GradeWiz - AI Grading Assistant

What

GradeWiz is an AI grading assistant for lab work, homework, and exams. It is designed for teachers and institutions that want to reduce manual grading time while giving students faster, more detailed feedback.

The product’s workflow centers on uploading student work, using computer vision to segment responses by question, applying an AI grading model against a rubric, and then letting the instructor review results before releasing grades and feedback. Based on the site, GradeWiz appears positioned as an institution-ready assessment tool for higher education and other structured teaching environments.

Features

• Submission segmentation and student matching: GradeWiz uses computer vision to split uploaded work into individual questions and match submissions to students, which reduces manual sorting effort.
• AI rubric completion and feedback generation: Its grading model fills out the rubric and produces individualized feedback that explains errors as well as correct reasoning.
• One-click rubric generation: Instructors can create detailed rubrics for an assignment quickly, which can speed up setup for repeatable assessments.
• Learning instructor grading style: Teachers can provide grading instructions and refine outputs so the system learns how they want work assessed over time.
• Instructor review before release: Grades and feedback can be checked by the instructor before being published, helping preserve oversight in the grading process.
• Performance analytics: The platform highlights common mistakes and broader class trends, which can help instructors adjust teaching based on observed misconceptions.

Helpful Tips

• Validate performance on your own assessment types: The site cites grading accuracy and speed claims, but teams should test with their actual lab formats, handwriting quality, and rubric complexity before broad rollout.
• Use the review step as a calibration phase: Early adoption will likely work best when instructors actively correct outputs so the system can better reflect local grading expectations.
• Confirm workflow fit for submission formats: Standard assignments support PDF submissions, while Flex Assignments support text, images, and multiple-choice inputs, so format planning matters.
• Plan around assignment editing limits: Published assignments cannot be edited, which means instructors should finalize structure and instructions before release.
• Check institutional requirements up front: The site states accessibility, FERPA, and HECVAT-related readiness, but procurement teams should still verify documentation against internal policies.