological ownership of outputs, and perceived meaningfulness of the task. These negative effects on self-efficacy and meaning persisted after participants returned to manual work. By contrast, an active, human-first collaboration mode (write a draft, then use AI to refine it) preserved self-efficacy, ownership, and meaning at levels comparable to no-AI work. Passive use produced higher immediate enjoyment and satisfaction with outcomes, but those short-term benefits reversed when participants later performed the task without AI. A complementary real-world survey produced correlational patterns consistent with the experiment.
Key Points
- Conditions compared (random assignment in experiment):
- No AI Use (manual work)
- Copy & Paste AI (passive use: directly using AI-generated content)
- First Human Then AI (active collaboration: human draft refined by AI)
- Primary outcomes (measured immediately after primary writing task):
- AI-independent self-efficacy: Copy & Paste M=5.16 (SD=1.32) < No AI M=5.63 (SD=1.38); ANOVA F(2,266)=3.54, p=.030, η²p=.026. First Human Then AI did not differ from No AI.
- Psychological ownership: Copy & Paste M=4.35 (SD=1.30) << First Human Then AI M=5.26, No AI M=5.34; F(2,266)=27.12, p<.001, η²p=.169.
- Meaningfulness: Copy & Paste M=4.94 (SD=1.59) < No AI M=5.54, First Human Then AI M=5.46; F(2,266)=5.26, p=.006, η²p=.038.
- Durability (assessed after a subsequent manual writing task):
- Self-efficacy remained lower for participants who had previously used Copy & Paste AI (M=4.95) than for No AI (M=5.66); F(2,266)=6.25, p=.002, η²p=.045.
- Psychological ownership rebounded after manual work (no significant differences post-task).
- Meaningfulness still lower for prior Copy & Paste users (F(2,266)=3.40, p=.035, η²p=.025).
- Secondary outcomes (enjoyment, outcome satisfaction):
- Immediate: Copy & Paste reported higher enjoyment (M=5.66) and substantially higher outcome satisfaction (M=5.80) than No AI (satisfaction M=4.50); outcome satisfaction F(2,266)=19.64, p<.001, η²p=.129.
- After subsequent manual work, prior Copy & Paste users reported lower enjoyment and satisfaction than the other groups (e.g., subsequent satisfaction M=4.12 vs First Human Then AI M=5.42; F=10.98, p<.001).
- Follow-up correlational survey (N≈270 working adults):
- Passive AI reliance correlated negatively with self-efficacy (r=−.45), psychological ownership (r=−.22), and outcome satisfaction (r=−.16).
- Active AI collaboration correlated positively with self-efficacy (r=.40), psychological ownership (r=.30), and outcome satisfaction (r=.14).
- Correlational patterns held when respondents imagined being without AI.
Data & Methods
- Preregistered between-subjects experiment (N = 269). Participants were professionals assigned to occupation-specific writing tasks (role-specific prompts). Randomly assigned to one of three conditions described above. Primary measures focused on AI-independent self-efficacy (pre-registered focal outcome), psychological ownership, and perceived meaningfulness; exploratory measures included task enjoyment and outcome satisfaction. After the primary task, all participants completed a second (subsequent) manual writing task to assess persistence of effects.
- Analyses: one-way ANOVAs with pairwise LSD comparisons; mixed ANOVAs reported in supplement. Effect sizes (partial η²) ranged from small-to-moderate for key effects (e.g., ownership η²p=.169, satisfaction η²p=.129).
- Complementary cross-sectional survey (N ≈ 270) of working adults measured self-reported styles of AI use (passive vs collaborative) across varied professional tasks and correlated these with the same psychological outcomes. Correlational design — no causal inference.
Caveats: manuscript provided is an unedited accepted version (final edits pending). Primary experiment focuses on writing tasks; generalizability to all task types is supported but not proven. Survey results are correlational.
Implications for AI Economics
- Productivity vs. psychological capital trade-off: Passive AI substitution can raise output quality/speed (and immediate satisfaction) but may erode workers’ task-specific self-efficacy and sense of meaning—forms of psychological capital that affect learning, persistence, and future productivity. Economic evaluations of AI adoption should account for these psychological externalities, not only short-term productivity gains.
- Human capital depreciation and dynamic labor supply: Repeated passive reliance on AI risks skill atrophy (lower confidence and possibly reduced effort to maintain competency), which can decrease long-run worker productivity, reduce human capital accumulation, and alter labor supply decisions (e.g., reduced willingness to perform tasks without AI). Models of automation should incorporate endogenous skill depreciation and morale effects.
