Apply an affective data governance framework: minimization, purpose limitation, consent, human oversight, and separation from evaluation¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Whenever AI-supported teacher-development systems involve affective, behavioral, or socio-emotional data, the article prescribes a minimum governance framework: "Apply data minimization, purpose limitation, informed consent, opt-in/opt-out mechanisms, access controls, retention limits, human oversight, and contestability." Data gathered for wellbeing must not be repurposed for productivity monitoring, ranking, or performance evaluation, and algorithmic inferences about affective states should function only as provisional signals requiring contextual human interpretation.

Design Implications¶

Context¶

Requirements¶

• Meaningful opt-in and opt-out mechanisms with explicit guarantees that non-participation produces no professional disadvantage; access restricted to roles responsible for support, not supervision or employment decisions

Constraints¶

• The article notes emotion recognition and soft biometrics can normalize surveillance of affective life under apparently consensual privacy arrangements, and regulatory caution increasingly treats affective inference as high-risk or prohibited in education and workplace settings

Target Learners¶

• teachers
• institutional data governance officers
• vendors of wellbeing platforms

Target Learning Goals¶

• protection of teacher privacy and agency
• ethical use of affective data
• institutional trust

Related Strategies¶

• Stage GAI use in assessment: permit exploration, restrict independent reasoning, require justification
• Deploy agentic AI in education with transparency about agentic decisions, human oversight and institutional responsibility
• Ethical governance safeguards for robot-supported mental health education with children
• Address ethics and privacy considerations — transparency, security, student control, accountability, and informed consent — at the initial stages of MMLA design in simulation-based training

Examples¶

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Key Sources¶

• Aponte ML, Vargas Sánchez AD, Chiappe A and Martínez-Pérez S. (2026). Artificial and emotional intelligence: two key forces for teachers' professional development in an era of uncertainty. Front. Psychol. 17:1935683. https://doi.org/10.3389/fpsyg.2026.1935683