Skip to content

Anne Traynor Use Case

Faculty & Institution

  • Faculty: Anne Traynor
  • Institution: Purdue University
  • EPP area / program: Classroom assessment

Course Information

  • Course title: EDPS 327: Classroom Assessment
  • Course context: Required course in Purdue’s teacher education program, delivered asynchronously online
  • Course duration: 15 weeks
  • Implementation timing: Approximately 2.5 weeks

AI Integration Overview

  • Use case title: Evaluating AI-Generated Assessment Tasks and Rubrics
  • AI role: Source of assessment task and rubric drafts for critical evaluation and revision
  • AI tools used: Specialized education AI tools and generative language models, with tool choice allowed
  • Purpose: Help preservice teachers evaluate assessment task quality, revise task instructions and rubrics, adapt tasks for diverse learners, and distinguish close from remote alignment to standards.

Activity / Task Description

In Part 1, students chose a curricular standard, used a specialized education AI tool to generate student instructions for a performance assessment task, and evaluated the task using McMillan’s Checklist for Effective Performance Tasks. They then prompted the tool to adapt the task for lower comprehension or greater challenge and judged whether all versions assessed the same standard well. In Part 2, students used AI to generate a rubric for an existing performance task, evaluated rubric quality, and decided whether improvement should come through prompting, manual revision, or both.

Do / Do-Not Guidance

  • Documented do’s: Ask preservice teachers to compare multiple AI outputs; prepare to discuss AI ethics from multiple angles; allow choice of AI platform or alternative when AI use is not the core objective; demonstrate prompting strategies when useful.
  • Documented do-nots: The deliverable form did not list formal “don’t” entries. Course policy does state that language-model use in the discussion board is not permitted except when instructions ask students to evaluate AI output, and long blocks of AI text should not be copied verbatim.

Evidence & Reflections

Traynor reported that students often judged a single AI output as acceptable regardless of quality, but comparing multiple outputs helped them distinguish stronger and weaker options. She also observed that few students had previous instruction in prompting strategies and that many could not yet evaluate AI output quality effectively.

Materials Reviewed

  • [Template] Deliverables_Traynor.docx
  • Design Memo_Traynor_v2.docx
  • Implementation Tips.docx
  • EDPS 327 Classroom Assessment syllabus_Spring 2026.docx
  • Work Sample 1.pdf
  • Work Sample 2.pdf

Library Tags

  • EPP area: Classroom assessment
  • AI integration type: Assessment task generation, rubric generation, AI-output critique
  • Student population: Preservice teacher education students
  • Implementation scale: 2.5-week asynchronous activity
  • Materials available: Design memo, syllabus, implementation tips, work samples, deliverable form