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

AI is already reshaping Australian education jobs by changing tasks more than whole occupations, so administrators, librarians and teaching assistants face faster task automation even as new opportunities emerge; PwC's AI Jobs Barometer shows demand for AI skills and productivity gains across industries, while governments have moved quickly to govern classroom use via the Australian Framework for Generative AI in Schools (government guidance) and practical school resources from state academies.

Local reporting captures both sides: a teacher used generative AI to build a story-driven game for her granddaughter Lilly, illustrating how the tech can empower creativity while displacing routine work.

That mix - rising value for AI-savvy workers plus real task disruption - means targeted reskilling matters now; short, workplace-focused courses and bootcamps that teach prompt-writing and AI workflow skills can help education staff stay relevant as roles evolve, not vanish (PwC AI Jobs Barometer report).

“It's not jobs that are at risk of AI, it's actual tasks and skills.” - Dr. Evan Shellshear

The top-five list was built by triangulating national guidance, sector studies and real-world pilots: prioritising roles that do routine, data-heavy tasks; those flagged as highly exposed in the Jobs and Skills Australia reporting and media coverage; and where equity or digital-access gaps could worsen displacement.

In practice that meant combining the Department of Education's responsible-use lens from the Australian Framework for Generative AI in Schools (Department of Education) with economy-wide vulnerability findings (administrative, record‑keeping and entry‑level roles) reported in the Jobs & Skills coverage, and checking against classroom pilots and literacy rollouts - including trials that found students re-engaged with AI feedback 87% of the time and saw around a 47% improvement in final responses - to capture both task‑level risk and where support or reskilling will matter most.

That mix of policy, empirical exposure and pilot outcomes keeps the list focused on likely near-term change rather than speculative job loss.

“While we haven't yet seen an impact on entry-level roles in Australia, it will be important that the labour market continues to provide these valuable formative roles, which provide foundational experiences in their careers.”

School administrative officers - the office clerks who manage enrolments, records and day‑to‑day paperwork - are among the most exposed to AI because their work is routine, data‑heavy and rules‑driven, making it ideal for automation; tools that digitise forms, route approvals and summarise records can turn multi‑step enrolment workflows into one click, while AI copilot features help draft clear parent emails and consolidate HR notes for faster payroll checks (education workflow automation tools by FlowForma).

Practical, low‑risk uses already saving time include auto‑screening applications, triggering attendance alerts and building single‑document staff observations so records don't live in a dozen places - exactly the kind of step‑by‑step admin Ryan Hoxie outlines for school leaders who want to use AI to reclaim time for students (AI workflows for school administrators - Edutopia).

The big so what: when an enrolment form that once took days can be verified and routed instantly, staff get back the hours to call anxious parents or help vulnerable students - not to mention the projected industry gains from automating routine tasks highlighted in AI automation case studies and forecasts (AI automation use cases in education - Activepieces).

Library technicians and library clerks - the people who turn messy digital deposits into searchable records - are squarely in AI's sights because metadata work is repetitive, data-rich and ripe for batch processing; experiments like the Library of Congress's Exploring Computational Description show machine learning can speed up description workflows and surface titles, authors and identifiers quickly, while research on AI metadata tagging (Doc2Vec/LDA) shows promise for improving discovery but also produces odd errors that underline the risk - one prototype even tagged a conservation thesis “Keg_stand” - a vivid reminder that automation without human review can mislead users (and embarrass catalogues).

Forums and case studies also flag practical gains in large‑scale metadata clean‑ups and batch processing, balanced by worries about bias, deskilling and environmental costs.

The clear takeaway for Australian library teams: AI tools can reclaim hours from routine cataloguing and improve findability if deployed with human‑in‑the‑loop workflows, careful tuning and ethical safeguards; learnings from the LOC experiments and metadata studies offer usable templates for cautious, staff‑centred adoption in AU libraries (Library of Congress - Exploring Computational Description (AI-assisted cataloging study), Choice360 - Using AI for metadata tagging to improve resource discovery, eForum - AI Insights: Revolutionizing Metadata Management).

“Since high quality catalog records are essential to the Library of Congress and libraries around the world who use our MARC records, the results are showing us that catalogers will need to review ML/AI output prior to publishing, which we expected.”

