AI for Whom? - A Critical Study of Equity and the Digital Divide in Thailand’s AI Education Policy

As Thailand advances its national agenda for artificial intelligence (AI) in education, questions arise about who stands to benefit and who risks being left behind. This study critically examines how equity and inclusion are represented in Thailand's emerging AI education policies and how these representations align with implementation realities across different school contexts. Adopting a qualitative interpretive design, it combines Critical Policy Analysis drawing on Bacchi's "What's the Problem Represented to Be?" approach, with the Digital Inclusion Framework. Three core official documents, the Thailand National AI Strategy and Action Plan (2022–2027), the relevant Government Gazette announcement, and the OBEC AI Usage Guide (2025), were analyzed alongside semi-structured interviews with six educational stakeholders, including school directors, vice-directors, a national policy actor, and an education entrepreneur from urban and rural settings. The analysis shows that Thai policy consistently frames AI as an instrument of national modernization, economic competitiveness, and future workforce readiness, while saying comparatively little about the unequal conditions under which schools are expected to adopt it. In practice, schools with stronger infrastructure, funding, and institutional support were far better positioned to engage with AI, whereas smaller and rural schools faced unstable connectivity, maintenance burdens, heavy teacher workloads, and limited professional support. AI was most often used for administrative efficiency rather than transformative pedagogy, with richer forms of engagement concentrated in better-resourced schools. The study argues that equal technological provision does not produce equitable participation, and that current policy silences around rural disadvantage and teacher capacity risk reproducing existing inequalities. It concludes that meaningful AI integration in Thailand requires more context-sensitive, equity-oriented policy design. Methodologically, the study shows how combining the WPR approach with the Digital Inclusion Framework, and reading policy texts against practitioners’ accounts, can make policy silences visible and grounded in practice, distinguishing between silences that are absent, disconnected from AI provisions, or left non-operational.