Using Large Language Models to Provide Explanatory Feedback to Human
  Tutors
Jionghao Lin Danielle R. Thomas Feifei Han Shivang Gupta Wei Tan Ngoc Dang Nguyen Kenneth R. Koedinger
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.