The AI-Native Professional
Our Mission
While there is consensus forming around the durable capabilities needed for the age of AI, how to provide learning opportunities for all, not just those who started with advantages, to develop those capabilities is unclear.
Our mission is to develop a discipline-neutral framework, grounded in research and tested across a wide range of audiences, to generate new knowledge on how to develop human value in the age of AI.
How It Works
Here’s the framework, explained step by step.
01 · The ScienceSelf-Determination Theory, 25 years of human motivation researchThree psychological needs: Autonomy, Connectedness and Competence+
A 25-year body of research (Deci & Ryan) finds people develop most durably when three psychological needs are met. We design for all three:
- Autonomy: we give learners real choices over their goals and work, so the learning is theirs.
- Connectedness: we complement every activity with human conversations, so learning stays socially anchored.
- Competence: we meet learners where they are and scaffold each step, so they build a genuine sense of capability.
Source: Self-Determination Theory, American Psychological Association
02 · Symbiotic ThinkingA human-led practice of pursuing wisdom in partnership with other intelligences, human or artificialRooted in daily habits and conversational skills+
Symbiotic Thinking shows up in two everyday practices:
Daily HabitsSlow Down · Know Yourself · Take the Lead▾
⏸️ Slow Down
While AI brings speed, human value lies in slowing down and getting the direction right.
🪩 Know Yourself
It is the starting point for everything, what you are curious about, your strengths and weaknesses, your preferences. These should be yours and yours alone.
🎯 Take the Lead
Be proactive in directing all your work toward your chosen goal and purpose.
Grounded in: Kahneman (2011); Flavell (1979); Bandura (2001).
ConversationsSynchronous (Listening · Talking) · Asynchronous (Reading · Writing)▾
Synchronous, in the moment
👂 Listening: finding the real problem, through peer conversations, stakeholder interviews, thought partnering.
🗣️ Talking: making the implicit explicit, through check-ins, presentations, defended positions.
Asynchronous, across time
📖 Reading: extracting meaning, from course content, documentation, AI outputs.
✍️ Writing: structuring your thinking, in reflection notebooks, goals, demonstrations.
Grounded in: Littleton & Mercer (2013); Chi et al. (1994); Bangert-Drowns et al. (2004).
03 · Durable CapabilitiesSelf-Directed Learner · Integrative Solver · Adaptive BuilderWhat the practice builds+
One loop, Symbiotic Thinking at the center: learn what the problem needs (SDL), connect it to the people and domains involved (IS), build, test, and adapt (AB), each round revealing the next thing to learn.
SDLSelf-Directed LearnerMetacognitive awareness for just-in-time learning▾
Acquiring sufficient knowledge in unfamiliar domains: knowing what is sufficient and how to learn, and the ability to evaluate their own learning.
In action
A builder starting on patient-communication tools needs medical terminology, HIPAA, clinical workflows, and patient psychology, in weeks, not semesters.
How we develop it
Giving learners autonomy to choose their goals, metacognitive guidance and personalized coaching delivered through AI tools, and practice in thinking with AI without cognitive decline.
Grounded in: Zimmerman (2002); Flavell (1979).
ISIntegrative SolverAbility to connect different domains and perspectives in pursuing a solution▾
Build a T- and M-shaped skill profile, connecting and transferring ideas across domains and integrating multiple perspectives learned through conversations.
In action
Asked for a sophisticated sentiment-analysis dashboard, an Integrative Solver talks to the complaint handlers first, applies knowledge from customer-relationship research, and identifies that 50 patients generate 60% of complaints, then builds a simple tool that solves the real problem.
How we develop it
Learners work on projects that cross domains to solve problems, rather than demonstrate narrow disciplinary skills, pursuing goals they select for their external relevance.
Grounded in: Hansen & von Oetinger (2001); Klein (1990); Akkerman & Bakker (2011).
ABAdaptive BuilderExecuting through cycles of build, test, and adapt▾
Ability to execute under uncertainty, building, testing, learning from what works and what doesn’t, and adapting.
In action
Rather than waiting to figure out answers to all questions, an Adaptive Builder ships a minimal version, tests it in real environments, learns, and lets the lessons shape the next iteration.
How we develop it
Build iterative development into all levels of the learning experience, give multiple opportunities for revisions, and never make individual activities one-and-done.
Grounded in: Kapur (2008); Thomke (2020); Edmondson (2023).
04 · OutcomesSuperagency · Human ValueWhat learners can demonstrate+
Two questions every learner learns to answer with evidence from their own work.
Grounded in: Hoffman & Beato (2025).
Our Design Principles
We did not build this from a single source. We designed it against four principles, the way a good theory is built.
Layered
It is built in layers: the science, the daily practice it points to, the capabilities that practice builds, and the outcomes we are after. Each layer rests on the one below, so you can trace any piece up or down.
Simple
We know there is a lot here. The goal is always to simplify: as few parts as possible, keeping only what earns its place, until the framework is small enough to hold in your head and teach to someone else.
Explanatory
It should make sense of what we actually see: why a learner is stuck, what a habit is building toward, how a capability shows up in real work.
Applied
It should be able to describe and be applied to design real learning experiences a learner can act on.
These principles guide every decision. We ground each idea in external research, and we treat our own testing with students as the real validation: the framework earns its place by working in practice.