From Hours to Minutes: Agentic Grading for Descriptive Responses

Automated Grading Assistant for Written Answers

Executive Summary

The product and AI engineering team delivered a lightweight grading assistant that automates scoring and feedback for descriptive answers in quizzes, exams, and assignments. Deployed as a browser extension, the assistant reads instructor grading pages, identifies questions and student responses, and applies rubric logic with large language models to propose scores and constructive comments. Human-in-the-loop review keeps instructors in control, while privacy-aware design minimizes data exposure and requires no changes to the underlying LMS.

Problem

Multiple-choice items are easy to auto-grade, but essays and short answers consume disproportionate time. Existing tools often produce inconsistent results, lack rubric alignment, or force instructors to copy-paste content between systems. Graders skip feedback when deadlines loom, reducing learning value. Institutions needed a solution that supports rubric-based scoring, generates meaningful, specific comments, integrates directly with existing grading pages, and scales without hiring additional assistants.

Solution

The assistant operates directly inside instructor workflows. A content script parses grading pages, segments prompts and responses, and extracts rubric criteria where available. An orchestration layer sends minimal, redacted snippets to an LLM with a structured prompt that enforces rubric weights, point ranges, and evidence-backed comments. Returned suggestions populate score fields and feedback text areas automatically, ready for quick adjustment and submission. Controls allow strictness tuning, tone selection for feedback, and one-click regeneration. The platform includes audit logs, role-based permissions, and an admin console for institution-level settings. The stack combines a modern TypeScript extension (MV3), a serverless backend, secure authentication, and a compliant document store; vendor-agnostic LLM providers power evaluation and drafting behind data-minimization guardrails.

Outcome

Instructors move from manual, repetitive grading to guided review. Time per submission drops dramatically, feedback coverage increases, and rubric consistency improves across cohorts. Students receive faster, clearer explanations of strengths and gaps, accelerating remediation. Institutions support larger assessment volumes without retooling the LMS, and academic integrity is maintained through transparent, editable scoring rationales.

What You Can Expect Working with Dreamloop Studio

Dreamloop Studio’s product and AI teams design assistants that fit existing academic workflows. Expect rubric-aware prompts, privacy-first data handling, and human-in-the-loop controls that keep educators in charge. The result is a grading process that scales, produces better feedback, and gives time back to teaching.