Update gt-arch-8833.md

Author: kastnerp

_teaching/gt-arch-8833.md

@@ -7,25 +7,4 @@ importance: 6
 category: Georgia Tech
 ---
 
-## Summary
-
-This research elective is designed for students interested in deepening their understanding of data-driven methods in architecture with an emphasis on sustainability. The course blends seminar and workshop formats, featuring input lectures from guest speakers in academia and industry. The topics range from
-environmental performance to augmented intelligence in design.
-
-## Deliverables
-
-Students will work on a hands-on project to analyze complex sustainability-related phenomena, such as energy consumption, mobility patterns, thermal comfort, daylighting, well-being, and quality of space. Students will develop an individual research question that they will answer with a data-driven approach, developed over the semester. The final deliverable is an
-
-1. implementation of their approach (using Rhino, Grasshopper, C#, Python, Javascript)
-2. conference-ready manuscript / slide deck where processes, findings, and
-   conclusions are documented.
-
-## Prerequisites
-
-Students should have intermediate to advanced digital skills and prior experience in
-environmental systems and computational design, as the course skips introductory workshops in favor of a more technical focus. Experience with Python/C# will be advantageous but is not required. The teaching methodology will emphasize evidence-based design, variant testing, and evaluation using existing and custom-
-built computational tools. The course fosters a collaborative learning environment, encouraging students to work with curiosity and rigor as they develop new tools and insights collective
-
-## Student Work
-
-[End of the Year Showcase Spring 2024](https://docs.google.com/presentation/d/1uE61tAisLvI9qhuCtqXEb0OJ5sR9ljYjdt6a3hoJJ7Q/edit?usp=sharing)
+{% remote_include https://raw.githubusercontent.com/SustainableUrbanSystemsLab/ARCH-8833/refs/heads/main/README.md %}