This paper introduces TRACE, an automated framework for analyzing textual revisions across drafts. TRACE aligns initial and revised texts and infers interpretable revision operations, such as insert, delete, replace, and move. TRACE supports AI-powered revision analytics, writing assessment, automated feedback, and studies of human–AI writing collaboration.

MCQ-Diag is a diagnostic tool for reviewing distractor quality in multiple-choice assessments through three interpretable indicators: semantic plausibility, semantic uniqueness, and lexical distinctiveness. Rather than assigning automated judgments, it presents these as evidence within an interactive review environment. Semantic plausibility and lexical distinctiveness show modest, statistically significant validity against expert ratings.