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.

Improving conversational proficiency is a key target for students learning a new language. While acquiring conversational proficiency, students must learn the linguistic mechanisms of Repair and Grounding (R&G) to negotiate meaning and find common ground with their interlocutor so conversational breakdowns can be resolved. Task-oriented Spoken Dialogue Systems (SDS) have long been sought as a tool to hone conversational proficiency. However, the R&G patterns for language learners interacting with a task-oriented spoken dialogue system are not reflected explicitly in any existing datasets. Therefore, to move the needle in Spoken Dialogue Systems for language learning we present GrounDialog: an annotated dataset of spoken conversations where we elicit a rich set of R&G patterns.

In this paper, we report on the shared task on metaphor identification on VU Amsterdam Metaphor Corpus and on a subset of the TOEFL Native Language Identification Corpus. The shared task was conducted as apart of the ACL 2020 Workshop on Processing Figurative Language.