Curriculum writers and instructional designers in Australia are standing at the practical front line of AI's push into routine content work: tools can crank out aligned learning outcomes, draft assessments and even propose adaptive pathways in minutes, but experiments show trade‑offs that matter for Australian classrooms - from USF's comparison of ChatGPT, Copilot and Gemini (different strengths in alignment, variety and speed) to wider evidence that AI can speed design yet often needs careful review to avoid shallow, one‑size‑fits‑all materials; in early childhood settings the risk is stark, where an AI might produce a neat early‑literacy activity that still fails to scaffold a multilingual four‑year‑old or recognise local curriculum standards.

That mix of efficiency and fragility means Australian teams should treat AI as a time‑saving assistant, not a shortcut: use model outputs to draft and iterate, embed human review for developmental appropriateness and equity, and map every AI‑generated module back to national standards like ACARA so learning remains locally valid and inclusive - practical steps that let designers reclaim hours for high‑impact tasks such as formative feedback, differentiation and co‑design with teachers and communities (USF study on AI in curriculum design, Teaching Strategies analysis of AI in early childhood education, ACARA curriculum connections - guide for Australian designers).

“Educators use their knowledge of each child and family to make learning experiences meaningful, accessible, and responsive to each and every child.”

Student-facing admissions and contact‑centre officers are squarely in the sights of conversational AI because routine, repeatable enquiries - course entry rules, document checklists and deadline reminders - can be handled by bots that offer instant, 24/7 responses; students expect that availability (99% in a Comm100 survey) and 95% are open to chatbot support, while some campuses report bots managing the majority of initial contacts (Comm100 higher education chatbot acceptance survey).

That efficiency can free staff for the complex, human‑centred work admissions teams do best, but it also brings real risks: Australian experts warn automated admissions could undo equity gains if systems reproduce biased data, so governance and training matter (Times Higher Education article on AI risks in university admissions and equity).

TEQSA's student guidance adds a practical guardrail: institutions must be transparent about AI use and protect academic integrity so bots complement rather than replace human judgement (TEQSA guidance on artificial intelligence for students).

The bottom line: bots can answer midnight FAQs, but a human must still steer tricky, equity‑sensitive or authenticity issues - that mix will shape which roles evolve and which skills to prioritise.

Entry-level teaching assistants and assessment clerks are already feeling the squeeze - routine marking, feedback and paperwork that once ate evenings are prime targets for Australian-built AI that promises big time savings: products like TeachersRocket advertise automated marking, personalised feedback and even automated Individual Education & Wellbeing Plans that can “reclaim 10+ hours per week” for classroom-facing work, while local platforms such as Teachers Assistant AI (Australian curriculum‑mapped templates) offer curriculum‑mapped templates and assessment generators tailored to Australian classrooms; these tools can turn stacks of scripts into rapid, consistent first-pass scoring and ready-made resources.

That efficiency comes with a clear caveat from national researchers: automated essay scoring and robo-marking need strong governance, transparency and stakeholder consultation to avoid de‑professionalisation, inequity or inappropriate use in high‑stakes contexts - the University of Sydney white paper urges national guidelines and careful roll‑out.

The practical path for Australian schools is pragmatic: deploy marking and admin automations to free time, but keep humans in the loop, invest in training and governance, and redeploy trusted staff into student-facing support where their judgement matters most.

“We know teachers are already experiencing heavy workloads and this new technology could help ease the pressure, so long as the implementation doesn't create even more work.”

For Australian education workers the next steps are pragmatic and immediate: treat AI as a tool to augment judgment, not a black box that replaces it, and invest in short, role‑focused reskilling that converts automation into reclaimed time for students and equity‑focused work.

National studies and industry analysis show both opportunity and risk - PwC's AI Jobs Barometer documents rising demand and real productivity gains for AI‑skilled workers, while Jobs & Skills Australia flags administrative and entry‑level roles as among the most exposed - so pair upskilling with clear governance, human‑in‑the‑loop checks and role redesign so released capacity funds higher‑value tasks rather than headcount cuts (PwC AI Jobs Barometer report on AI job impacts in Australia, Jobs & Skills Australia vulnerability summary of roles exposed to AI).

Practical routes include short bootcamps that teach prompt craft and everyday AI workflows - for example Nucamp's AI Essentials for Work (15 weeks: AI at Work, Writing AI Prompts, Job‑Based Practical AI Skills) - so staff can move from being exposed to AI to being the people who steer it for better student outcomes; industry modelling even suggests well‑deployed AI can free multiple hours a week for high‑impact activities.

Start with small pilots, demand transparency and equity guarantees, build internal communities of practice, and make AI literacy a payroll‑level priority so every role keeps the human strengths students need.

“Adaptability will be critical”