Mixed-methods UX research leader.
I build the systems that help organizations turn user understanding into better product and learning decisions at scale.
This portfolio is shared by request. Enter your password, or email to request access.
Need access? Email mndmclean@gmail.com.
I build the systems that help organizations turn user understanding into better product and learning decisions at scale.
I'm a mixed-methods UX research leader focused on building the systems that help organizations turn user understanding into better product and learning decisions at scale.
Most recently, I built and led the UX research function at Guild, scaling it from 1 to 9 researchers and supporting 100,000+ working adult learners across product, design, and strategy. My focus was on building research infrastructure, standards, and enablement that allowed research to operate across teams.
The throughline in my work is systems: how research is structured, shared, and used inside organizations.
At Guild, I also built AI transformation across research and product workflows, including AI-enabled research operations, governance for responsible use, and company-wide enablement through training and practice.
Previously, I completed my PhD in STEM education where I studied how people learn and how learning systems shape participation and outcomes.
Today, I work with education organizations and school districts on AI and learning systems, focusing on how AI is changing instruction, assessment, and learner behavior.
Click any section to expand. Confidentiality has been respected throughout — Guild proprietary details have been anonymized or generalized.
As Guild's first researcher, I built the templates, training, and tiered governance that let teams across the company run their own tactical research while the central team focused on the studies that actually needed it. The team grew from 1 to 9 because the work scaled by reach, not headcount.
I joined Guild in 2018 onto the product design team. Guild was early-stage, growing fast, and figuring out what working adult learners actually needed from an education benefit. Research was scattered across functions, with no shared standard for how questions got asked or how answers traveled back into decisions.
I was hired to do research within product design. But as I built credibility on early projects, requests started coming from outside product — marketing, sales, strategy, leaders trying to understand the workforce we were serving. Within a year, the demand had made the case for a dedicated function. By the time I formally moved out of product design and into a research role, I'd been operating as a shared resource across the company for months. The function existed before it had a name.
Both happened in parallel. The team grew from one to nine over seven years. But the team grew because we leaned into democratization — building templates, training, and tiered governance that let partner teams run their own tactical research. Hiring more researchers wouldn't have kept up with the demand. Empowering more people to do research did.
I made the case for a different model in different rooms over a long stretch of time. There was no executive sponsor handing me runway. The function got built because I kept showing up — to product reviews, strategy offsites, roadmap discussions — with research that mattered to the decision in front of us. The lesson I kept relearning: being in the room mattered more than being right on paper. A perfectly designed study delivered to people who weren't waiting for it changed nothing. A directionally correct insight raised in the moment a decision was being made changed the company.
Each layer answered a different question. Templates let designers and PMs do their own tactical research without reinventing the basics. Standards kept quality from drifting as the team grew. Curriculum turned partner teams into capable collaborators. The system worked because all three did.
The methodological tension between "what would the rigorous answer take" and "what does the business need by Friday" was constant. I made peace with it early: rigor isn't a fixed bar; it's calibrated to the consequences of being wrong. Some questions warranted six-month studies. Others needed a tight survey by end of week. Knowing the difference was the work.
I built that judgment into a tiered model that taught partner teams when to call the central team in.
The tiers weren't bureaucracy. They were a shared vocabulary that let a designer or PM walk into a planning conversation and self-assess whether what they needed was something they could run alone, something they should call in for help on, or something only the central team could do well. It moved the methodological judgment closer to the work, and freed the central team to focus where it mattered most.
By 2024, partner teams across the company were running their own usability tests, surveys, and discovery work as routine practice. The central team's nine researchers could focus on the longitudinal, cross-product, and high-stakes studies — including the ones in Case Studies 02 and 03 — that genuinely required their depth.
Several of the people I hired and grew on this team have gone on to lead research at other companies. That's the impact metric I'm proudest of.
Starting in early 2023, weeks after ChatGPT's launch, I led the company's adoption of generative AI as both a research operations capability and a workforce-wide transformation.
ChatGPT launched in late November 2022. By January 2023 I was already integrating it into my own research workflows — synthesis, lit review, prompt design for survey item generation, faster pattern-finding across qualitative data — and quietly testing where it actually helped versus where it produced confident nonsense. Most of the company hadn't started yet. There was no AI policy, no enterprise tooling, no shared sense of what was acceptable use.
I wasn't asked to lead AI work. I started doing it because it was already changing my own job, and because the questions colleagues were bringing me — "Can I use this for X? Is this risky? What tool should I pick?" — needed answers nobody else was set up to give. By 2024, "AI Transformation" was part of my title. The role grew around the work, not the other way around.
I structured the AI work the same way I'd structured the research function — enablement for non-experts, governance for shared practice, and a regular rhythm to keep the company connected to a fast-moving field.
The newsletter was the spine of the program. Each week I translated frontier AI research, product releases, and policy debates for a non-technical company audience — Stanford labor papers, OpenAI benchmarks, compute-economics primers, the evolving conversation from "AI safety" to "responsible AI." Every issue followed the same shape: a current development, a plain-language explanation of why it mattered for our work, links to the original sources, and a "what's changing" framing for decisions ahead.
Reach grew quickly. By steady-state the newsletter was reaching most of the company. People forwarded it to their teams. Other companies asked for copies. It became the artifact people associated with Guild's AI culture.
