Using Large Language Models to Provide Explanatory Feedback to Human Tutors
This work addresses the problem of improving tutor training and feedback in educational settings, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackles the challenge of providing real-time explanatory feedback to human tutors in domain-specific environments by presenting two approaches for binary classification of praise responses, achieving an F1 score of 0.811 for effective praise and 0.350 for ineffective praise, and introduces progress towards using large language models for enhanced explanatory feedback.
Research demonstrates learners engaging in the process of producing explanations to support their reasoning, can have a positive impact on learning. However, providing learners real-time explanatory feedback often presents challenges related to classification accuracy, particularly in domain-specific environments, containing situationally complex and nuanced responses. We present two approaches for supplying tutors real-time feedback within an online lesson on how to give students effective praise. This work-in-progress demonstrates considerable accuracy in binary classification for corrective feedback of effective, or effort-based (F1 score = 0.811), and ineffective, or outcome-based (F1 score = 0.350), praise responses. More notably, we introduce progress towards an enhanced approach of providing explanatory feedback using large language model-facilitated named entity recognition, which can provide tutors feedback, not only while engaging in lessons, but can potentially suggest real-time tutor moves. Future work involves leveraging large language models for data augmentation to improve accuracy, while also developing an explanatory feedback interface.