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

Brunei stands at a pivotal moment as deep learning begins to reshape classrooms, from adaptive tutoring and predictive analytics to back‑office automation that can free teachers for higher‑value coaching; explore the BytePlus report on how deep learning is transforming education in Brunei for concrete examples of personalized learning and the infrastructure, privacy, and skills challenges that come with it.

The same technologies that threaten routine lecture delivery or manual grading also create clear pathways to adapt: building workforce AI fluency, adopting ethical data practices, and reskilling through practical programs such as the AI Essentials for Work bootcamp, which teaches prompt writing and day‑to‑day AI tools for educators and administrators.

Picture a classroom where each student's lesson plan evolves like a streaming playlist - precise, timely, and human‑supervised - and the choice for Brunei's schools is whether to lead that change or be reshaped by it.

Methodology blended three complementary lenses to pinpoint the five education roles most exposed in Brunei: a task-level audit inspired by David Autor's automation framework at Stanford HAI that classifies routine, abstract and manual tasks; a cognition lens from Arvind Mehrotra's analysis distinguishing high‑cognition versus low‑cognition work (to see which classroom duties demand transferable thinking skills); and practical workforce findings about automation's tendency to replace repetitive duties while creating new roles in analytics and AI operations.

Local validation used Brunei‑focused resources - aligning these frameworks with on‑the‑ground use cases and reskilling pathways described in The Complete Guide to Using AI in the Education Industry in Brunei Darussalam - to check which tasks show up on school timetables as repetitive “clockwork” candidates for automation and which map to human strengths like empathy and creativity.

The result is a task-by-task risk score that flags routine grading, templated lesson delivery and administrative scheduling as high risk, while highlighting where targeted upskilling can shift roles toward supervision, curriculum design and AI‑augmented coaching.

“Exposure is not a very useful term,” Autor said.

Routine classroom teachers who rely on one‑size‑fits‑all, lecture‑style delivery face high exposure as Brunei schools adopt AI tools that individualize learning and automate assessment: BytePlus documents how intelligent tutoring systems, adaptive platforms and automated grading are already being trialled in the country to tailor content, give instant feedback and flag at‑risk learners (Impact of Artificial Intelligence on Education in Brunei).

When an adaptive dashboard can reshuffle each student's lesson path in real time, the value of repeating the same slide to everyone diminishes, especially where predictive analytics and NLP‑driven tutors supply targeted practice and explanations (Use cases of AI in Brunei's education).

That doesn't make teachers obsolete, but it does change the job: success will hinge on moving from content delivery to higher‑value roles - mentoring, interpreting data, designing rich classroom interactions - and on overcoming practical barriers such as the digital divide, educator training and privacy safeguards.

The “so what?” is clear: without deliberate reskilling and classroom redesign, routine lecturing risks being sidelined; with purposeful adoption teachers can reclaim time for coaching and complex, human‑centered work that AI cannot replicate.

Next on the risk list are graders and standard‑assessment markers - roles already feeling pressure as automated grading and AI‑assisted evaluation can rapidly score objective items and scale feedback across large cohorts, a capability overview detailed in Ohio State's review: Ohio State review of AI and Auto‑Grading in Higher Education (capabilities, ethics, evolving role of educators).

But efficiency comes with real harms: research and reporting show generative systems can replicate human prejudices and struggle to distinguish outstanding work from mediocre responses, producing score shifts that matter - Leon Furze's experiments found identical essays could receive wildly different marks with minor input changes, and a study covered by The 74 Million highlights racial disparities in ChatGPT scoring (see Leon Furze: Don't Use GenAI to Grade Student Work, The 74 Million: AI Shows Racial Bias When Grading Essays).

For Brunei, the practical takeaway is clear: adopt hybrid workflows where AI accelerates routine scoring and formative feedback while final judgments, moderation, transparency, consent and regular bias audits remain human‑led - otherwise a single misgraded essay could alter a student's scholarship path or placement.

Designing rubrics for AI, keeping teachers in the loop, and publishing audit results will be essential steps for fair, trusted assessment.

“The new risk of algorithmic bias is that it is more systematic than human bias.” - Stéphan Vincent‑Lancrin, OECD

Administrative staff who run enrolment desks, build timetables and handle routine parent‑teacher messages are squarely in AI's sights in Brunei because the same systems already being piloted locally can automate attendance tracking, enrolment processing, timetable scheduling and routine communications - freeing time but also reshaping jobs.

