AAEL Labs: Building an AI Tool That Automatically Generates VALUE-Aligned Rubrics from Course Assignments
Many instructors spend more time designing rubrics than designing the actual learning activity.
What if an AI system could generate the rubric automatically from the assignment itself?
This morning, I built a small prototype that explores this idea using the AI-Augmented Exploratory Learning (AAEL) framework.
The system takes a course assignment and performs a simple pipeline:
• Extract problems from the assignment text
• Classify problem types (conceptual, interpretation, coding, etc.)
• Map them to AAC&U VALUE learning domains
• Generate a structured rubric with suggested point allocations
For a simple Linear Regression assignment, the tool automatically identified:
• Conceptual understanding questions
• Regression interpretation tasks
• Python implementation
• R implementation
From there it generated a rubric aligned to domains such as:
• Quantitative Literacy
• Inquiry and Analysis
• Problem Solving
• Written Communication
Rubric design can now be generated in seconds and then refined by the instructor.
From a systems perspective, the prototype is surprisingly simple:
• Python for assignment parsing and classification
• lightweight NLP to detect problem types
• rule-based mapping to VALUE rubric criteria
• automatic rubric generation and point distribution
In other words, this is not speculative technology. It is something instructors could realistically use today.
This connects directly to my research framework:
AI-Augmented Exploratory Learning (AAEL)
Ask → Adapt → Analyze
Instead of replacing instructors, AI becomes a design partner for teaching and learning.
The goal is not automation of thinking.
The goal is better learning design.
I will post the GitHub repository in the Comments.
I would be interested to hear how others in education, analytics, and EdTech are thinking about tools like this.
Should AI help instructors design assignments and rubrics?
Or should this remain a purely manual process?
Robert Foreman
Doctoral Student – Educational Technology
Central Michigan University
Research Focus: AI-Augmented Exploratory Learning (AAEL)
Contact: forem1r@cmich.edu
Check out the Python script at my GitHub page:
robazprogrammer/aael-rubric-generator: AAEL Labs prototype: an AI-assisted tool that extracts problems from course assignments and generates VALUE-aligned rubrics automatically using Python and lightweight NLP.