TBR AI EXCHANGE
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As both an Instructional Technology Specialist and adjunct instructor, I regularly develop assignment rubrics designed to support instructional alignment, grading consistency, and clear student expectations within online learning environments. Creating detailed, performance-based rubrics can be a time-intensive process, particularly when ensuring alignment between course outcomes, assignment requirements, and measurable performance criteria. I wanted to explore how AI could support rubric development workflows while still maintaining instructional quality, faculty oversight, and pedagogical intentionality.
I implemented an AI-supported workflow using tools such as ChatGPT to assist with the initial development and refinement of assignment rubrics for courses I teach as an adjunct instructor. Within this workflow, I provided assignment descriptions, learning objectives, grading expectations, and course context to generate draft rubric categories, performance criteria, and achievement-level descriptors.
I used AI to help organize rubric structure, improve wording clarity, refine measurable criteria, and align rubric components with assignment expectations and course outcomes. The workflow was particularly helpful in generating performance-level language, brainstorming rubric categories, and identifying opportunities to improve consistency across grading criteria. All AI-generated content was reviewed, revised, and finalized through my own instructional and professional judgment prior to implementation within the course.
The AI-supported rubric development workflow improved efficiency during assignment design and revision while helping create more detailed, organized, and student-friendly rubrics. The process reduced some of the time required to draft performance descriptors and allowed me to focus more intentionally on instructional alignment, assignment quality, and student expectations.
The workflow also reinforced the importance of human oversight within AI-supported instructional design processes. While AI was effective in assisting with structure, wording, and idea generation, faculty expertise remained essential for ensuring pedagogical quality, contextual accuracy, and alignment with course goals. This experience demonstrated how AI can responsibly support instructional workflows in higher education when used as a collaborative support tool rather than a replacement for instructional expertise.