Article

AI Interfaces for Reducing Cognitive Load in Decision-Making: Dynamic Adaptation of Information Presentation

Nodirbek YusupbekovTashkent state technical university named after Islam Karimov, Tashkent, UzbekistanUrinjon ChorievTashkent state technical university named after Islam Karimov, Tashkent, UzbekistanAbdulla KhudoyberdievTashkent state technical university named after Islam Karimov, Tashkent, Uzbekistan

Open MINDrepository2026

ABI

Abstract

Complex engineering environments produce heterogeneous information that can overload attention and working memory, reducing decision performance. This study frames information presentation as an adaptive variable and proposes a closed-loop AI interface that adjusts granularity, ordering, modality, and uncertainty encoding using context and interaction signals. The framework targets lower cognitive burden without hiding critical evidence and is evaluated via response time, accuracy, and confidence calibration with machine-learning–based policy learning.

Topics

Human-Automation Interaction and Safety Personal Information Management and User Behavior Neural and Behavioral Psychology Studies

Identifiers

DOI: 10.5281/zenodo.18648308

Citations and references

Cited by 00 references

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