The AI Ready Schools Erasmus+ project is currently undertaking a literature review focusing on themes of AI Readiness. Here's are some preliminary findings;

An AI-ready school appears to be characterised by a comprehensive mixture of institutional policy, teacher preparation, and a commitment to human agency and ethics. It moves beyond tool adoption to foster an environment where technology serves as a "thinking partner" while maintaining the "human in the loop".

1. Institutional and Policy Frameworks

• Clear Guidelines: AI readiness requires school-level or national policies that define the legal, ethical, and pedagogical boundaries of AI use.
• Safety and Ethics: Readiness is defined by adherence to standards like Safety, Accountability, and Transparency, ensuring that personal data is protected and that AI does not introduce new safeguarding risks.
• Equitable Infrastructure: Schools must provide reliable digital devices and internet access to prevent the integration of AI from widening the digital divide.
• Leadership Support: School leaders must be trained to identify and select appropriate AI tools and develop whole-school strategies for their implementation.

2. Teacher Readiness and Pedagogical Shifts

• Targeted Professional Development: Success is "closely dependent on the readiness of teachers," who require training that covers both technical mechanics and pedagogical strategies.
• Assessment Transformation: An AI-ready school shifts from product-oriented evaluations to process-based assessments, valuing the learning journey over AI-generatable final products like traditional essays.
• Role Redefinition: Teachers act as facilitators who model the critical evaluation of AI, helping students move from passive users to active "data agents".
• Workload Management: AI readiness involves leveraging tools to streamline administrative tasks and lesson planning, allowing teachers to dedicate more energy to direct student interaction.

3. Curricular Integration and Literacy

• Universal AI Literacy: Knowledge of AI is treated as a basic skill, encompassing an understanding of what AI is, how it works (including machine learning), and its impact on society.
• Computational Thinking 2.0: Schools evolve from teaching rule-based programming (CT 1.0) to data-driven problem-solving (CT 2.0), where students learn to train models using sets of data.
• Adaptable Modules: Curricula are designed to be modular and adaptable to local contexts, allowing teachers of any subject to integrate AI literacy transversally.

4. Student Agency and Critical Thinking

• Thinking Partners, Not Replacements: Students are taught to use AI as a collaborator (e.g., as a tutor, coach, or teammate) while remaining responsible for verifying outputs for hallucinations and bias.
• Originality and Ideation: Students are encouraged to develop their own ideas first before involving AI to ensure that their authentic voice and cognitive engagement are preserved.
• Verification Habits: AI-literate students develop the habit of cross-checking AI-generated facts, citations, and logic against reliable sources.

5. Inclusive and Social Practices

• Collaborative Dynamics: Schools monitor the impact of AI on social interactions, ensuring that students do not become over-reliant on individual AI interaction at the expense of peer-to-peer collaboration and mentorship.
• Demystifying Myths: A ready school helps students move past misconceptions—such as believing AI is inherently "dangerous" or that it only exists within physical robots—to see it as a software tool for real-world problem-solving.

Follow the AI Ready project on LinkedIn to see how the research develops.