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

AI matters for education jobs in the Netherlands because it's not just a distant tech trend but a near-term force reshaping work: ING's analysis found AI could significantly boost Dutch labour productivity (43% of jobs are complementary to AI), which means both opportunity and disruption for schools and universities (ING report on AI and Dutch labour productivity).

At the same time, a Media & Learning survey shows roughly 80% of institutions lack clear AI policies, leaving Dutch educators exposed as generative AI use surges and Gartner predicts that by 2028 over 70% of teaching, research and student-submitted content will be produced with AI support.

That combination - strong national readiness and weak institutional guardrails - could quickly shift routine tasks like admin and exam scoring; the practical step forward is skills, not panic, which is why short, work-focused courses matter (see Nucamp AI Essentials for Work bootcamp for practical prompts, tool workflows and job-ready application).

“AI brings tremendous opportunities in making banking more personal, easier, and empowering for customers.” - Görkem Köseoğlu, ING Chief Analytics Officer

Selection began with measures used in recent Dutch and eurozone analyses: occupations were scored for both AI exposure and AI complementarity (the two-axis approach discussed in ING's Think piece, which builds on Pizzinelli and Felten's work) so roles that AI can either perform or productively augment are identifiable; the result is a simple, comparable ranking that highlights which education tasks are most likely to shift first.

That framework was combined with Netherlands‑specific signals - most notably ING's finding that 43% of Dutch jobs are complementary to AI and that recent job growth has concentrated in AI‑compatible service roles - to prioritise routine, high‑volume tasks within schools and universities (administration, scoring, template curriculum work, entry‑level language practice and routine support).

Where possible, the analysis weighed growth trends and adoption gaps (public sector caution, firm‑level uptake) to flag both near‑term automation risk and realistic adaptation windows for Dutch educators; think of it as a 2x2 map driven by national numbers, not speculation (NLTimes: ING report on AI's potential to elevate the Dutch labour market, ING Think: Eurozone labour market exposure vs. complementarity to AI).

“AI brings tremendous opportunities in making banking more personal, easier, and empowering for customers.” - Görkem Köseoğlu, ING Chief Analytics Officer

Student administration clerks and education secretaries in Dutch schools and universities sit at the intersection of high-volume routine work and rapidly maturing AI tools: research from the INFINITE project shows chatbots and management systems are already streamlining administrative tasks and offering real‑time student support, while regional projects in the Netherlands use predictive dashboards to flag at‑risk learners so teams can act before drop‑out becomes reality (INFINITE research on AI in higher education, AnnieAdvisor: AI & data in student support).

That means routine duties - FAQ triage, scheduling, enrolment checks and first‑line follow‑up - are the most likely to be automated first, freeing time for casework that needs judgment and human contact (think: a dashboard that leads to a text or even a home visit for a student in danger of leaving).

At the same time, university guidance like Leiden's AI notes remind administrators that handling student data with commercial AI is legally and ethically sensitive, so automation must be paired with clear policies, consent and secure vendor arrangements to keep privacy risks in check (Leiden University: AI in education guidance).

“We have the infrastructure, we have the data, and now we're moving toward more predictive approaches. But we need to combine this with human care.” - Arno den Otter, Student Affairs Office, Zuid‑Holland Oost

Assessment graders and test‑scoring staff in the Netherlands face one of the clearest near‑term disruptions from AI: automated scoring systems and the new wave of LLM‑based scorers can slash hours of repetitive marking and deliver instant results, as studies show these tools already tackle essays and constructed‑response math items (see the NAEP R&D Hub working paper on LLMs for automated scoring performance), and practitioners note the same efficiency and consistency benefits highlighted in industry reviews.

Yet the trade‑offs matter: investigations find machines can reproduce and amplify human biases and sometimes reward formulaic or even nonsensical text, so Dutch exam boards and university assessment teams should pair any pilot with human checks, subgroup audits and transparent rubrics rather than full replacement (for concerns about fairness and rollout, see the EdSurge article on AI fairness and accuracy in automated grading).

Picture a gibberish essay that gets a high machine score - a small, alarming detail that underlines why vigilance, mixed human‑AI workflows and strong evaluation metrics must guide adoption.

