Top 5 Jobs in Education That Are Most at Risk from AI in Thailand - And How to Adapt

Thailand's push under Thailand 4.0 and recent national AI roadmaps means artificial intelligence is moving from policy papers into classrooms and school offices - from adaptive learning tools and AI speaking coaches to off‑the‑shelf automated grading systems that are already saving Thai teachers hours each week.

That surge brings opportunity and risk: national analyses warn of major job disruption, and routine education roles (clerical staff, markers, content‑assembly tasks) are particularly exposed unless workers reskill.

Practical steps matter - understanding how AI augments daily tasks is as important as policy debates - and targeted, job‑focused training can help educators stay relevant; see why Thailand's AI strategy matters for schools via the Asia Society briefing and consider hands‑on upskilling like Nucamp's AI Essentials for Work bootcamp to learn promptcraft, tool use, and workplace AI skills for immediate impact.

Selection of the top five education roles combined Thailand‑specific policy signals, published risk estimates, and practical evidence of automation in schools: roles were flagged when they matched (1) high exposure to repetitive, rule‑based tasks; (2) prevalence across Thai classrooms and administration; and (3) early signs of AI adoption or cost‑saving tools in the field (for example, off‑the‑shelf automated grading systems that are already saving Thai schools hours each week).

Risk scores drew on national planning (Thailand's National AI Strategy and Action Plan) and regional studies - including the TDRI and ILO findings that show millions of jobs in Thailand face high automation exposure - while triage weightings followed NAIS priorities for human‑capacity and sectoral adoption so the list is practical for Thai educators who must either adapt or reskill.

The methodology therefore blends policy intent, measured job‑risk (probability and scale), and concrete classroom signals so readers get a

“so what?”

if a routine task can be completed by a model in minutes, the job's durable value hinges on uniquely human skills like mentoring, judgement and curriculum design; see Thailand's National AI Strategy and OECD's policy summary for the roadmap and examples of sector uptake, and examples of automated grading in practice.

School administrative and clerical roles in Thailand face clear exposure because so many day‑to‑day duties are rule‑based - data entry, enrollment processing, fee management, routine communications and report generation are prime targets for automation.

Robotic process automation (RPA) and AI workflow tools are already proven to trim administrative costs and speed work (Gartner and case studies show up to ~30% cost savings and enrollment processing time cut by roughly half), and some grading and report tasks can be reduced by 50–90% in real implementations; see a practical overview of the RPA in education case study for schools at RPA in education case study.

In Thailand this matters even more because digital tech - computers, the internet and mobile use - is now integral to young people's lives, creating both demand for faster school services and easier pathways for automation; see the digital learning in Thailand case study and trends at digital learning in Thailand case study.

Adaptation means shifting from pure data entry to supervisory and design work: learn to manage bots and exceptions, own data quality, and use automated outputs to free time for student support and local problem solving; practical examples such as the automated grading systems in Thai schools case study at automated grading systems in Thai schools show how clerical time can be repurposed into higher‑value tasks that machines cannot do well.

Exam markers and grading assistants in Thailand are among the most exposed - AI tools are already grading open‑ended answers and generating assessment items, which can shrink weeks of manual marking into minutes; pilots like SCB 10X's RISA (powered by the Typhoon LLM) report up to 90% accuracy on open responses, while local case studies show automated grading systems saving Thai schools hours of teacher time (SCB 10X RISA pilot (automated grading, Typhoon LLM); Automated grading systems in Thai schools case study).

Adaptation is practical: shift from bulk scoring to oversight - calibrating rubrics, validating AI outputs, triaging edge cases, and converting saved time into richer, human feedback and remediation.

Professional development and governance matter too (training, privacy and ethics), so markers become evaluators of AI quality rather than manual counters; equipping teams with these skills follows recommendations for teacher competency and system readiness in Thai guidance on AI in schools.

The result is not fewer educators but different work - less red ink, more targeted coaching that actually improves student learning.

“These mechanisms allow RISA to help teachers grade open-ended exam questions with up to 90% accuracy.”

Teaching assistants and paraprofessionals in Thailand face clear exposure as intelligent tutoring systems (ITS), adaptive learning platforms and AI tutors take on routine scaffolding, instant feedback and small‑group practice - but these same tools create a practical upgrade path if embraced strategically.

