AI & Future of Product Careers

How AI Is Changing the Boundary Between Designers and Engineers

A mixed-methods research study exploring how generative AI is changing workflows, professional identity, and career preparation for future designers and engineers.

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Timeline & Status

1 semester

team of 1

Role

UX Researcher

Skills

UX Research, Research Planning, Interview Design, Survey Design, Qualitative Analysis, Quantitative Analysis, Thematic Coding, Affinity Mapping, Insight Synthesis, Data Visualization, Research Presentation

Instructor Reflection

“Excellent. These insights are so important and timely. This research changed how I think about AI in education. I used to treat IA as a tool with clear boundaries, but this project showed me that AI is reshaping entire workflows rather than simply assisting individual tasks. It has inspired me to rethink how AI should be incorporated into my future syllabus.”

— NYU Professor Qingyan Ma

Why This Matters?

Companies no longer hire designers and engineers based solely on their discipline. AI is reshaping expectations for junior talent.

My Research Process

 

1. Define the Research Scope
Defined the research question, target participants, and research direction

4. Collected Data
Conducted stakeholder interviews and gathered survey responses from students

2. Reviewed Existing Research
Reviewed existing literature to identify gaps and refine the research focus

5. Analyzed the Data
Coded interview data, compared survey patterns, and identified key themes

3. Designed the Study
Created Interview guides, surveys, and participant recruitment plans

6. Synthesized Insights
Combined qualitative and quantitative findings into actionable research insights

Research Design

 

Qualitative Research - Interviews

Semi-structured interviews helped me understand how students use AI in their projects, how they define their roles, and how AI affects their career preparation.

Quantitative Research - Surveys

The survey validated interview findings and revealed broader patterns in AI adoption and career preparation.

Research Execution

 

How I Conducted Research

Interview:

Create a relaxed conversation where participants feel comfortable sharing their experiences. Share your own thoughts when appropriate instead of only listening.

Survey:

Emphasize that the survey can be completed in under 5 minutes. Explain how the insights from the survey may benefit participants.

Qualitative Data Analysis

 

Step1: Organizing raw interview data

  • Collected handwritten notes and cleaned transcripts from participant interviews

  • Reviewed the transcripts to find meaningful statements related to AI use, role identity, and career preparation

  • The goal was to move from messy raw data into text that could be coded

Step2: From quotation to initial code

  • Organized codes into a more readable way

  • Create definition for each code to explain the meaning behind the codes

Step3: Identifying Repeated Codes and Patterns

  • Grouped similar codes and counted repeated patterns across the participants

  • The frequency list helped me see which ideas appeared most often

  • Strong repeated codes included AI efficiency, AI coding, trust, and junior job pressure

  • Helps me to filter final codes

Step4: Affinity Mapping

  • Grouped similar codes into categories

  • Connected categories into high-level categories and final themes

  • This helped me move from individual interview statements to broader research findings

Quantitative Data Analysis

 

Step1: Survey Analysis Method, From Charts to Patterns

  • I first reviewed Google Forms charts question by question

  • Then I compared related questions instead of reading each chart separately

Step2: Survey pattern Comparison

  • Reviewed the original google Forms charts and selected data points most related to the research question

  • Compared related questions instead of reading each chart separately

  • Created comparison charts to connect AI use, career preparation, and role-boundary change

Key Findings

The most important patterns and insights identified through interviews and survey research

Finding 1: AI is already part of students’ workflow, but AI does not generate the final work

Survey evidence: 81.5% use AI for writing/editing, and 59.3% use AI for coding less than 30% participants use AI for portfolio and job preparation

Interview evidence: Participants described AI as a tool for speeding up work and polishing ideas. However, several participants also said that critical decisions and final responsibility still belong to humans

Combined insight: AI is not separate from students’ work anymore. But for many students, it is still mainly used to complete tasks faster, not to generate the final work directly

Finding 2: AI blurs task boundaries but does not change the professional identity

Survey evidence: 59.3% students use AI to code 25.9% used AI to debugging

Interview evidence: More than half of the participants used AI for coding, websites, PCB design, or technical prototypes, but many still described themselves as designers, artists, or product people rather than engineers

Combined insight: AI is making design and engineering tasks more connected, but it has not fully erased professional identity or expertise boundaries

Finding 3: Students recognize AI is important, they haven’t take actions on actual job preparation

Survey evidence: 85.1% think AI learning is important for future career preparation, but only 18.5% said AI changed their career preparation

Interview evidence: Participants talked about AI as necessary for staying competitive, but many still planned to stay in their original field because of their personal passion and knowledge

Combined insight: AI awareness is high, but its impact is still in the early stages

Finding 4: Students need support at both skill and career levels

Survey evidence: 55.6% want more AI-related courses, 48.1% want faculty guidance, and 44.4% want more internship opportunities

Interview evidence: Participants mentioned uncertainty about junior roles and might need more support from school

Combined insight: Students already understand that AI is becoming important, but many are still unsure how to respond to that change in a concrete way. For some students, AI may not feel strong enough to change their career path yet; for others, they may know AI matters but do not know which tools, skills, or career steps to focus on. This suggests that the main issue is not only AI awareness, but how to translate that awareness into clearer career preparation

Takeaways / Actions

Teach AI as workflow, not only as tools

Students need to understand how AI fits into design, engineering, research, and production workflows

Help students translate AI use into career preparation

Many students use AI but fewer know how to connect it to portfolios, role choices, or junior job readiness

Keep human expertise central

AI can help students cross task boundaries, but judgment, user research, design thinking, and technical review still matter

Looking Forward: AI as a New Career Shift

“AI may be like the internet era. It will change many jobs and may even remove some of them, but it will also create new roles that require new skills. We are still at the early stage of this wave, so students should pay attention to where these changes are going.”

— Interview Participant 03