Summary reasoning traces from LLMs maintain task performance and increase trust and appeal relative to answer-only or full-trace conditions, but none of the formats improve users' metacognitive calibration on reasoning tasks.
Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry
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The authors introduce Agentivism as a learning theory for human-AI interaction that explains how durable capability develops through selective delegation, epistemic monitoring, reconstructive internalization, and transfer under reduced support.
LLM use for essay writing correlates with reduced brain network connectivity, lower self-reported ownership, and poorer recall of one's own content compared to unaided or search-based writing.
LLM-based multimodal feedback matches educator feedback in learning outcomes but exceeds it in student perceptions of quality, engagement, and reduced cognitive load.
Proposes a modular agentic architecture for educational LLMs with stage-specific modules to incorporate pedagogical advice and improve controllability over monolithic chatbots.
Advanced LLMs improve EFL writing scores and diversity for lower-proficiency students but correlate with lower expert ratings on deep coherence, acting more as crutches than scaffolds.
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