Profiling Writing Skills at Scale: A Hybrid Stylometry-LLM Pipeline for Formative Feedback

Abstract

Providing timely, personalised feedback in large-scale learning environments remains a persistent challenge. Generative artificial intelligence offers scalability, yet limited interpretability and concerns around ungrounded feedback constrain its use for formative assessment. This Work-in-Progress paper presents an early-stage hybrid pipeline that combines stylometric analysis with large language models (LLM) to support interpretable writing feedback at scale. Linguistic features are projected into low-dimensional, pedagogically interpretable indicators using Principal Component Analysis. Across two essay corpora comprising 2,828 student texts (PAN14 and ASAP), we observe stable patterns cor..