AI teaching-assistant skills and supporting resources for APMonitor courses.
These course-aware assistants help students understand concepts, practice with guided questions, debug code, and locate relevant course material. They are designed to support learning—not replace it or complete graded work on a student's behalf. Each assistant follows the AI-use policy and instructional approach of its course.
Clara is the virtual teaching assistant embedded throughout the APMonitor courses. Clara provides students with a convenient way to ask course questions, review concepts, and receive guided help directly from the course environment.
The downloadable skills in this repository bring the same course-aware teaching-assistant approach to external AI tools, including:
- Claude and Claude Code
- ChatGPT and OpenAI Codex
- Google Gemini and Gemini CLI
The packaged skills are in AI-Skills/.
| Course | Subject | Download |
|---|---|---|
| CHE 263 | Computational Tools for Engineers | che263_TA_Skill.zip |
| ChE 436 | Process Dynamics and Control | che436_TA_Skill.zip |
| DDE | Data-Driven Engineering | dde_TA_Skill.zip |
| DO | Dynamic Optimization | do_TA_Skill.zip |
| ME 575 / CE 575 | Design Optimization / Optimization Techniques in Engineering | me575_TA_Skill.zip |
| PDS | Machine Learning for Engineers | pds_TA_Skill.zip |
Each ZIP archive includes a course-specific set of resources such as:
| File | Use |
|---|---|
README.md |
Detailed setup and usage instructions for that course |
*.skill |
Installable Claude skill package |
*-SKILL.md |
Human-readable skill definition |
*-instructions.md |
Portable instructions for a ChatGPT Custom GPT or Project, or a Gemini Gem |
*-knowledge.md |
Course map, notes, conventions, and other reference knowledge |
AGENTS.md |
Instructions for OpenAI Codex and compatible coding agents |
GEMINI.md |
Instructions for Gemini CLI |
Exact filenames use the course identifier as a prefix.
- Download the ZIP file for your course.
- Extract it and open the included
README.md. - Follow the instructions for your preferred AI platform.
- Begin with a course question, a concept you want to review, or code you want help debugging.
Example prompts:
- Explain this concept using the notation from the course.
- Quiz me one question at a time and give feedback after each answer.
- Help me diagnose this error without completing the assignment for me.
- Review my model formulation and ask me questions about anything that is missing.
- Point me to the most relevant APMonitor lesson or example.
This repository will also collect additional teaching-assistant resources for faculty and students. Course skills can be adapted to include:
- course schedules and learning objectives;
- lecture notes, examples, and reference material;
- assignment context and approved levels of AI assistance;
- course-specific notation, software, and modeling conventions;
- tutoring, questioning, feedback, and debugging strategies; and
- links to course pages, applications, and supporting exercises.
When adapting a skill, clearly state the course AI policy and verify that the assistant guides student reasoning instead of supplying prohibited solutions.
Contributions that improve compatibility, accessibility, instructional quality, or course coverage are welcome. Please keep resources organized by course, document the intended AI platforms, and avoid including restricted student data or copyrighted material that cannot be redistributed.
This project is available under the MIT License.
