Grading paper-based exams and complex STEM assignments for large student cohorts creates significant bottlenecks, especially when managing consistency across multiple teaching assistants. Gradescope addresses this friction by digitizing physical student work and restructuring the evaluation process from student-by-student to question-by-question. Instructors upload scanned exams or have students submit images of their work directly through a mobile interface. The platform then uses artificial intelligence to scan handwriting, text, or formulas, grouping matching answers together so you can evaluate identical responses all at once rather than clicking through them individually. One of the platform's most practical structural benefits is its dynamic rubric system. If you realize halfway through grading a problem set that a specific error deserves a different point deduction, you can adjust the rubric item once, and Gradescope automatically propagates that point adjustment to every single student paper already processed. For computer science departments, the code autograder platform runs student submissions against custom test suites automatically while maintaining an archive of version history to protect academic integrity. By organizing student workflows around specific item blocks, the system keeps grading parameters identical across your entire teaching staff, lowering the time spent on grading while providing students with highly detailed, itemized feedback.