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

Italy's education sector needs practical realism, not panic: global generative AI is forecast to surge toward roughly USD 1 trillion by 2034, and AI is already embedding itself into everyday workflows (see the StartUs Insights generative AI market forecast and the Stanford HAI 2025 AI Index report), which raises exposure for administrative and library roles - educational instruction and library occupations show about 29.5% task exposure to automation.

That makes two responses essential for Italy's schools and edtech teams: deploy privacy‑first pilots (for example, a GDPR‑compliant class performance dashboard to spot students at risk) and upskill staff in workplace AI skills; short, focused programs like the Nucamp AI Essentials for Work bootcamp (15 weeks) teach promptcraft and practical tool use so routine grading, enrollment triage, and content drafting move from bottlenecks to streamlined, supervised workflows.

“AI is poised to be the most transformative technology of the 21st century.” - Stanford HAI

To build this list, sources were triangulated for Italy-specific patterns rather than global headlines: primary weighting came from the Rome Business School student survey (3,600 respondents, including an >800‑person Italian sub‑sample) and sector research that maps market growth, regional adoption and digital skills gaps; these informed which education roles show the strongest mix of routine tasks and high AI exposure.

Key inputs included measured student behaviour (89% of Italian university students use AI; 76% use generative tools, 64% use AI as a personal assistant), regional adoption and market figures that show rapid generative‑AI growth, plus expert roundtable input to interpret training gaps and on‑the‑ground risks.

Roles were ranked by task automation exposure, prevalence of repetitive workflows in day‑to‑day duties, and the ease of substituting those workflows with current AI tools; findings were cross‑checked against Italy's digitalisation reports and recent surveys on classroom AI use to keep recommendations practical and locally grounded.

For source details see the Rome Business School study and the Digitisation & AI report linked below.

“It's not enough to teach how to use tools; we must develop a mindset capable of adapting to change, integrate cross-disciplinary skills, and enhance critical thinking. The future of education will not only lie in the transmission of knowledge but in the ability to train individuals who can ethically and consciously navigate technological transformations.” - Emanuele Cacciatore

Registrars and enrollment officers in Italy are on the frontline of automation because so many of their duties - application capture, transcript evaluation, enrollment triage and student records management - map neatly to workflow automation and content services that already exist for education; platforms such as education workflow automation software can shorten time‑to‑enrollment and give staff a single, searchable view of student records, while Hyland content services for education highlights automated transcript capture and routing that can cut decision windows from weeks to days.

Italy's institutions should pair digital forms and secure document management with GDPR‑aware controls - precisely the promise of modern systems like DocuWare document management software for higher education - so routine bottlenecks become supervised, auditable workflows that free experienced staff for the exceptions and personal advising that AI can't replace.

“If we are not future-ready as educators, we can't expect the same of our students. Laserfiche has supported us in being future-ready.” - Dr. Duana Kindle

Graders and assessment markers in Italy should expect their routines to change fast: automated assessment tools already excel at objective, structured tasks (multiple‑choice, short answers and unit‑tested code), while AI‑assisted systems and LLMs are increasingly used to scale feedback and manage large cohorts, speeding turnaround and grouping similar responses for batch marking - think trading an overnight pile of essays for a “digital red pen” that highlights common issues at once.

Research and campus pilots show the best practice is a human‑in‑the‑loop approach - use platforms like Gradescope to auto‑grade and form answer groups for review, supplement LLM feedback on essays, then have instructors refine and contextualize results to guard nuance and creativity.

But this efficiency comes with caveats: AI can introduce bias, “hallucinate” details, and operate opaquely, so institutions must audit outputs, disclose AI use, and protect student data when integrating AI into assessment workflows.

For a practical overview see Ohio State's review of auto‑grading and MIT Sloan's guidance on balancing promise and pitfalls of AI‑assisted grading.

“It (AI) has the potential to improve speed, consistency, and detail in feedback for educators grading students' assignments.” – Rohim Mohammed, Lecturer at University College Birmingham

Course content writers and instructional designers - especially those designing IT and programming tracks for Italian universities and bootcamps - are already seeing AI take over routine drafting: from syllabi, rubrics and scaffolded lesson plans to sample code and formative quizzes that can be auto‑generated and iterated in minutes.

Seventy‑five percent of faculty in recent surveys report using AI to create teaching materials, so the competitive edge for designers will be in shaping learning outcomes, discipline‑specific examples and secure, GDPR‑aware workflows rather than writing every lesson from scratch (see Ithaka S+R's analysis).

Best practice is to treat AI as a first‑draft engine: use tools to speed content creation but follow ACUE's playbook - set clear learning objectives, pick the right tool for the task, and build student activities that require critical evaluation of AI outputs - while auditing for bias, accuracy and data privacy.

The payoff is concrete: less time on boilerplate and more time testing real classroom scenarios, personalised pathways, and the pedagogical choices only humans can make.

