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

AI matters for education jobs in Denmark because the technology is already reshaping demand for skills even if broad economic shocks remain contested: university research finds clear “winners and losers” after ChatGPT's launch, cutting demand for routine writing and boosting roles in chatbots, machine learning and creative production (University of Copenhagen study on AI and the labour market), while the Danish government's new AI strategy and AI Competence Pact aim to upskill 1 million Danes and fund Danish Foundation Models to keep education relevant and local (Danish national AI strategy and AI Competence Pact announcement).

For teachers, clerks and librarians the takeaway is practical: routine tasks face automation but new assessment, data and digital-pedagogy roles will grow - job-focused training such as Nucamp's AI Essentials for Work helps education staff pivot into those higher-value tasks.

“The real disruption isn't in how we teach, but in how our students' minds are reshaped by constant AI interaction.” - Jeppe Klitgaard Stricker

Selection of the top five at‑risk education roles leaned on Denmark‑specific evidence and clear, practicable criteria: prioritise tasks already targeted by automated decision‑making (ADM) or routine documentation, flag roles with frequent citizen contact or high stakes for vulnerable groups, and weigh legal/regulatory exposure plus the documented presence of pilots or deployed systems.

That meant giving extra weight to administrative, grading and support roles because Denmark's public sector experiments and signature projects show heavy use of ADM in administration and profiling (see the Automating Society Denmark chapter), while the ADD project research highlights a political shift toward “boring AI” that automates administrative workflows rather than invasive citizen‑level profiling.

The Amnesty International findings about Udbetaling Danmark - where fraud‑detection models and mass data merging already affect benefit recipients - also guided the selection toward jobs tied to welfare, records and case control.

Finally, responsible‑AI guidance (compliance, data minimisation, human oversight) was folded into the methodology: roles were ranked by likelihood of automation, impact on rights, and realistic upskilling pathways so education workers can pivot into oversight, data‑analysis or pedagogical‑tech roles rather than be displaced.

“This mass surveillance has created a social benefits system that risks targeting, rather than supporting the very people it was meant to protect.” - Hellen Mukiri‑Smith, Amnesty International

Administrative clerks - think student‑stipend officers and benefit processors - are squarely in RPA's sights because their work is high‑volume, rules‑based and full of document checks: Blue Prism's breakdown of common RPA use cases flags registration, form processing and compliance as ideal automation targets (Blue Prism RPA use cases for automation).

Danish public bodies already prove the point - the Central Denmark Region turned a pharmacy task that once needed over 1 million keystrokes into a 40,000‑keystroke job and trained dozens of local developers to roll out almost 80 automations, freeing tens of thousands of hours for higher‑value work (Central Denmark Region RPA case study (UiPath)).

Combine RPA with intelligent document processing and bots can read and route stipend applications, leaving humans to manage exceptions, appeals and data‑quality oversight - exactly the kinds of supervisory roles ABBYY says extend RPA into new jobs rather than just cut headcount (ABBYY RPA and intelligent document processing overview).

The practical takeaway: automation will shrink routine inbox work but create demand for process designers, IDP supervisors and case‑level decision makers in Danish education administration.

“That one robot has reduced someone's work by a factor of 50.” - Børge Knudsen, Head of the Customer and Support Department

For exam graders and marking assistants, Denmark's 2026 pilot that permits students to use generative AI while preparing for the English oral exam signals a practical but tricky shift: once a student is handed a topic they may have one hour to use “all available tools, including generative AI,” then must deliver the presentation live to an examiner, so human judgement of performance and authenticity becomes front-and-centre (Denmark pilot AI in high school English oral exams).

Officials are keeping a safety valve - parts of the written test will still be handwritten to curb overreliance on digital aids - so marking workflows will need to blend traditional checks with new oversight practices (Denmark exam pilots and handwriting reintroduction).

The practical effect for graders is a pivot from purely scoring outputs toward designing assessment rubrics, spotting AI‑assisted prep in live settings, and collaborating with teachers to preserve academic standards while integrating useful AI tools; the one-hour prep window - AI at a student's elbow, then a live performance under scrutiny - makes that change vividly real.

