Tina Austin presenting on Bloom’s Taxonomy and AI-era learning

I am building

Researcher · Educator · Advisor

Designing learning
for minds in the age of AI.

Tina Austin helps educators and institutions move beyond tool adoption—to design for human reasoning, discernment, and agency in an AI-mediated world.

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Institutions, media, and global education communities

Organizations including Oxford, UNESCO, SXSW EDU, Microsoft, Marconi Society, UCLA, Caltech, OpenAI, CNN, Rutgers, USC, ASU+GSV, Duolingo, Canva, NBC, and others

The premise

“When AI can produce the answer,
learning lives in the reasoning.”

Generative AI has made finished work an unreliable proxy for learning. Tina’s work asks a more useful question: how do we make thinking visible, traceable, and worth doing?

Her answer is UnBlooms™—a recursive, problem-centered approach to learning design that develops judgment before, during, and after AI use.

UnBlooms™

A framework for thinking with AI

Not a ladder.
A living loop.

Bloom’s Taxonomy is often treated as a climb toward a finished product. But learning is rarely linear—and AI can now shortcut the visible output at every level.

UnBlooms begins with a meaningful problem, then cycles through inquiry, making, critique, reflection, and revision. The goal is not to avoid AI. It is to know when to use it, when to challenge it, and when to think without it.

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Selected research & writing

Ideas built to be used.

Tina’s work connects learning science, assessment design, AI literacy, and institutional change—moving evidence into practice.

Journal article · 2026

When AI Agents Can Complete the Assignment: Practical Strategies for Designing Tasks That Still Require Human Thinking

Published in the Journal of Instructional Design and Technology. Presents practical assignment redesigns that make students’ reasoning, critique, and decisions visible when AI agents can complete tasks independently.

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Peer-reviewed paper · 2026

Toward a Metric for Disciplinary Learning in the Age of AI

Introduces the UnBlooms™ Metacognitive Awareness Scale and Discernment Rate—measures designed to distinguish polished output from durable learning in AI-mediated environments.

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Conference paper · LAK ’26

Using learning analytics to measure AI-critical literacy

A within-subjects study of the UnBlooms Framework, presented at the Learning Analytics for Human-Centered Generative AI-Enhanced Smart Learning Environments workshop.

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Open framework · 2025

The UnBlooms™ Model

A problem-centered framework for learning design in the AI era: recursive rather than hierarchical, with reasoning made visible before, during, and after AI use.

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Book · 2025

The UnBlooms™ Workbook

A practical guide to designing, teaching, and assessing human reasoning when AI can generate the finished product—built for educators who need methods, not slogans.

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Read Tina’s research notes on Substack

From framework to fieldwork

Research, translated into institutional change.

Tina works with universities, nonprofits, policy groups, and philanthropic organizations on responsible AI adoption—connecting governance, faculty development, assessment, and culture.

1,800+faculty supported

5continents reached

2022UC AI Initiative launched

OpenAI

Higher Education Summit · Invited faculty speaker

UNESCO House, Paris

IASEAI ’26 · AI Safety Literacy Framework

University of Oxford

AIEOU · Research collaboration

California

AI & Workforce Board · Policy advising

Microsoft

Innovative Educator Expert · 2025–26

UCLA + institutions worldwide

Faculty development · 1,800+ educators

Tina Austin speaking to a large audience at the OpenAI Higher Education Summit — On stage
Teaching with AI across disciplinesOpenAI Higher Education Summit · 2025

About Tina

Science-trained.
Human-centered.
Unusually practical.

Tina Austin is a globally recognized researcher, educator, author, and advisor working at the intersection of AI, learning, and institutional change.

A former biomedical researcher and longtime university lecturer, she has taught at UCLA, USC, CSU, and Caltech. She brings the skepticism of a scientist and the empathy of an educator to a question that matters everywhere: how do we gain the benefits of AI without giving away human judgment?

Read the full bio →LinkedIn ↗Substack ↗GAInable.ai ↗

Selected appearances & work