The Future of EdTech 2026: The Next Wave for Australian Schools

The next wave of educational technology in Australian schools is not landing as a single “must-have” platform. It is showing up as an operational change.

AI tools are being embedded into lesson planning, feedback, and communication workflows. Hybrid delivery models are being refined and standardised, rather than redesigned from scratch. Wellbeing and accessibility features are becoming baseline expectations, not premium add-ons. And privacy, ethical AI, and student data governance are moving from policy footnotes to procurement gatekeepers.

For school leaders, the question is no longer “Can we afford new technology?” It is “What are we already paying for in duplicated systems, teacher workload, and unmanaged risk?”

Four moves that will define EdTech success in Australian schools

Schools looking to get maximum value from the Edtech stack over the next four years will do four things consistently:

First, they will integrate AI adoption into education and governance, rather than treating it as a novelty or siloed side project. Australia already has a national framework to guide safe, ethical, responsible use of generative AI in schools, and several jurisdictions have published complementary policy guidance.

Second, they will build measurement into rollout from day one. Evidence shows that EdTech outcomes vary widely, and the impact depends on implementation quality, teacher practice, and the selection of the best-fit tools from the outset.

Third, they will make student data privacy and online safety a priority from the outset, not a checkbox to tick at the procurement stage. Software used in Australian schools should align with the Privacy Act, the Australian Privacy Principles, and the upcoming OAIC's Children's Online Privacy Code, which is scheduled to be in place by 10 December 2026.

Fourth, they will evaluate equitable access through the lens of student success rather than availability. Digital inclusion gaps in regional and remote Australia are real and directly relevant to any educational strategy that assumes constant connectivity, always-on analytics, or access to AI tools at home.

The next wave of EdTech is practical, not flashy

The key takeaway from recent global Edtech conversations, including the Consumer Electronics Show 2026, is that headline innovations and advanced technologies are now less important to education professionals than operational direction: the future of EdTech involves solutions that reduce friction, integrate into real classrooms, and survive the governance questions schools can no longer ignore.

The focus has shifted from flash to functionality, with a focus on practical AI for feedback and planning, smoother hybrid teaching environments, wellbeing and accessibility tools, and a stronger emphasis on privacy and ethical AI in product conversations.

Three major factors are driving this shift.

1. Curriculum and capability expectations: The Australian Curriculum, Assessment and Reporting Authority makes it explicit that general capabilities, including Digital Literacy, are developed through learning areas and are not 'extra'.

2. Regulatory guidelines: The Privacy Act, Australian Privacy Principles, and the Notifiable Data Breaches scheme create real compliance obligations. The Online Safety Act 2021 and associated regulatory mechanisms shape the broader online child safety environment in which schools operate.

3. Equity and infrastructure constraints: Digital inclusion is uneven, and remote connectivity issues remain common enough to break 'cloud-only' assumptions.

Future skills and the Australian school value proposition

If we strip EdTech down to first principles, it should help schools do two jobs better.

First, it should lift learning outcomes and learner agency. Second, it should build transferable capabilities students will need in an AI-saturated world, including critical thinking, digital literacy, data literacy, collaboration, and safe, informed use of AI tools.

The question for leaders is which technologies actually move the dial on those jobs in real classrooms, not just in product demos. The next sections look at key categories where schools are planning investment, and what each of them changes in practice.

The technologies schools should plan for and what they change in practice

Personalised learning and AI tutoring

'Personalised learning' is often oversold. Done poorly, it leads to high resource demand, increased teacher workload, and social isolation for students. Passive screen time, less collaboration, and a lack of deep learning experiences are further challenges. Done well, personalised learning tailors instruction, pacing, and learning environments to each student’s unique needs, skills, and interests, freeing up teachers to focus on fostering engagement, critical thinking, and higher-order skills.

Evidence shows that the most effective solutions are intelligent tutoring systems and assistive technologies used within STEM, where feedback loops are tight, and mastery can be measured.

A meta-analysis of intelligent tutoring systems found a median improvement of 0.66 standard deviations over conventional instruction, with results shaped by test alignment and implementation quality.

A helpful question for schools to ask themselves when evaluating these tools is: 'Can we define and measure the impact they have on learning outcomes?'

Practical recommendations for leaders:

• Require any adaptive or AI tutoring rollout to declare the learning target, the evidence standard, and the assessment method used before purchase.

• Ensure any AI-generated feedback is teacher-supervised by default, particularly for younger learners in primary schools and students with diverse learning needs.

• Pilot any new technology with a defined cohort and use outcome-based assessment rather than satisfaction surveys as your main outcome signal.