“We are launching pilot schemes to try to find the right balance.” - Mattias Tesfaye, Danish Education Minister

Teacher aides and classroom support staff face a clear shift as AI tutors move from novelty to everyday help: Denmark's DTU-backed DTU ChatTutor AI tutor for Danish classrooms already lets teachers upload slides, notes and exercises so an AI can answer student questions and “refer students to the relevant sections,” with tougher queries escalated to TAs during a 120‑student pilot - freeing human aides from repetitive Q&A while making escalation and judgment the core value they add (DTU ChatTutor AI tutor for Danish classrooms).

International evidence shows AI tutors act as allies that deliver round‑the‑clock feedback, tailor practice and boost equitable access when used carefully (Analysis: How AI tutors are supporting educators in 2025), and Danish experts urge treating AI as a structuring assistant rather than a content replacement - so TAs focus on pedagogy, nuance and safeguarding against bias (University of Southern Denmark AI-in-education best practices).

The practical picture is vivid: imagine a tireless study buddy handling low‑level queries at midnight while a TA spends contact time coaching small groups, spotting misunderstandings and stepping in when the AI reaches its limits.

“AI has really just changed how we can do our jobs.” - Andrea Hinojosa

School and municipal planners and education data analysts are shifting from reporting past trends to running predictive models and translating those outputs into actionable local policy: Denmark's DREAM microsimulation explicitly

simulates lifetime educational behavior

using the full Danish population as an initial cohort, with each person coded by age, gender, origin and educational status and transition probabilities estimated from historical register data via conditional inference trees (DREAM microsimulation model for educational forecasting), while Cedefop's national skills forecast shows the labour market moving toward more professionals and higher qualifications - roughly 34% of future job opportunities for professionals and about 49% of the workforce with high‑level qualifications by 2025 - so planners must blend demographic microsimulations, AI forecasting and scenario testing to decide school capacity, VET pipelines and teacher supply (replacement demand is about seven times expansion demand) (Cedefop skills forecast for Denmark).

Imagine a planner fast‑forwarding a digital cohort to spot where shortages will cluster - that clarity is what turns data into timely hiring, reskilling and municipal budgeting decisions.

Librarians and learning‑resource curators in Denmark are being nudged from tradition into systems work as AI automates cataloguing, tagging and basic content generation: cross‑European research names Det Kongelige Bibliotek among national libraries experimenting with automated metadata and digitisation workflows (LibrarIN research: national libraries embracing AI for digital transformation), while commercial platforms like Ex Libris Primo promise conversational discovery and AI research assistants that change how patrons find and use collections (Ex Libris Primo AI-powered library discovery and research assistant).

Practical pilots show real gains - CRL's AI script produced item‑level metadata up to 60× faster - so routine cataloguing work will shrink even as demand grows for curators who can audit models, manage local indexes and teach AI literacy to students and faculty (CRL pilot: AI accelerates metadata production).

The clear “so what?”: the role shifts from creating records to designing trustworthy pipelines, spotting hallucinations and protecting patron privacy, making upskilling and ethical guardrails central to future library careers in Denmark.

“Without discovery, there is no access.” - Andrea Duntz, CRL

For Danish education workers the path forward is clear and practical: treat AI as a toolbox, not a replacement - start by building AI literacy (many Danes already use generative AI: ~37% in 2024) and learn to use models for structuring lessons, generating study aids and automating low‑value admin while keeping human judgment central; follow the University of Southern Denmark's guidance to “use AI for structuring rather than content creation” and run small classroom pilots that pair AI tutors with teacher oversight, insist on clear institutional rules for exams and deepfake protections, and pivot toward roles that audit models, design assessments and coach students in critical AI use.

Upskilling needn't be theoretical: practical courses such as Nucamp AI Essentials for Work bootcamp registration teach prompt writing, tool workflows and job‑focused AI skills in 15 weeks, giving staff usable techniques to reclaim time from routine tasks and invest it in pedagogy, safeguarding and inclusive learning.

“we should try to combine artificial intelligence with human intelligence and not with human stupidity.” - Mushtaq Bilal, University of Southern Denmark