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

Argentina's schools are already at the crossroads of opportunity and disruption: global analysis like PwC's 2025 AI Jobs Barometer shows that

AI can make people more valuable, not less,

a reminder that automation often reshapes tasks rather than simply replacing roles (PwC 2025 AI Jobs Barometer report).

At the same time, national policy is catching up - Argentina is drafting laws and rolling out public‑sector guidance modeled on a risk‑based EU approach (IAPP global AI legislation tracker for Argentina) - so clerical staff, routine graders and admissions teams face fast-moving rules and new tools like automated grading and machine‑vision assessments now being trialed in local classrooms (automated grading with machine vision in Argentine classrooms).

That combination - rising AI capability, active regulation, and clear productivity gains - makes upskilling (especially practical AI and prompt skills) an urgent, practical step for Argentine education workers seeking control over how AI changes their work.

AI can make people more valuable, not less – even in the most highly automatable jobs.

To assess AI risk and adaptation for Argentina, the analysis combined global evidence from PwC's 2025 AI Jobs Barometer - which mined close to a billion job ads and highlights metrics like 3x higher revenue‑per‑worker growth in AI‑exposed industries, faster skill‑change rates and a rising wage premium for AI skills - with locally relevant signals: national policy direction and classroom pilots in Argentina (see the national AI strategy for education and reports of automated grading with machine vision in local schools).

Roles were classified using PwC's exposure framework (augmentable vs. automatable), weighted by indicators such as likely task automation, speed of skill change, and measurable productivity upside, and then cross‑checked against on‑the‑ground use cases and regulatory trends in Argentina to prioritise practical upskilling and governance steps for education workers.

The result is a risk ranking that pairs global labour-market rigour with specific Argentine examples to make recommendations that are realistic, actionable and time‑sensitive.

AI can make people more valuable, not less.

School administrative and clerical roles - data‑entry clerks, registrars and secretaries - are often the first to feel the push of AI because much of their day is structured, repeatable work that algorithms and dashboards can automate or accelerate.

In Mendoza, for example, a province‑wide early‑warning system designed by the University of Buenos Aires gives heads a class map with an indicator light beside each name and flags students at risk based on four variables (results, absences, family education and age‑grade gap), a tool that runs on at least two years of enrolment data and already covers roughly 80% of pupils in the region (Mendoza province early-warning dashboard to prevent school dropout).

That kind of automation can free time formerly spent on paperwork, but it also shifts jobs toward data stewardship, privacy governance and sensitive follow‑up - risks and equity trade‑offs underscored by regional studies mapping AI in classrooms (ProFuturo regional analysis mapping AI in Latin American classrooms) and by practitioners urging careful implementation (World Bank guidance on implementing early-warning systems to prevent dropouts).

The practical “so what?”: clerical teams who learn dashboard interpretation, basic data ethics and how to translate algorithmic alerts into human outreach will turn potential displacement into more meaningful, higher‑impact school work.

“Three out of ten secondary school pupils in Argentina do not complete their education.”

For test graders and routine assessment staff - those who score standardized exams or process multiple‑choice sheets - the recent blind studies are a clear red flag and an invitation to adapt: researchers at the University of Reading found AI‑generated exam answers that were virtually undetectable and on average scored about half a grade boundary higher than real students, with 94% of AI essays not raising concerns from markers (University of Reading “Examinations Turing Test” coverage in The Guardian), a result others summarised as AI submissions beating real students and often slipping past detection systems; at the same time, AI tools can also augment grading, especially in STEM, by speeding visual recognition and analytics and freeing time for richer feedback (how AI is reshaping grading for STEM teachers).

For Argentine grading teams this means two practical moves: insist on human oversight and rubric transparency for any automated scoring, and learn to configure, audit and explain AI‑assisted marks so that reliability, equity and academic integrity are defended rather than eroded - because a machine that grades faster can also change what gets rewarded in the classroom, for better or worse.

“Our research shows it is of international importance to understand how AI will affect the integrity of educational assessments,” said Dr Peter Scarfe.

Routine tutors and basic‑content instructors face a clear near‑term squeeze - AI tutoring is scaling fast and is designed for the very tasks these roles perform: drill, repetition and just‑in‑time explanation - yet the local evidence shows the outcome depends on how human and machine are combined.

