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

AI is already reshaping education work across Czechia: a recent report notes one in four business leaders expect staff cuts due to automation, and national analysis flags that about 35% of Czech jobs sit in high‑risk occupations - well above the OECD average - so schools and colleges can't wait to act (see the coverage on Expats.cz).

The government's updated AI strategy stresses lifelong learning and teacher training to turn risk into opportunity, while academic research on AI literacy highlights that Czech teachers need more practical skills to embed AI safely in classrooms.

That makes targeted reskilling essential: practical programs that teach promptcraft, tool use, and job‑based AI workflows can help exam markers, TAs and admin staff shift into higher‑value roles - consider short, work‑focused options such as the AI Essentials for Work bootcamp to build usable skills fast.

“Artificial intelligence represents a huge potential for our economy and society and can significantly improve our quality of life.”

The methodology for this piece triangised Czech reporting, university research and peer‑reviewed classroom studies to keep the focus squarely on Czechia: official and press coverage from ČTK (captured in local reporting) established the policy and rights context, experiments and commentary from Charles University's journalism centre showed how routine newsroom outputs can already be automated, and a recent multi‑author teacher‑education study used nationwide questionnaires to map AI literacy gaps among Czech teachers - together these sources show both the pace of technical change (robot‑generated election writeups were trialled by ČTK) and the practical training shortfalls that make reskilling urgent.

Sources were read for direct quotes, examples of automation in practice, and empirical methods (survey design, template‑based NLG trials and archival experiments) so that job risks and adaptation pathways in Czech schools and higher education rest on documented Czech cases and measured teacher needs rather than general speculation; where national policy frames the analysis, the Czech AI strategy's emphasis on lifelong learning and vocational reskilling was used to interpret implications for workforce transition.

For primary references see the CTK coverage, Charles University reporting on automated journalism, and the ECEL AI‑literacy study.

“I think that artificial intelligence and human rights will be one of the topics that I and my successors will deal with, also taking into account that we are now a national human rights institution,” said Krecek.

The Czech system's heavy reliance on continuous assessment and termly school reports - documented in the Eurydice overview of assessment in Czechia - creates a steady flow of routine scoring work that automated graders are designed to absorb; with upper‑secondary schools also using both continuous and final assessments, the volume and regularity of clearly structured marks (the national 1–5 classification is used across levels) makes exam‑marking an obvious automation target (Eurydice overview of assessment in Czechia, Czech grading scale (1–5) - Ministry overview).

That may sound efficient, but local reporting flags a downside: wide differences in teacher grading and calls for more verbal assessment suggest algorithms could both reduce variance and harden unfair patterns -

a machine that “standardises” a bias still produces unfair results (Expats.cz article on Czech grading bias).

The upshot for Czech exam markers is pragmatic: objective, rubric‑based tasks and routine paperwork are most at risk, while roles requiring nuanced judgement, oral assessment or pedagogical feedback will remain valuable - imagine dozens of term reports scored in seconds, but without the human note that can spot a student's changing motivation or context, and the reason to invest in assessment literacy and AI‑aware reskilling becomes clear.

Teaching assistants form a backbone of Czech classrooms - more than 21,000 were working in schools in 2018 supporting pupils with special educational needs - but many lack methodological support and clear communication channels with teachers and parents, which makes routine tutoring tasks especially exposed to AI disruption Support for Teaching Assistants in Czech Schools - People in Need.

Government and programme-backed assistants such as Fulbright ETAs illustrate the scale of everyday language practice: cohorts of 20–30 assistants co‑teach with dozens of Czech teachers and, in one campaign, 25 assistants worked with 174 teachers and reached 7,620 students - precisely the kind of repeating speaking drills, vocabulary practice and simple remediation that chatbots and AI tutors can now run at scale Fulbright English Teaching Assistants Czech Republic program.

Eurydice's overview of teachers and education staff clarifies that these roles are officially classed as education staff, so automation pressure won't just be technical but organisational too Eurydice overview of teachers and education staff in Czechia.

The human edge remains in relationship work - parental communication, supervised group reflection, and specialist methods training offered in conferences and blended courses - so reskilling should pair AI tool literacy with those interpersonal, coordination and methodological skills that machines can't replicate; imagine a chatbot running practice exercises while an assistant uses a monthly supervised meeting to tackle classroom inclusion issues in ways an algorithm cannot.

