Merge pull request #5 from BU-Spark/project-outline

Project outline

README.md

@@ -54,7 +54,7 @@ Developed as part of a **Boston University SPARK** project for **BU MET ETI**, t
 🔵 **Others**:  
    - LLM integration (multiple providers)  
    - Vector databases (for retrieval-augmented generation)  
-   - Web crawling utilities  
+   - Web crawling utilities (Selenium, requests)
 
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@@ -95,7 +95,7 @@ Below is a visual representation of our current workflow for the MET BU Autograd
 
 ## 📜 License  
 
-This project is licensed under the **GNU General Public License (GPL)**. See the [LICENSE.txt](LICENSE.txt) file for more details.
+This project is licensed under the **GNU General Public License (GPL)**. See the [LICENSE](LICENSE) file for more details.
 
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project-document-template.md

@@ -4,11 +4,7 @@
 
 ## 📝 Overview
 
-The **AI-Assisted Grading Tool** for written answers and complex assignments is a project for **Boston University’s Metropolitan College Office of Education Technology and Innovation (MET ETI)**. This tool is designed to enhance grading consistency, accuracy, and alignment with instructor expectations for **CS 581 quizzes and assignments** using:
-
-✅ **Azure AI Studio**\
-✅ **GPT-4o**\
-✅ **Retrieval-Augmented Generation (RAG)**
+The **AI-Assisted Grading Tool** for written answers and complex assignments is a project for **Boston University’s Metropolitan College Office of Education Technology and Innovation (MET ETI)**. This tool is designed to enhance grading consistency, accuracy, and alignment with instructor expectations for **CS 581 quizzes and assignments**.
 
 The AI model will be capable of:
 
@@ -80,7 +76,7 @@ To define the **successful completion** of this project, we aim to deliver:
 
 ## D. Outline a Path to Operationalization
 
-The goal is to deliver a **production-ready API**. The final deployment strategy includes:
+The problem at hand is improving the consistency, accuracy, and reliability of AI-assisted grading for short-answer quizzes and file-based assignments. The AI model must extract a clear score and justification while referencing structured supplemental data, rubrics, and student-uploaded files. The solution should also support file processing, external links, and potential web browsing capabilities for retrieving relevant material. The goal is to deliver a **production-ready API**. The final deployment strategy includes:
 
 ### 🌐 **Integration with Blackboard & LMS systems**
 
@@ -124,8 +120,8 @@ This diagram provides a (preliminary) step-by-step breakdown of how requests flo
 ### 📖 References
 
 - 📄 **MET ETI AI-Assisted Grading Requirements Document**.
-- 📌 **Azure AI Studio Documentation**.
-- 🤖 **GPT-4o, Claude, LLaMA API Docs**.
+- 📌 **Azure Documentation**.
+- 🤖 **GPT-4o, Claude, LLaMA, DeepSeek, Grok3 API Docs**.
 
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