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.
The Agency Allocation Framework reframes learner agency in AI-mediated education as the explicit allocation of decision authority across human and artificial actors, supported by a literature review and illustrative example.
Critical Inker scaffolds critical reflection during AI-assisted writing via Socratic questioning and visual logical-error feedback, reporting 91.2% argument overlap with ground truth and 87% validity accuracy in a pilot evaluation.
RelianceScope is a new analytical framework that maps AI reliance into nine engagement patterns across help-seeking and response-use, situated in students' prior knowledge and instructional context, validated on programming course logs.
Trust-driven routine use of generative AI is linked to reduced cognitive engagement in STEM students, with higher technophilic traits increasing vulnerability.
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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.