No. Eudemonic AI is a decision-support and insight tool, not a diagnostic or clinical system. It is designed to help institutions interpret anonymised, aggregated cohort-level patterns in questionnaire responses. It does not diagnose mental illness, produce clinical assessments or replace professional judgement.

The platform is designed around anonymised, aggregated cohort-level outputs. Access to data and outputs should be governed through institution-specific arrangements, with role-based access controls and agreed governance processes. Institutions remain responsible for their own data controllership and implementation decisions.

Yes. Eudemonic AI is intended to be explored through a structured pilot approach so an institution can review the questionnaire process, dashboard style, cohort-level outputs and governance fit before considering broader adoption.

Eudemonic AI is designed for a broad range of educational institutions, including universities, further education colleges, schools and multi-academy trusts. It is particularly relevant to wellbeing leads, pastoral teams, safeguarding leaders, academic quality teams and student success functions that want stronger cohort-level insight.

The full questionnaire is not published openly. This helps protect the integrity of the methodology, privacy considerations and intellectual property. More detailed review can be discussed during formal pilot conversations and, where appropriate, under non-disclosure arrangements.

Institution-specific discussion is possible around dashboard emphasis, reporting priorities and how outputs are presented. The core analytical approach remains structured, but presentation and pilot scope can be shaped around institutional context and decision-making needs.

The product is designed with privacy, governance awareness and human oversight in mind. It is intended for cohort-level staff review, not automated student-level action. The approach emphasises anonymised outputs, role-based access, institutional governance and responsible use within existing wellbeing and safeguarding structures.

Development has been shaped through collaboration and innovation engagement with eight UK universities, including work connected to feasibility study, machine-learning model testing, software engineering, pilot-informed analysis, dashboard design, UX/UI development, software security review and evidence-building for responsible EdTech development. These collaborations do not imply formal endorsement, adoption or certification.