The Complete Guide to Using AI in the Education Industry in Thailand in 2025

Introduction: AI is reshaping Thailand's classrooms in 2025 - from Bangkok hosting the Global Forum on the Ethics of AI, which showcased 36 selected papers, to practical pilots that show real learning gains - and the debate is no longer only technical.

Generative AI tools are now common in universities and schools, promising personalized, bilingual support and administrative relief while raising fresh questions about integrity and access; a Bangkok case study reported a 30% boost in engagement using tools like SplashLearn and ChatGPT, and national sentiment is optimistic (about 77% see AI as more beneficial than harmful).

Balancing that promise means investing in AI literacy, ethics and teacher training; practical upskilling pathways such as the AI Essentials for Work bootcamp help educators learn promptcraft and classroom workflows to use AI responsibly and equitably in Thai schools.

“While generative AI holds great potential to advance higher education goals, its benefits will only be fully realised if accompanied by deliberate investment in AI literacy training. Without it, we risk deepening existing inequalities and compromising the integrity and quality of academic practice.”

Artificial intelligence (AI) in Thai education is less about sci‑fi than about everyday classroom lift - tools that tailor lessons to each student, cut teacher admin time, and extend quality resources beyond Bangkok; as BytePlus outlines, Thailand is using AI to personalise learning, bridge inequalities and ready students for a tech economy (BytePlus report: AI in Thailand education).

Industry pilots show concrete gains: SCB 10X and partners developed RISA, an AI teaching assistant powered by a Thai‑aware LLM (Typhoon) that can help grade open‑ended answers with up to 90% accuracy and free teachers to coach deeper skills (SCB 10X case study: RISA AI teaching assistant), while Nucamp AI Essentials for Work syllabus examples highlight automated grading and personalised learning paths that scale one‑to‑one support without blowing budgets.

The promise is tempered by access gaps: UNICEF data shows over 97% of Thai schools have internet but only 16% of households own a computer, so AI's benefit hinges on equipping teachers, protecting student data and reaching rural classrooms (UNICEF: Can AI close the learning gap in Thailand?).

A vivid way to picture the opportunity: imagine an island classroom where an AI tutor fills in for missing specialists, giving every child a tailored lesson while the teacher focuses on human skills AI cannot teach.

“My dream school should encourage the development of coding and AI skills through hands-on practice,” said Yuparat, 15.

Thailand's national AI strategy has moved from blueprint to action in 2025: a newly formed National AI Committee (established April 2025 and chaired by Prime Minister Paetongtarn Shinawatra) is steering a five‑year agenda that blends ethics, infrastructure, skills and sector uptake to make AI a practical engine for growth and public services.

Building on the Thailand National AI Strategy and Action Plan (2022–2027), policy highlights include ethics and regulation, expanded cloud and HPC infrastructure, open platforms and a national databank, plus human‑capital targets - 10 million people with basic AI user skills, 90,000 trained professionals and 50,000 developers - backed by a pledge of at least 500 billion baht in public and private investment to scale cloud services, GPUs and data centres.

Education is explicitly in scope: ministry guidance and the national research directions name AI‑enabled assessment and language learning as priorities, while Thailand's role hosting global ethics forums signals a parallel push for governance that keeps innovation accountable.

The result aims to link Bangkok labs to rural classrooms via shared platforms so teachers can focus on human skills while secure, regulated AI handles routine personalization and assessment.

Thailand is already moving from experiments to visible pilots: banks and startups are fielding teacher assistants and speaking coaches in classrooms, student apps and entire schools - for example, SCB 10X's “RISA” assistant (powered by the Thai‑aware Typhoon LLM) is being trialled to cut grading time and can score open‑ended answers with up to 90% accuracy, while Learn Corporation uses AI quietly in the back end to craft personalised learning paths without letting students outsource thinking to a chatbot (SCB 10X RISA and Typhoon LLM case study on AI in Thai education).

Language practice pilots led by Edsy reached six Bangkok schools and have been shown to nudge scores higher (Samsen Wittayalai reported ~6% average gains) after students used the Edsy AI Speaking Coach - an interactive tool that asks prompts like “Tell me about your dream job,” corrects sentences and coaches pronunciation so every student can practice at their own pace, like carrying a pocket native speaker into the classroom (Thaiger report on Edsy AI Speaking Coach Bangkok pilot and student gains).

