T6. RESPONSIBLE, SECURE & TRUSTWORTHY AI IN EDUCATION

T6. RESPONSIBLE, SECURE & TRUSTWORTHY AI IN EDUCATION

SCOPE:

Research, design, and governance frameworks ensuring that AI systems for learning are safe, transparent, equitable, and institutionally sustainable. This track unites ethical design, privacy engineering, and policy innovation for human-centered AI in education.

A. ETHICAL DESIGN, FAIRNESS & SAFETY

Ethical AI frameworks; fairness-aware learning systems; bias detection and mitigation; inclusivity in educational AI; transparency and explainability; value-sensitive design; algorithmic accountability; participatory AI design; ethics-by-design methodologies; AI safety protocols; responsible innovation ecosystems; diversity and representation in datasets; trust calibration and reliability; interpretability benchmarking; fairness metrics and auditing; harm and risk assessment; socio-technical alignment; moral reasoning in AI tutors; equitable access to AI tools; ethical review and oversight in educational AI.

FOCUS: ensuring AI-driven learning systems are fair, ethical, inclusive, explainable, and socially responsible, through ethical design and validation methodologies.

B. PRIVACY, SECURITY & DATA GOVERNANCE

Educational data privacy; secure data architectures; differential privacy; population groups; federated learning frameworks; encryption and secure multi-party computation; anonymization and pseudonymization; identity management in learning ecosystems; access control models; blockchain-based credentialing; zero-knowledge proofs; data provenance and lineage tracking; audit trails and accountability; adversarial robustness; cyber-physical security in learning systems; privacy-preserving analytics; compliance with GDPR/FERPA; secure cloud-based education services; resilience to data breaches; ethical data collection practices; data trust and stewardship.

FOCUS: developing technical and procedural safeguards for protecting learner data, ensuring privacy, and maintaining institutional trust.

C. POLICY, LEGAL & INSTITUTIONAL READINESS

AI policy in education; national and institutional AI strategies; accreditation and quality assurance frameworks; algorithmic governance; AI-readiness assessment models; regulatory compliance mapping; education-sector data legislation; procurement and ethical sourcing standards; public–private partnerships in AI; teacher AI literacy programs; policy implementation frameworks; risk and compliance auditing; institutional capacity-building; legal accountability in AI use; governance of automated decision systems; strategic foresight for educational AI; AI ethics education; standards harmonization (ISO/IEEE); socio-legal studies of educational AI; policy co-design with stakeholders.

FOCUS: translating AI ethics and safety principles into policies, institutional frameworks, and governance practices that support sustainable, trustworthy AI adoption in education.