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Education-centered critical policy analysis of AI: Ghana's AI strategy as a case

Matthew Nyaaba, Vida Awinime Bugri, Eric Kojo Majialuwe, Bismark Nyaaba Akanzire, Ibrahim Nantomah, Felicia Boateng, Patrick Kyeremeh, Benjamin Quarshie, Ellen Kwarteng, Macharious Nabang

arXiv:2608.16910v1cs.CYcs.AI

TL;DR

National AI strategies increasingly shape education, yet their pedagogical, cultural, ethical, and implementation demands remain insufficiently understood. This study critically analyzes Ghana’s 2025–2035 strategy using an Education-Centered AI Policy Framework and finds strong national AI-readiness ambitions but underdeveloped school-level guidance.

  • Problem

    Existing frameworks do not fully connect national AI policy with teacher agency, curriculum, pedagogy, assessment, learner protection, language, culture, and participatory implementation.

  • Method

    The study uses critical qualitative document analysis to deductively examine Ghana’s strategy through six education-centered policy components.

  • Results

    Ghana’s strategy emphasizes AI literacy, youth skills, TVET, workforce readiness, inclusion, local language data, and responsible governance, but underdevelops school-level implementation.

  • Takeaways & Limitations

    Ghana needs clearer curriculum pathways, teacher preparation, assessment guidance, learner protection, multilingual pedagogy, locally responsive tools, and participatory governance.

  • Takeaways & Limitations

    The analysis used only the publicly available strategy and may not reflect inaccessible preparatory, implementation, consultation, or sector-specific documents.

Abstract

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National AI strategies increasingly guide governance, workforce development, innovation, and competitiveness, but less is known about how they frame education as a sector with pedagogical, cultural, ethical, and implementation demands. This study develops and applies an Education-Centered AI Policy Framework to analyze Ghana's National Artificial Intelligence Strategy, 2025-2035. Using critical qualitative policy document analysis, we examined the strategy through six components: policy purpose, teacher agency and professional learning, curriculum and assessment, language and culture, responsible AI and learner protection, and participation and implementation governance. Findings show that Ghana's strategy is ambitious and timely, especially in its emphasis on AI literacy, youth skills, TVET, workforce readiness, rural outreach, local language data, inclusion, and responsible AI governance. However, the education agenda is stronger on national AI readiness than on school-level implementation. Teacher agency, pre-service teacher education, curriculum progression, assessment guidance, AI disclosure, multilingual pedagogy, culturally responsive AI use, child-centered safeguards, and participatory governance remain underdeveloped. We also identify document-level concerns about transparency and coherence, including apparent AI-styled visual content without visible disclosure and a mismatch between a vision and mission figure and its textual explanation. We argue that Ghana needs a sector-specific, education-centered AI policy and implementation pathway that connects workforce readiness with teacher preparation, curriculum reform, assessment redesign, learner protection, infrastructure, local language instruction, culturally responsive pedagogy, locally responsive AI tools, and participatory governance.

Introduction · Literature Review · National AI Strategies and Education

National AI strategies increasingly shape education, workforce development, innovation, governance, and competitiveness, but research shows they often prioritize producing AI talent over addressing education’s pedagogical, cultural, ethical, and implementation needs. Ghana’s 2025–2035 strategy makes education a major pillar of national transformation, creating a need to examine its fit with multilingual, unequal, and culturally diverse educational conditions.

