Academic Journal
Does Event Density Change When AI Explanations Are Most Effective for Monitoring and Safety Recovery?
| Τίτλος: | Does Event Density Change When AI Explanations Are Most Effective for Monitoring and Safety Recovery? |
|---|---|
| Συγγραφείς: | Li, HeSen1 (AUTHOR), Pan, WeiCheng1 (AUTHOR), Zhang, WanQing1 (AUTHOR), Wu, JieHong2 (AUTHOR), Lou, WenXing1 (AUTHOR), Tan, YuPeng3 (AUTHOR), Chen, Hong4 (AUTHOR) chenhong_kgu@163.com |
| Πηγή: | International Journal of Human-Computer Interaction. Jul2026, p1-18. 18p. 6 Illustrations. |
| Θεματικοί όροι: | *Flight crews, *Commercial aeronautics, Flight simulators, Evaluation methodology, Synchronization |
| Περίληψη: | AbstractThis study examined how Explanation Timing (immediate, delayed, none) and Event Density (low, high) in an AI-assisted system affect pilots’ monitoring and safety recovery during cruise operations in general aviation. A 3×2 between-subjects flight simulator experiment was conducted with 90 aviation students. Performance was measured using state-change detection rate (SDR), recovery success rate (RS), and recovery time (RT). Results showed significant main effects of Explanation Timing on all outcomes, with immediate explanations producing the best performance. A significant interaction between Explanation Timing and Event Density was observed for RS, with stronger effects under high event density. Findings indicate that the effectiveness of AI explanations depends on both their presence and temporal alignment with pilots’ ongoing situation assessment. These results provide evidence for optimizing explanation timing in AI systems to enhance monitoring and recovery performance in flight operations. [ABSTRACT FROM AUTHOR] |
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