This study examines the use of Generative Pre-Trained Transformers (GPT) in delivering automated explanatory feedback in tutor training. It addresses the difficulties of providing nuanced, domain-specific feedback in real time. By employing sequence labeling and evaluating praise components, the research demonstrates that GPT can effectively identify paired feedback types. The study’s findings include a correlation of assessment quality between human judgment and a Modified Intersection over Union score, showing promising results for real-time educational applications.
Automated explanatory feedback systems play a crucial role in facilitating learning for a large cohort of learners by offering feedback that incorporates explanations...
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