Auriga GuidanceAuriga Guidance

The science

Built around established psychological frameworks. Currently in pilot validation.

Auriga’s assessment draws on ten established areas of psychological and educational research. Here they are, named plainly, alongside an honest account of what’s proven and what’s still in progress.

Self-Determination Theory

How autonomy, competence and connection shape genuine motivation — not just compliance.

Social Cognitive Career Theory

How self-belief and expectations about outcomes shape the paths someone considers at all.

Holland’s RIASEC Model

A widely used way of describing interest patterns — used internally, never shown to students as a label.

Career Construction Theory

Careers as an unfolding story a person builds, not a single fixed destination to be identified.

Identity Development

How adolescents explore and gradually commit to a sense of who they are.

Executive Function

Planning, focus and self-regulation — skills that shape how a student pursues any goal.

Growth Mindset

Whether a student sees ability as something that can develop with effort.

Emotional Intelligence

Recognising and managing emotion — in oneself and in working with others.

Grit / Perseverance

Sustained effort and interest toward long-term goals.

NEP 2020 / Holistic Progress Card alignment

Designed with an eye toward India’s national policy direction on holistic, multi-dimensional student development.

Multi-informant design

Why student, parent and teacher perspectives — not just one

A student’s own self-report is valuable but incomplete — people (especially teenagers) aren’t always the most accurate narrator of their own patterns. Combining a student’s answers with a parent’s and a teacher’s independent observations gives the profile more than one vantage point, and the profile is explicit about where those views agree or where evidence is still thin.

Validation status

An honest account, not a marketing claim

Auriga’s assessment is a research-backed instrument adaptation, built around the frameworks above. It is not yet population-normed, and pilot validation is currently in progress.

We deliberately avoid language like “scientifically validated,” “clinically validated,” or claims that AI “determines” a student’s personality or “predicts” their career. Those claims would go beyond what current evidence supports.

What AI does, and doesn’t

AI assists. People remain responsible.

AI helps identify patterns across a student’s evidence, personalise recommendations, and generate first drafts of guidance content. Psychological interpretation, assessment quality, safeguarding and any consequential guidance decision are reviewed by people — never issued by an algorithm alone.

Curious how this looks in practice?