The Czech system's layered administration - where schools are run within public administration and responsibilities are split across the central government, regions and municipalities - creates a tangle of enrolment forms, timetables and reporting lines that make routine back‑office work especially exposed to automation (see the Eurydice overview of the organisation of the Czech education system).

Tools already in use elsewhere - most notably predictive enrollment analytics for Czech schools that help providers forecast demand and optimise faculty allocation - show how admissions, data‑entry and scheduling can move from spreadsheets into automated dashboards that adjust staffing and class lists as patterns emerge.

With student AI use rising even where rules lag, routine reconciliation, record‑keeping and repeat communications are the obvious first targets, so administrators who learn workflow design, analytics oversight and vendor integration will avoid being the “paper shuffle” replaced by a calendar that updates itself after each enrolment wave (student AI usage statistics in Czech schools (2025)).

Library assistants and routine information‑services staff in Czechia are front‑line candidates for AI disruption because the very tasks they do best - resource discovery, cataloguing and simple reference - are already being automated: Ex Libris' whitepaper shows generative AI boosting discovery, metadata creation and personalized recommendations, while practical reports describe AI sharpening classification and recall so patrons get better answers via natural‑language search.

Ex Libris whitepaper: Generative AI and the future of library services - Library Journal and AJE report: 5 Ways AI Impacts Libraries.

“The adoption of AI is likely to produce an impact and changes that go far beyond the local improvements that libraries may initially be looking for.”

That doesn't mean libraries vanish; it means routine cataloguing workloads that once took cataloguers decades (a vinyl backlog example would need centuries at current rates) can be handed to AI, freeing staff for inclusion work, complex reference, and community programming that machines can't do well.

For Czech institutions the takeaway is tactical: pair metadata automation with roles in AI oversight, workflow design and public AI literacy so library assistants transition from repetitive entry work into stewarding trustworthy discovery and human‑centred services that keep libraries vital to students and researchers.

Course content editors, proofreaders and junior instructional writers in Czechia are squarely in the path of today's AI writing and editing tools: domestic training like the Prague-based AI for Creators course shows how promptcraft and workflow automation can compress tasks that once needed hours into a few guided steps (Authentic Media - AI for Creators course (Prague)), while Czech newsroom experiments with template‑based summaries and automated reporting underscore that routine, templateable text (syllabus blurbs, quiz feedback, module summaries) is already automatable in local practice (AI-powered journalism experiments in Czechia - EJO/Charles University).

The risk is not just speed but standards: publishers and universities are relying on AI‑checker and integrity workflows to catch hallucinations, bias and improper attribution, so editors who learn AI oversight, verification and ethical editing will be the ones steering quality rather than being sidelined (AI writing and research integrity best practices - Turnitin).

A vivid takeaway: a junior writer's week of line‑editing can be reduced to an hour of supervising a draft and curating sources - which makes prompt and policy literacy the new editorial muscle.

“when it comes to AI, one of the biggest threats is its lack of originality, creativity, and insight.”

Adaptation in Czech schools needs to be practical, ethical and fast: evidence shows teachers lag on AI literacy while students adopt tools quickly - more than three quarters of secondary pupils report using AI regularly - so the immediate priority is pairing clear ethics and rules with hands‑on reskilling (see the ECEL study on AI literacy in teacher education and the recent Czech student survey).

National planning already flags education reform and Digital Innovation Hubs as levers in this shift, and the EU's Ethical Guidelines for AI in teaching give concrete classroom prompts and assessment safeguards that should shape in‑service training.

For educators that means three tidy moves: (1) short, work‑focused courses to build promptcraft, verification and workflow oversight; (2) new local leadership roles - AI coordinators or DIH liaisons - to translate policy into practice; and (3) blended support (mentoring, conferences and vetted toolkits) so that routine tasks are automated under human supervision rather than outsourced.

The payoff is immediate and vivid: routine paperwork and template feedback can be reduced to oversight tasks, freeing time for the relational, inclusion and critical‑thinking work that machines can't do - turning risk into a concrete pathway for career growth.

“Teachers should actively share their experiences of using AI in the classroom and look for ways to effectively guide students in how to use it,” said AI engineer Matthew Eibich.