Uncovering Cognitive Costs of Using Artificial Intelligence Tools at Work: A Daily Diary Study
Bibliographic record
Abstract
Despite the growing implementation of artificial intelligence (AI) artifacts in the workplace, little is known about how utilizing AI in completing jobs may impact employees’ productivity and wellbeing. Extending prior research that assumed the potential positive impact of AI usage, we drew on the cognitive load theory and conducted a daily diary study to uncover the cognitive demands employees may encounter when utilizing AI-based information tools at work. We collected data over five workdays from 231 call center representatives working for a major bank located in South Korea. These representatives utilize an AI-based supporting system to address customers’ requests over phone calls on a daily basis. We found that on days of frequent AI usage, representatives were more likely to experience information overload, and thereby receiving poorer performance ratings from supervisors and having difficulty detaching from work in the evening. In addition, we found that the detrimental effect of using AI at work is exacerbated by job tenure and is mitigated by openness to experience. Our research has implications for understanding the AI-human collaboration dynamics as well as AI’s impacts on employees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".