通过认知对齐避免AI导致思维被动，提升数据素养
Disrupting Cognitive Passivity: Rethinking AI-Assisted Data Literacy through Cognitive Alignment
- 提出认知对齐框架，匹配用户思维需求与AI交互方式
- 不同组合下，可避免思维被动或认知摩擦
- 适合教育、数据分析等需培养独立思考的场景
AI聊天机器人正越来越多地担任数据解析、可视化与推理中的协作者或导师角色。然而，其默认的全面一次性回应模式可能削弱从业者通过自主思考发展数据素养的机会，导致认知被动。基于实证研究与理论分析，我们提出：打破认知被动需采用更动态、自适应的策略——认知对齐框架。该框架将用户认知需求（接收型或思辨型）与AI交互模式（传递型或思辨型）进行匹配，不匹配则引发认知被动或摩擦。本文进一步探讨其对数据素养的启示，并提出未来研究的开放问题。
原文摘要 · Abstract (English)
AI chatbots are increasingly stepping into roles as collaborators or teachers in analyzing, visualizing, and reasoning through data and domain problem. Yet, AI's default assistant mode with its comprehensive and one-off responses may undermine opportunities for practitioners to develop literacy through their own thinking, inducing cognitive passivity. Drawing on evidence from empirical studies and theories, we argue that disrupting cognitive passivity necessitates a nuanced approach: rather than simply making AI promote deliberative thinking, there is a need for more dynamic and adaptive strategy through cognitive alignment -- a framework that characterizes effective human-AI interaction as a function of alignment between users' cognitive demand and AI's interaction mode. In the framework, we provide the mapping between AI's interaction mode (transmissive or deliberative) and users' cognitive demand (receptive or deliberative), otherwise leading to either cognitive passivity or friction. We further discuss implications and offer open questions for future research on data literacy.
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