Optimizing Operator–Agent Interaction in Intelligent Adaptive Interface Design: A Conceptual Framework
Bibliographic record
Abstract
Intelligent adaptive interfaces (IAIs) are emerging technologies that promise opportunities for enhancing performance in complex sociotechnical environments, such as multiple uninhabited aerial vehicle (UAV) control. However, a lack of established design guidelines for such advanced interfaces makes many designs costly and ineffective. In this paper, a generic conceptual framework for developing IAIs is proposed to guide interface design. The framework integrates a user-centered design approach with the concept of proactive use of adaptive intelligent agents (AIAs), aiming at maximizing overall system performance. Based on existing design approaches, identified challenges, and IAI design needs, the framework uses a multiple-agent hierarchical structure to allocate tasks between operators and agents for optimizing operator-agent interaction. These AIAs provide interface aids as a means of reducing operator workload, and increasing situation awareness and operational effectiveness. The framework and associated IAI models provide guidance to design a knowledge-based system, such as a UAV control station interface.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".