Newsletter screenshot. Drop in a representative issue here.
Enablement without governance is irresponsible. The fastest way to lose trust in AI inside a company is to let an early, confident, wrong AI output reach a customer, a partner, or a board. The role I held wasn't just to evangelize AI; it was to be the person who could tell when an AI output was credible and when it wasn't, and to teach others to do the same.
I worked with the team to develop a working pattern for AI-assisted synthesis. AI surfaces candidate themes; the researcher tests them against the raw data; the researcher writes the final claim.
The work that mattered most was the rhythm — a weekly artifact that signaled the company was paying attention, a working group that turned questions into shared practice, and a culture that took AI seriously without taking it as gospel.
A longitudinal mixed-methods study with 12 working adult learners over a full year, designed to surface the moments where motivation and persistence break down.
Learner disengagement was a known problem. Most paused learners told us they intended to return; most never did. Fewer than one in four came back, even though over 80% had said they would. The behavioral data could tell us when a learner paused, what they reported at the moment of pause, and which segments paused at higher rates. What it couldn't tell us was the texture of the weeks before a pause.
To redesign anything that mattered — coaching, communications, program structure, support — we needed to understand the trajectory, not just the endpoint.
I designed a year-long video diary study with 12 working adult learners, purposively sampled across employer partners, program types, and demographic segments. The full study ran a calendar year per participant — long enough to capture the seasonal rhythms, life shocks, and recovery cycles that shorter studies kept missing.
The methodological choices were deliberate. Single in-depth interviews would have been retrospective. Surveys at scale already existed and told us what, not why. Focus groups would have let social pressure suppress the unguarded disclosure. The diary study traded sample size for time depth — and time depth was what was missing.
Disengagement was a cascade that began weeks before a learner disappeared. Across the 12 participants, the shapes of their years were different in detail but recognizably the same in structure: an initial lift, an inevitable dip, and then a fork.
Life shocks couldn't be prevented — illness, caregiving, financial stress — but the conditions at the fork could be shaped. By the time a learner paused, they were typically already behind, and being-behind was itself a demotivation signal. We tested the spiral pattern against behavioral data and found the holidays didn't produce the V-shape everyone assumed; they produced a U with a long bottom.
Working adults stacked courses in their first term. Coaches encouraged this — "how much can you handle?" was the default. But semester one is where academic re-acclimation happens, especially in gateway math. The capacity frame was wrong: semester one isn't a test of throughput, it's a re-entry into the role of student. Learners who took fewer courses up front persisted at meaningfully higher rates.
Each insight reshaped a different part of how the company supported learners. Coaches got vocabulary for disengagement patterns they could feel but couldn't name. Comms timing moved from immediate post-pause contact to the weeks after, when learners were actually trying to come back. The pre-existing pause survey was rewritten to feed a real-time retention dashboard. Industry-specific shock calendars (retail's back-to-school, healthcare's shift demands) became standing input to campaign timing.
The spiral framework was adopted across product, coaching, marketing, and strategy as a shared vocabulary for retention.
A mixed-methods diagnostic of a critical funnel step that had been declining year-over-year since 2020. The finding reshaped how Guild engaged learners in a part of the journey nobody had been watching.
Guild's funnel had a step between application approval and program start that had been declining year-over-year since 2020. It wasn't a dramatic drop. It was a slow erosion easy to miss in any single quarter. The trend was visible only when I pulled together a longer view of the data during ongoing review.
The puzzle was simple to state and hard to answer. These were people who had cleared eligibility, completed an application, gotten approved. And then a meaningful share never actually started. Existing assumptions ranged from "life got in the way" to "the product was confusing." Neither was specific enough to fix.
I designed a two-track study. A new ongoing touchpoint survey reached members at the moment they hit that funnel step — hundreds per month — capturing self-reported readiness, intent, and barriers in close-to-real-time. Eight to ten in-depth interviews with members who had paused at exactly that point gave us the texture the survey couldn't.
The hardest design choice was the touchpoint survey itself. Surveys at moments of decay are notoriously underpowered — the people most likely to disengage are also the least likely to respond. I wrote it to be answerable in under a minute, with one open-text field that turned out to be where most of the signal lived. I had the survey running before the qualitative interviews were scheduled, so the qualitative recruiting could be informed by what the survey was already showing.
The headline insight was simple and not what anyone expected. Motivation eroded in the gap. Approval wasn't a permanent state. "Yes" was a perishable signal, and without something to keep the commitment alive between approval and start, it quietly decayed.
It wasn't primarily life events. It wasn't primarily product friction. The interviews showed people who had been excited at approval describing themselves weeks later in language that revealed slipping commitment. Inertia, not friction, was the failure mode.
The recommendations I made were structural, not tactical. We didn't try to remove the gap — for many members it was unavoidable, driven by enrollment cycles and program calendars. We built engagement into it.
Several teams owned the implementation. Research had diagnosed the problem and named the mechanism, but coaching, comms, and product each held different pieces of the response.
The funnel step rate improved measurably after the changes shipped. The exact numbers are confidential, but the direction was clear and the change held over time. More importantly, the framing — that motivation is perishable in the gaps between commitment and action — became a shared mental model across the teams that owned the journey. Subsequent work on other funnel steps adopted the same lens.