"Finding the Limits of AI for Web Development in 2023"

Introduction

The project, titled "Finding the Limits of AI for Web Development in 2023," conducted by AI Limits Team 1 from Kennesaw State University, encompasses a comprehensive exploration of artificial intelligence (AI) in the domain of web development.

The study focuses on the year 2023 and primarily employs ChatGPT, an advanced AI model, as the driving force for the development of a fully functional Auction House Website.

Scope Analysis:

Project Objectives

Innovative Web Development: The creation of an Auction House Website entirely generated by ChatGPT, challenging traditional methods.
AI-Generated Content: The generation of both code and content, including item descriptions, user interactions, and auction-related information, showcasing AI's versatility.
Frontend and Backend Integration: Complete integration of AI into both frontend and backend development, highlighting AI's contribution to various aspects of web development.
Research and Documentation: Extensive research to understand ChatGPT's capabilities and limitations, with the creation of comprehensive research papers and reports.

Achievements

The project has yielded significant achievements, providing valuable insights into AI's role in web development:

Innovative Web Development: Successful creation of an Auction House Website using entirely AI-generated code.
AI-Generated Content: Content generation by ChatGPT, emphasizing its capabilities in content creation.
Frontend and Backend Integration: Seamless integration of AI into both frontend and backend development.
Research and Documentation: Extensive research that has added to the body of knowledge on AI-driven web development.

Project Scope Components

Objective: The project's primary objective is to explore AI's capabilities and limitations in web development, focusing on 2023. It encompasses both frontend and backend aspects of web development and the integration of AI components to enhance user experiences.
Frontend: The project involves creating a user-friendly and visually appealing Auction House Website using AI-generated content, including layout design, responsive design principles, and user interactions.
Backend: The project includes building a robust backend infrastructure for handling user data, payment processing, and database management. It explores AI's potential in payment processing.
Research: The project incorporates extensive research into AI trends in web development, utilizing academic papers, case studies, and relevant information to inform project objectives and findings.
Hosting: Hosting the AI-driven Auction House Website is an integral part of the project, involving the assessment of various hosting platforms.

Deliverables

The project includes several key deliverables:

Research Paper Detailing Findings: A comprehensive research paper that documents the project's findings and insights.
AI-Generated Auction House Website: The creation of a fully functional Auction House Website entirely generated by AI.
PPT Milestones: A series of milestone presentations in the form of PowerPoint (PPT) to communicate project progress and achievements effectively.
Wix Website: The creation of a dedicated Wix website to serve as an online hub for disseminating project information.

Team Contribution

The project team comprises individuals with distinct responsibilities:

Jackson Chastine: Leads the group through well developed plans, and also responsible for last look before submissions.
Joel Eve: Leads comprehensive research and analysis.
Alexandre Garcia: Focuses on backend development, payment processing, and coding efforts.
Danish Khan: Takes on the primary coding role, handles AI integration, and collaborates with other team members.
Jessica Chavez: Records project milestones and outcomes, contributes to wireframe creation, and ensures accurate project documentation.

Challenges and Lessons Learned

Challenges encountered in the project include dealing with ChatGPT's knowledge limitations, troubleshooting bidding functionalities, and addressing technical challenges.

Valuable lessons include improving code combination processes and implementing a piece-by-piece approach to AI prompting.