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

Slovenia's education workforce is at an inflection point: the national AI strategy calls for updating curricula, broad upskilling and stronger language‑technology support, while research hubs in Ljubljana are moving AI from lab to classroom - a shift that changes routine administrative and teaching roles overnight (Slovenia national AI strategy report).

Global showcases of AI in language learning show how quickly tools can augment grading, tutoring and registrar tasks (Global AI and language-learning trends in Slovenia), so practical reskilling matters: targeted courses like Nucamp's AI Essentials for Work bootcamp syllabus teach prompt‑crafting and workplace AI skills that help clerical staff, TAs and content assemblers adapt - imagine a registrar's inbox triaged in minutes instead of hours, and the “so what?” becomes immediate.

With artificial intelligence now advancing to practical applications, the event aims to explore its broader integration into education.

Assessment combined national strategy scans, large‑scale survey work and hands‑on audits to judge which school jobs face the fastest change: Slovenia's draft National programme (NpUI) set a policy lens and funding baseline for human capital and infrastructure (including a EUR 110 million commitment to 2025) that framed risk thresholds (Slovenia AI strategy and human capital measures (AI Watch report)); national case studies such as the METIS early‑warning pilot exposed practical limits - built on “more than 30 million grades” yet hampered by small budgets and parental consent problems - showing why technical feasibility alone can't define risk (Automating Society METIS and ADM in schools report).

Methodologically, researchers layered indicator frameworks from the University of Ljubljana's multi‑method project (mapping technologies, data sources and monitoring models) with survey and design‑based evidence like REDS (stratified sampling, weighted analysis and replication methods) to capture both exposure to automation and capacity to adapt (training pipelines, eTorba digital resources, teacher upskilling).

The result: a mixed‑methods rubric that flags roles where repeatable administrative inputs and predictable grading patterns meet weak oversight and limited reskilling - a recipe for rapid displacement unless concrete retraining and governance steps are taken.

“You do not need AI to see that a pupil has got worse grades in the second semester. You can do that with Excel.”

Registrars and enrollment officers in Slovenia face fast, visible change as national digitalisation pushes routine work onto shared platforms and smarter back‑end systems: the ANDI rollout that spawned the 2023 eTorba e‑bag project is building a single national platform for electronic textbooks and learning resources, which cuts paper handling and centralises student materials (see the eTorba national platform project summary eTorba national platform project summary); at the same time, new infrastructure ambitions - including an EU‑backed AI factory and high‑performance supercomputer hosted in Slovenia - will make powerful automation tools more accessible to schools and higher‑education offices (Slovenia Ministry of Higher Education, Science and Innovation news).

That means tasks once done with paper forms and phone calls - enrolling students, validating documents, scheduling intakes - can be streamlined or partially automated, while simultaneous reforms like growing interest in micro‑credentials reshape admissions workflows (University of Ljubljana contribution on micro‑credentials).

The vivid image: a registrar's desk that used to hold a tower of printed course packs becomes a dashboard of verified digital records - a shift that rewards staff who upskill in platform management, data validation and micro‑credential processing rather than those who cling to paper.

Graders and routine assessment staff in Slovenia should brace for AI systems that can score answers, flag inconsistencies and generate feedback at scale: an IntechOpen chapter on automating grading outlines how machine learning tools are already enhancing efficiency and feedback loops in education (IntechOpen chapter on AI in automating grading), while practical write‑ups on natural language processing show how NLP can handle automated essay scoring, MCQ checking and personalized feedback - speeding turnaround and improving consistency but also concentrating risk where oversight is light (Article on NLP for automated grading and assessment).

For Slovenia this means routine markers and exam proctors may find bulk scoring replaced by models that detect patterns across thousands of responses, so the human role shifts toward validation, handling edge cases and safeguarding fairness; picture a pile of red pens traded for a dashboard that flags atypical answer patterns in real time.

Practical adaptation includes learning to audit model outputs and craft prompts or rubrics that reflect local language and curricula - skills explored in local use‑case roundups and prompt libraries for Slovenian classrooms (Top AI prompts and use cases for Slovenian education).