  • Introduction: National AI strategies guide government priorities for innovation, workforce development, regulation, public services, and competitiveness across sectors.Education is central because AI readiness depends on human capacity, technical expertise, digital skills, and public understanding.
  • Introduction: Ghana’s National Artificial Intelligence Strategy, 2025-2035, sets a ten-year vision to position Ghana as a leading AI hub in Africa by 2035.It identifies education as a first and major pillar of national AI transformation.
  • Introduction: Education priority does not ensure that national AI policy addresses education as a complex social, cultural, pedagogical, and ethical system.Existing research indicates that policies frequently frame education primarily as a route to producing technical experts and future workers.
  • Introduction: Ghanaian education presents multilingual classrooms, cultural diversity, rural-urban inequalities, uneven digital access, teacher preparation challenges, and ongoing curriculum reforms.These conditions shape how AI policy and tools can be implemented in schools.
  • Introduction: AI tools and policies are more meaningful when they fit local infrastructure, teacher preparation, pedagogical traditions, cultural expectations, languages, indigenous knowledge, curricula, and learners’ lived experiences.Research cautions against importing models without examining their suitability for low-resource and culturally diverse education systems.
  • Introduction: AI governance commonly gives greater visibility to government, technical, academic, and private-sector actors than to affected education communities and end-users.Relevant participants include teachers, students, parents, school leaders, teacher educators, disability advocates, language communities, rural schools, and local communities.
  • National AI Strategies and Education: Research distinguishes “Education for AI,” focused on experts and labour-market preparation, from “AI for Education,” including classroom practice, assessment, learner protection, teacher agency, and equity.The latter receives less attention in many national strategies.
  • National AI Strategies and Education: Workforce preparation, higher education reform, reskilling, and lifelong learning dominate national education priorities, positioning education as a pipeline for AI readiness rather than a sector needing its own implementation, ethical, and pedagogical frameworks.This distinction is especially consequential for Ghanaian classrooms shaped by multilingualism, urban-rural disparities, examination pressures, teacher preparation needs, uneven digital access, and curriculum reform.

AI in Education · Responsible AI, Student Data, and Educational Ethics · Culturally Responsive AI

The literature frames AI in education around AI literacy, transformed teaching and learning, assessment, and teacher agency, while emphasizing that responsible use requires concrete safeguards for privacy, fairness, transparency, and human oversight. In Ghana, culturally responsive AI additionally depends on local-language resources, indigenous knowledge, curriculum alignment, and attention to multilingual and rural-urban educational realities.

  • AI in Education: AI literacy should help students understand AI’s operation, limitations, responsible use, and progression across K–12 learning.Research identifies age-appropriate learning goals but calls for stronger curriculum continuity and evaluation.
  • AI in Education: Generative AI may support tutoring, feedback, lesson planning, differentiated instruction, and administration, but risks misinformation, bias, over-reliance, academic-integrity problems, and weak transparency.
  • AI in Education: Assessment research highlights feedback, academic integrity, task design, and assessment literacy, yet school-level guidance remains limited, particularly for younger learners and examination-driven systems.
  • AI in Education: Teachers require agency to evaluate AI outputs, adapt materials, protect learners, and override automated suggestions using expanded technological, pedagogical, and contextual knowledge.
  • Responsible AI, Student Data, and Educational Ethics: Responsible AI policy must specify privacy, transparency, accountability, safety, fairness, disclosure, consent, human oversight, and protections for sensitive learner data.
  • Responsible AI, Student Data, and Educational Ethics: Because biased data and poorly evaluated systems can reproduce inequality, schools need practical rules for data ownership, storage, transfer, profiling, disclosure, review, and academic integrity.
  • Culturally Responsive AI: English-dominant AI systems can marginalize African languages and cultural knowledge, while scarce Ghanaian-language corpora, annotated datasets, and speech resources constrain inclusion.
  • Culturally Responsive AI: Culturally responsive AI should support local languages, indigenous knowledge, curriculum-aligned examples, meaningful scenarios, and Ghana’s multilingual and rural-urban educational contexts.

Stakeholder Participation in AI Policy · Existing AI Policy and Education Frameworks · Toward an Education-Centered AI Policy Framework

The paper argues that AI policy must move beyond expert-led governance and national readiness toward education-centered frameworks that address classroom realities, cultural context, learner protection, and implementation power. It develops a critical framework examining policy purpose, curriculum and assessment, language and culture, responsible AI, and participation governance.

