From Foundation to Transformation

Develop and publish the MDRS and MARS, defining the minimum infrastructure, systems, data, governance, safeguarding, support and professional-capacity conditions required for reliable digital implementation and responsible AI-dependent deployment.

Assess schools and education systems against MDRS and MARS — connectivity, power, devices, networks, technical support, cybersecurity, core systems, data governance, interoperability, teacher readiness and implementation capacity.

Classify schools and systems by readiness level and use the results to prioritise infrastructure investment, implementation support and phased deployment of digital and AI-enabled services.

Require approved digital and AI tools to demonstrate appropriate offline, low-bandwidth and continuity functionality under Caribbean operating conditions.

Define the core digital and AI competencies learners should develop across education levels, including critical use, creativity, ethics, safety, information literacy and responsible participation.

Translate the competency framework into clear progression expectations for early childhood, primary, secondary and post-secondary education.

Map digital and AI competencies, foundational mathematics and literacy, and responsible technology use across existing subjects, grade levels and learning outcomes.

Require digital curriculum resources and AI-supported learning materials to meet accessibility and assistive-technology requirements for learners with disabilities.

Develop practical guidance for using digital and AI tools in lesson planning, resource development, differentiation, feedback and classroom support.

Require teachers to review and remain accountable for AI-generated content, feedback, recommendations and other outputs used in teaching or student support.

Provide guidance that ensures digital and AI use strengthens writing, reasoning, creativity, inquiry and independent problem-solving rather than replacing them.

Review the implications of digital tools and AI for classroom assessment, national examinations, academic integrity, certification, accessibility and equity.

Define what use of digital and AI tools is permitted, prohibited, supervised, disclosed or required in formative and summative assessment.

Provide guidance on plagiarism, AI-generated work, disclosure, verification, authorship and appropriate teacher and institutional responses.

Define Tier 1 foundational capability, Tier 2 applied practice, and Tier 3 coaching, leadership, technical and implementation capability for teachers, school leaders and education personnel.

Deliver practical training on digital and AI literacy, responsible use, pedagogy, assessment integrity, safeguarding, data protection and change leadership.

Establish the Regional Coordination Body, national focal points and cross-functional national steering arrangements to coordinate standards, national adaptation, implementation oversight and reporting.

Define how EMIS, SIS, LMS, assessment, identity, data and school-management systems should connect — common data definitions, APIs, master data and access-management requirements.

Establish criteria for evaluating tools and providers against curriculum alignment, accessibility, safeguarding, privacy, interoperability, offline functionality, implementation support, local contextualisation, evidence of effectiveness and commercial viability.

Establish shared principles and minimum requirements for the responsible design, procurement, deployment and use of digital and AI technologies in education.

Require clear disclosure of AI use, meaningful human oversight and identifiable institutional accountability for decisions affecting learners, teachers or schools.

Require evaluation of approved tools for unequal impacts across disability, gender, geography, language, income level and other relevant groups.

Establish minimum requirements for lawful and responsible education data use — data minimisation, purpose limitation, access control, retention, consent, child safeguarding and protection from harmful digital and AI-enabled interactions.

Define national roles, accountability, data stewardship, school-level responsibilities, safeguarding procedures, parent communication and escalation pathways.

Assess the quality, completeness, interoperability, governance and usability of education data. Establish common definitions, identifiers, metadata, research-access rules and priority research questions.

Establish the regional coordination function, national focal points, implementation teams, governance arrangements, financing responsibilities and initial delivery plans required to coordinate implementation.

Establish baseline measures, reporting definitions, data-collection arrangements, annual reporting cycles and learning mechanisms across readiness, capacity, adoption, equity, safety and outcomes.

Establish teams to provide school onboarding, data migration and cleanup, system configuration, helpdesk coordination, implementation coaching, managed reporting, adoption monitoring and targeted intervention.

Develop multi-year financing plans for regional public goods, national systems, infrastructure, capacity building, implementation support, research and recurrent operations.

Upgrade connectivity, school networks, devices, power resilience, secure hosting, backup, maintenance, cybersecurity and technical support in line with national readiness plans.

Embed approved competencies across language, mathematics, science, social studies, TVET, arts and other subjects rather than treating them as a standalone course.

Develop, adapt and curate accessible learning resources, examples, case studies and datasets that reflect Caribbean culture, language, geography, industries and development priorities.

Integrate source evaluation, misinformation, manipulated media, synthetic content and responsible online participation into age-appropriate curriculum guidance.

Establish approaches for using digital and AI-supported teaching to improve access and participation for learners with disabilities, remote learners, multilingual learners and learners needing extra support.

Support schools to use approved tools, learner data and teacher judgement to adapt instruction, pacing, practice, feedback and intervention.

