GenAI can power simulated student agents that provide opportunities for educators to engage in core teaching practices, such as leading a small group argumentation-based science discussion. To realize the potential of such simulations and support teacher reflection and learning, it is necessary to provide participants with timely feedback on their performance in the simulation. This study investigates systems for automated evaluation of and feedback on teacher performance in a simulation along the dimension of making use of student ideas to move the discussion forward. We address three research questions: (a) How well do models fine-tuned on transcripts of teacher performance in a matching human-puppeteered teaching simulation (that served as the model during the development of the GenAI one) perform in evaluating transcripts from the GenAI teaching simulation? (b) How well does a system using a few-shot LLM perform on the same task? (c) How do educators perceive the quality and usefulness of the automatically generated feedback? The findings underscore the importance of a rigorous evaluation of automated evaluation and feedback systems.

This work aims to employ natural language generation (NLG) to rapidly generate items for English language learning applications: this requires both language models capable of generating fluent, high-quality English, and to control the output of the generation to match the requirements of the relevant items. We experiment with deep pretrained models for this task, developing novel methods for controlling items for factors relevant in language learning: diverse sentences for different proficiency levels and argument structure to test grammar. Human evaluation demonstrates high grammatically scores for all models (3.4 and above out of 4), and higher length (24%) and complexity (9%) over the baseline for the advanced proficiency model. Our results show that we can achieve strong performance while adding additional control to ensure diverse, tailored content for individual users.

We describe the AGReE system, which takes user-submitted passages as input and automatically generates grammar practice exercises that can be completed while reading. Multiple-choice practice items are generated for a variety of different grammar constructs: punctuation, articles, conjunctions, pronouns, prepositions, verbs, and nouns. We also conducted a large-scale human evaluation with around 4,500 multiple-choice practice items. We notice for 95% of items, a majority of raters out of five were able to identify the correct answer, for 85% of cases, raters agree that there is only one correct answer among the choices. Finally, the error analysis shows that raters made the most mistakes for punctuation and conjunctions.