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A rubric to assess generative AI-based feedback on student writing assignments
Daniel R Rankins1, Erick N Tran2, Valerie T La2
1Department of Ecology and Evolutionary Biology, University of California, Irvine, Irvine, California, USA.
Journal of Microbiology & Biology Education
|August 3, 2026
Summary
A new rubric helps educators evaluate generative artificial intelligence (GenAI) tools for science writing feedback. This systematic approach assesses GenAI based on accuracy, constructiveness, clarity, strength recognition, and originality.
Area of Science:
- Science Writing Education
- Artificial Intelligence in Education
Background:
- Generative artificial intelligence (GenAI) tools are increasingly prevalent in educational settings.
- A systematic method is needed to evaluate the rapidly evolving landscape of GenAI tools for classroom use.
- GenAI can supplement instructor feedback in science writing, offering students more revision opportunities.
Purpose of the Study:
- To develop and describe a rubric for evaluating GenAI feedback in science writing education.
- To enable instructors to systematically assess GenAI models.
Main Methods:
- A rubric was created to evaluate GenAI feedback across five key areas: accuracy, constructiveness, clarity and readability, recognition of strengths, and originality.
- Data were collected comparing feedback from multiple GenAI models on student work.
Main Results:
- The study presents data comparing GenAI feedback based on the developed rubric and course guidelines.
- The rubric provides a framework for assessing the quality and utility of GenAI-generated feedback.
Conclusions:
- The developed rubric serves as a valuable resource for instructors seeking to evaluate GenAI tools for scientific writing feedback.
- This systematic evaluation method supports the effective integration of GenAI into science writing pedagogy.
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