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Supporting AI Literacy Teaching Through the Development of Assessments for Classroom Use
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Presents exit tickets and teacher- and student-facing rubrics for the RAICA "Picture This" AI literacy module, iteratively revised after a beta test with nine teachers and 282 students.
Problem
AI literacy assessment research has emphasized competency frameworks over practical, teacher-usable assessment materials for regular classroom use, and few studies assess learning within multi-lesson classroom settings.
Approach
Develops formative assessments (exit tickets per lesson) and rubrics aligned to AI4K12 guidelines for a middle school image classification module, then revises them using teacher usage data, student responses, and rubric application to student projects, yielding design insights on clear language and visual scaffolds.
Key results
- Of 20 exit ticket items, 13 had instructional utility; 9 of these showed clear evidence of learning and 4 showed emerging development.
- In the beta test with 9 teachers and 282 students, 6 revisions were surfaced for useful items and 7 items were judged not useful, with clarity of language accounting for 46% of revision codes.
- 88% of students (n=98) who used the student-facing rubric for self-reflection gave a response referencing one of the five rubric categories.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use the student-facing rubric during project building | Picture This students · Picture This lesson 6 rubric activity | responses involving one of the five rubric categories: 88% | Helped |
Surprising findings
- Only three of nine teachers used the neural network exit ticket as originally written; teachers who modified it all added a diagram, motivating visual scaffolds.
Limitations
- Small sample (9 teachers, 282 students) across specific classroom contexts; findings not generalizable.
- Teacher-selected student projects may be biased toward the best work, affecting rubric findings.
- Exit ticket response counts varied widely (19 to 121), so conclusions from low-count items may be less representative.
Experimental setup
Datasets: Student responses to exit tickets (n ranging 19 to 121 per item) Student projects submitted to showcase (8 projects) Teacher fidelity tracker usage data and feedback
Metrics: teacher usage categories (fidelity tracker) mean/median/mode of Likert responses evidence-of-learning rubric score (1-3) percent of students referencing rubric categories (88%)
Builds on: AI4K12 guidelines RAICA curriculum PBLWorks Rubric for Rubrics
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