Global forecasts put the AI tutoring services market at about USD 1,422.3 million in 2025 with a projected rise to USD 5,754 million by 2035, driven by personalization, 24/7 availability and lower unit costs (AI tutoring services market forecast - FactMR); at the same time, an Argentine randomized pilot of remote tutoring in Buenos Aires and Mendoza used short, 20‑minute weekly phone calls over eight weeks and found no clear average effect, although the treated subgroup showed a modest 0.15 standard‑deviation gain and tutor commitment strongly predicted how many sessions students completed - a reminder that relationships and implementation matter as much as technology (Argentina remote tutoring pilot - IADB).

The practical “so what?” for tutors: basic drilling tasks are the most automatable, but those who steward AI-driven practice - curating correct content, supervising accuracy, coaching students through tricky problem‑solving and mobilising caregiver engagement - will convert risk into new, higher‑value roles rather than disappear.

Front‑desk teams and admissions staff in Argentina are on the front line of AI change: conversational systems can now field common questions, guide applicants through forms and even triage cases before a human steps in, freeing time during peak enrolment seasons but also shifting the role toward oversight, escalation and data stewardship.

A systematic review of AI in Latin American higher education shows chatbots that “understand common language requests and respond automatically,” while practical writeups on the rise of AI chat assistants spell out clear benefits - 24/7 responses, FAQ handling, and ERP integration that smooth the admissions funnel (systematic review of AI in Latin American higher education (SpringerOpen), analysis of AI-powered chatbots in educational institutions (eduCase)).

Providers and vendors promise dramatic efficiency gains - case studies report up to a 50% cut in administrative time - yet implementation is not plug‑and‑play: projects require careful training, multilingual tuning and audit trails to protect fairness and privacy (school admissions automation case study by Darwin AI).

The practical “so what?” is vivid: a midnight parent query answered instantly by a bot can convert confusion into enrolment, but only if staff retain control of complex cases, data quality and the human touch.

“Three out of ten secondary school pupils in Argentina do not complete their education.”

Curriculum content editors, copy editors, and routine instructional designers in Argentina are squarely in the line of AI's first wave: large language models and machine translation can draft templated lesson plans, simplify texts, or produce worksheets in seconds, but local research and pilots show that speed without local curation risks shallow or culturally tone‑deaf materials - see the Argentina case study of the AI Curriculum Development Project documenting an AI‑prompted bird‑feeder activity and its classroom outcomes (Argentina case study: AI Curriculum Development Project).

Surveys of Argentine instructors also reveal reluctance to hand control to black‑box tools, especially in language classes where accuracy and pedagogy matter (Íkala study on Argentine teachers' attitudes toward AI).

Practical adaptation means leaning into strengths: use AI to generate first drafts, then apply local expertise - Program.AR's culturally appropriate materials and teacher training provide a blueprint for large‑scale editing that preserves Spanish‑and‑Argentina‑specific content (Program.AR curriculum resources for Argentina).

The payoff is clear: editors who master prompt‑crafting, fact‑checking, and pedagogical tuning can turn automation from a threat into a time‑saving partner that preserves classroom relevance and equity.

“At UADE, we are glad to have ASU supporting our faculty to develop this learning and skills to deliver quality education for our students.”

Practical checklist for Argentine education workers: start with short, practical upskilling (learn prompt craft, tool‑configuration and auditing) - a focused option is the AI Essentials for Work syllabus that teaches workplace prompt and AI skills in 15 weeks (AI Essentials for Work syllabus); press provincial and federal channels to fund the teacher retraining and broadband rollout envisioned in the San Juan bill so every school can access AI tools and safeguards (San Juan AI education bill); lean on proven national networks and materials - Program.AR's teacher workbooks, Pilas Bloques toolkit and large teacher‑training network (5,000+ trained) show how culturally appropriate content and PD keep automation pedagogically sound (Program.AR teacher training and materials); prioritise three operational moves at the school level - (1) demand human oversight and transparent rubrics for any automated grading, (2) learn basic data stewardship and ethical checks to protect equity (echoing teacher concerns about accuracy and over‑reliance in language classes), and (3) run small pilots that pair AI drafts with local teacher curation so speed becomes time for deeper feedback, not shallow content.

These steps align policy, pedagogy and practical skills to keep Argentine educators in charge of how AI reshapes classrooms.