Learning analytics for data-based decision making

Learning analytics are transforming how educators track performance and tailor their teaching approaches. They provide insight into engagement patterns, help to identify students who are falling behind, and enable early interventions. However, as with most analytics, they are only as good as the data quality, teacher interpretation, and the ethics of how data is measured and stored.

K-12 learning analytics is still an emerging field. Adoption is highest in high schools and is motivated by the desire to predict improved outcomes, understand learning processes, and support teachers.

Academic reviews of the use of learning analytics to guide mathematics instruction in K-12 settings show largely positive effects on student learning. However, these gains tend to be strongest among already high-achieving students, raising questions about equity, differentiation, and how these tools can better support learners who are struggling or have diverse needs.

The use of learning analytics also raises governance questions for schools. The OAIC notes that modern data analytics can generate new personal information and encourage longer data retention, and provides specific guidance on how the Australian Privacy Principles apply to data analytics. School leaders need to be clear about what data is being collected, how long it will be kept, and who can access it.

Practical recommendations for leaders:

• Start with a small data set aligned with student support outcomes such as attendance, participation signals, formative assessment checkpoints, and teacher observations.

• Separate dashboards for learning from those for compliance and well-being, so the tools do not inadvertently create a surveillance culture.

• For analytics platforms, require clarity on where data is stored, how long it is retained, and why it is necessary for student learning or support. 

AI-powered tools for teaching, assessment, and productivity

AI is becoming increasingly embedded in the tools schools already use. Learning management systems now include automated marking suggestions and engagement alerts. Writing platforms offer grammar correction, tone refinement, and generative drafting support. Adaptive maths programs adjust question difficulty in real time. Coding environments provide AI copilots that suggest syntax and troubleshoot errors. Even email and productivity suites now summarise threads, draft communications, and generate lesson materials. In many cases, these features are enabled by default, meaning schools are no longer deciding whether to adopt AI, but how to manage tools that already contain it.

Australian legislation (at both state and federal levels) is clear about how AI should be addressed in education. Schools should not pretend generative AI does not exist; they must govern its use.

The national Australian framework for generative AI in schools contains principles and guiding statements for safe, ethical, and responsible use. Several states have also created additional guidelines.

• New South Wales: The NSW Education Standards Authority (NESA) states that the unapproved use of AI in completing tasks is a breach of academic integrity. NESA advises schools to teach students how to acknowledge all materials appropriately, including AI, in line with NESA’s published guidelines

• Queensland: The Queensland Curriculum and Assessment Authority has published formal AI guidance. From 2026, students graduating with a QCE will be required to complete an academic integrity course, directly responding to advances in generative AI tools.

• South Australia: The SACE Board of South Australia has reinforced expectations that student work must be their own, with sources acknowledged, even in the presence of generative AI tools.

AI use in academia is no longer theoretical. Assessment frameworks are adapting in real time, and schools are expected to align their teaching practices, policies, and student guidance accordingly.

Practical recommendations for leaders:

• Redesign assessment, not just detection: Higher education is already reworking tasks so that learning processes are visible and verifiable, rather than relying solely on AI policing and K-12 schools are following suit.

• Define what AI use is allowed by the task: Be explicit. Some tasks may be no-AI. Others may allow AI-assisted drafting. Some may fully integrate AI tools. Clarify expectations and teach students how to disclose and reference AI use appropriately.

• Make staff training mandatory for approved GenAI tools: If a tool is sanctioned, the capability needs to follow. Provide structured professional learning so teachers understand functionality, risks, data implications, and classroom application. Across Australian guidance, one theme is consistent: governance and capability determine outcomes, not the tool itself.

Immersive learning via VR, AR, and virtual labs

Immersive learning tools can be extraordinarily powerful, especially when used for experiences that are otherwise hard to replicate: think hazardous experiments, scaled science phenomena, remote geography, and authentic rehearsal of complex procedures.

Evidence suggests that Gamification and immersive learning with VR and AR enhance educational experiences.

Virtual laboratories also demonstrate consistent benefits. Research reports a medium positive effect size for virtual laboratory activities on student achievement, particularly in science contexts.

For Australian schools, immersive tools succeed or fail based on infrastructure, equity, and instructional design, not on the technology's novelty.

• Connectivity and device capability are non-negotiable: Immersive tools rely on strong internet and capable hardware. If either struggles, the lesson quickly falls apart. To protect equity, high-bandwidth experiences are often best kept classroom-based, where all students can access the same infrastructure.

• Accessibility must be verified, not assumed: Claims about differentiated learning and wellbeing support should be tested in your environment. Check that accessibility features work as intended and that students are not pushed into separate or lower-quality experiences. Inclusion should be built into the core platform, not added as an afterthought.

• Use immersive experiences deliberately and sparingly: Research suggests that shorter, well-structured sessions often produce stronger learning gains than extended use. Immersion works best when tightly aligned to clear learning objectives, with strong scaffolding and follow-up, rather than as a standalone novelty.

