I’m Matt Ward, a Senior UX Designer at Amazon and a Georgia Tech industrial design alumnus. I design AI experiences—and help emerging designers build the judgment to shape what comes next.
Designer. Practitioner. Mentor.
Explore the course proposal
My career spans 10 years across connected products, financial technology, marketing platforms, enterprise software, and generative AI. That range brings students a practical view of how design skills transfer across industries.
At Amazon, I design experiences for emerging generative AI capabilities and create new interaction patterns and foundational guidelines adopted across teams. My work actively shapes how employees use AI and how designers build AI experiences, helping establish design practices for this evolving field.
I’ve mentored over 100 design students through ADPList (opens in a new tab), providing career guidance, portfolio reviews, and practical feedback.
My Skillshare classes have reached over 6,000 students, with 198,007 total minutes watched and over 135 positive reviews. View my teaching profile (opens in a new tab).
My academic involvement includes 10 guest lectures and appearances at Georgia Tech and Hunan University, and 10 guest reviews at Georgia Tech and Virginia Tech. Topics span undergraduate and graduate design studios, portfolio development, career planning, and MS-HCI job-search workshops.
Georgia Tech, BS in Industrial Design. My work connects systems thinking, research, interaction design, and the craft of making usable products.
02 / Course proposal
Proposed studioA project-based studio where teams design a mobile or web application through human-led problem framing and ideation, using AI selectively for research support, prototyping, and evaluation—and learning to justify when it adds value.
Download full course proposal (PDF)
AI features are optional. Project quality rests on research, craft, and well-supported decisions about AI use.
Teams develop their own understanding and concepts in response to a shared brief, then build and test a polished prototype. Short lessons support sustained studio work, critique, and iteration.
Define the problem, conduct user research, and develop evidence-based insights.
AI judgment: Compare AI-supported planning and synthesis with direct user evidence; verify sources and separate observation from AI inference.
Generate original concepts through human-led sketching, reframing, and critique, then develop scenarios and user flows.
AI judgment: Begin ideation independently. Use AI later to challenge concepts, identify edge cases, or check constraints.
Build interactive prototypes, observe people using them, and prioritize improvements.
AI judgment: Compare traditional and AI-assisted prototypes and evaluation, checking generated outputs against usability, accessibility, and real user evidence.
Present a polished prototype and portfolio case study supported by research, testing, and design rationale.
AI judgment: Document where AI added value, failed, required verification, or was intentionally avoided in an AI Practice Audit.
The central learning outcome
Know when to use AI.
Know when human expertise should lead.
Students leave with a portfolio-quality prototype, presentation, case study, and AI Practice Audit documenting their design decisions.
A starting point for a conversation
I’d welcome a discussion about the course’s fit within your program and student needs. This proposal is a starting point for shaping the studio together.
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