  • Stakeholder Participation in AI Policy: AI policy affects groups differently by location, resources, language, role, and power, yet national processes often privilege government, academia, industry, and technical actors over marginalized communities.This imbalance can leave teachers, students, parents, school leaders, teacher educators, and rural communities peripheral to policy decisions.
  • Stakeholder Participation in AI Policy: Meaningful participation requires affected groups to influence policy outcomes rather than merely receive consultation, making participation a condition for trustworthy and implementable educational AI.Teachers contribute knowledge of classroom realities and constraints, while students, parents, and communities identify concerns involving surveillance, fairness, privacy, consent, trust, and cultural legitimacy.
  • Existing AI Policy and Education Frameworks: Existing AI strategies commonly prioritize national competitiveness, regulation, risk management, and technology adoption, providing limited attention to the educational questions raised by classroom AI.Comparative research identifies different emphases in China, the European Union, and the United States but does not fully address educational implications.
  • Existing AI Policy and Education Frameworks: K-12, AI literacy, and STEM frameworks address responsible use, privacy, ethics, assessment redesign, contextualization, and learner agency, but school-district AI policy remains emergent and incomplete.These frameworks extend AI policy debates while leaving important policy gaps.
  • Toward an Education-Centered AI Policy Framework: Grounded in critical policy analysis, the study examines whose interests, assumptions, voices, silences, and definitions of education and AI readiness are constructed in Ghana’s national strategy.The approach treats policy as more than a neutral technical response to public problems.
  • Toward an Education-Centered AI Policy Framework: The framework’s core dimensions examine whether AI policy frames education as workforce preparation or broader transformation, and how it defines AI literacy, classroom practice, learning, authorship, assessment, and academic integrity.This dimension also considers generative AI’s effects on feedback, task design, transparency, and human judgment.
  • Toward an Education-Centered AI Policy Framework: The framework further assesses local languages, indigenous knowledge, cultural identities, Ghanaian realities, learner safeguards, and participation across AI policy design, implementation, monitoring, and revision.Its responsible-AI dimension covers privacy, consent, transparency, fairness, disclosure, accountability, and human oversight, while governance analysis asks whose knowledge counts.

Researchers’ Positionality · Method · Research Design

The study combines Ghanaian education and AI expertise with critical qualitative policy document analysis to examine how Ghana’s national AI strategy frames education, stakeholders, implementation, and responsible AI. It treats the strategy as a policy text that constructs meanings of education, AI readiness, inclusion, governance, and national development.

  • Researchers’ Positionality: The research team includes Ghanaian education researchers, teacher educators, and AI-in-education scholars.The team has experience in teacher preparation, educational technology, AI policy, rural education, and culturally responsive pedagogy.
  • Researchers’ Positionality: The team’s backgrounds supported attention to Ghanaian classroom realities, local language inclusion, teacher agency, and implementation concerns.
  • Researchers’ Positionality: Because several authors are professionally invested in AI and education in Ghana, the team used reflexive attention to its positionality.The supplied passage introduces this reflexive approach but is truncated after “ref”.
  • Method: The study used critical qualitative policy document analysis guided by the Education-Centered AI Policy Framework in Figure 1.The approach draws on Bowen (2009) and Diem et al. (2014).
  • Research Design: The analysis examined how Ghana’s National Artificial Intelligence Strategy, 2025–2035 frames education, positions stakeholders, defines implementation priorities, and addresses responsible AI in educational settings.
  • Research Design: Rather than treating the strategy as a neutral technical roadmap, the analysis approached it as a policy text constructing meanings of education, AI readiness, inclusion, governance, and national development.

Data Sources and Policy Context … Document-Level Transparency and Coherence Concerns

The study analyzes Ghana’s National Artificial Intelligence Strategy, 2025-2035 using qualitative document analysis and an Education-Centered AI Policy Framework. It finds an ambitious but uneven education agenda and identifies transparency and coherence concerns within the policy document itself.

