Our research and development of an AI-assisted grading platform have provided valuable insights into improving the grading process for open-ended exam questions. These practical tips are designed to help lecturers apply these learnings to their own grading workflows, even if they do not use AI tools.
- Prioritize E-Assessment where sensible: The project highlighted the importance of focusing on exams written on computers rather than handwritten submissions. E-Assessments eliminate the need for hand-writing recognition technology, which is error-prone and time-consuming. By using digital formats, lecturers can streamline the grading process while enabling advanced AI-assisted features that require high-quality input data like content highlighting and answer grouping.
- Work with predefined grading rubrics: Our research and experiments revealed that predefined grading criteria significantly enhance grading consistency. Lecturers should invest time in documenting clear, detailed rubrics before grading begins. This reduces inter-rater variability and helps stream-line the grading process, particularly in team-based scenarios.
- Leverage AI to draft grading rubrics: AI tools can assist in generating initial rubric drafts based on question content, example solutions, or grading instructions. While these rubrics should always be reviewed and refined by human graders, using AI in this way can save time and ensure that criteria are comprehensive and measurable. For further resources, check out our guidelines on creating effective rubrics for AI-assisted grading, as well as the prompt examples on rubric generation.
- Grade a subset on the rubrics, then refine: Before applying rubrics to the entire set of responses, grade a small subset of answers using the initial rubrics. This process allows you to identify ambiguities or gaps in the criteria and refine them based on real examples. Iterative refinement ensures that the rubrics are practical and applicable across all responses, improving both efficiency and fairness.
- Use assisted-grading platforms: One significant finding from our pilots was that grading platforms with AI assistance could significantly reduce correction time while improving consistency across responses. By automating repetitive tasks like answer ordering or keyword identification, such platforms free up valuable time for educators to focus on more complex aspects of teaching and assessment. Even without full automation, these tools can streamline workflows and enhance grading reliability.
