Abstract: A cloud-native grading environment that leverages advanced language models to assess and comment on open‐ended student submissions. Implemented with a modern JavaScript framework and Firebase’s real-time backend, the platform offers dedicated upload portals for instructors’ exemplar responses and learners’ work. An AI-driven analysis engine transforms text into semantic representations, compares student answers against reference solutions, and generates bespoke feedback statements. By automating scoring and commentary, the system not only lightens educators’ workloads but also ensures uniformity in evaluation and supplies students with clear, actionable insights. The platform also supports continuous learning by refining its feedback strategies based on historical assessment data. Additionally, it incorporates adaptive analytics dashboards for instructors to monitor class performance trends and intervene proactively.

Keywords: Automated assessment, Semantic embeddings, real-time synchronization, Personalized feedback, educational AI, adaptive learning, performance analytics

| DOI: 10.17148/IARJSET.2025.12460