As generative AI becomes increasingly embedded in educational tools, one of the most pressing questions for K–12 educators is how to ethically and effectively leverage it in assessment practices.
While the allure of AI-assisted grading is strong, particularly when teachers face overwhelming workloads, most districts and experts advise against allowing AI to make evaluative decisions about student performance. For example, in my own experience of experimenting by having Copilot grade against my assignment rubrics, it inflates grades significantly over my own assessment.
The risks around accuracy, bias, transparency, and student trust remain too high at present to utilize AI to actually grade student papers. Ruling out AI-generated grading does not mean ruling out AI in assessment, however. In fact, generative AI can play a powerful, responsible role in supporting teachers around the assessment cycle without replacing their professional judgment.
When used thoughtfully, AI enhances clarity, consistency, and efficiency while preserving the teacher’s expertise at the center.
Use AI to Design High-Quality Assessments, Not Score Them
Generative AI can help educators create assessments that better align with learning goals, state standards, and instructional strategies. Teachers can use AI to:
- Generate draft questions at varying levels of Bloom’s Taxonomy
- Propose performance tasks aligned to standards
- Write multiple-choice distractors that mirror common misconceptions
- Adapt existing assessments to different reading levels
- Suggest rubric criteria and indicator language (a personal favorite is rubric development)
The teacher remains the designer and editor, but generative AI accelerates early drafting. Teachers must then review and refine all items for accuracy, cultural relevance, age appropriateness, and alignment. AI is a brainstorming partner, not an author.
Use AI to Strengthen Rubrics and Calibration
AI can help educators articulate performance expectations more clearly, especially in subjective or skills-based areas like writing, science inquiry, or collaborative problem-solving. It can:
- Rewrite rubric descriptors in student-friendly language
- Suggest examples of “looks like/sounds like” for each level
- Compare two versions of a rubric to identify gaps or misalignments
- Provide calibration scenarios teachers can use in professional learning communities (PLCs).
AI should never analyze or score actual student work. Instead, it helps teachers refine their tools for making professional judgments. Occasionally, when I can’t find the correct constructive feedback for a student, I will ask AI to rephrase my feedback in a more positive and constructive way.
Use AI to Provide Feedback to Teachers, Not Students
Feedback to students must be authentic and contextualized. AI can support teachers as they craft feedback by helping:
- Rephrase teacher-written feedback more clearly or concisely (as mentioned above to provide a more positive spin)
- Provide sentence stems for various subject areas
- Suggest variations based on reading level or language needs
- Align feedback to rubric criteria
Teachers should not upload student writing for AI-driven feedback. Instead, AI improves teacher-generated feedback templates or clarity. As mentioned earlier, when I experimented with Copilot in a FERPA-compliant environment, Copilot significantly inflated the grades even based upon a strong rubric.
Use AI to Create Assessment Supports and Scaffolds
AI tools can help teachers build resources that support student success in assessments, such as:
- Graphic organizers for students to fully understand assignments
- Vocabulary lists can be developed from existing materials
- Checklists for self- or peer-review can be created for assignments
These supports can assist in supporting student understanding while reducing teacher workload.
Use AI to Analyze Trends in Teacher-Collected Data
As mentioned, generative AI should not evaluate individual student writing. However, it can help teachers see patterns in their own observations or assessment results. This includes asking AI to summarize common errors outlined in student papers or outline the common errors on quizzes and exams.
Historically, I would share exam summaries with students so they could see how they generally compared to the class as a whole. AI can be used to do that item analysis more efficiently. It can also be used to automatically look for any questions that everyone got correct or wrong, so the teacher can review to make it more challenging or effective in the future.
In this situation, teachers enter the results, not student work, allowing them to maintain control over evaluation.
Prioritize Transparency, Privacy, and Local Policy Compliance
AI cannot understand student intent, creativity, or growth. Nor can it reliably avoid bias or error. Teacher insight remains the most ethical and accurate way to assess student work.
When using AI to support assessment, teachers should:
- Follow district-approved tools and policies
- Avoid uploading identifiable student data into public AI systems (ensure FERPA compliance)
- Communicate clearly with families about how AI will be used
Conclusion
Generative AI can offer powerful opportunities to improve assessment design, clarity, scaffolding, and data analysis, without replacing teacher judgment. When used thoughtfully and transparently, AI strengthens both teaching and learning, supporting educators while maintaining trust and integrity.