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Personalized Degree Page Content

Personalized Content Demo

Business problem we are solving for

Currently, students can suffer from choice paralysis when trying to select a degree from all the ones we offer at ASUO. In addition even when a degree is selected the student can struggle understanding the full scope of a degree program, including the courses involved, the type of work they will engage in, and the potential career paths. This leads to uninformed decisions, changing majors, and potentially increased time and cost for degree completion.

Summary of project

Our goal is to create an AI-based tool that can offer personalized sections of content on the Arizona State University (ASU) website. This tool will help prospective students understand whether a specific degree aligns with their interests and goals. The tool will provide personalized information about courses in the degree, examples of the type of work, and potential career paths.

Primary Features:

Personalized content on ASUO ​We will be able to serve truly personalized content about whether a degree might be good for you at scale ASUO Degrees Fined Tuned AI Model ​We will have a fine tuned model trained on ASU brand voice that can speak to any offering in our degree catalog

Resources/teams involved

Project Manager: Responsible for overall project coordination, communication, and timeline adherence.
Product Owner: A project stakeholder who will act as the voice of the user and make decisions about the product direction.
AI/ML Engineers: To build, train, and test the AI models.
Data Engineers/Analysts: To handle data aggregation, cleaning, and preprocessing.
Software Developers: For system integration, API management, and front-end interaction.
QA & UX Research: To conduct rigorous testing on the results of the AI models
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Additional Considerations

Data Privacy and Security: Ensure that user data and prompt inputs are handled securely and in compliance with relevant regulations.
Model Bias and Fairness: Take measures to identify and mitigate potential biases in the AI models and consider fairness during the evaluation process.
Scalability and Maintenance: Design the tool in a modular and scalable manner to accommodate future additions of AI models and support ongoing maintenance and updates.
User Feedback and Iteration: Establish mechanisms for users to provide feedback on the tool's performance, usability, and suggest improvements for future iterations.

Next Steps

From POC to Phase 1




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