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

AI is already reshaping Chilean classrooms and the jobs that support them: adaptive tutoring and automated assessment are moving from pilot projects to everyday tools, threatening routine grading and administrative roles unless workers upskill to higher‑value tasks.

Research from OPED at Universidad Católica de Chile shows teacher training is being redesigned with modules on “AI as an Academic Tutor” and ethical use, while regional trend reports highlight rapid EdTech adoption and personalized learning EdTech Trends 2025 report on AI and personalized learning.

National curriculum updates that add programming and AI signal policy momentum, and practical pathways such as the 15‑week AI Essentials for Work 15-week bootcamp syllabus (Nucamp) offer concrete routes for educators and staff to adapt their skills for this next wave.

"it is important to provide people with digital tools from childhood since in the new digital society whoever is not endowed with digital skills will be marginalized and we cannot allow this to happen with our children."

To identify which education roles in Chile face the biggest disruption and which can realistically adapt, the analysis borrowed a task‑level lens used in the Stanford/CENIA study “Generative Artificial Intelligence: Opportunities for the Future of Work in Chile,” mapping common school‑sector jobs into hundreds of discrete tasks and scoring each task on a 0–1 scale that captures whether GenAI can cut its completion time by at least half; aggregating those scores by hours worked per task produced an “acceleration opportunity” for each job and revealed where routine, repeatable work - think form‑filling or batch grading - looks like a stopwatch that could be halved with automation (SSRN: Generative AI in Chile).

That quantitative core was complemented with Chile‑relevant adaptation pathways drawn from practical resources - rapid literature syntheses and classroom prompts that speed local action research, plus examples of chatbots and connected‑classroom tools - to prioritize upskilling that shifts workers toward supervision, evaluation, and genuinely interpersonal tasks (Literature Reviews & Research Support).

School administrative assistants and secretaries face one of the clearest near‑term jolts from AI because so much of their day is predictable, repeatable work - scheduling, intake forms, routine student inquiries - that can be automated or dramatically sped up; Chilean education providers are already deploying 24/7 chatbots to provide low‑cost student support and shave administrative load during peak enrollment seasons 24/7 chatbots automating Chilean education student support.

That doesn't mean roles disappear so much as shift: high‑value tasks will be oversight, quality control, and handling exceptions that AI can't resolve, which requires training in prompt design, basic analytics, and vendor‑tool governance - skills that can be accelerated with rapid syntheses and classroom action research using targeted resources like Nucamp AI Essentials for Work syllabus and use‑case guides.

Leaders in AI and operations research also emphasize the need for deliberate evaluation and reskilling pathways so staff move from doing routine work to supervising AI systems and improving student-facing services INFORMS plenaries on AI, operations research, and reskilling, a practical pivot that turns a threat into a chance to coordinate services, reduce errors, and expand access across Chilean districts.

Education data‑entry and registry staff in Chile sit squarely on the front line of automation: the Stanford deep‑dive flags data entry as a “quick‑win” for Generative AI and estimates roughly 31% of public‑sector tasks could be accelerated, while administrative duties in education are especially exposed (teachers' admin tasks are estimated 65–75% suitable for automation) (Stanford report on generative AI impact in Chile).

Mature tools like robotic process automation and intelligent virtual assistants - already used to speed application processing and cut routine inquiries - can shrink bulk registry work and reduce errors, with industry vendors reporting dramatic time savings in government workflows (RPA and intelligent automation for public sector workflows).

The practical pivot for registry teams is clear: move from manual entry to supervising automated pipelines, auditing records flagged as exceptions, and owning data governance and algorithmic transparency - skills reinforced by Chile's GobLab guidelines on ethical procurement and transparency for public AI systems (GobLab guidelines for ethical AI procurement in Chile).

Imagine desks once buried in enrollment forms replaced by a real‑time dashboard that highlights the handful of files needing human review - a vivid example of how time reclaimed can be redirected to defend fairness, privacy, and citizen trust.

“We found it intriguing to focus on public procurement. We saw an opportunity to foster public-private collaboration, raise awareness, and build capacity in ethical AI, while developing concrete tools to ensure a positive social impact.”

Career and guidance counselors in Chile face a subtle but real squeeze from AI: chat agents like Kai - developed to scale Elige Educar's “Quiero Ser Profe” outreach via WhatsApp - show that more students will start a conversation with an AI (35% vs 28%), and the bot can answer far faster and with crisper factual replies, yet those exchanges are shorter and less oriented toward personal reflection (3.7 messages vs 5.9 for human counselors and 62.6% of Kai's queries are factual vs 25.8% for humans).

That pattern points to a practical adaptation rather than an existential one: use AI to handle high-volume, factual queries (admissions rules, course requirements, scholarship deadlines) while redirecting human counselors to the higher‑value, subjective work - career conversations, motivation, and nuanced vocational fit - that drove the program's impact on applications and enrollments.

