The Framework

The TEACH-AI framework

TEACH-AI is thegurucool's proprietary competency framework for teacher AI readiness, built around seven domains and a multi-level proficiency scale. It was developed to give teachers a way to measure, and build, their ability to teach well in an AI-integrated classroom, not just their ability to use AI tools.

What it measures

Most AI training for teachers stops at tool literacy. It shows you how to prompt a chatbot or generate a worksheet, then calls that professional development. TEACH-AI starts from a different premise: knowing how to use an AI tool and knowing how to teach well with one are not the same skill.

The framework breaks that second skill into seven domains, each measured against a proficiency scale. A teacher does not start at zero. The diagnostic places you at the level that reflects where your practice actually is, then builds a pathway from there.

Seven domains
T
Technology Fluency
Knowing which AI tools belong in your classroom, and the judgment to tell the difference.
E
Ethical Intelligence
Bias, data privacy, and what responsible AI use looks like when the people affected are children.
A
Assessment Mastery
Building assessments that hold up when students have access to tools that used to be called cheating.
C
Community Engagement
Using AI to plan and build lessons faster, without losing the judgment that makes the lesson good.
H
Hybrid Pedagogy
Working alongside AI without becoming dependent on it, and without being afraid of it either.
A
Agile Learning
Personalising learning at a scale that reaches the students who used to fall through the gaps.
I
Impact Amplification
Making sure AI use in the classroom makes things more equitable for every student.
Grounded in pedagogy

Four decades of research, applied.

TEACH-AI is not a new theory of teaching. It applies four established, well-researched pedagogical bases to the specific problem of AI-integrated classrooms.

TPACK
Technology Fluency · Community Engagement
Universal Design for Learning
Impact Amplification
Hattie's Visible Learning
Agile Learning
Bloom's Taxonomy
Assessment Mastery

Ethical Intelligence and Hybrid Pedagogy draw on all four bases together. Both are less about a single instructional technique and more about the judgment that sits above every technique a teacher uses.

A proficiency scale, not a starting line
Emerging Expert

Full level definitions are part of the diagnostic, not published here.

Common questions
What is the TEACH-AI framework? +
TEACH-AI is thegurucool's proprietary framework for teacher AI readiness. It covers seven domains, each scored against a proficiency scale, and is used to diagnose a teacher's starting point and build a personalised path from there.
Is TEACH-AI the same as TeachAI.org? +
No. TeachAI.org is a separate initiative. TEACH-AI is thegurucool's own framework, built specifically for classrooms in Southeast and South Asia.
What research is TEACH-AI built on? +
TEACH-AI draws on four established pedagogical bases: Bloom's Taxonomy, TPACK, Universal Design for Learning, and Hattie's Visible Learning, applied specifically to AI-integrated teaching rather than repackaged as something new. The seven domains are also mapped to the UNESCO AI Competency Framework for Teachers, so the framework is grounded in established pedagogy and aligned to a recognised global standard.
Do teachers start at the same level? +
No. The diagnostic places each teacher at the proficiency level that matches their actual practice, then builds their pathway from there.
How do I earn a formal AI credential as a teacher in Southeast Asia? +

If the credential you want is in AI-integrated teaching practice, thegurucool's TEACH-AI framework awards verified, per-domain credentials mapped to the UNESCO AI Competency Framework for Teachers.

You earn each credential by demonstrating mastery in a scenario-based diagnostic, not by completing a course, and the process is self-serve, with no district procurement or cohort start date required. TEACH-AI measures teaching practice specifically, so it is not the same as a general computer-science or machine learning qualification.

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