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
7 Pith papers cite this work. Polarity classification is still indexing.
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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.
citing papers explorer
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Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition
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.
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Agentivism: a learning theory for the age of artificial intelligence
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.
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Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task
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.
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LLM-based Multimodal Feedback Produces Equivalent Learning and Better Student Perceptions than Educator Feedback
LLM-based multimodal feedback matches educator feedback in learning outcomes but exceeds it in student perceptions of quality, engagement, and reduced cognitive load.
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Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance
Proposes a modular agentic architecture for educational LLMs with stage-specific modules to incorporate pedagogical advice and improve controllability over monolithic chatbots.
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The Crutch or the Ceiling? How Different Generations of LLMs Shape EFL Student Writings
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.
- An Empirical Study to Understand How Students Use ChatGPT for Writing Essays