  • Data Sources and Policy Context: Ghana’s National Artificial Intelligence Strategy, 2025-2035 was selected as the primary document because it is official, public, recent, national, and relevant to education and governance.The selection followed the Center for AI and Digital Policy’s Significant AI Policy News criteria and Bowen’s qualitative document-analysis approach.
  • Analytical Framework: The Education-Centered AI Policy Framework examines policy purpose; teacher agency and professional learning; curriculum, pedagogy, and assessment; language and culture; responsible AI and learner protection; and implementation governance.These six connected components guided analysis of the strategy’s first pillar and are documented in Appendix A.
  • Analytical Procedure: The research team used iterative skimming, close reading, interpretation, extraction of education references, and a shared coding matrix to analyze the strategy.The first reading examined structure, policy priorities, implementation language, and education-related claims before education references were coded.
  • Analytical Procedure: Weekly meetings over approximately six weeks supported coding consistency by comparing codes, resolving disagreements, refining definitions, and checking interpretations against textual evidence.Document-level transparency and coherence was treated as a cross-cutting code connected to responsible AI, disclosure, policy communication, and internal consistency.
  • Findings: The strategy presents an ambitious, developmental, future-oriented policy vision, but translates education unevenly while emphasizing talent production, employability, and economic competitiveness.Classroom-level questions about teachers, preparation, pedagogy, assessment, and learner protection receive less emphasis than national AI transformation priorities.
  • Document-Level Transparency and Coherence Concerns: The document appears to contain AI-styled or AI-assisted visual content without visible disclosure, raising a transparency concern that mirrors the strategy’s own responsible-AI commitments.Figure 2 is identified as an example of apparent AI-styled visual content without visible disclosure.
  • Document-Level Transparency and Coherence Concerns: A mismatch between the vision-and-mission figure and its textual development raises concerns about accuracy, clarity, and internal policy alignment.Figure 3 documents a mismatch between a representative icon image and the text; the concern is framed as a Responsible AI issue as well as a communication problem.
  • Document-Level Transparency and Coherence Concerns: Clear disclosure of AI-assisted content and stronger figure-text alignment would improve the strategy’s consistency, credibility, and educational usefulness before implementation in schools and teacher education.The analysis treats Responsible AI as beginning with the policy document itself rather than dismissing the strategy’s value.

Policy Purpose: Education as AI Workforce Readiness · Stakeholder Representation

Ghana’s strategy frames education chiefly as a national pipeline for AI talent, workforce readiness, and economic participation, while giving limited attention to broader educational purposes and classroom-level agency. Although it coordinates government and technical stakeholders, teachers, learners, families, and school communities remain insufficiently represented as policy-shaping actors.

  • Policy Purpose: Education as AI Workforce Readiness: Education is positioned primarily as a national talent pipeline spanning schools, universities, TVET institutions, training centres, and professional development programmes.The strategy links these institutions to preparing Ghana’s population for participation in an AI-driven economy.
  • Policy Purpose: Education as AI Workforce Readiness: The strategy links curriculum planning to labour-market needs by calling for a baseline study of AI-related talent requirements and availability.The stated purpose is to inform curricula in schools and universities.
  • Policy Purpose: Education as AI Workforce Readiness: The proposed AI Ready Ghana programme aims to train over 1,000,000 AI-ready youth by 2033, from the first year of high school through tertiary education.This is presented as the strategy’s most ambitious education target and a major AI-literacy commitment.
  • Policy Purpose: Education as AI Workforce Readiness: Education is narrated mainly through employability, productivity, entrepreneurship, innovation, and economic transition, while civic, cultural, ethical, creative, and democratic purposes receive less visibility.Learners are largely imagined as future workers and contributors to the digital economy.
  • Stakeholder Representation: The strategy presents AI development as a multi-stakeholder national agenda, but government agencies, technical institutions, universities, entrepreneurs, and industry actors are most visible.The acknowledgements foreground MoCDTI, the Data Protection Commission, and other ministries, departments, and agencies.
  • Stakeholder Representation: Teachers, students, parents, school leaders, teacher unions, parent-teacher associations, Colleges of Education, and basic school communities are not strongly positioned as policy actors.This creates a mismatch because education is expected to carry much of Ghana’s AI transformation.
  • Stakeholder Representation: The limited representation is an implementation concern because AI in education will affect curriculum, assessment, learner data, teacher workload, academic integrity, multilingual instruction, and classroom equity.The strategy would be stronger if these education-facing groups helped shape responsible AI education rather than appearing mainly as beneficiaries.