Social learning platforms and flexible learning models

Social learning platforms have been the subject of much hype and high adoption, particularly in the past two years.  Studies show that these tools can increase learning engagement by up to 30% and knowledge retention by up to 60%. For learning, there are clear benefits. However, schools may have overlooked that these tools are not simply ‘learning’ platforms. Yes, they are used for learning, but in reality, they are also online social environments that shape identity and behaviour, and like other social platforms, they come with safety risks.

Governance of these tools shouldn't focus solely on student-to-computer interaction; it should also account for student-to-student dynamics, peer visibility, digital reputation, and how online behaviour carries over into the classroom.

Unlike classroom conversations, digital interactions are persistent. A comment posted in frustration can remain visible long after the lesson ends, and can be shared beyond its original audience.

Australian schools do not operate in a policy vacuum. The Australian Curriculum embeds online safety across rights and responsibilities, respectful relationships, consent, wellbeing, media literacy, and the safe operation of digital tools. When schools adopt social learning platforms, they are not just selecting instructional software. They are creating a new digital social space that must align with, and be governed by, these principles.

Hybrid and flexible learning models are now often built into curriculum design, with CES commentary highlighting how hybrid classroom setups have matured since the pandemic. While this can help reduce technical friction and improve hybrid teaching, it adds a layer of complexity to ensuring equitable access.

Practical recommendations for leaders:

• Treat collaboration tooling as part of your online safety program. Ensure students understand help-seeking pathways and create a clear incident response framework.

• Define “when learning happens” models (in-class, at-home, asynchronous) and ensure the tech stack supports students who cannot reliably access home internet or devices.

Measuring whether your EdTech is actually working

One way to keep EdTech decisions honest is to decide up front how you will measure impact. The table below outlines practical KPIs schools can use to track whether new tools are improving learning, equity, workload, safety, and security, rather than just adding complexity.

Online Safety and the duty of care in the Edtech stack

Online safety obligations are no longer covered by blu-tacking a poster on the computer lab (as helpful as this may be). Online safety is now a systems design and deployment issue that spans devices, platforms, policies, and response frameworks.

When schools adopt new EdTech, they are not just buying functionality; they are introducing new data flows, new behavioural environments, and new cyber safety implications. The question is not just whether the platform is educational and equitable, but also whether it can be governed safely.

Before approving a new educational technology solution, leaders might ask themselves the following questions.

Safety controls and student interaction

• Does the platform allow student-to-student messaging, commenting, or file sharing?

• Are moderation controls configurable by the school?

• Are there audit logs that show who posted what and when?

Data handling and privacy

• What student data is collected by default?

• Where is it stored and for how long?

• Is data shared with third parties, including for model training?

• Can the school delete or de-identify data on request?

Governance and exit

• Who controls configuration settings: the school or the vendor?

• Can data be exported in a usable format if the contract ends?

Network Monitoring: Turning online safety policy into operational reality

Australian schools are under increasing pressure to ensure student safety online while also keeping up with the future of edtech. While AUPs and other policy frameworks are essential, they are not protective in themselves; they cannot keep students safe on their own. A document stored in a shared drive does not provide visibility, identify risk, or trigger timely intervention.

If a school introduces AI tools, social platforms, hybrid environments, and cloud-based learning systems, it must also ensure it has visibility across all of them. Otherwise, the governance work described earlier, including AI use policies, academic integrity expectations, privacy compliance under the Australian Privacy Principles, and online safety commitments aligned to curriculum and duty of care, remains theoretical. Without structured visibility and documented response, those obligations cannot be measured, enforced, or evidenced.

In Australia, there is no single statute that mandates a specific monitoring product. Instead, expectations flow from duty of care obligations, state education policies, privacy legislation, and child safety frameworks. The consistent legal test is whether a school has taken reasonable steps to protect students and can demonstrate that it did so. This makes visibility the essential foundation for accountable EdTech governance.

If your school cannot see patterns of concerning online behaviour, you will miss early warning signs. If you cannot generate timely alerts, your wellbeing and pastoral care teams are left responding after harm has already escalated. If you cannot produce logs or documented actions, you may struggle to demonstrate that you took reasonable steps to filter, supervise, and protect students in line with your duty of care.

In practical terms, effective monitoring in an Australian school context means:

• Structured visibility into student internet activity at the network level

• Clear alerting for high-risk behaviour aligned to wellbeing and academic integrity priorities

• Defined escalation pathways between IT, leadership, and wellbeing staff

• Audit logs that show what was identified, when, and how it was handled

This is not about blanket surveillance or expanding unnecessary data collection. It is about using the data schools already generate through their networks and platforms to create accountable oversight.