Project Four / Workshop Three
Rubric-Based Assessment with Artificial Intelligence (AI-LDP) & Standard Lesson Design
Introduction
In this workshop, we move beyond subjective grading and explore how rubrics can provide a transparent and fair framework for evaluating creative student work. A rubric becomes the shared language between teacher and learner, clarifying expectations and supporting constructive feedback. This session is part of the IAT Model – where pedagogy meets technology to shape the future of education.
Traditional Assessment vs. Rubric-Based Assessment
| Dimension | Traditional Assessment | Rubric-Based Assessment |
|---|---|---|
| Main Goal | Recall of facts and correct answers (What) | Analyzing, creating meaning, and transferring skills (How) |
| Dominant Tool | Paper-based tests and essays | Performance tasks with analytical rubrics |
| Evaluation Criteria | Correctness of answers, absolute score | Quality of performance, depth of analysis, behavioral descriptors |
| Transparency | Low – criteria revealed only after grading | High – expectations shared before the task begins |
| Feedback | Often just a score or symbol | Detailed, constructive feedback for future improvement |
| Fit with IAT Model | Misaligned with the role of the designer-teacher | Key tool for reflective practice and performance-based learning |
The Prompt Box
To design a professional rubric with AI support, we use a sequence of prompts:
- Extracting key criteria (e.g., creativity, analysis, structure)
- Defining performance levels (Excellent, Good, Needs Improvement)
- Generating behavioral descriptors for each level
- Compiling the final rubric table
This structured dialogue with AI (AI-LDP) saves teacher time and ensures clarity in assessment.