Teacher Preparation, Curriculum, and Assessment

Ghana’s strategy presents curriculum reform as a major route to AI readiness, extending learning beyond programming to coding, data science, data ethics, and data protection. However, it leaves teacher preparation, classroom progression, pedagogy, and assessment guidance insufficiently developed for everyday implementation.

  • Curriculum: The strategy calls for coding, AI skills, data science, data protection, and data ethics across schooling, including foundational coding and data science in primary education.It also proposes practical tools such as Scratch and no-code resources, alongside awareness of AI and digital-field jobs.
  • Curriculum: The strategy names curricular content but provides no age-appropriate progression or classroom roadmap across primary, junior high, senior high, TVET, and teacher education.It also does not explain how teachers should scaffold AI concepts, adapt tools for diverse learners, or assess AI learning.
  • Teacher Preparation: Teacher preparation is underdeveloped because the strategy lacks a detailed AI teacher competency framework and gives limited attention to pre-service preparation and continuous professional development.Unspecified areas include AI literacy, prompt use, bias, data protection, academic integrity, assessment redesign, learner privacy, inclusive pedagogy, and responsible classroom use.
  • Assessment: The strategy broadens ethical priorities beyond data protection and cybersecurity, but does not translate them into school-level rules for disclosure, acceptable assistance, authorship, feedback, or examinations.Consequently, its national curriculum ambition does not provide the detailed preparation, pedagogical guidance, and assessment rules needed for classroom implementation.

Culture and Language · Inclusion and Classroom Practice · Responsible AI Governance and Learner Protection

Ghana’s strategy links AI inclusion to local-language data, rural and targeted access, and national Responsible AI governance. However, it underdevelops culturally responsive classroom practice, school-level inclusion, learner-specific safeguards, and transparency in official materials.

  • Culture and Language: The strategy proposes using local languages to structure datasets and funding their collection, transcription, and labeling to make AI more inclusive and nationally relevant.Its treatment remains focused on language data rather than educational use.
  • Culture and Language: AI-supported education could use familiar languages and Ghanaian cultural contexts to improve comprehension, confidence, identity, and inclusion, but the strategy leaves this opportunity underdeveloped.The passage specifically contrasts dataset processing with classroom explanation and culturally connected instruction.
  • Inclusion and Classroom Practice: The strategy expands AI access through rural tech hubs, mobile labs, and local TVET centres, especially for rural youth, women, persons with disabilities, and underserved communities.This frames inclusion through geographically distributed and community-based training delivery.
  • Inclusion and Classroom Practice: Scholarships, stipends, free materials, regional centres, mentorship, local media, radio, and local-language outreach support flexible, community-based AI education beyond elite institutions.The proposals emphasize regional distribution and multiple access routes.
  • Inclusion and Classroom Practice: Disability and gender inclusion are addressed through specialized training, accessibility resources, and a Women in AI initiative offering scholarships, mentorship, and venture support.The strategy also sets a 40% gender target, although the supplied passage ends before completing its description.
  • Inclusion and Classroom Practice: Inclusion is framed mainly as access to programmes and opportunities, with limited guidance on classroom support, connectivity barriers, gender stereotypes, or leadership in under-resourced settings.This identifies a gap between participation in programmes and sustained inclusion in ordinary classrooms.
  • Responsible AI Governance and Learner Protection: Responsible AI governance is nationally prominent through a proposed independent authority addressing algorithmic bias, personal-data misuse, and high-risk AI outputs.The proposed authority would coordinate, monitor, and guide ethical AI development.
  • Responsible AI Governance and Learner Protection: School-level protections remain unclear regarding learner profiling, data commercialization, surveillance, consent, automated grading, behavioural monitoring, and learner tracking.The strategy is therefore stronger on national ambition and workforce planning than on educational implementation and child-centered safeguards.