Bangkok also launched HOG International Academy as Thailand's first AI‑centred high school, pairing RevisionSuccess tools with teachers to make AI an amplifier, not a replacement; across pilots the recurring themes are clear - real time feedback, admin relief and adaptive pathways - but risks around accuracy, pedagogy alignment and misuse mean human oversight and teacher training remain essential (IT Brief Asia report on HOG International Academy AI‑centred high school launch).

“We believe AI is not just an enhancement, but a foundation for the future of learning.”

Which country is leading AI and introducing it to education - lessons for Thailand: regional front-runners show two complementary pathways Thailand can borrow - the coordinated, soft‑governance model of Singapore and the risk‑based legal clarity emerging in places like South Korea (and China's contrasting hard‑law approach offers cautionary lessons).

Singapore's Smart Nation playbook and national teacher‑training push - where an “AI‑enabled companion” is meant to give customized feedback, automated grading and motivation while researchers, government and industry align - demonstrates how a small state can scale personalised learning responsibly (AI in education lessons from Singapore and other nations); South Korea's move to embed AI coursework and distinguish “high‑impact” systems shows why Thailand's new National AI Committee should couple broad guidance with stricter rules for sensitive assessment or student‑data uses.

At the same time, Asia's regulatory spectrum - from China's more prescriptive rules to Singapore and ASEAN's voluntary frameworks - highlights a practical tradeoff between rapid innovation and enforceable safeguards (comparative analysis of AI regulation across Asia).

For Thailand, the takeaway is concrete: pair national coordination and investment with clear red lines for high‑risk applications, invest in teacher training and local oversight, and pilot tools that augment classroom teaching so every rural school can get a reliable “pocket tutor” without handing control to opaque vendors - one balanced approach that keeps agency, equity and accountability front and centre.

“It's irresponsible to not teach (AI). We have to. We are preparing kids for their future”.

AI in Thai classrooms in 2025 delivers clear, classroom‑ready benefits: personalised learning paths that adapt to each student's pace, bilingual English‑Thai support for vocabulary and speaking practice, and major teacher relief through automated grading and admin so instructors can focus on coaching and critical thinking; concrete pilots show the difference - a Bangkok classroom using SplashLearn and ChatGPT reported a 30% rise in engagement and faster feedback loops, while SCB 10X's RISA assistant (with its “3‑layer AI shield”) can grade open‑ended answers with up to 90% accuracy, freeing time for deeper learning (AI tools for Thai classrooms, SCB 10X RISA case study).

Practical classroom use cases range from AI speaking coaches and pronunciation drills to back‑end personalised curricula that nudge students along mastery pathways without replacing teacher judgment; schools can start small with free or low‑cost pilots, pair automated scoring with human‑in‑the‑loop validation, and adopt prompt‑crafting upskilling so educators control outcomes (Automated grading and feedback).

The payoff is simple and vivid: a teacher who once spent nights marking essays can now spend that time mentoring a student through a tricky concept, while specialised AIs (an “army of AIs” for language skills) give interpretable, skill‑level feedback that turns practice into progress.

“Learning First, Technology Second.”

By 2025 Thai classrooms commonly mix global EdTech with home‑grown systems: teachers rely on adaptive platforms like SplashLearn for personalised math and reading, Writable for instant writing feedback, and ChatGPT for bilingual prompts and speaking drills - a combination that a Bangkok pilot linked to a 30% jump in engagement and peerable, real‑time practice (see the Kids English Thailand AI tools guide for Thai classrooms).

Schools also trial Thai‑aware solutions such as SCB 10X's RISA, whose “3‑layer AI shield” (Identifier, Explainer, Safety Guard) vets responses and helps grade open‑ended answers with reported accuracy up to 90% - a vivid reminder that automation can save nights of marking while leaving teachers in charge of judgement (SCB 10X RISA 3‑layer AI shield case study).

Specialist language tools from workshops (Diffit for adapted Thai texts, VoiceThread for spoken practice) pair well with research showing ChatGPT can improve English learning outcomes in controlled studies, so the practical recipe for Thailand is selective pilots, teacher upskilling, and human‑in‑the‑loop checks that turn an “army of AIs” into reliable classroom partners rather than crutches for students (Kids English Thailand AI tools guide for Thai classrooms, SCB 10X RISA 3‑layer AI shield case study, IJELS ChatGPT English learning study).