Local tool rundowns show adaptive platforms and AI‑powered tutoring are already being used to personalise learning and automate routine tasks across Thai schools (Best AI tools for education in Thailand), and ITS pilots deliver measurable learning gains - Thai research found significant improvements in aural dictation, interval recognition and chord recognition after sustained ITS use, so the tech can genuinely supplement instruction (Long-term ITS study on ear training outcomes (IJSASR)).

Practical adaptation for TAs means shifting from repeating drills to supervising and calibrating ITS: run dashboards, validate AI feedback, triage flagged students and focus on socio‑emotional coaching and differentiated remediation that models can't do well.

Use AI to generate assessment items and free time for higher‑value support (see sample assessment‑generation use cases), but plan professional development and data‑privacy safeguards so assistants become skilled integrators of tools rather than being sidelined; when humans and ITS split the work well, the job becomes less about counting marks and more about mentoring the students the system highlights.

Basic language tutors (Thai/English) are exposed because the same automation that churns out assessment items and keyed exams can produce endless practice worksheets and quizzes for vocabulary, grammar drills and short‑answer tasks - see the assessment item generation examples that create Grade 9 exams with answer keys and difficulty tagging for quick teacher use.

Automated grading systems are already saving Thai schools hours of teacher time while maintaining high accuracy, so routine scoring and low‑level feedback are increasingly handled by tools rather than people; this shifts the durable value of tutors toward nuanced, human skills.

Adaptation means learning to use AI as a materials engine - curate and validate generated exercises, triage unclear AI feedback, and redeploy freed time into live conversation coaching, cultural nuance, motivation and remediation that machines can't replicate.

Practical support exists: targeted teacher professional development for AI helps educators integrate these workflows without losing classroom control, so tutors become orchestrators of personalised practice rather than repeaters of it.

Content‑assembly roles - lesson planners, worksheet creators and teams that churn out standardised materials - are among the clearest near‑term targets in Thailand because generative models and course assistants can now draft full lesson modules from a course title and description, auto‑tag difficulty levels, and spin out ready‑to‑use assessment items; Chulalongkorn's pilots and national guidance make this plain and urge AI literacy plus clear rules for use (Chulalongkorn generative AI pilot and national guidance for Thailand).

Practical adaptation for Thai educators means treating AI as a materials engine rather than a replacement: curate and validate generated content, set evaluation scopes and attribution rules, embed privacy safeguards, and reallocate time to differentiation, classroom coaching and local curriculum design that models cannot do well; tools like the Blackboard AI Design Assistant for course content show how systems can assemble modules but also why human oversight must set learning objectives and assessment criteria.

Upskilling matters - short, hands‑on training and promptcraft for quality control turn speed gains into better classrooms rather than lost roles, and practical item‑generation examples demonstrate how to pair machine output with teacher judgement (AI assessment item generation examples for Thai education).

The memorable test: when a draft lesson appears in seconds, its real value is measured by the human decisions that make it worth teaching.

“AI Literacy will be one of the three fundamental competencies that young people need to learn besides language and computational competencies.”

Practical next steps for Thai education workers over the next 6–12 months start with a quick task audit (flag repetitive, rule‑based work), a compliance check for PDPA risks, and a short, hands‑on upskill plan so saved time converts into coaching, differentiation and curriculum design - not redundancy.

Begin by learning core AI literacy and ethical practice aligned with Thailand's national approach to transparent, accountable systems (see Thailand AI Ethics and Regulation for why governance matters), pair that with PDPA‑focused data minimisation and redaction practices (see PDPA guidance for compliance), then run small classroom pilots that include clear oversight, rubric calibration and student‑privacy safeguards.

Prioritise roles where automation already frees dozens of teacher‑hours - reassign those hours to live feedback, socio‑emotional support and targeted remediation - and formalise the change with short professional development or a practical bootcamp like Nucamp's AI Essentials for Work to learn promptcraft and tool workflows.

The memorable test: when a draft lesson appears in seconds, its real value is the human decisions that make it worth teaching; plan to measure outcomes (learning gains, time reallocated, privacy incidents) and scale the pilots that pass those tests.

“AI Literacy will be one of the three fundamental competencies that young people need to learn besides language and computational competencies.”