Using Augmentation-Based AI Tool at Work: A Daily Investigation of Learning-Based Benefit and Challenge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Augmentation-based artificial intelligence (AI) artifacts are increasingly being incorporated into the workplace. The coupling of employees and AI tools, given their complementary strengths, expands and expedites employees’ access to information and affords important learning opportunities. However, existing research has yet to fully understand the learning-based benefits and challenges for employees in augmentation. Integrating insights from AI augmentation literature and cognitive load theory, we conducted a daily diary study to understand employees’ experience using augmentation-based AI at work on a daily basis. We theorized and found that, on the one hand, frequent usage of augmentation-based AI during a workday was associated with greater knowledge gain and subsequently better task performance at the end of the workday. On the other hand, using augmentation-based AI frequently also led employees to experience information overload, which in turn impaired their performance and recovery at the end of the workday. In addition to elucidating the countervailing mechanisms, we identified employee openness to experience as a dispositional factor, and positive affect as a momentary state that shaped the effects of using augmentation-based AI over the workday. Our research has implications for understanding AI augmentation dynamics from a learning-based perspective, as well as AI’s impact on employees at large.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it