GradeBoard vs Pawtograder

GradeBoard is a web-based grading platform for educators that uses AI to provide feedback aligned with rubric outcomes. It accepts PDF, DOCX, TXT, and image uploads, and OCR extracts text from images. Its analysis compares learner work semantically with rubrics and generates inline comments, summative feedback, and annotations that can be edited before export. Batch processing is listed for 50 or more submissions at once. Analytics track performance trends, time savings, and student progress distribution. Free costs nothing and includes 10 learner submissions, a standard AI model, limited rubric uploads, annotated DOCX downloads, basic analytics, 30-day data retention, and email support. Pro costs £24.99 a month for 200 submissions, a priority AI model, batch grading, advanced developmental feedback, OCR, and six-month retention. Enterprise is listed from £250 a month, billed From, and includes unlimited submissions, SSO, team management, custom API/LMS integration, 12-month retention, and a dedicated success manager. Paid plans list annotated DOCX and batch feedback ZIP downloads. The registration page says GradeBoard complies with GDPR and other data protection regulations.

Pawtograder combines autograding, manual grading, office hours, discussion, a gradebook, and analytics for computer science courses. Each commit can trigger tests through GitHub Actions, with results shown by test in the grading interface. Rubrics support additive, deductive, and multi-level criteria, line-level annotations, and separate scoring for group members. The gradebook includes MathJS expression columns, drop-lowest calculations, CSV import, and a student what-if mode. Office hours queues support text, video, and screen sharing, and can show linked submissions and failing tests. Discussions can surface relevant threads for known errors and allow public or staff-only posts. Calendar feeds and Discord channel mirroring cover course events; LTI 1.3 integrations with Canvas, Blackboard, Brightspace, and Moodle are listed as roadmap items. Optional AI-generated test hints and an MCP server for compatible AI clients are also listed. Instructors can use the project-hosted community instance or self-host on Kubernetes with Helm charts. The project is GPLv3 open source. The free community instance is for evaluation; ongoing use at scale requires contacting the project.