AI-assisted data literacy benefits from a cognitive alignment framework that maps AI modes (transmissive or deliberative) to user demands (receptive or deliberative) to reduce passivity and friction.
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HINA introduces heterogeneous interaction networks to model and analyze multi-entity learning processes at individual, dyadic, and group levels, demonstrated via a case study on AI-mediated collaborative learning.
Trust-driven routine use of generative AI is linked to reduced cognitive engagement in STEM students, with higher technophilic traits increasing vulnerability.
citing papers explorer
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Disrupting Cognitive Passivity: Rethinking AI-Assisted Data Literacy through Cognitive Alignment
AI-assisted data literacy benefits from a cognitive alignment framework that maps AI modes (transmissive or deliberative) to user demands (receptive or deliberative) to reduce passivity and friction.
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Heterogeneous Interaction Network Analysis (HINA): A New Learning Analytics Approach for Modelling, Analyzing, and Visualizing Complex Interactions in Learning Processes
HINA introduces heterogeneous interaction networks to model and analyze multi-entity learning processes at individual, dyadic, and group levels, demonstrated via a case study on AI-mediated collaborative learning.
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Thinking Less, Trusting More: GenAI's Impacts on Students' Cognitive Habits
Trust-driven routine use of generative AI is linked to reduced cognitive engagement in STEM students, with higher technophilic traits increasing vulnerability.