Preparing for Tomorrow's Teamwork: Insights From eSports on How Human Expertise Shapes Training Needs for AI‐Integrated Work
Bibliographic record
Abstract
ABSTRACT As organizations increasingly adopt human‐AI teams (HATs), understanding how to enhance team performance is paramount. A crucially underexplored area for supporting HATs is training, particularly helping human teammates to work with these inorganic counterparts. Indeed, research on training for HATs is limited, often relying on human team training frameworks, failing to consider the humans' expertise‐based training needs and how this may affect collaboration with AI. To bridge this gap, we interviewed competitive eSports athletes ( N = 22), a group experienced in training with AI, to discuss the gaps in current human–AI training and their desires for future training that better supports humans in AI‐integrated work. Using the quantitative ethnography (QE) tool, epistemic network analysis (ENA), we examine these training needs and how they vary based on the participants' task expertise. Our findings indicate that current training methods focus on using AI for taskwork training, with significant expertise differences identified due to diverging perceptions on this taskwork focus as well as tensions related to balancing adaptability with predictability and clashing attitudes toward training with AI. Future training must evolve to deepen understanding and trust between humans and AI, focusing on socio‐emotional bonds and role awareness to offer greater benefits for teaming. We conclude with three actionable recommendations for organizational research on training for HATs to expand these findings to broader contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".