The study introduces novel approaches for fine-tuning pre-trained LLMs to predict item response theory parameters directly from item texts and structured item attribute variables. The proposed methods were evaluated on a dataset over 1,000 English Language Art items that are currently in the operational pool for a large scale assessment.

We investigate the reliability of two scoring approaches to early literacy decoding items, whereby students are shown a word and asked to say it aloud. Approaches were rubric scoring of speech, human or AI transcription with varying explicit scoring rules. Initial results suggest rubric-based approaches perform better than transcription-based methods.

We present preliminary evidence on the impact of a NLP-based writing feedback tool, Write-On with Cambi! on students’ argumentative writing. Students were randomly assigned to receive access to the tool or not, and their essay scores were compared across three rubric dimensions; estimated effect sizes (Cohen’s d) ranged from 0.25 to 0.26 (with notable variation in the average treatment effect across classrooms). To characterize and compare the groups’ writing processes, we implemented an algorithm that classified each revision as Appended (new text added to the end), Surface-level (minor within-text corrections to conventions), or Substantive (larger within-text changes or additions). We interpret within-text edits (Surface-level or Substantive) as potential markers of metacognitive engagement in revision, and note that these within-text edits are more common in students who had access to the tool. Together, these pilot analyses serve as a first step in testing the tool’s theory of action.