“Learning First, Technology Second.”

Ethics, privacy and governance are the hinge points that will decide whether AI helps or harms students across Thailand: national guidance led by the Ministry of Digital Economy and Society and draft ETDA notices set an ethics‑first frame, but the landscape still carries three persistent risks - privacy breaches, algorithmic bias and opaque decision‑making - that demand sector‑specific rules and human‑in‑the‑loop checks (see the Nemko overview of Thailand AI ethics and regulation overview (Nemko Digital)).

Practical education policy must therefore pair PDPA enforcement and clear oversight for “high‑risk” systems with teacher upskilling, routine audits and transparent procurement so schools don't outsource judgment to black‑box vendors; classroom pilots that use automated scoring or personalised paths should be coupled with human validation and data minimisation practices (for how automated grading can speed turnaround without losing oversight, see Automated grading and feedback in Thai education - AI use cases).

Critical voices warn that guidelines so far lean toward industry priorities, not public protection, and real‑world surveillance examples - like thousands of AI cameras in the Deep South - make the stakes visible: restoring public trust will require open consultations, audit trails, and education‑centred governance that keep Thai values and student rights front and centre (background and critique summarized by Asia Society and national analyses: AI governance and critiques in Thailand (Asia Society Policy Institute)).

“AI systems should be designed with the principle that seeks to protect personal data in mind,”

Practical rollout in Thailand starts by aligning pilots with national strategy and ethics: use the NAIS and ethics frameworks as guardrails, mandate PDPA compliance, and open consultations so communities trust school‑level deployments (see the Thailand AI governance overview for context).

Begin with low‑risk, high‑value pilots in schools and homes - automated grading systems that preserve teacher oversight are a clear first step because they speed turnaround and free teachers for mentoring; pair each AI score with human‑in‑the‑loop validation to keep judgement local (Automated grading and feedback systems for Thai schools).

Next, scale targeted personalised learning paths where connectivity and devices permit, using off‑the‑shelf models adapted to Thai language and curricula and leveraging shared platforms to connect urban labs with rural classrooms.

Invest heavily in practical teacher upskilling - short courses on prompt engineering and classroom workflows turn vendors' tools into reliable pedagogy, not crutches (Prompt engineering training for Thai educators).

Finally, bake in simple governance: routine audits, data minimisation, and community reporting so an AI that helps grade a draft becomes a tool that strengthens learning and trust, not a black box - picture a teacher validating AI‑scored drafts in minutes and spending that reclaimed evening coaching a single struggling child.

Conclusion and roadmap: Thailand's path to safe, practical classroom AI in 2025 is clear: marry national strategy with grounded pilots, protect learners, and build teacher capacity.

Start by aligning school‑level pilots with the Thailand National AI Strategy and the new National AI Committee targets (skills for 10M users, 90k professionals, 50k developers) so projects feed into shared infrastructure and the planned national databank rather than creating vendor silos (Thailand National AI Strategy - official government AI strategy, Thailand National AI Committee launch - OpenGovAsia).

Prioritise low‑risk, high‑value wins the Ministry already endorses - automated grading with human‑in‑the‑loop checks and AI‑assisted language practice - while enforcing PDPA, routine audits and open procurement so student data stays local and understandable; these moves echo the Ministry of Education's 2025–2026 policy that calls for AI‑enabled language learning, lifelong learning platforms and reduced teacher burden (Ministry of Education AI policy 2025–2026).

Invest in shared cloud/GPU capacity and sandboxes before wide rollouts, and invest equally in short, practical upskilling - microcourses in prompt engineering and classroom workflows so teachers control outcomes (for example, Nucamp's AI Essentials for Work syllabus offers a 15‑week path on prompts and practical AI skills: Nucamp AI Essentials for Work syllabus (15-week practical AI for work)).

The simplest, most vivid test: an AI that speeds essay scoring to minutes but still requires a teacher to validate and then spend that reclaimed evening mentoring a struggling child - measure impact by learning gains, equity of access, and transparent governance, not by novelty alone.