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

Cambodian educators should pay close attention to AI risk because the same tools that can tailor lessons and free teachers from paperwork can also displace routine roles: automated grading, scheduling, and simple tutoring are already mature enough to reshape jobs in schools and tutoring centers.

Research shows AI personalizes learning, streamlines administrative tasks, and enhances instructional quality, but also brings thorny issues - privacy, algorithmic bias, and unequal access - that matter anywhere students are learning with limited resources.

Rather than wait, teachers and school leaders can treat AI as a practical lever: learn how assessment and tutoring tools work, redesign assignments that require original reasoning, and build skills to supervise and improve AI outputs.

For practical next steps and Cambodia-focused use cases, see the University of Iowa overview of AI in education and Nucamp's local guide, The Complete Guide to Using AI in the Education Industry in Cambodia in 2025; for hands-on training consider Nucamp AI Essentials for Work bootcamp (15-week program).

AI in education offers transformative potential by personalizing learning, streamlining administrative tasks, and enhancing instructional quality.

Methodology combined an employment-weighted automation-risk chart (sourced from the Will Robots Take My Job? site) with peer-reviewed and university research to avoid alarmist conclusions - studies from Notre Dame, Boston University, and BYU helped distinguish where machines tend to complement workers rather than fully replace them - and then grounded those signals in Cambodia-specific guidance and use cases from Nucamp's local resources.

Rankings began with the chart's risk scores (which reflect how automation risk scales with workforce share), were cross-checked against empirical findings on displacement and task-creation, and were adjusted for Cambodian realities documented in Nucamp's Complete Guide to Using AI in the Education Industry in Cambodia in 2025 and the Student Performance Analyzer use cases; practical limits like connectivity, cost, and local deployment models were used to down-rank roles that are technically automatable but unlikely to be replaced soon.

The result: a shortlist that balances global evidence with on-the-ground Cambodian context and produces actionable adaptation steps for educators rather than headlines.

“When robots were initially introduced, the intent was to cut costs and replace human workers. But now companies are using ‘collaborative robots' or ‘cobots,' which are designed to work together with humans.”

Exam graders and standardized test administrators are especially exposed because their work is repetitive and rule-based - automated systems can quickly provide surface-level reads of answer patterns, much like how vulnerability assessments use automated scans to achieve surface-level visibility; that means cheap tools can replace routine scoring but will miss nuance unless humans recalibrate them.

In Cambodia, the practical response is not resistance but role redesign: shift emphasis from manual marking toward tasks that require judgment, feedback quality, and authenticity checks, and use AI to amplify insight rather than substitute for it.

Tools such as the Student performance analyzer can turn raw grades into action plans and reveal class-wide trends and targeted interventions, freeing time for teachers to craft richer prompts and oral exams that reveal reasoning.

When schools adopt scoring models, pick deployment options that control costs and protect data - platforms like BytePlus ModelArk help education providers balance token billing with enterprise security - so administrators can upgrade technical literacy, audit AI outputs, and keep accountability where it matters: in the classroom, not just on a spreadsheet.

Routine back-office roles - data entry, rostering, fee processing and schedule changes - are squarely in the sights of automation because robotic process automation (RPA) and simple cloud integrations can copy the exact keystrokes staff perform today, cut errors, and free time for higher‑value work; see practical RPA use cases for education in Savvycom's RPA guide.

In Cambodia, the sensible path is not to resist bots but to redesign the work: pilot RPA for repeatable flows (enrolment imports, invoice runs, roster updates) while moving critical systems to secure cloud providers, adopting offline backups and a cyber response plan so recovery is possible if something goes wrong.

Compliance and privacy must be first priorities - vendor oversight, role‑based access, and regular cyber training (the RPA cyber cover requires NCSC Cyber Security Training and Police CyberAlarm registration in other jurisdictions) keep student data safe and preserve community trust; Nucamp's Complete Guide to Using AI in Cambodia collects local deployment examples for education teams.

Start with one high‑volume process, measure time saved, and use those reclaimed hours to audit data practices - one automated roster can translate into a week of focused data hygiene, not fewer jobs.

“When a cyber incident occurred in our school, we found the reporting process quick and easy, which is just what was needed when something like this happens. We would recommend if a cyber incident happened to others that use a cashless catering system, that they try and get an alternative in place as soon as possible e.g., bring in the use of a cash register. You need cyber-attack insurance. Cyberclan was invaluable, leading the whole investigation, guiding our IT staff on the rebuild, advising the ICO officer, holding regular online update meetings and providing a constant point of contact for school queries and support. This is an important learning point, to stop using plug-in memory devices and store documents in the cloud. Initially you just don't know what has caused the problem and so you can't assure people one way or the other and anyone with a connection will block your access to protect themselves.”

