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Found 5 papers, 2 papers with code
How Can I Get It Right? Using GPT to Rephrase Incorrect Trainee Responses
Our findings indicate that: 1) using a few-shot approach, the GPT-4 model effectively identifies correct/incorrect trainees' responses from three training lessons with an average F1 score of 0. 84 and an AUC score of 0. 85; and 2) using the few-shot approach, the GPT-4 model adeptly rephrases incorrect trainees' responses into desired responses, achieving performance comparable to that of human experts.
Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis
Taken together, offering early conceptual support to students with low learning rates could make classroom practice with AI tutors more effective.
Using Think-Aloud Data to Understand Relations between Self-Regulation Cycle Characteristics and Student Performance in Intelligent Tutoring Systems
We discuss system re-design opportunities to add SRL support during stages of processing and paths forward for using machine learning to speed research depending on the assessment of SRL based on transcription of think-aloud data.
Comparative Analysis of GPT-4 and Human Graders in Evaluating Praise Given to Students in Synthetic Dialogues
We found that both zero-shot and few-shot chain of thought approaches yield comparable results.
Designing for human-AI complementarity in K-12 education
Recent work has explored how complementary strengths of humans and artificial intelligence (AI) systems might be productively combined.