Discussion · Implications and recommendations

Ghana’s AI strategy prioritizes national AI readiness, skills, and workforce preparation, but leaves school-level implementation underdeveloped. The paper recommends a participatory, sector-specific education framework linking teacher preparation, curriculum and assessment reform, learner protection, local responsiveness, and infrastructure.

  • Discussion: The strategy frames education mainly through AI-ready youth, TVET, talent development, data science, and labour-market-aligned curricula rather than AI enacted in classrooms.This reflects a stronger emphasis on education for AI than on AI for education.
  • Discussion: Although education is the strategy’s first pillar, it lacks clear AI learning progressions across basic education, secondary education, TVET, and teacher education.The document names coding, AI skills, data science, data ethics, and data protection without specifying developmental sequences.
  • Discussion: Teacher courses and collaboration with ICT coordinators are recommended, but teachers’ agency as curriculum interpreters, assessment designers, ethical decision-makers, and learner protectors remains underdeveloped.The analysis emphasizes that AI integration requires expanded technological, pedagogical, and contextual knowledge and strong teacher judgment.
  • Discussion: School-level guidance on AI-supported work is limited, leaving disclosure, assessment, grading, and examination treatment unclear in Ghana’s examination-driven system.This silence may produce confusion, uneven enforcement, and mistrust.
  • Discussion: Responsible AI provisions address governance, rural outreach, gender, disability, and local-language access, but provide limited guidance on profiling, consent, surveillance, and student data.Apparent AI-styled visuals without visible disclosure also raise concerns about whether the policy models the transparency it expects.
  • Discussion: Education-related stakeholder engagement insufficiently represented teachers, students, families, communities, and guardians, although participation is an implementation condition for school-level AI.The analysis identifies a recurring policy tendency to privilege government, industry, academia, and technical experts.
  • Implications and recommendations: Ghana should create a sector-specific, education-centered AI implementation framework that preserves workforce ambitions while addressing children, teachers, assessment, languages, families, communities, infrastructure, and inequality.The framework would translate national AI ambition into education-specific implementation requirements.
  • Implications and recommendations: Framework development should be participatory, involving educators, learners, families, school leaders, teacher-education institutions, rural communities, disability advocates, language communities, researchers, and curriculum bodies.Recommended methods include consultations, classroom-based research, regional dialogues, and studies across education levels.

Limitations · Conclusion

The study’s education-centered analysis finds Ghana’s AI strategy ambitious and nationally important, but stronger on national readiness than school-level implementation. It concludes that Ghana needs clearer education-specific pathways while acknowledging limits from document access and sectoral scope.