Routine teaching assistants and entry‑level tutors - those who handle FAQ queues, run drill exercises and provide on‑demand practice - are already seeing their role reshaped in Slovenia: teachers report mostly occasional AI use today but expect to rely more on classroom tools over the next five years, provided uses show clear pedagogical value and respect ethical limits (2024 study of upper‑secondary teachers' perspectives on AI in Slovenian classrooms); national plans that fund training and curriculum updates under the draft NpUI mean public support for upskilling TAs is on the table (Slovenia national AI strategy report and NpUI training plans).

In practice, FAQ helpers and drill tutors may find repetitive prompting and basic feedback tasks automated, freeing time for small‑group coaching and social‑emotional support - but only if staff learn to validate outputs, guard against “invisible influencers” and check for bias the way recent risk assessments warn (risk assessment report on AI teacher assistants in classrooms).

The memorable image: a TA's morning no longer spent repeating the same short answers, but curating AI suggestions and stepping in where empathy, nuance and classroom knowledge matter most.

AI tools won't replace the human connection and support TAs provide, but they can significantly enhance a TA's ability to serve diverse student needs effectively

Curriculum and content assemblers - those who cobble together lesson plans, worksheets and low‑level learning modules - are squarely in AI's sights in Slovenia: national strategy documents push for curriculum updates and large‑scale upskilling while forums like EdReNe are actively steering how AI can safely generate and curate digital educational content, authoring tools and teacher repositories (Slovenia National AI Strategy report, EdReNe 2025 strategic seminar on AI in education).

Practical benefits are clear in sector writeups - AI can speed course updates, produce tailored worksheets and visual aids, and help keep materials aligned with evolving syllabi - but that efficiency comes with quality, bias and copyright questions that national guidance and teacher training must address (best practices for AI content creation in education).

The smart play for assemblers is to move from one‑off packet production to curating and validating AI‑generated modules: imagine trading a stack of photocopied worksheets for a verified, editable playlist of curriculum‑aligned activities that a teacher reviews and localises in minutes - skills that the NpUI and European discussions are already funding and framing for schools.

“We need a future that is broad and democratic, a future in which people widely understand how AI works - its strengths as well as its dangers and limitations,”

Language lab instructors and routine language‑practice facilitators face a very practical form of disruption: modern labs are no longer just rows of headsets but cloud‑enabled platforms with speech recognition, auto‑grading and AI conversation tools that can multiply student speaking time and run personalised drills at scale.

That efficiency is valuable in Slovenia's push to modernise classrooms, but it also means routine tasks - running repetitive pronunciation drills, logging simple practice scores, queuing standard exercises - are the most likely to be automated.

The smart response is a shift from doing drills to designing, curating and validating AI‑generated practice: instructors become the pedagogical gatekeepers who localise content, interpret automated feedback, protect student privacy and coach where empathy and nuance matter.

Picture a lab where a teacher's console highlights problematic pronunciation patterns flagged by software while the instructor steps in to model nuance - the vivid payoff is more meaningful speaking practice for every student, provided educators learn to wield these tools.

For practical guidance on classroom benefits and personalised assignments see Sanako language lab benefits.

SmartClass even advertises the ability to “increase students' speaking time” by up to 10x while offering AI‑powered pronunciation and auto‑graded activities. SmartClass language lab features

Concrete next steps for Slovenia start with a three‑track approach: (1) staff - fast, practical reskilling focused on AI literacy, prompting and evaluation skills so routine roles (registrars, graders, TAs, content assemblers and language‑lab facilitators) can move from execution to oversight; the recent IEEE study flags Slovenian students as having

“the greatest need for improvement” in foundational, practical and ethical AI knowledge

, so targeted short programs matter (IEEE Access study on AI literacy in Slovenian students).

A ready option for workplace skills is a 15‑week, hands‑on course like Nucamp's AI Essentials for Work 15‑Week Bootcamp - Nucamp that teaches prompts, tool use and job‑based applications.

(2) school leaders - accelerate ANDI rollouts such as eTorba, embed non‑technical AI modules across teacher PD and curriculum reviews, and require local audit protocols so automated grading and content generation are validated against Slovenian language and pedagogy (ANDI eTorba implementation in Slovenian schools - Cedefop summary).

(3) policymakers - sustain NpUI commitments (including the EUR 110M human‑capital focus), fund teacher upskilling, maintain HPC and language‑tech infrastructure, and stand up the proposed National AI Observatory to monitor impact and fairness.

The payoff is tangible: fewer piles of photocopied worksheets and more verified digital playlists and dashboards - but only if training, governance and monitoring move at the same pace as deployment.