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

Canada is already feeling AI's ripple: Statistics Canada's experimental estimates show a large share of Canadian workers sit in jobs “highly exposed” to AI and that many higher‑educated roles - including education professionals - may be transformed or augmented rather than simply eliminated; about 31% of employees fell into high‑exposure/low‑complementarity and 29% into high‑exposure/high‑complementarity categories in 2021 (Statistics Canada experimental AI exposure estimates).

At the same time, student uptake of generative tools is real - one survey found 20% of post‑secondary students use generative AI most or all of the time (FSC report: generative AI use among post‑secondary students) - so adaptation matters: practical AI literacy and prompt‑writing skills can turn risk into advantage.

For educators and support staff in Canada, targeted upskilling (for example the AI Essentials for Work bootcamp) offers a clear path to use AI as a productivity multiplier instead of a threat, whether streamlining admin workflows or personalizing remediation for struggling learners.

“This is contrary to prior waves of technological transformation, which mainly affected less educated workers.”

Methodology - How we picked the top five education jobs focused on measurable exposure and real task‑level risk: occupations were first ranked using Statistics Canada's complementarity‑adjusted AI occupational exposure index (C‑AIOE), which maps O*NET abilities to Canada's NOC and flags jobs in the high‑exposure/low‑complementarity quadrant as relatively most vulnerable to substitution; those in the high‑exposure/high‑complementarity group were treated as likely to be transformed or augmented rather than eliminated (Statistics Canada experimental AI occupational exposure estimates).

To capture generative‑AI specific threats to routine tasks, the shortlist was cross‑checked against task‑and‑skill level automation risk from IRPP's generative‑AI analysis (OaSIS + ChatGPT prompts), which highlights clerical, data‑processing and certain writing tasks as highest‑risk - useful for distinguishing, say, administrative versus human‑centred classroom roles (IRPP Harnessing Generative AI analysis on task-level automation risk).

Selection also accounted for industry and regional concentration, enterprise size and the static nature of the indices (2016/2021 census inputs), and prioritized occupations where both the index and task scores signalled high exposure and low complementarity - a combination that, in May 2021, captured roughly 4.2 million Canadian employees and roughly three in ten workers who face the clearest substitution risk.

School administrative staff - from general office support workers to registrars and admissions officers - land squarely in the line of fire when research peels back the task-level risks: Statistics Canada's LISA analysis shows Office support occupations carry the highest concentration of workers predicted to be at high automation risk (35.7%), while the Institute for Research on Public Policy finds clerical activities (entering, transcribing and storing information) score the highest generative‑AI risk (4.29/5) and flags general office support with a 3.67 automation score (Statistics Canada report: Automation and Job Transformation in Canada, IRPP research: Harnessing Generative AI).

That matters in practice: routine tasks that once filled an admissions clerk's day - data entry, scheduling, routine letters - are the very activities most easily handled by models and workflow automation, meaning the human advantage shifts toward oversight, complex student-facing judgment and data privacy stewardship; regions and institutions that move quickly to retrain staff for those complementary skills will turn disruption into a productivity win rather than a layoffs story.

“AI's impact on work depends on a lot more than just the technology itself. Companies also need the right infrastructure, capital, legal permissions and organizational readiness. That means many jobs are only at risk if these other pieces fall into place,” study co‑author Matthias Oschinski said in a statement.

Graders and assessment markers are squarely in the crosshairs: automated essay scoring now matches human raters on agreement in multiple studies and is deployed at scale, so routine rubric checks and surface‑feature scoring are increasingly automated (human–computer agreement research on automated essay scoring); RAND's eRevise work shows AES/AWE can drive measurable gains on targeted features like number and specificity of evidence - especially when systems are designed to sit inside teachers' workflows rather than replace them (RAND eRevise automated writing evaluation research) - and large vendors already use hybrid models that route hard cases to humans for quality control (Pearson automated scoring Continuous Flow approach).

For Canadian markers the practical takeaway is clear: automate what's repeatable, keep humans on nuance, fairness and accommodations, and build hybrid processes and teacher‑training so that the “weekend pile of essays” becomes a short list of flagged exceptions requiring professional judgment rather than an endless inbox.

“We can get those scores back to the district immediately, as soon as they're done, that opens up a whole new possibility for getting these multi tiered systems of support in place…”

Curriculum and instructional‑material developers - the folks who turn standards into lesson plans, worksheets and slide decks - face both risk and opportunity as AI tools move from rough drafts to production-ready assets: generative systems can rapidly draft aligned learning objectives, module outlines and even multimedia scripts, and in some cases “create a 30‑minute training video in as few as 10 minutes” (a workflow many Canadian teams can use to scale localized content), but these gains hinge on good prompts, careful SME verification and data safeguards.

