AI-Assisted Rubric Development and Student Submission Analysis

AI was used as a drafting tool during the development of rubrics for programming assignments in a college-level C++ programming course. Assignment requirements, learning objectives, and grading priorities were provided to the AI tool, which generated an initial rubric structure and performance criteria. The AI-generated rubric was then reviewed, revised, and refined to ensure alignment with course outcomes, assignment expectations, and disciplinary standards.

In addition, AI was used to analyze patterns across student programming submissions. By reviewing multiple student solutions, AI helped identify common programming errors, recurring misconceptions, and areas where students struggled to apply course concepts. These observations were used to inform instructional adjustments, provide targeted feedback, and improve future assignments and learning materials.

Using AI reduced the time required to create initial rubric drafts and helped identify assessment criteria that may have otherwise been overlooked. The process improved consistency across assignments and provided students with clearer expectations regarding performance standards. Analysis of student submissions helped reveal common patterns in student thinking and highlighted concepts that required additional instruction or clarification.

Lessons Learned

A key lesson learned was that AI-generated recommendations and analyses require faculty review and validation to ensure accuracy and relevance. AI proved to be an effective tool for supporting assessment design and instructional decision-making, while instructor expertise remained essential for interpreting results, refining materials, and ensuring alignment with learning objectives. Analysis of student performance using these rubrics informed subsequent revisions to instructional materials and helped identify common areas of difficulty for future course improvements.