This code is from Rivier University's Professional Seminar class (COMP-699) from the Summer of 2024, the Fall of 2024, and the Spring of 2025.
It uses Microsoft's autogen to specialize an agent for each function of an adaptive learning system. The model used is OpenAI's ChatGPT-4o.
It uses panel for the chat interface. It opens in a web browser.
There is a paper published by the Rivier Academic Journal describing the system.
Dokku, S., Gumpula, A., Gudati, S., Nagisetty, M., Thimmarayappa, R., Yeruva, J. P., & Glossner, J. (2024). Multi-agent adaptive learning for mathematics. Rivier Academic Journal, 19(1), Summer 2024. Rivier University.
~/Adaptive-Learning$ uv run python -m src.UI.panel_gui_tabs_vtUse a github codespace
https://learn.microsoft.com/en-us/windows/wsl/install
open PowerShell in admin mode (right click on program)
wsl --install -d Ubuntu-24.04
To see all the available Linux distributions
wsl --list --online
reboot your machine
https://docs.docker.com/desktop/setup/install/windows-install/
reboot
start Docker desktop and configure it to start on Windows boot (Settings->General)
In Windows search, type Ubuntu and select Ubuntu-24.04
create your userid
create a password <---- DON'T FORGET IT
From here, the directions for Linux and Windows running Linux are the same except where noted.
sudo apt update && sudo apt install -y \ software-properties-common \ curl \ zip \ unzip \ tar \ ca-certificates \ git \ wget \ build-essential \ vim \ jq \ firefox \ wslu \ && sudo apt clean
https://docs.astral.sh/uv/#installation
curl -LsSf https://astral.sh/uv/install.sh | sh
https://code.visualstudio.com/sha/download
git clone https://github.com/Rivier-Computer-Science/Adaptive-Learning.git
cd into Adaptive-Learning and initialize a venv environment
uv venv --python 3.12
Activate the environment
source .venv/bin/activate
uv pip install -r requirements.txt
Note: Linux users should not need to perform this step.
If your Windows browser does not open automatically:
Option 1: All http requests use the Windows browser:
sudo apt install wslu echo 'export BROWSER=wsluview' >> ~/.bashrc
Option 2: Only this project uses the Windows browser:
sudo apt install wslu
echo 'export BOKEH_BROWSER=wsluview' >> ~/.bashrc`
Option 3: Run the browser from within Ubuntu: :
echo 'export BOKEH_BROWSER=firefox' >> ~/.bashrc
Sign up to get an OpenAI Key
export OPENAI_API_KEY=sk- # available form platform.openai.comNote: for Windows use set instead of export
There are 11 agents used in the adaptive learning system:
| Agents | Role |
|---|---|
| Student | A user proxy for a student who wants to learn mathematics. |
| Knowledge Tracer | Determine the Student's mathematical capabilities. |
| Teacher | Present new material in multiple formats. |
| Tutor | Explain answers to a Student. Help a Student to learn new material or review previous materials. |
| Problem Generator | Generate math problems at the appropriate level for the Student. |
| Solution Verifier | Check the Student's solutions to math problems. |
| Programmer | Write python code to solve math problem. |
| Code Runner | Execute python code and display the result. |
| Level Adapter | Determine when to increase or decrease the level of difficulty for a Student. |
| Learner Model | A model of the Student's learning level. |
| Motivator | Provides positive and encouraging feedback to the Student. |
We had hoped to use an unconstrained autogen.GroupChatManager to manage agent communications. Try as we may, we could not get agents to stay within their assigned roles. We eventually resorted to a state machine for agent selection using the panel gui. The limitation of this approach means there is a very specific learning path. See the paper for details.
The console knowledge tracer program uses direct agent communication.
The UI contains multiple tabs in various states of completion. The Learn tab is the main interface.
The Model tab interacts with the LearnerModel agent and provides an assessment of the student's capabilities.