- Compensation, retention, and returns to skills: Firms that substitute AI for core tasks may face higher turnover, lower intrinsic motivation, and reduced discretionary effort—costs that can offset productivity gains. Conversely, designing workflows that position AI as an augmentation tool (human-first collaboration) preserves ownership and meaning and may sustain higher long-run returns to skills and investment in training.
- Task and technology design: Policy and firm-level interventions (training, interface design, role definitions, metrics) should favor augmentation/collaboration modes that preserve human judgment and responsibility. Incentive structures and performance metrics should avoid encouraging pure copy-paste AI workflows without human engagement.
- Measurement and policy: Productivity statistics and welfare analyses should include measures of worker well-being, task meaningfulness, and skill trajectories. Labor market policy (retraining subsidies, occupational licensing, job redesign incentives) should consider psychological impacts of AI integration.
- Research directions for economists: quantify long-run effects of passive vs. collaborative AI use on wage trajectories, career mobility, firm-level productivity, turnover, and human capital investment; run field experiments on AI tool design and usage norms; model firm adoption decisions that internalize psychological externalities.
Bottom line: how AI is integrated into work matters economically. Active, collaborative use appears to capture AI’s performance benefits while preserving worker agency, competence, and meaning; passive substitution risks short-term gains at the expense of durable psychological and human-capital costs.
Assessment
Claims (15)
| Claim | Direction | Confidence | Outcome | Details |
|---|---|---|---|---|
| Passive use of AI (copying AI-generated output) reduces workers' self-efficacy. Worker Satisfaction | negative | high | self-efficacy (confidence to complete tasks without AI) |
n=269
0.6
|
| Passive use of AI reduces psychological ownership of the produced outputs. Worker Satisfaction | negative | high | psychological ownership of outputs |
n=269
0.6
|
| Passive use of AI reduces perceived meaningfulness of work. Worker Satisfaction | negative | high | perceived meaningfulness of work |
n=269
0.6
|
| Some declines (in self-efficacy and meaningfulness) from passive AI use persist after participants return to manual work. Worker Satisfaction | negative | high | self-efficacy; perceived meaningfulness (measured post-return to manual work) |
n=269
0.6
|
| Passive AI use produced an initial increase in enjoyment/satisfaction that reversed once participants returned to manual work. Worker Satisfaction | mixed | medium | enjoyment/satisfaction |
n=269
0.36
|
| Active, collaborative AI use (human drafts first, then uses AI to refine) preserves self-efficacy at levels comparable to independent (no-AI) work. Worker Satisfaction | null_result | high | self-efficacy (confidence to complete tasks without AI) |
n=269
0.6
|
| Active, collaborative AI use preserves psychological ownership of outputs at levels comparable to independent work. Worker Satisfaction | null_result | high | psychological ownership of outputs |
n=269
0.6
|
| Active, collaborative AI use preserves perceived meaningfulness of work at levels comparable to independent work and does not produce the lasting psychological costs seen with passive use. Worker Satisfaction | null_result | high | perceived meaningfulness of work (including post-return) |
n=269
0.6
|
| A complementary real-world survey (N = 270) across diverse tasks reproduced the experimental pattern, suggesting external validity beyond the lab writing tasks. Worker Satisfaction | positive | medium | self-reported relationships between AI-use mode and psychological outcomes (self-efficacy, ownership, meaningfulness) across real-world tasks |
n=270
0.36
|
| The experiment was pre-registered, used occupation-specific writing tasks, and employed a between-subjects design with three conditions (No-AI, Passive AI, Active collaboration). Other | null_result | high | n/a (methodological claim) |
n=269
0.6
|
| Outcomes reported are primarily self-reported psychological measures rather than objective productivity metrics. Other | null_result | high | measurement type (self-reported psychological outcomes) |
n=269
0.6
|
| Firms that adopt passive, copy-based AI workflows risk psychological costs that could offset short-run productivity gains from AI. Firm Productivity | negative | speculative | inferred organizational outcomes (productivity offsets, not directly measured) |
0.06
|
| Persistent declines in self-efficacy after passive AI exposure suggest potential for skill atrophy and slower reversion when tasks must be performed without AI. Skill Obsolescence | negative | speculative | inferred human-capital outcomes (skill atrophy, reversion costs; not directly measured) |
0.06
|
| Design and policy interventions that encourage active human contributions (e.g., draft-first workflows, co-creation interfaces, training) can help preserve worker agency and mitigate psychological costs. Training Effectiveness | positive | medium | inferred mitigation of psychological harms (not directly measured at firm scale) |
0.36
|
| Economic evaluations of AI adoption should include psychological and human-capital externalities (effects on self-efficacy, skill depreciation, job satisfaction) to fully account for welfare and productivity dynamics. Other | positive | speculative | recommended evaluation scope (inclusion of psychological/human-capital measures) |
0.06
|