Machi Shimmei

Steven Coyne, Diana Galvan-Sosa, Ryan Spring et al.

This study examines feedback in English as a Foreign Language (EFL) writing contexts, focusing on written corrective feedback (WCF). Large language models (LLMs) can provide WCF at scale, but aligning them with pedagogical best practices remains an ongoing challenge. WCF meeting criteria like factuality or relevance may still be unsuitable for learning contexts, highlighting the need for extrinsic evaluation based on the learner's perspective. We deployed WCF systems in a university-level EFL class with nearly 2,000 students, collecting over 20,000 drafts. We evaluated the generated WCF from two perspectives: intrinsic evaluation by experienced English teachers using a rubric, and extrinsic evaluation via student feedback and engagement metrics. Results revealed low alignment between teacher expert ratings and student feedback. These findings suggest that traditional expert evaluation alone may not fully capture WCF's usability or helpfulness from the learner's perspective, highlighting the importance of learner-centered evaluation frameworks for AI-based applications in language education.

Jeanne McClure, Machi Shimmei, Noboru Matsuda et al.

In this paper, we explore the potential of Large Language Models (LLMs) with assertions to mitigate imbalances in educational datasets. Traditional models often fall short in such contexts, particularly due to the complexity and nuanced nature of the data. This issue is especially prominent in the education sector, where cognitive engagement levels among students show significant variation in their open responses. To test our hypothesis, we utilized an existing technology for assertion-based prompt engineering through an 'Iterative - ICL PE Design Process' comparing traditional Machine Learning (ML) models against LLMs augmented with assertions (N=135). Further, we conduct a sensitivity analysis on a subset (n=27), examining the variance in model performance concerning classification metrics and cognitive engagement levels in each iteration. Our findings reveal that LLMs with assertions significantly outperform traditional ML models, particularly in cognitive engagement levels with minority representation, registering up to a 32% increase in F1-score. Additionally, our sensitivity study indicates that incorporating targeted assertions into the LLM tested on the subset enhances its performance by 11.94%. This improvement primarily addresses errors stemming from the model's limitations in understanding context and resolving lexical ambiguities in student responses.

Machi Shimmei, Masaki Uto, Yuichiroh Matsubayashi et al.

The primary goal of this study is to develop and evaluate an innovative prompting technique, AnaQuest, for generating multiple-choice questions (MCQs) using a pre-trained large language model. In AnaQuest, the choice items are sentence-level assertions about complex concepts. The technique integrates formative and summative assessments. In the formative phase, students answer open-ended questions for target concepts in free text. For summative assessment, AnaQuest analyzes these responses to generate both correct and incorrect assertions. To evaluate the validity of the generated MCQs, Item Response Theory (IRT) was applied to compare item characteristics between MCQs generated by AnaQuest, a baseline ChatGPT prompt, and human-crafted items. An empirical study found that expert instructors rated MCQs generated by both AI models to be as valid as those created by human instructors. However, IRT-based analysis revealed that AnaQuest-generated questions - particularly those with incorrect assertions (foils) - more closely resembled human-crafted items in terms of difficulty and discrimination than those produced by ChatGPT.