  • Limitations: The analysis relied on the publicly available online strategy and may have excluded inaccessible preparatory, implementation, consultation, or sector-specific materials.The study also focused mainly on education-related dimensions, particularly AI talent development, youth readiness, training, and responsible AI implementation.
  • Conclusion: The study developed and applied an Education-Centered AI Policy Framework to examine Ghana’s National Artificial Intelligence Strategy, 2025–2035 through an education-sector lens.This addresses limited knowledge about how national AI strategies frame education’s pedagogical, cultural, ethical, and implementation demands.
  • Conclusion: Ghana’s strategy is ambitious and timely, emphasizing AI literacy, youth skills, TVET, workforce readiness, rural outreach, inclusion, local language data, and responsible AI governance.The findings characterize the document as nationally important while recognizing its broad education-related priorities.
  • Conclusion: The education agenda is stronger on national AI readiness than on school-level implementation, leaving teacher agency, pre-service education, curriculum progression, assessment guidance, and AI disclosure underdeveloped.The passage also identifies multilingual pedagogy, culturally responsive AI use, child-centered safeguards, and participatory governance as underdeveloped.
  • Conclusion: AI education must extend beyond preparing youth for the digital economy to rethinking learning, teacher preparation, assessment, community involvement, and learner protection.The study frames these demands as central to Ghana’s education response to AI.
  • Conclusion: Ghana needs clearer AI curriculum pathways across basic, secondary, TVET, higher, and teacher education, supported by professional development systems.The conclusion calls for education-wide clarification rather than youth training alone.
  • Conclusion: The authors disclosed using ChatGPT/Claude and Grammarly for grammar correction, language refinement, and clarity, while retaining responsibility for the publication’s content.They state that the tools did not generate new data, conduct analysis, or substantively alter interpretations.

Appendix A · Coding Framework

The Education-Centered AI Policy Framework applies critical policy analysis to examine how Ghana’s AI strategy constructs education, represents stakeholders, and addresses implementation, context, learner protection, and document coherence. Its coding questions span educational purpose, teacher agency, curriculum and assessment, language and culture, responsible AI, participation, and transparency.

  • Coding Framework: Overall, the framework asks how policy assumptions, visible and absent voices, and potential effects shape schools, teachers, and learners under Ghana’s AI strategy.This approach is grounded in critical policy analysis and examines both policy construction and its implications.
  • Policy purpose: The framework examines whether Ghana’s AI strategy frames education as workforce preparation, human capital development, social transformation, or democratic participation, while balancing competitiveness with equity, inclusion, ethics, and citizenship.It also identifies emphasized, minimized, or silenced educational purposes.
  • Participation and implementation governance: Participation and implementation coding asks whether teachers, learners, parents, school leaders, teacher educators, and rural communities are visible policy actors with consultation, feedback, accountability, and local participation mechanisms.It also examines which institutions hold implementation authority and whose voices are privileged or marginalized.
  • Teacher agency and professional learning: Teacher-focused coding distinguishes professional agency from training receipt and examines teachers’ roles in curriculum interpretation, assessment, ethical judgment, learner protection, and pre-service and in-service preparation.The framework also considers teacher educators, Colleges of Education, and teacher education universities in AI capacity-building.
  • Curriculum, pedagogy, and assessment: Curriculum coding assesses AI-literacy guidance and pathways across basic, secondary, TVET, higher, and teacher education, alongside pedagogical use, assessment, integrity, authorship, grading, disclosure, and acceptable assistance.The questions also address creativity and critical thinking in AI-supported education.
  • Language, culture, and contextual responsiveness: Language and culture coding asks whether Ghanaian languages, cultural knowledge, and local educational realities function as pedagogical resources rather than only data resources.It also considers rural and under-resourced schools, multilingual classrooms, culturally responsive materials, and risks of cultural bias or language marginalization.
  • Responsible AI and learner protection: Responsible-AI coding evaluates learner safeguards covering privacy, consent, surveillance, profiling, automated decisions, human oversight, appeals, fairness, transparency, accountability, bias mitigation, and vulnerable learners.The framework asks whether these principles become concrete school-level guidance.
  • Document-level transparency and coherence: Document-level coding tests whether the policy models responsible AI through disclosure of AI-generated or assisted content, consistent figures and explanations, clear terminology, and coherence across its policy components.It specifically examines alignment among vision, mission, pillars, implementation plans, and education recommendations.
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