Ithaka S+R report on making AI generative for higher education • ACUE best practices for AI assignments in higher education

“AI can undoubtedly enhance efficiency and accuracy, yet it lacks the essence of human intelligence and personal interaction.”

Teaching assistants and routine tutors in Italy's IT courses are already being reshaped by AI: studies show AI tutors can substantially boost learning (one Harvard‑led study in physics reported students learned more than twice as much) and campus projects like the University of Michigan's Maizey chatbot have helped programming students progress through distributed‑systems projects by fielding the frequent, conceptual questions that otherwise clog office hours; see the EdTech overview of AI‑powered teaching assistants.

AI tools also strengthen human tutors - Stanford's Tutor CoPilot trial raised mastery rates and proved especially effective for novice tutors - so the practical model for Italian bootcamps and university labs is human‑in‑the‑loop support that lets bots handle routine diagnostics and hints while humans focus on deep debugging, design critique and mentoring.

But promise comes with clear guardrails: hallucinations, bias and privacy risks mean pilots must limit datasets, audit outputs, and follow GDPR‑aware designs (for example, a GDPR‑compliant class performance dashboard for risk‑flagging), plus train tutors to interpret and challenge AI suggestions rather than accept them wholesale.

Think of an AI TA as a 24/7 lab partner that points out where code fails - but not the professor who draws the architectural map.

“I would love it if the bot could answer the easy questions, and then they could come to me with the hard ones.” - Andrew DeOrio

Library and information services staff and instructional technologists in Italy's IT departments are at the sharp end of a RAG-driven change: instead of only curating catalogs and PDFs, they will be asked to build, vet and maintain the knowledge‑bases that feed retrieval‑augmented generation systems so answers are current, auditable and GDPR‑safe.

That means practical skills - chunking documents, tagging rich metadata, choosing embeddings and vector stores, and running re‑ranking and provenance checks - are now as important as classification rules; practitioners should prioritise data quality and schema consistency to avoid retrieval noise (see Best practices for structuring retrieval-augmented generation (RAG) datasets).

Infrastructure matters too: high‑throughput storage and low‑latency access speed up retrieval for large corpora (WEKA RAG guide: retrieval-augmented generation performance and caching) , while architectural patterns let sensitive records stay local to meet privacy rules (Confluent RAG overview: designs to minimise hallucinations and support local data controls).

The payoff is concrete and memorable - think of a digital librarian that can fetch the exact paragraph from a 200‑page manual to fix a student's code in seconds - so libraries should reposition as guardians of curated, labeled knowledge and train on RAG toolchains, vector search, and GDPR‑aware pipelines to keep IT students and faculty working reliably and compliantly.

Across administrative, teaching and library teams in Italy's IT departments, the practical route to resilience is a shared, role‑specific playbook: adopt an AI literacy framework that maps functional, ethical and pedagogical skills across tiers, run short, hands‑on microlearning and job‑embedded coaching so staff learn by doing, and formalise policy through a stakeholder committee - start with TeachAI's AI Guidance for Schools toolkit to draft GDPR‑aware guardrails.

Training should be tactical: use SchoolAI's quick‑start blueprint to launch week‑long pilots that let registrars, graders and TAs test human‑in‑the‑loop workflows (bots triage routine queries; humans handle exceptions), then scale what measurably saves time.

Libraries and instructional technologists must add RAG maintenance, metadata and vector‑store hygiene to their skillset so knowledge bases stay auditable and local; create dedicated roles such as AI coaches/mentors who run faculty cohorts, learning‑data stewards who manage provenance and privacy, and RAG engineers who keep retrieval precise - imagine a digital librarian pulling the exact paragraph from a 200‑page manual to fix a student's broken code in seconds.

Prioritise small, GDPR‑compliant pilots with clear ROI, embed peer sharing and PLCs for rapid iteration, and tie each new role to concrete outcomes (reduced turnaround, fewer manual exceptions) so staff see that upskilling leads to better work, not fewer jobs.

Italy's path to keeping IT‑focused education staff employable is pragmatic: national policy and pockets of funding now make real upskilling possible rather than optional, so registrars, TAs, graders and library technologists should treat training as an immediate survival skill rather than a distant luxury.

The recent New Skills Fund top‑up (EUR 318.8M) expands employer‑led upskilling through ITS, universities and accredited providers and aims to involve over a million workers, creating clear channels for businesses and schools to reimburse staff training - see the Cedefop summary of Italy New Skills Fund FNC boost for details.

At the same time, Italy's 2024–2026 AI Strategy stresses training, public‑private labs and pilots that already include AI in classrooms, signalling long‑term demand for AI literacy across education roles; read the Italy AI Strategy 2024–2026 overview.

For hands‑on, job‑focused reskilling, short practical courses matter: a 15‑week program like Nucamp's AI Essentials for Work teaches promptcraft and workplace AI skills in concrete modules and can be funded or tested via employer training hours and national schemes - see Nucamp AI Essentials for Work - course details & registration.

Think of it as swapping months of anxious uncertainty for a 15‑week credential and a playbook to make AI a productivity booster - not a replacement.