Tailor AI deployment to specific applications and stakeholder groups rather than applying a one-size-fits-all integration approach¶

Strategy · All strategies
Evidence · 6 claims (6 for) · 1 study (1 causal), q3 · 0 of 1 report an effect size · 6 claims rest on one study

Description¶

The article recommends that educational institutions prioritise transparency in AI operational processes, proactively incorporate explainable AI methodologies to clarify decision-making criteria, and establish clear privacy protocols that balance confidentiality and transparency. It concludes that "applying a one-size-fits-all approach to AI integration in education is infeasible" and that careful consideration of specific AI applications and their impact on perceptions of global utility, justice, and confidence is needed. Educators must also be trained not only in how to use AI tools but in understanding their implications.

Design Implications¶

Context¶

Requirements¶

• Clear institutional policies addressing the problem of agency, building confidence and trust, and insisting on explainable AI systems

Constraints¶

• Transparency and explainability mean different things to different stakeholders, and their effectiveness is context-dependent

Target Learners¶

• K-12 students
• teachers
• parents

Target Learning Goals¶

• Enhancing stakeholder acceptance and trust in AI education solutions

Affordances¶

• Confidence Trust Ai Acceptability Education Framework

Claims¶

• Higher AI agency is associated with lower intention to use AI in education, while transparency, explainability, and privacy do not directly predict intention to use [+W]
• For parents, higher AI agency lowered confidence, which indirectly reduced intention to allow AI to correct student work (Correct-AI) [+W]
• For students, AI agency reduced intention to use Correct-AI indirectly through lower perceived global utility and justice, with no direct effect [+W]
• For students, privacy (Answer-AI) and explainability (Emotion-AI) each positively influenced intention to use indirectly through perceived global utility [+W]
• For teachers, higher privacy levels lowered confidence regarding an AI supporting student emotional health, indirectly reducing intention to use (Emotion-AI) [+W]
• For teachers, explainability (Correct-AI) and both agency and explainability (Answer-AI) influenced intention to use indirectly through global utility and perceived usefulness [+W]

Related Strategies¶

• Tailor GenAI chatbot onboarding, training, and support to identified adoption personas
• Move beyond generic AI policies to method-based ethical policies and process-oriented assessment
• Deploy agentic AI in education with transparency about agentic decisions, human oversight and institutional responsibility
• Establish comprehensive institutional AI governance frameworks including task forces, roadmaps, and transparent policies
• Foster communities of practice for AI governance instead of rigid top-down policies
• Design AIED deployments to meet school operational criteria such as support, stability, and availability
• Design AI-mediated cognitive supports with transparency, clear boundaries, and teacher evaluation
• Tailor active methodologies to the specific educational context and subject matter

Examples¶

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Key Sources¶

• Alexander John Karran, Patrick Charland, Joé Trempe-Martineau, Ana Ortiz de Guinea Lopez de Arana, Anne-Marie Lesage, Sylvain Sénécal & Pierre-Majorique Léger. (2025). Multi-stakeholder perspective on responsible artificial intelligence and acceptability in education. npj Science of Learning 10:44. https://doi.org/10.1038/s41539-025-00333-2