Systemic, human-centred approach treating digital data use as a school-and-teacher system, with AI integrated into professional development¶

Strategy · All strategies
Evidence · no claims cited

Description¶

Following research on human-centred learning analytics, the authors suggest "the use of digital data in schools should be understood as a systemic issue that involves both school and teacher characteristics", calling for a broader framework considering school context, leadership support, and teacher collaboration alongside technical skills and beliefs. They further propose integrating AI-powered tools such as personalized learning platforms into professional development to help teachers analyze and interpret data more efficiently, while ensuring adequate training and careful consideration of ethical implications such as data privacy and bias.

Design Implications¶

Context¶

Requirements¶

• Involvement of various stakeholders including school leaders, teachers, and local policy makers in decision-making

Constraints¶

• The article cautions that AI integration should ensure teachers are adequately trained and that ethical implications such as data privacy and bias are carefully considered

Target Learners¶

• School teachers and school leaders in K–12 settings

Target Learning Goals¶

• Teacher data literacy and effective, ethically responsible use of digital data in instruction

Related Strategies¶

• Personalized, subject-specific professional support combining teacher characteristics with school-level collaboration and leadership support
• Implement a systems change approach addressing five factors simultaneously for AI and digital equity
• Design AI literacy tools with transparency in development and establish district policies for vetting AI tools
• Adopt a systems approach to AI integration rather than spotlights or gaps
• Involve teachers as co-designers of AI-based intelligent systems, with agency to train, correct, interpret, and override system recommendations
• Ground ethical inquiry into AI in the realities of teaching, assessment, leadership, and professional judgement rather than treating it as external compliance
• Train teachers in prompt engineering, evaluating AI-generated responses, ethical and privacy considerations, and integrating AI outputs into pedagogical decision-making
• Provide teachers with adequate training and support for AI use, embedded in leadership-led professional development
• Educators should treat themselves as philosophers of teaching, learning, and digital objects

Examples¶

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Key Sources¶

• Michos, K., Schmitz, M.-L., & Petko, D. (2025). Learning Analytics in Schools: Is Digital Data Use Influenced by Teacher-Level or School-Level Factors? Journal of Learning Analytics, 12(3), 87–101. https://doi.org/10.18608/jla.2025.8567