The Human Side of AI at Work: How AI Usage Affects the Perceptions of Work and Workplace Behaviors
Bibliographic record
Abstract
The rapid integration of Artificial Intelligence (AI) into workplace settings has fundamentally transformed the way employees perform their tasks, communicate, and perceive their own and others’ roles at work. As AI usage continues to grow, it is reshaping many aspects of the organizational landscape in ways that remain underexplored, presenting both opportunities and challenges. Grounded in the intersection of psychology and organizational behavior, this symposium brings together researchers to explore these critical issues related to AI adoption. Our discussions focus on five important topics that connect AI usage with psychological and organizational dynamics. These include the impact of AI on perceptions of hard work and task engagement, the implications of varying levels of AI integration for self-efficacy and work meaningfulness, the navigation of AI disclosure in the workplace, individuals experiences of receiving empathy from AI, and the observer’s perceptions of a coworker’s AI usage. AI Increases Quality of Work but Diminishes its Perceived Value Author: Alexander Eng; Asia School of Business How Confident Are You With and Without AI? Psychological Consequences of Using Generative AI Author: Elena Hayoung Lee; USC Marshall School of Business, University Of Southern California Author: Yidan Yin; Author: Cheryl Wakslak; University of Southern California To Share or Not to Share: Understanding AI Disclosures in the Workplace Author: Alys Ferragamo; University of Southern California Author: Yidan Yin; Choosing To Receive Empathy from AI versus Human Expressers Author: Joshua Wenger; The Pennsylvania State University Author: C. Daryl Cameron; The Pennsylvania State University Author: Michael Inzlicht; University of Toronto Challenge and Hindrance Appraisals of a Coworkers' Use of Artificial Intelligence Author: Sang Hoon HAN; The Ohio State University Author: Hun Whee Lee; The Ohio State University Author: Kaifeng Jiang; Peking University
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".