Disrupting Cognitive Passivity: Rethinking AI-Assisted Data Literacy through Cognitive Alignment
This addresses the issue of ineffective AI-assisted learning for practitioners in data analysis, though it appears incremental as it builds on existing theories without new empirical validation.
The paper tackles the problem of AI chatbots inducing cognitive passivity in data literacy by proposing a cognitive alignment framework to map AI interaction modes with users' cognitive demands, aiming to improve human-AI collaboration without specifying concrete numerical results.
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.