Customer Experience combines instructional design and applied data science, a combination well-suited for workforce-focused AI training. Her academic training was completed in the United States, where she earned a Bachelor of Arts, Master of Education, and Master of Applied Data Science.
In a leadership position at a large U.S. research university, she leads program development and learning infrastructure for a master’s program in computational data science. Responsibilities extend beyond course content to include evaluation of design, learner support systems, and operational processes that affect program scalability. Through this work, she has improved learner engagement and completion outcomes while reducing the administrative burden typically associated with large, technically intensive programs.
As attorney Messina observed: “Most AI cases involve the implementation of AI in an industry. This one was intended to help more people learn AI and use it in the job market.”
This same orientation shapes his proposed project. She plans to develop an open source AI learning ecosystem framework that universities, workforce development organizations, and employers can adapt to their own needs. The framework is designed to support practical, job-relevant AI training for non-traditional learners, including working adults and career changers, with skills that transfer directly to the roles they already occupy.
The challenge
Beyond a single institution
The main challenge was to demonstrate both national relevance and real-world progress. The petition needed to show that the client’s work extended beyond a single academic role and met the needs of a broader workforce.
Demand for AI skills continues to grow, while many training pathways remain expensive, inflexible and difficult to scale. These barriers disproportionately affect working professionals and career changers. The petition therefore needed to demonstrate how the customer systems approach could transcend institutional boundaries and support broader adoption across the United States.
As lawyer Messina explained, the case required careful balancing. Her current role in the United States was to serve as evidence of credibility and dynamism, while demonstrating that the systems she had developed could support a national framework.
Strategic approach
Use current U.S. work to demonstrate nationwide impact
The legal strategy emphasized a simple distinction. Many AI petitions focus on the application of AI in a specific sector. This case aimed to expand the number of people who can learn AI and bring these skills into the workforce.
The petition leveraged the client’s current role in the United States to demonstrate progress and capabilities, without limiting efforts to a single employer. His work has been characterized as a platform for developing, testing and refining these tools. The proposed effort then expanded these tools for broader use across the United States.
The supporting evidence reflected this approach. The brief included a tangible work product and documented results, showing that the learning ecosystem was already operational and ready to scale.
Simple approval
USCIS approved the EB-2 NIW petition through premium processing without issuing an RFE.
The approval reflected the client’s determination that the client’s work met a national workforce need and that it was already making measurable progress in the United States. The decision recognizes both the value of expanding access to AI education and the documented results of its work to date.
Case Overview
Category |
Details |
| Immigration category | EB-2 National Interest Waiver (NIW) |
| Professional field | AI Education |
| Country of origin | Vietnam |
| Senior lawyer | Paul Messina |
| Type of treatment | Premium processing |
| RFE | No |
| Result | Approved |