Low-level private tutors and teaching assistants in Cambodia face real pressure as the tutoring market booms across poorer parts of Asia and AI-driven services scale: the Economist notes private tutoring's surge -

the incentive is clear: in Cambodia, low-paid teachers who offered tutoring doubled their salaries

so demand is real, but the growth of AI tutors means many routine, scripted lessons can be replicated cheaply; the AI Tutoring Services Market is projected to jump from USD 3,716.6M in 2025 to USD 21,625.2M by 2035 with

pure AI tutors

taking a large share, making homework help and repetitive drill work especially vulnerable (FutureMarketInsights).

Cambodian tutors can adapt by leaning into what machines can't easily replicate locally: relationship-driven diagnostics, oral and live problem‑solving sessions, and supervising AI outputs for cultural accuracy, while using tools that turn scores into targeted actions - see the Student Performance Analyzer - to show measurable impact and command premium rates; when adopting AI for blended offerings, compare deployment options and cost controls so small providers don't get priced out.

Content and lesson‑plan assemblers and curriculum distributors are especially vulnerable because generative AI now creates polished lesson drafts in seconds, turning what used to be a specialist's craft into a commodity; students already use these tools far more than instructors (a national survey found 27% of students were regular generative‑AI users versus just 9% of instructors), so off‑the‑shelf plans can propagate quickly unless checked (University of Illinois).

Yet research shows these tools often misalign with learning objectives or fail to integrate local textbooks and context without human review, so the smartest Cambodian response is not resistance but redesign: use AI to draft outlines and varied activity ideas, then assign a human editor to map every lesson to Cambodia's curricula, local language norms and cultural examples, and to audit for bias and accuracy (see the USF study on curriculum design and lesson planning).

Pair that workflow with affordable assessment analytics - for example, the Student performance analyzer that turns raw grades into action plans - and schools can protect teacher‑student relationships, keep instructional quality local, and sell premium, culturally grounded lesson bundles instead of competing on low‑cost, generic content.

“As an educator, I've seen the challenges and increasing pressure on faculty and instructional designers to create engaging and aligned course content.”

Language‑practice conversation providers that focus on scripted exchanges are especially vulnerable in Cambodia because Khmer remains a low‑resource language for AI: limited digital corpora, a complex abugida script with stacked characters and no clear word spacing, and few Khmer‑specific pretrained models make off‑the‑shelf chatbots prone to mistakes (see the analysis of Analysis of Khmer as a Low‑Resource Language for AI).

In practice this means a cheap AI tutor may handle vocabulary drills but fumble real conversational turns - mis‑segmenting words or missing local idioms - so scripted exchanges can be automated without delivering real communicative gains.

The local opportunity is to shift from replaceable scripts to premium, human‑guided services: combine live tutors who give culture‑aware corrective feedback with emerging Khmer models and datasets (homegrown efforts like SEA LION and homegrown Khmer AI initiatives), supervise fine‑tuning with real human utterances, and measure impact with analytics tools such as the Student Performance Analyzer education analytics tool.

A vivid test: when a system can't reliably parse Khmer's ligatures, live tutors who coach pronunciation and cultural context become the difference between a hollow scripted lesson and a learner who actually speaks Khmer fluently.

Finish with a clear, Cambodia-focused 3-step action plan that keeps classrooms local and students protected: Step 1 - strengthen access and governance: press for predictable education financing, national data‑protection rules and shared infrastructure so EdTech benefits reach rural and marginalised learners (CDRI notes only 22% of schools have functional computers and roughly 55.6% internet access); read the policy roadmap at CDRI for guidance.

Step 2 - train teachers in ethics and redesign assessment: deliver practical workshops on prompt design, verification and formative assessment so tools like large language models enhance pedagogy without undermining academic integrity (the ChatGPT review recommends targeted training rather than bans).

Step 3 - pilot the right tools and upskill staff: start with analytics that turn marks into action (pilot the Student performance analyzer to find class‑level trends and targeted interventions) and invest in applied courses that teach prompt-writing, tool selection, and deployment controls - for example, consider Nucamp's AI Essentials for Work bootcamp (15-week) to build workplace-ready AI skills.

Policy, people, and practical pilots together make AI a lever for inclusion rather than displacement in Cambodian schools.

“ChatGPT has shown a powerful ability to organize and draft components of articles.”