Strategic framework and global trends of national smart education policies

The paper addresses how nations can construct smart education at scale and what global policy trends shape these efforts. In the context of a widely recognized crisis in education and accelerating digital transformation, the study notes both advocacy for intelligent technologies and critiques about limited theoretical grounding. It frames smart education as a rising national policy theme and seeks to (1) propose a national-level framework to guide smart education and (2) identify global trends in smart education policy. The introduction situates the work within calls from the UN and UNESCO to expand access, ensure equity, and develop resilience through technology-enabled, high-quality, lifelong learning.

The article outlines four developmental stages of smart education: (1) Emerging (1983–2007), when 'smart' centered on cultivating wisdom and creativity, with early initiatives like Malaysia’s Smart School (1997). (2) Evolving (2006–2011), shifting toward integrating ICT, ubiquitous learning, and early national policies (e.g., Korea’s SMART Education, 2011). (3) Theorizing (2012–2018), marked by the growth of intelligent technologies (IoT, cloud, big data, AI) and conceptualizations emphasizing learner-centered, personalized, adaptive, and efficient learning supported by smart environments. (4) Application (2019–present), with smart education embedded in national policies and UNESCO documents, large-scale initiatives (e.g., China’s Smart Education Pilot Zones, Korea’s Green Smart Future School), and proposed frameworks and solutions leveraging 5G, AI, IoT, and big data. Despite numerous conceptual frameworks, a national-level policy framework for large-scale application had remained underdeveloped, motivating the current study.

The study combined a Delphi method with textual analysis of policy documents. Delphi: 14 experts from UNESCO (3), China (7), and the U.S. (4), all professionals in educational technology or digital education, participated. A draft National Smart Education Framework was prepared from the literature. Step 1: experts received the draft via email, provided feedback; Step 2: revisions and videoconference discussions; Step 3: iterative email evaluations until consensus was reached. Textual analysis: Two rounds of policy selection using keywords ('smart education', 'digital education', 'digital transformation', 'educational technology policy') across official ministry and organization websites, with inclusion criteria (education sector, official issuance, dated Jan 2019–May 2024, full-text available) and corresponding exclusions. From 48 retrieved items, 24 representative national/organizational policies were retained (one per country/organization). Analysis steps: (1) close reading and decomposition into goals, strategies, actions; (2) dual coding by two researchers using 9 dimensions from the first three leveraging points of the framework, deriving 26 core indicators; discrepancies were discussed to consensus; (3) frequency counts of indicators to identify trends, complemented by bubble charts for cross-policy visualization.

Framework: The validated National Smart Education Framework comprises four leveraging points with three dimensions each: (A) Forward-thinking governance and policy initiatives—Develop a national vision and plan; Build infrastructure capacity; Invest in human capacity. (B) Digital learning environments—Seamless connectivity; Learning devices and support; Ethical use of technology. (C) Transformative teaching and learning—Student-centered pedagogy; Reimagined assessments; Learner community building. (D) Overarching considerations—Inclusion and equity; Continuous improvement culture; Multi-sector cooperation and partnerships. Quantitative trends across 24 policies:

• Forward-thinking governance and policy initiatives • Develop a national vision and plan (DNVP, n=353): DNVP1 Promote high-quality, inclusive, and accessible education (130); DNVP2 Create a high-performing digital education ecosystem (119); DNVP3 Set visions for technology in learning (104). Examples: Netherlands’ Digitalisation Agenda references DNVP1 nine times; UK strategy highlights DNVP2 eight times; Singapore EdTech Plan emphasizes DNVP3 nine times. • Build infrastructure capacity (BIC, n=311): BIC1 Increase Internet connectivity and access to digital tools (118); BIC2 Improve digital services (104); BIC3 Enhance effective investment (89). Singapore frequently emphasizes seamless learning environments and ICT infrastructure. • Invest in human capacity (IHC, n=310): IHC1 Enhance digital skills (154); IHC2 Explore effective training methods (100); IHC3 Offer AI courses to cultivate AI literacy (56). Maldives’ ICT in Education Master Plan strongly emphasizes IHC1 (10 mentions).
• Digital learning environments conducive to smart education • Seamless connectivity (SC, n=189): SC1 Promote fair and equal Internet access (75); SC2 Provide high-speed, convenient, free, and safe services (61); SC3 Increase Wi‑Fi coverage (53). EU’s Digital Education Action Plan focuses on nationwide school Wi‑Fi coverage. • Learning devices and support (LDS, n=153): LDS1 Enhance utilization of digital devices (56); LDS2 Pool open educational resources (54); LDS3 Build public service platforms (43). Saudi Arabia’s Madrasati platform exemplifies LDS3. • Ethical use of technology (EUT, n=193): EUT1 Ensure information security and privacy (82); EUT2 Guarantee availability, integrity, confidentiality, and cybersecurity (57); EUT3 Develop ethical guidelines on AI and data usage (54). Wales and France include concrete online safety and certification modules (e.g., Pix).
• Transformative teaching and learning enabled through technology • Student-centered pedagogy (SCP, n=209): SCP1 Implement new pedagogies (blended, human-computer collaboration, immersive) (82); SCP2 Provide real-time feedback and personalization (76); SCP3 Use learning data for personalized teaching (51). U.S. NETP 2024 frequently references SCP2. • Reimagined assessments (RA, n=224): RA1 Emphasize critical thinking, problem-solving, creativity (97); RA2 Harness technology for learner-centered assessments (67); RA3 Record and analyze learning-process data (60). U.S. NETP 2024 highlights RA1 and RA3 with specific tools and practices. • Learner community building (LCB, n=155): LCB1 Develop digital citizens (97); LCB2 Promote cooperative learning and co-creation (58). The U.S. and Singapore showcase makerspaces, STEAM labs, and community-connected learning. Overall policy trends include: promoting inclusive, high-quality, and accessible education; expanding connectivity and device access; strengthening digital skills; ensuring data privacy and cybersecurity; adopting innovative pedagogies; enabling real-time feedback and personalized learning; and prioritizing higher-order thinking in assessments.

The findings address the research questions by providing a validated, consensus-based national framework and empirically mapping global policy emphases. Policies most frequently stress governance and strategic planning, followed by infrastructure and human capacity, while digital learning environment supports (especially public service platforms) receive comparatively fewer mentions—reflecting economic and infrastructural constraints. The analysis suggests tailoring priorities by national context: economically underdeveloped regions may focus on vision-setting and infrastructure, whereas developed contexts should emphasize sustained investment in educator and learner capacity. Overarching considerations—equity and inclusion, continuous improvement, and cross-sector partnerships—emerge as essential to sustainable implementation. Examples include the U.S. NETP’s periodic updates, Korea’s five-year ICT in education plans, and Singapore’s multi-cycle EdTech plans, which institutionalize continuous improvement, and coordinated partnerships in the UK to translate innovation into practice.

This study contributes a National Smart Education Framework endorsed by UNESCO IITE and demonstrates its utility by analyzing 24 national/organizational policies to surface global strategies and trends. The framework and evidence base can guide governments and stakeholders in crafting or updating national smart education plans to improve equity, quality, and efficiency. Future research should strengthen quantitative validation of the framework, broaden policy coverage across more countries and regions, and conduct empirical studies to test the effectiveness and implementation outcomes of smart education initiatives in diverse contexts.

The study is primarily qualitative, relying on Delphi consensus and textual analysis; future work should incorporate stronger quantitative validation. The policy sample includes 24 countries/organizations and does not cover most of the world; expanding the dataset is needed. Finally, further empirical research is required to test the effectiveness of smart education and deepen understanding of implementation across varied contexts.