Disrupting Cognitive Passivity: Rethinking AI-Assisted Data Literacy through Cognitive Alignment

TL;DR

This paper proposes a cognitive alignment framework to enhance AI-assisted data literacy by balancing AI interaction modes with users' cognitive demands, aiming to reduce cognitive passivity.

Contribution

It introduces a nuanced approach to human-AI interaction through cognitive alignment, connecting AI modes with user demands to improve data literacy and engagement.

Findings

Mapping between AI interaction modes and user cognitive demands

Framework to prevent cognitive passivity and friction

Implications for designing adaptive AI educational tools

Abstract

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.