📅 2025-06-19 — Session: Developed and Evaluated AI Model Rubrics
🕒 00:00–23:55
🏷️ Labels: Ai Models, Rubric, Evaluation, Machine Learning, Education
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop and evaluate rubric criteria for AI model assessments, focusing on physics-heavy AI challenges, scientific prompt citations, and grading AI model responses.
Key Activities
- Created MECE rubric criteria for evaluating AI challenges in physics, ensuring criteria are atomic and self-contained.
- Established guidelines for citing sources in scientific prompts, distinguishing between common knowledge and niche information.
- Developed a framework for grading AI models using rubrics, aligning quantitative scores with qualitative assessments.
- Proposed a correction scheme for evaluating prediction model responses, including evaluation criteria, common errors, and expected scores.
- Evaluated student performance on bias and variance concepts in machine learning models using a grading scale.
Achievements
- Successfully developed comprehensive rubric criteria and grading frameworks for AI model evaluation.
- Clarified the distinction between bias and overfitting in machine learning, enhancing understanding of model evaluation.
Pending Tasks
- Further refine the rubric criteria for specific AI challenges and model types.
- Implement the proposed correction scheme in actual model evaluations to validate its effectiveness.