Centralised platforms such as a strategic SRMS report for post‑pandemic Brunei (Student Resource Management System - SRMS) can pull attendance, health and performance records into one dashboard and generate reports and schedules automatically, a capability highlighted as a strategic recovery and efficiency tool for post‑pandemic Brunei; meanwhile, conversational agents are already proving they can answer FAQs and provide 24/7 help to students and parents, reducing inbound email and phone bottlenecks (education chatbots for 24/7 student and parent support).

Practical vendor briefs and roadmaps for school systems show how targeted automation (from auto‑filing attendance to workflow reminders) can cut tedious work while demanding new skills: data governance, privacy safeguards and staged training for staff (vendor briefs on AI tools for school administrators and implementation roadmaps).

The

“so what?”

is stark: with the right SRMS, a once‑clogged enrolment season can become a streamlined service - and without clear policies and upskilling, those administrative roles risk being reduced to oversight of algorithms rather than the trusted human touch families still rely on.

Private and low-skill tutors who focus on drill-based, standardized content are especially vulnerable in Brunei as scalable AI tutoring moves from research into classrooms: BytePlus's survey of AI in Brunei shows government initiatives, institutional partnerships and educator training are building the foundation for wider AI use in schools, while LLM research suggests those systems can increasingly mimic one-to-one instruction (threatening commodity tutoring that depends on predictable, repeatable tasks).

MBZUAI's MRBench work and the long-standing "two sigma" finding both make the "so what?" stark - a low-cost app or conversational tutor on a phone can, in principle, deliver the sort of personalized remediation that once required a private instructor, undercutting price-sensitive local markets and changing parental expectations.

The risk is not only economic: uneven infrastructure and limited teacher training in Brunei mean quality and equity could worsen unless rollout pairs tools with oversight, bias checks and clear guidance on where human tutors add irreplaceable value (motivation, socio-emotional support, and complex problem coaching).

For tutors, the adaptive challenge is simple: move from delivering canned drills to designing bespoke learning sequences, mentoring, and supervising AI-augmented practice to stay relevant.

“With AI, we can provide everyone with a personalized tutor, something like a personal digital assistant on a phone,” Kochmar says.

Instructional content creators who churn out routine, templated curriculum materials are squarely in AI's crosshairs in Brunei: modern lesson‑planning engines can generate curriculum‑aware plans, export ready‑to‑teach slides and produce differentiated activities in minutes, turning what used to be a week of prep into a single editing session - see the TeachBetter.ai lesson-planner guide to automated lesson planning (TeachBetter.ai lesson-planner guide to automated lesson planning).

At the same time, research warns these generators often default to teacher‑centered, low‑agency designs unless prompts are carefully engineered - an important caution for Brunei schools seeking equitable, student‑centered learning - see the Social Innovations Journal analysis of pedagogical biases in AI lesson plan generators (Social Innovations Journal analysis of pedagogical biases in AI lesson plan generators).

The practical course is clear: reuse AI to speed routine drafting but embed local curriculum context, bias checks and teacher‑led revisions so materials reflect Brunei's learning goals and cultural needs; paired with national initiatives around personalized adaptive learning, this approach can free creators to move from content mills to bespoke designers who add the human judgments AI lacks - example local use cases and prompts for Brunei are collected in Nucamp's AI Essentials for Work syllabus on personalized adaptive learning (Nucamp AI Essentials for Work syllabus on personalized adaptive learning).

Move from alarm to action: Brunei's coming Personal Data Protection Order (PDPO 2025) and the new Digital Brunei Transformation Plan create a legal and strategic runway for schools to embed safe, accountable AI into everyday practice - start by mapping data flows to PDPO requirements, publishing clear consent and retention rules, and running bias audits on any assessment or tutoring models used.

Anchor procurement and deployment in Brunei's own AI Governance and Ethics Guide (and ASEAN's principles) so systems remain human‑centric and auditable, and design hybrid workflows that keep teachers making final judgments while AI accelerates routine scoring and scheduling.

Invest in shared infrastructure - student record systems and teacher dashboards - to avoid fragmented pilots, and pair rollout with cohort-based reskilling so staff can move from clerical tasks to data‑literate coaching; practical, workplace-focused training like Nucamp AI Essentials for Work bootcamp can accelerate prompt literacy and everyday tool use.

Finally, make transparency a habit: publish model inventories, testing results and governance roles so families trust AI tools and leaders can iterate safely as Brunei builds its smart‑nation vision.

For specifics on the PDPO and national plan see the coverage of Brunei's AI governance reforms and the regional governance brief.

“Therefore, effective implementation of artificial intelligence requires shared responsibility by all stakeholders.”