Practical playbooks from the field show common best practices: start every AI task with clear learning objectives, use AI for brainstorming and first drafts (not final authority), and refine outputs to protect accuracy and equity; see an accessible primer on integrating AI into instructional design (AI-powered instructional design practical guide for educators) and a sector snapshot of adoption and risks (AI in instructional design adoption and risks - Clarity Consultants).

For Canadian developers, pairing these approaches with local privacy and compliance practices is essential - review regional guidance on student data handling to avoid handing sensitive inputs to public models (student data privacy and compliance guidance for Canadian education).

The sharply practical takeaway: use AI to automate repetitive drafting so humans can spend time on nuance, inclusion and assessment alignment - otherwise a polished slide deck risks being fast but hollow.

“If you release accountability for yourself for the information that's presented to your learners, you also release your learners from the accountability of learning it correctly.” – Jeremy Tuttle, Director of Learning Design, Niche Academy

Tutors and classroom support roles - from one‑to‑one tutors to in‑class remediation staff - are at a crossroads in Canada: AI can scale evidence‑backed, high‑dose tutoring (defined as at least three 30‑minute sessions per week) and give every struggling learner a patient, personalized coach, but only if districts pair tools with teacher oversight, thoughtful rollout and privacy safeguards.

Research shows well‑designed human–AI hybrids (for example Tutor CoPilot's RCT gains) raise mastery and let weaker tutors reach stronger outcomes, while intelligent systems can deliver adaptive practice, instant feedback and lower per‑student costs that make high‑dosage models feasible at scale (see NORC on AI‑enhanced high‑dose tutoring and an EdWeek primer on classroom use and limits).

The practical rule for Canadian schools: treat AI tutors as amplifiers not replacements - train staff to interpret analytics, keep teachers “in the loop,” and protect student data with regionally appropriate controls (see guidance on privacy and compliance for Canadian education).

Picture a once‑overwhelmed tutor's caseload shrinking to a shortlist of nuanced cases flagged by AI - that's the efficiency boost, provided human judgment stays central.

“Teachers are crucial facilitators of learning, and any attempt to introduce AI into classrooms without teacher input is likely to fail.”

Teaching assistants and education assistants in Canada are uniquely positioned to gain from - and be reshaped by - AI: lightweight, always‑on tutors like Khan Academy's Khanmigo can take routine Q&A and practice drills off a TA's plate, giving staff time for the human work that matters most (differentiation, behaviour support, and accommodations), while bespoke campus chatbots and course‑specific assistants have shown they can answer the easy questions so humans can focus on the hard ones; see Khanmigo for a teacher‑focused assistant and practical examples in the field (Khanmigo - Khan Academy's AI teaching assistant, EdWeek: The New Teachers' Aides - AI tutors in education).

Pilots and trials suggest hybrid approaches help weaker tutors boost outcomes and let TAs triage caseloads into a short list of “human‑only” interventions, but Canadian districts must pair tools with training, clear guardrails and strict student‑data rules - see our guidance on privacy and compliance in Canada for education AI tools - otherwise the tech risks amplifying bias or hallucinations instead of learning.

The most practical path for Canadian TAs: experiment with AI to multiply reach, not to replace the irreplaceable human judgments that keep classrooms fair and safe; imagine every assistant with the equivalent of “three TAs in their pocket” to handle repetitive queries while people handle nuance and care.

“AI has really just changed how we can do our jobs.”

Canada's path forward is clear: protect students and staff while seizing the productivity gains of AI by pairing stronger governance with practical upskilling.

Start with a multi‑level governance plan that reflects the CTF/FCE's calls for equity, transparency and clear classroom rules and that plugs into federal tools like the Government of Canada's Directive and Algorithmic Impact Assessment for public systems (CTF/FCE: AI in Public Education, Responsible use of AI in government).

Use the PowerSchool breach - which affected over 80 boards and exposed data for roughly 1.5 million Toronto students - as a stark reminder: cybersecurity and procurement standards must be non‑negotiable.

Invest in teacher and staff AI literacy, co‑design procurement and pilots with educators, fund district cybersecurity upgrades, and require “human‑in‑the‑loop” safeguards so AI augments rather than replaces judgment.

At the school‑level, practical training pathways (for example, short, job‑focused programs like Nucamp's AI Essentials for Work) can rapidly equip administrative and instructional staff with prompt‑writing, tool‑selection and data‑stewardship skills so districts turn disruption into improved supports for learners (AI Essentials for Work - register).

Coordinated policy, funding for EDIA‑centred rollouts, and mandatory impact assessments will keep classrooms safe, fair and forward‑looking.