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

Yongsu Ahn, Nam Wook Kim, Benjamin Bach

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.

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

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.

Paper Structure

This paper contains 12 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Challenges: When AI meets Data Literacy
  3. AI's default mode forces cognitive passivity
  4. AI short-circuits multi-level reasoning in data problem
  5. Disrupting cognitive passivity is possible, but context-dependent
  6. Cognitive Alignment: Rethinking AI-assisted Literacy
  7. Future Research Directions
  8. Toward adaptive alignment in AI tools
  9. Reasoning as a measure of data literacy
  10. Multi-level reasoning as design consideration
  11. Questions for the community
  12. Conclusion

Figures (1)

  • Figure 1: Study overview. The current AI's default mode, producing ready-made responses at a one-off response, easily induces cognitive passivity, harming opportunities for practitioners to foster data literacy. Beyond simply transforming AI into practicing reflective thinking, we suggest that this should be understood as a function of alignment between user's cognitive demand and AI's interaction mode. We provide a high-level description of potential cases where (mis)aligned cases can lead to a successful AI-assisted data analysis or different types of cognitive effects.