AI in Education Glossary

The deliberate, ethical and human-centred use of artificial intelligence — guided by clear purpose, human values and reflective practice rather than trend or convenience. Intentional AI asks why before which tool, and keeps people at the centre of every decision.

In contrast to reactive AI use — where tools are adopted quickly without strategic thought — intentional AI requires educators to pause, reflect and evaluate whether a tool genuinely serves the learning goal and the people involved.

The five interconnected domains of competency that form the Tech-Telligence Framework: Human Intelligence, Emotional Intelligence, Pedagogical Intelligence, Technological Intelligence and Ethical Intelligence. Together they form a complete model for intentional AI practice in education.

Each intelligence represents a different dimension of how educators think about, use and are affected by AI — and all five must be developed together for truly intentional practice.

An approach to artificial intelligence that keeps human needs, values, dignity and wellbeing at the centre of every design, deployment and evaluation decision. In education, human-centred AI means that technology serves the teacher-student relationship — it never replaces it.

Human-centred AI resists the tendency to optimise for efficiency or output at the expense of empathy, relationships and the irreplaceable human dimensions of learning.

The ability to make sound teaching and learning decisions when using AI — understanding how AI tools impact student cognition, motivation, struggle and growth. A pedagogically intelligent educator asks: does this AI deepen learning, or simply speed it up?

Pedagogical Intelligence is the third intelligence in the Tech-Telligence Framework, and it guards against the most common pitfall of AI in classrooms: mistaking productivity for learning.

The capacity to recognise, reason about and act on the ethical implications of AI use — including bias, privacy, fairness, transparency and the potential for harm. In education, ethical intelligence means asking: whose data is being used? Who benefits — and who might be harmed?

The fifth intelligence in the Tech-Telligence Framework, Ethical Intelligence is not optional. Every AI decision in a school has human consequences, and educators who use AI ethically must anticipate them.

The ability to understand, evaluate and choose AI tools with informed judgement — not expert-level programming knowledge, but enough understanding to use technology responsibly and choose wisely. A technologically intelligent educator knows what an AI tool can and cannot do before trusting it.

The policies, frameworks and processes a school or district uses to guide the ethical, safe and effective use of artificial intelligence. Good AI governance defines who can use what tools, how student data is protected, how bias is monitored and how accountability is maintained.

Without intentional governance, AI adoption in schools defaults to individual teacher decisions — which leads to inconsistency, risk and a lack of shared values around technology use.

A formal or informal document that sets out how a school, university or education system expects AI to be used — by teachers, students and administrators. A strong AI policy addresses academic integrity, data privacy, acceptable tools, bias mitigation and the human oversight required for any AI-assisted decision.

The Tech-Telligence Framework provides the philosophical and practical foundation that schools need before writing effective AI policy.

A measure of how strategically, ethically and effectively a school or organisation uses AI. AI maturity moves from ad-hoc, reactive use at the lowest level to intentional, governed and continuously improving practice at the highest. The Tech-Telligence Intentional AI Audit maps maturity across all five intelligences.

The practice of knowing when to use AI and when not to — protecting students' capacity for deep thinking, creative struggle and independent reasoning. Cognitive balance recognises that when AI removes all cognitive effort, it removes the learning.

One of the seven teacher competencies in the Tech-Telligence Framework, cognitive balance is essential for maintaining the quality of education in an AI-rich environment.

The skill of crafting clear, goal-directed instructions to an AI system to generate relevant, accurate and educationally useful outputs. Prompting with purpose means understanding what you want the AI to do, why it matters and how to evaluate whether the output actually serves the learning goal.

The tendency of AI systems to produce unfair or discriminatory outputs because of biased training data, flawed design or unexamined assumptions. In education, algorithmic bias can affect automated grading, learning recommendations, predictive analytics and college admissions tools — often disadvantaging students from under-represented groups.

Recognising and actively countering algorithmic bias is a core responsibility of Ethical Intelligence in the Tech-Telligence Framework.