01⌁
Inquiry learningAI literacy
Can Nature Teach Children to Question AI?
A five-session inquiry experience that helps children question AI-generated answers through observation, evidence, and reflection.
View case studyLearning science · thoughtfully applied
I design learning experiences
for an AI-rich world.
I combine learning science, human-centered design, and research to help learners think critically, question confidently, and make better decisions— and to help public institutions create the conditions that make this possible.
Learning Experience
Design
AI Literacy &
Critical Thinking
Evidence-Based
Iteration
The goal isn't to teach learners to distrust AI. It's to build the habits of mind that make trust evidence-based.
Selected work
Four ways I design learning
From a single learning moment to the ecosystem around it.
02◎
Systems designAdult learning
Designing Responsible AI Learning at Scale
A learning ecosystem for educators, leaders, families, and students—designed to make responsible AI practice coherent across a district.
View case study03✦
Scenario designAssessment
Would You Trust the AI?
An interactive scenario that invites learners to step into a student’s shoes and practice making better judgments with AI.
Try the experience04◇
Online learningInclusive design
Designing Learning for the Online Classroom
Inclusive online experiences that support understanding, deliberate practice, meaningful feedback, and learner belonging.
View case studyBeyond research
I turn evidence into learning that people can actually use.
Research is one part of my practice. I also translate theory into experiences, environments, and tools—then study how they work and improve them.
Frame the learning problem
Clarify who is learning, what meaningful performance looks like, and what is getting in the way.
Design for cognition & motivation
Use prior knowledge, scaffolding, practice, feedback, agency, and belonging with intention.
Prototype authentic experiences
Build scenarios, activities, facilitation plans, and digital interactions around real decisions.
Measure what matters
Combine observable performance, learner thinking, and qualitative evidence—not just satisfaction.
Improve the whole system
Iterate the learning experience and the surrounding conditions that help it transfer into practice.
The question behind my work
Who receives what kind of teaching from AI, under what institutional conditions, and with what consequences?
AI increasingly allocates explanation, challenge, patience, attention, and trust. My scholarship makes those pedagogical choices visible—and helps educators and public institutions redesign them around human capability.
Research program
Auditing the systems that teach.
I connect computational evidence, human learning, and institutional governance to protect every learner's opportunity for intellectual growth.
Educational AI Auditing
I examine whether AI tutors distribute cognitive challenge, scaffolding, dialogue, and expectations differently across learners.
Computational auditsPedagogical equityLLMs
Human–AI Learning
I study how AI changes learning, verification, intellectual agency, and the opportunity to become an independent thinker.
Learning & transferEpistemic agencyDoctoral formation
Policy & Public Governance
I ask how schools and public institutions can adopt, monitor, and redesign AI systems under conditions of uncertainty.
District governanceImplementationPublic accountability
Current inquiries
Research questions in motion
How do AI tutors vary instructional opportunity across learner profiles?
A computational audit of cognitive challenge, dialogue, and expectations across large language models.
When do resources become real opportunities for AI-supported learning?
A cross-system analysis connecting socioeconomic context, readiness, and creative thinking.
What turns access into meaningful participation?
A comparative study of school disadvantage, teacher capacity, and AI-supported inclusion.
My intellectual journey
One question, coming into focus.
My path began with digital power and moved steadily closer to the places where technology shapes human possibility.
- 2022Digital Rights
Privacy, speech, platforms, and the boundaries of technological power.
- 2023Algorithmic Governance
Data governance, discrimination, market power, and generative AI.
- 2024Learning
Critical-thinking pedagogy, curriculum, and institutional readiness.
- 2025Opportunity
Scientific literacy, ICT, schools, and unequal learning conversion.
- 2026Audit & Governance
AI-mediated teaching, epistemic agency, and public accountability.
From production to verification: Generative AI, doctoral formation, and the leadership of digital education
Frontiers in Education
From integrity to identity: Course-level generative AI governance and scholarly subject formation in graduate education
Frontiers in Education
Analyzing Scientific Literacy in Asia's Top Five PISA 2022 Performers Using Hierarchical Linear Modeling and Bourdieu's Theory of Practice
Asia-Pacific Science Education
ICT and Its Impact on the Scientific Literacy of Secondary School Students: A Comparative Study Between Singapore and the USA in PISA 2022
Science Journal of Education
Critical Thinking Pedagogics Design in an Era of ChatGPT and Other AI Tools—Shifting From Teaching “What” to Teaching “Why” and “How”
Journal of Education and Development
My design philosophy
Good learning leaves learners more capable.
Start with the learner
Understand goals, contexts, prior knowledge, identity, and the choices learners need to make.
Design for thinking
Make reasoning visible through questions, representations, practice, explanation, and reflection.
Iterate with evidence
Prototype early, notice where learning breaks down, and use evidence to improve the experience.
Make learning reachable
Reduce unnecessary friction and create multiple ways to engage, participate, and demonstrate growth.
Where I bring this work
Across classrooms, research teams, and public institutions.
My practice has developed through policy training, learning-sciences scholarship, teaching across formats, and embedded work with institutions making decisions now.
Doctoral researcher
Ph.D. candidate in Education Policy and Leadership, with training in Comparative and International Education and Social Data Analytics.
Learning sciences · evaluation · computational inquiryEnterprise GenAI Research Fellow
Translating stakeholder evidence into governance recommendations, professional-learning priorities, and structures for organizational learning.
25+ interviews · 18 focus groups · 600+ surveysPublic policy & data science
M.P.P. training shaped how I approach algorithmic power, institutional decision-making, evidence, and public value.
Policy analysis · data visualization · leadershipTeaching across contexts
From university seminars to professional learning.
Policy AnalysisInnovation, Strategy & LeadershipData VisualizationNegotiationsSecurity PolicyInnovation & EntrepreneurshipEducational Methodology in the Multimedia Age
About Evelyn
Designer, researcher, policy thinker, and public partner.
I am Evelyn (Yi) Wu, a doctoral researcher in Education Policy and Leadership at Penn State, with training in Comparative and International Education and Social Data Analytics. I earned my M.P.P. from UC Berkeley, where public policy and data science shaped how I first approached questions of algorithmic power.
My work is grounded in a simple commitment: every learner possesses epistemic dignity—the right to be treated as capable of reasoning, questioning, and intellectual growth. I want AI and learning design to enlarge those possibilities, not quietly narrow them.
Let's continue the conversation
Have a learning or public-interest challenge worth thinking deeply about?
I welcome conversations about learning design, educational AI, human–AI learning, public-sector governance, and research collaboration.