UX RESEARCH · MIXED-METHODS RESEARCH
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TIMELINE & STATUS
1 semester · Team of 1
ROLE
UX Researcher
SKILLS
UX Research · Research Planning · Interview Design · Survey Design · Qualitative & Quantitative Analysis · Thematic Coding · Affinity Mapping · Insight Synthesis · Data Visualization · Research Presentation
INSTRUCTOR REFLECTION
— NYU Professor Qingyan Ma
01
Defined the research question, target participants, and research direction
02
Reviewed existing literature to identify gaps and refine the research focus
03
Created interview guides, surveys, and participant recruitment plans
04
Conducted stakeholder interviews and gathered survey responses from students
05
Coded interview data, compared survey patterns, and identified key themes
06
Combined qualitative and quantitative findings into actionable research insights
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.
The survey validated interview findings and revealed broader patterns in AI adoption and career preparation.
Create a relaxed conversation where participants feel comfortable sharing their experiences. Share your own thoughts when appropriate instead of only listening.
Emphasize that the survey can be completed in under 5 minutes. Explain how the insights from the survey may benefit participants.
• 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.
• Organized codes into a more readable way.
• Created a definition for each code to explain the meaning behind the codes.
• 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.
• This helped me filter the final codes.
• 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.
• I first reviewed Google Forms charts question by question.
• Then I compared related questions instead of reading each chart separately.
• 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.
The most important patterns and insights identified through interviews and survey research.
Survey evidence: 81.5% use AI for writing/editing, and 59.3% use AI for coding; fewer than 30% of 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.
Survey evidence: 59.3% of students use AI to code, and 25.9% use AI for 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.
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.
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.
01
Students need to understand how AI fits into design, engineering, research, and production workflows.
02
Many students use AI but fewer know how to connect it to portfolios, role choices, or junior job readiness.
03
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
— Interview Participant 03