I’m a hands-on Product Designer with 15+ years leading end-to-end UX across B2B and B2C SaaS, human-AI decision systems, identity & trust, and complex workflows at global scale. I’m based in Minneapolis–St. Paul and open to local or U.S. remote product design opportunities.
I am Michael Hawkins, a Product Designer. During my time at Pearson VUE, I worked in an industry where trust is the product. High‑stakes credentialing, remote proctoring, biometric admissions, and enterprise SaaS all share the same pressure. In that environment, a confusing screen, a bad default, or an unclear system output can affect the integrity of the exam.
Across that work, I keep coming back to the moment when someone has to act on what a system tells them. They may need to accept a candidate, escalate a review, trust a score, or override a recommendation. The interface has to help them understand the evidence and make a sound decision.
That work spans human-in-the-loop systems, model explainability, computer-vision UX, identity verification, biometrics, and recovery when automated decisions are uncertain.
That mix of system behavior and human judgment is where I focus my design work.
My path started in children’s education software and led into testing, credentialing, enterprise SaaS, and human‑AI decision systems. Questions about clarity, trust, and action followed me through each stage.
I came up in product design when "UX" and "graphic design" were still fighting for the same chair. My early work was at a children’s education software company, where the constraint was simple and unforgiving. An eight‑year‑old has no patience for friction and no vocabulary for complaints. That taught me to design as though the user has no manual, because they usually do not. It is also where I learned to lead through standards, critique, and mentorship. Giving other designers honest feedback and room to grow has been part of my practice ever since.
During my time at Pearson VUE, I worked across proctoring, admissions, and credentialing. I designed surfaces where a confusing interaction could strand a candidate, delay a credential, or create a contested exam. That work made the stakes concrete: when someone's livelihood depends on the next click, the interface has to be clear and recoverable.
More recently, I’ve focused that practice on human‑AI decision systems. AI‑Assisted Review Workstation is the clearest example: the system narrows attention while the interface shows evidence, preserves the audit trail, and keeps interpretation, override, and escalation with accountable people.
More products now use AI in consequential decisions. The interface determines how people understand and act on that output.
Accuracy matters, but the harder design problem is calibration between the model's confidence and the operator's confidence. When that calibration is wrong, the operator either over‑trusts the AI or ignores it entirely. Both outcomes waste the investment.
Most of my recent work has focused on designing that calibration. I care about what the model surfaces, how it shows its work, what evidence it presents, where the override lives, and how the audit trail reads back later. For me, explainability means giving the operator enough evidence to act and defend the action later.
That is the kind of AI product work I want to keep building.
These principles show up across the case studies. They are also how I work with product managers, engineers, researchers, and stakeholders.
I want the evidence a user needs close to the decision: model signals, audit trails, or comparisons that explain a change. The interface should make the basis for action visible.
I use workshops, prototypes, and reviews to get product, engineering, research, and operators working from the same model. That shared understanding has to show up in the product, not disappear after the meeting.
I map the policy, data, parallel surfaces, downstream audit, and recovery paths around a screen before I treat it as a finished interaction.
I design override, escalation, retry, and sync‑later states with the same care as the primary flow because those are the moments when users need the product most.
These recommendations come from product, engineering, and analyst partners across multiple programs. Names are retained, and affiliations remain limited where confidentiality requires it.
"He's the kind of designer you want on complex platforms where the user experience directly impacts trust and operational outcomes. He's exceptional at turning messy constraints into intuitive flows, aligning stakeholders quickly, and delivering high‑quality iterations at speed."
Greg Anderson Senior Product Manager · Pearson
"Michael has a strong ability to turn detailed technical requirements into intuitive, user‑friendly experiences. He demonstrated excellent systems thinking, balancing user needs, workflows, and platform constraints to deliver a streamlined and scalable solution."
Mani Anna Product Architect · Pearson
"Always impressed by the clarity and comprehensiveness of the designs and prototypes he created. He knows how to make designs that are clean, functional, and straightforward to implement and develop."
Eric Boothe Senior Software Developer · Certiport
"Whether building from scratch or tackling a difficult revamp, Michael consistently delivered thoughtful, scalable designs. He has a rare talent for articulating the 'why' behind every interaction, which helped the final implementation match the original vision."
Michele Huspek Senior Business Systems Analyst · Pearson