Writing development is often assessed through aggregate improvements in surface-level features, yet less attention has been given to how multiple linguistic dimensions evolve jointly over time. We model writing development as a multidimensional system shaped by stable individual variation and instructional progression across staged assignments, using interpretable linguistic features from the Writing Analytics Toolkit (WAT) and transformer-based sentence embeddings.Variance partitioning reveals substantial between-student stability alongside stage-dependent change. Mixed-effects models identify non-uniform developmental trajectories: academic focus, information density, and conventional language increase, whereas development of ideas and lexical variety decline, indicating tradeoffs across competing dimensions. Cross-lagged analyses further show dynamic dependencies between dimensions, suggesting coordinated change rather than independent progression.Embedding-based analyses capture stage-dependent shifts in semantic representation, with larger changes in earlier stages and increasing stability over time. Although assignment structure contributes to observed variation, stable individual differences and cross-stage dependencies indicate underlying developmental processes that generalize across tasks.Together, these findings characterize writing development as structured change in a multidimensional representational system, highlighting the need for computational models that capture stable variation, non-monotonic trajectories, and interactions among linguistic components.

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.