Augmented Cognition Meets AI: Enhancing Human Performance with Real-Time, Adaptive, and Trustworthy Intelligence
Bibliographic record
Abstract
This panel explores how advancements in artificial intelligence (AI) are transforming the field of augmented cognition (AC). Traditionally focused on adapting system behavior based on user state, augmented cognition is now expanding into new territory with AI technologies that can sense, interpret, and respond in real time. Emerging AI (e.g., large language models, multimodal machine learning, and generative agents) enables new modes of measurement, prediction, and interaction for AI-based augmented cognition. Panelists will highlight how AC systems differ from earlier automation approaches, offering examples from diverse application domains. Use cases include AI copilots that monitor driver fatigue and distraction, adaptive digital teammates that guide workers through complex tasks, and cognitive assistants that support rapid decision- making in high-pressure environments. The conversation will address key design considerations such as the level of human involvement, how to calibrate interaction, and what AI capabilities are still needed. They will also examine critical implementation challenges, including data privacy, cybersecurity, user trust, and ethical concerns. As AI increasingly acts as a synthetic collaborator, the panel will consider how augmented cognition is defined and applied. They will identify research gaps, propose future directions, and explore how human-AI systems can enhance safety, performance, and decision-making across complex domains.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".