Gradescope AI-Assisted Grading

Gradescope supports answer grouping and AI assistance for certain fixed-template assignments. In the product's current documentation, instructors can form groups themselves, while AI can suggest groups for selected question types such as multiple choice, math fill-in-the-blank, and text fill-in-the-blank. The system is meant to reduce repeated rubric work by letting an instructor review a group and apply grading consistently. Grouping can improve efficiency when many students give equivalent answers, but similarity is not the same as correctness. A group may combine answers that look alike but differ in reasoning, units, signs, or a key qualification. Handwriting, symbol recognition, and formatting can also affect grouping. Instructors should inspect representative responses and review group membership before applying rubric items. A practical grading workflow begins with a clear rubric and question setup. Review suggested groups, split them when answers differ in a meaningful way, and create groups for responses the tool missed. Grade ambiguous, novel, or partially correct answers individually. Then sample the scored submissions and compare them with the intended rubric. If the group changes, recheck affected scores before releasing grades. AI-assisted grouping does not determine a student's understanding or replace instructor judgment. A correct answer may come from different reasoning, and an incorrect answer may reveal a useful misconception. Use feedback that reflects the actual work, not a generic response copied to every student in a group. Student submissions can contain identifiable education records. Follow the institution's approved-tool and data-retention policies, limit access to submissions, and avoid exporting more information than needed. Instructors remain responsible for accommodations, rubric fairness, grade appeals, and explaining how a score was assigned. Verify current product support because question types and availability can change.