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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2504.13684 |
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| _version_ | 1866912334235041792 |
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| author | Xiangrong Zhu Xu, Yuan Liu, Tianjian Sun, Jingwei Zhang, Yu Tong, Xin |
| author_facet | Xiangrong Zhu Xu, Yuan Liu, Tianjian Sun, Jingwei Zhang, Yu Tong, Xin |
| contents | Human cognition is constrained by processing limitations, leading to cognitive overload and inefficiencies in knowledge synthesis and decision-making. Large Language Models (LLMs) present an opportunity for cognitive augmentation, but their current reactive nature limits their real-world applicability. This position paper explores the potential of context-aware cognitive augmentation, where LLMs dynamically adapt to users' cognitive states and task environments to provide appropriate support. Through a think-aloud study in an exhibition setting, we examine how individuals interact with multi-modal information and identify key cognitive challenges in structuring, retrieving, and applying knowledge. Our findings highlight the need for AI-driven cognitive support systems that integrate real-time contextual awareness, personalized reasoning assistance, and socially adaptive interactions. We propose a framework for AI augmentation that seamlessly transitions between real-time cognitive support and post-experience knowledge organization, contributing to the design of more effective human-centered AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13684 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intelligent Interaction Strategies for Context-Aware Cognitive Augmentation Xiangrong Zhu Xu, Yuan Liu, Tianjian Sun, Jingwei Zhang, Yu Tong, Xin Human-Computer Interaction Human cognition is constrained by processing limitations, leading to cognitive overload and inefficiencies in knowledge synthesis and decision-making. Large Language Models (LLMs) present an opportunity for cognitive augmentation, but their current reactive nature limits their real-world applicability. This position paper explores the potential of context-aware cognitive augmentation, where LLMs dynamically adapt to users' cognitive states and task environments to provide appropriate support. Through a think-aloud study in an exhibition setting, we examine how individuals interact with multi-modal information and identify key cognitive challenges in structuring, retrieving, and applying knowledge. Our findings highlight the need for AI-driven cognitive support systems that integrate real-time contextual awareness, personalized reasoning assistance, and socially adaptive interactions. We propose a framework for AI augmentation that seamlessly transitions between real-time cognitive support and post-experience knowledge organization, contributing to the design of more effective human-centered AI systems. |
| title | Intelligent Interaction Strategies for Context-Aware Cognitive Augmentation |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.13684 |