Training and workflows should therefore pivot to supervising agents, auditing tricky cases, and designing hybrid journeys that blend fast, low‑cost AI touchpoints with scheduled human follow‑ups; rapid syntheses of Chilean evidence can speed that transition for districts and NGOs (IDB report on scaling AI career guidance in Chile: From human counselors to AI agents - Inter-American Development Bank), while practice-focused reviews and prompts help teams test hybrid models in weeks not years (Education AI prompts and use cases literature review for Chile: Literature reviews and research support for AI in Chilean education).

With Chile facing a projected teacher shortfall by 2030, the vivid image of a counselor who now spends freed-up hours mentoring students through tough vocational choices - rather than answering routine queries - captures the “so what?”: AI can expand reach, but human judgment remains the linchpin of effective guidance.

Entry‑level graders and routine tutors are especially exposed because much of their work - scoring constructed responses, giving patterned writing feedback, and running repeatable tutoring scripts - maps neatly onto what today's scoring models and chat agents can speed up or mimic; as A.J. Juliani warns, the old “Scantron” era has evolved into AI systems that can reward formulaic answers and therefore shift what gets taught and valued, and experts urge caution before handing over high‑stakes judgment to machines (A.J. Juliani: The BIG Problem with Using A.I. for Assessment).

Research and practitioner guidance stress that automated grading can be inconsistent, biased, and poor at judging nuance or metacognition, and detection tools are unreliable - so the practical adaptation is not resistance but re‑role: train staff to be “human‑in‑the‑loop” moderators who curate AI feedback, audit flagged items, design AI‑resistant performance tasks (oral exams, process portfolios, local projects), and teach students AI literacy so feedback becomes a learning conversation rather than a black‑box score (Don't Use GenAI to Grade Student Work - Guidance for Educators; Education Week: Will AI Transform Standardized Testing?).

The memorable image: the familiar “tat‑tat” of a Scantron may be gone, but unless schools keep human judgment at the centre, the system could end up rewarding formula over insight - so routine roles can pivot to supervising, contextualizing, and deepening feedback instead of simply dispensing scores.

“AI scoring could result in basic writing being scored at higher rates, as we have seen from the AI-scored [Texas Success Initiative assessment]”

Library and media specialists in Chile are at particular risk because their daily work - curating collections, guiding source evaluation, and teaching research methods - collides with two ugly tendencies in generative search: confidently wrong answers and fabricated or broken citations, as the Tow Center's tests found when chatbots “provided incorrect answers to more than 60 percent of queries” and often returned fake URLs (AI Search Has a Citation Problem - Columbia Journalism Review/Tow Center analysis of AI search engines).

At the same time, open‑access content is being repurposed for model training in ways that complicate provenance and credit, so librarians must move beyond traditional gatekeeping to become forensic curators: teach students how to spot hallucinations, demand provenance from vendor tools, and embed AI‑aware citation practices into library instruction (see practical guidance on the pros and cons of LLMs for literature searching at University of Tennessee Libraries' AI literature-search guide).

Tactical adaptations include using AI to speed systematic review screening while always auditing outputs (recent studies show promise but also pitfalls), publishing curated, provenance‑checked resource lists, and negotiating crawler and licensing terms with publishers and platforms; rapid syntheses and local research support can help schools prototype these shifts in weeks (Literature Reviews & Research Support - Nucamp AI Essentials for Work syllabus).

Picture a midnight student trusting a chatbot link that leads to an error page - libraries can prevent that by making verification a taught, practiced habit.

“without the ability to opt out of massive scraping, we cannot monetize our valuable content and pay journalists. This could seriously harm our industry.”

Chile's practical roadmap begins with scale and specificity: expand high‑quality, hands‑on training so entire workforces - not just pilots - can shift from routine tasks to oversight, evaluation, and human‑centred work; large initiatives like the free online program that aims to train 4,000 educators show how rapid, cohort-based teacher reskilling can work (ChileMass AI teacher training program for 4,000 educators).

At the same time, institutionalize AI competencies through teacher‑education curricula - OPED's modular model (assessment, AI as tutor, bibliographic analysis) provides a blueprint for integrating ethics, pedagogy, and sequential skills across pre‑service courses (OPED: "Developing teacher digital competencies in the age of AI" study).

For non‑teaching staff, short applied programmes that teach prompt design, tool governance, and auditing - such as a 15‑week, workplace‑focused pathway - turn vulnerability into an upskilling opportunity (Nucamp AI Essentials for Work bootcamp (15‑week workplace AI training)).

Pair these supply‑side moves with explainable AI pilots for retention and tutoring, rapid local evidence syntheses to guide procurement, and clear roles that move people from data entry and mass grading into exception‑handling, verification, and mentoring - so that Chile's classrooms gain reach without losing human judgment.

“Leading countries are embedding AI at the heart of their education strategies. Chile must do the same,” says Sofía Matte.