Improving the Validity of Automatically Generated Feedback via Reinforcement Learning

Automatically generating feedback via large language models (LLMs) in intelligent tutoring systems and online learning platforms has the potential to improve the learning outcomes of many students. However, both feedback generation and evaluation are challenging: feedback content has to be valid esp...

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Veröffentlicht in:	arXiv.org
Hauptverfasser:	Scarlatos, Alexander, Smith, Digory, Woodhead, Simon, Lan, Andrew
Format:	Paper
Sprache:	Englisch
Veröffentlicht:	Ithaca Cornell University Library, arXiv.org 12.12.2024
Schlagworte:	Alignment Annotations Data augmentation Distance learning Error analysis Feedback Large language models Machine learning Tutoring
ISSN:	2331-8422
Online-Zugang:	Volltext
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