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.
Generative AI: A double-edged sword for creative thinking learning — Evidence from facial expressions and fNIRS
4 Pith papers cite this work. Polarity classification is still indexing.
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LLM-based multimodal feedback matches educator feedback in learning outcomes but exceeds it in student perceptions of quality, engagement, and reduced cognitive load.
Low dropout risk CS1 students exhibited three distinct weekly learning strategies while high-risk students showed nine varied patterns, some temporary and recoverable and others signaling imminent dropout.
Survey of Chinese math teacher trainees finds basic AI-TPACK levels, self-efficacy helps, and strong teaching beliefs may hinder progress.
citing papers explorer
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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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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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Understanding Self-Regulated Learning Behavior Among High and Low Dropout Risk Students During CS1: Combining Trace Logs, Dropout Prediction and Self-Reports
Low dropout risk CS1 students exhibited three distinct weekly learning strategies while high-risk students showed nine varied patterns, some temporary and recoverable and others signaling imminent dropout.
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The Status Quo and Future of AI-TPACK for Mathematics Teacher Education Students: A Case Study in Chinese Universities
Survey of Chinese math teacher trainees finds basic AI-TPACK levels, self-efficacy helps, and strong teaching beliefs may hinder progress.