Support selected schools to test and share effective models of digital and AI-supported teaching under structured monitoring and implementation support.

Increase the use of oral assessment, projects, portfolios, practical demonstrations, supervised tasks and other approaches that strengthen validity in AI-enabled learning environments.

Work with national examination and assessment bodies (including CXC) to develop options for adapting high-stakes assessment, certification and examination systems.

Define requirements for accessibility, security, offline functionality, identity verification, data protection, auditability and continuity for digital assessment tools.

Identify and prepare school-level champions to coach peers, share practice, support implementation and escalate barriers requiring national support.

Expand practice-based learning in AI-supported pedagogy, differentiated instruction, assessment redesign, data use and school-level implementation through coaching and peer networks.

Work with teacher education institutions to embed digital and AI literacy, ethics, safeguarding, inclusive pedagogy, assessment and classroom practice into pre-service programmes.

Develop applied capability in systems architecture, data governance, interoperability, vendor assurance, financing, implementation support, monitoring and reporting.

Create and maintain a regional registry of evaluated tools, content and providers that meet agreed standards and can be adapted to national requirements.

Require service levels, security assurance, data portability, implementation support, continuity arrangements, exit provisions and post-deployment performance monitoring.

Enable shared procurement, framework agreements, pooled purchasing and common requirements that improve access, affordability and quality, particularly for smaller states.

Support Caribbean-owned EdTech development, piloting, evaluation, procurement access, research partnerships, investment readiness and export potential within regional standards.

Require enhanced review before AI is used for high-impact functions, including student profiling, behavioural prediction, disciplinary decisions, admissions or automated decision-making.

Establish procedures for reporting, investigating, escalating and responding to harmful, biased, unsafe, inaccurate or inappropriate AI outputs and related ethical concerns.

Provide clear guidance on responsible AI use, rights, reporting channels, limitations of AI outputs, misinformation and appropriate participation in digital learning environments.

Establish requirements for authentication, access control, encryption, backup, vulnerability management, monitoring and secure identity management across education systems.

Provide clear guidance on consent, data use, AI-enabled services, safeguarding, reporting channels, and the rights of learners and parents.

Establish procedures for detecting, reporting, investigating, escalating, communicating and resolving data breaches, cyber incidents and child safeguarding concerns.

Support schools to apply data-quality controls, access management, safeguarding procedures, incident escalation and parent communication within daily operations.

Build regional and national capacity to assess the effectiveness, fairness, accessibility, safety and implementation quality of digital and AI-enabled tools.

Develop de-identified, representative and governed datasets in priority areas such as learning, attendance, assessment and student support — subject to data protection and research governance requirements.

Link Ministries, schools, universities, researchers and data professionals through research networks, fellowships, secondments and shared priority programmes.

Publish evidence on tool performance, implementation lessons, equity effects, risks and effective practice to inform policy, procurement, funding and professional learning.

Develop a standard approach for readiness assessment, implementation planning, leadership orientation, teacher induction, parent communication, data preparation, system configuration and early-stage adoption monitoring.

Provide practical support to digitise records, improve data quality, configure reporting and enable schools and Ministries to use dashboards and reports for decision-making.

Establish service levels, escalation procedures, incident management, knowledge bases, support tracking and coordination with approved providers.

Develop dashboards that track readiness, infrastructure, training, system use, data quality, onboarding, helpdesk performance, adoption risks, equity gaps and vendor service performance.

Track readiness and access by geography, school type, disability, income level and other relevant groups, and direct targeted support where gaps persist.

Review curriculum guidance periodically to reflect emerging technologies, implementation evidence, labour-market needs and Caribbean development priorities.

Examine the effects of sustained AI use on learner independence, reasoning, creativity, knowledge retention and teacher practice.

Test AI-assisted feedback tools in controlled settings, with teacher review, learner safeguards and independent evidence of effectiveness.

Create recognised advanced pathways for teachers, leaders, champions and specialists who demonstrate sustained competence in digital and AI-enabled education practice.

Monitor dependency on external platforms and suppliers, assess market concentration and continuity risks, and update regional strategies where necessary.

Review evidence on the effects of AI use on learner autonomy, equity, wellbeing, reasoning and public trust, and update ethical safeguards where required.

Review emerging risks from new technologies, changing threats, implementation evidence and incident trends, and update safeguards and standards where required.

Use accumulated evidence, implementation data and independent evaluation to update standards, investment priorities and future policy directions.

Conduct periodic independent review of implementation quality, equity, safety, financing sustainability, learning and system outcomes and emerging risks, and update the roadmap accordingly.

Use monitoring data, stakeholder feedback, evaluation findings and implementation experience to update delivery plans, standards, financing priorities and support models.