“The problem is that bias is another kind of pattern, and so these machine learning systems are also going to pick it up.” - Emily M. Bender

Basic‑content curriculum developers and creators of learning resources are on the front line of AI's twin promise and risk: generative tools can draft module outlines, produce initial lesson text, generate quizzes, captions and voiceovers, and even spin up visuals and translations in minutes - so routine drafting, curation and localization tasks are the likeliest to shift first - yet these same tools need careful human curation to ensure pedagogical alignment, cultural nuance and accuracy (for example, AI can draft a Dutch‑language lesson quickly but miss a local idiom or accessibility requirement).

Practical guides show how AI speeds research, storyboarding and content production while still requiring a “human‑in‑the‑loop” for objectives, assessment alignment and quality control (see the University of Cincinnati overview of AI for instructional designers and Engageli course-creation best practices), and product examples like Disco AI highlight swift lesson drafting, image generation and personalization that can free designers to focus on higher‑order tasks.

For Dutch teams, that means shifting from producing boilerplate lessons to supervising adaptive pathways, auditing for bias and localising materials - in short, let AI do the repetitive night‑shift work, and keep humans on the daytime jobs that require judgement and local expertise (see Nucamp AI Essentials for Work syllabus: tailored translation & transcription workflows for Dutch projects).

Entry‑level language tutors and routine language‑practice instructors in the Netherlands are among the roles most exposed to near‑term AI shifts because learners can now get adaptive, round‑the‑clock practice, instant pronunciation corrections and automatic feedback that used to come only from human partners; studies and industry guides note speech‑recognition tutors, chatbots and grading tools can scale practice and free teachers from repetitive correction tasks (see practical overviews on AI tutors and instant feedback at American Public University: American Public University - AI in Language Learning and Teaching overview, and Intellectsoft: Intellectsoft blog - How AI Improves the Language Learning Experience).

That doesn't mean human tutors vanish - rather, their value shifts toward coaching complex conversation, cultural nuance and assessment audit; think of an evening lesson where an AI chatbot handles drills at 2 a.m.

while the tutor reviews subtle pragmatic mistakes and GDPR‑safe data use flagged by the system. Practical adaptation steps for Dutch programs include supervising AI partners, auditing automated feedback for bias, and integrating AI workflows into local lesson plans and translation/transcription pipelines (Translation and Interview Transcription Workflows for Dutch Projects), so tutors can move from repeat correction to higher‑value mentoring and curriculum design.

Teaching assistants and paraprofessionals in Dutch schools are among the roles that can gain the most from smart AI use - if the shift is managed rather than feared.

AI can automate roll calls, routine feedback and progress tracking so TAs spend less time on paperwork and more time on small‑group coaching, behaviour support and the nuanced, in‑person interventions machines can't replicate; see eSchoolNews' overview of how AI is changing the role of teaching assistants for concrete examples (eSchoolNews: AI is changing the role of teaching assistants).

National guidance that centres teacher agency is crucial: ACER argues AI should augment educators and be implemented through co‑design, training and clear privacy safeguards so Dutch TAs can supervise automated tutors, audit feedback for bias, and lead the human side of blended lessons (ACER: ensuring teacher agency in a technology‑empowered world).

Picture an always‑awake assistant that takes perfect notes and flags a slipping learner - TAs who learn to conduct these tools will move from chasing admin to delivering the one‑on‑one conversations and adaptive support that actually keep students on track.

AI is not here to replace you - it's here to enhance your effectiveness.

For Dutch education professionals the path forward is practical and local: build AI literacy, pilot with clear human‑in‑the‑loop safeguards, and pair pilots with governance so privacy, fairness and the upcoming EU AI Act don't become afterthoughts.

Start by using evidence‑based guides and shared curricula - Npuls' AI GO framework offers a compact, practice‑focused roadmap for teacher competencies and institutional policy (AI GO! - AI literacy framework (Npuls)) - and VU Amsterdam's AI competency tips show how staff can move from “Explorer” to “Integrator” by combining short e‑modules, critical ethics checks and classroom pilots (How to enhance your AI competencies as a teacher - VU Amsterdam).

Prioritise quick wins that free time for judgement‑heavy work (automate routine grading or scheduling, then spend the saved hour on targeted coaching), require vendor transparency and subgroup audits in every pilot, and fold learnings into staff development and curriculum renewal.

A small, well‑measured pilot that documents harms and gains will beat vague promises: one clear dashboard alert and a trained teacher's timely conversation can stop a dropout, which is the exact “so‑what” benefit that turns AI from risk into real educational value.