AI or Human? A Practical Decision Framework Schools Can Use to Choose Tutoring Providers

School leaders are no longer choosing between “tutoring” and “no tutoring.” The real decision in 2026 is how to allocate limited intervention budget across online tutoring platforms for UK schools, AI-powered tutors, and human-led provision in a way that is scalable, safe, curriculum-aligned, and demonstrably effective. That choice matters because tutoring is now under sharper scrutiny than ever: governors want value for money, senior leaders want measurable outcomes, and safeguarding teams want confidence that every pupil interaction is properly managed. The best procurement decisions are no longer made by brand familiarity or price per hour alone; they are made by comparing models through a clear framework that weighs impact, fit, risk, and equity.

This guide is designed as a decision framework for school leaders, trust executives, MAT procurement teams, and assessment and intervention leads. It will help you compare AI tutoring and human tutors across the factors that matter most in schools: curriculum alignment, safeguarding, dosage, affordability, pupil experience, and the ability to scale without compromising quality. If you are already comparing providers like Third Space Learning, MyTutor, Fleet Tutors, Spires, Tutorful or similar platforms, the right question is not “which is best in general?” but “which is best for this subject, this cohort, this timetable, and this outcome target?”

One reason this choice is so consequential is that online tuition has become the default delivery model in many contexts. The source material notes that 88% of in-school tutoring is now delivered online, which makes platform selection a strategic operational decision rather than an occasional commissioning task. At the same time, the market has diversified: schools can now buy AI-led one-to-one maths support such as Skye, or contract DBS-checked human tutors across dozens or hundreds of subjects. As procurement options multiply, the safest way to avoid waste is to apply a disciplined rubric rather than rely on marketing claims.

1) Start With the School Problem You’re Trying to Solve

Define the academic goal before comparing providers

The first mistake schools make is treating tutoring procurement as a vendor search instead of a problem-definition exercise. Before comparing AI tutoring and human tutors, define the actual intervention need: is the aim to raise maths attainment in Year 6, to provide GCSE exam prep, to close gaps after absence, or to support stretch and challenge for high prior attainers? Different goals demand different delivery models, and a provider that excels at rapid, low-cost maths practice may not be ideal for nuanced essay feedback or multi-subject catch-up. This is where a framework outperforms a shortlist: it forces alignment between the problem and the solution.

A useful approach is to classify the intervention into one of four buckets: high-volume foundational practice, exam preparation, subject-specific mastery, or pastoral-confidence rebuilding. AI tutoring tends to be strongest where the learning objective is narrow, repeatable, and easy to diagnose, such as fluency practice in maths or structured retrieval. Human tutors tend to be strongest where the task requires live explanation, emotional reassurance, or adaptable dialogue across misconceptions. For leaders planning wider provision, pairing the right model with the right intervention can reduce overdelivery costs and improve uptake.

Think in cohorts, not just individuals

Procurement decisions often fail when schools assume all learners need the same kind of support. In reality, one cohort may need intensive literacy intervention, another may need maths catch-up, and a third may need support for A-level sciences or SEN-friendly scaffolding. The best school leaders segment demand by year group, subject, baseline attainment, and attendance patterns, then match the tutoring model to each segment. This is how you preserve equity while still making spending decisions that are efficient and measurable.

That cohort-based mindset also helps with budgeting. Schools working under strict intervention envelopes often need an answer to the cost vs impact question, not just the headline price. A lower-cost AI tutor may enable broader coverage, while a higher-cost human tutor may justify itself in higher-stakes or more complex cases. If you want a deeper lens on procurement trade-offs, our guide to total cost of ownership is a useful reminder that sticker price rarely tells the full story.

Separate “support,” “practice,” and “instruction”

Another practical distinction is between support, practice, and instruction. Support means helping a pupil stay on task and feel guided; practice means repeated retrieval and structured exercises; instruction means explaining new material and adapting in real time to misconceptions. AI tutoring usually shines in support and practice. Human tutors are usually better for instruction, especially when the learner’s barriers are complex or confidence is low. The more explicitly you define which of those three jobs your tutoring should do, the easier your selection process becomes.

Schools that miss this distinction often overpay for human hours where a cheaper platform would suffice, or underbuy human expertise where deeper teaching is needed. A balanced model can be highly effective: AI-led practice for routine reinforcement, human-led sessions for targeted gaps, and teacher oversight for curriculum coherence. That blended logic is already visible in how some school leaders think about designing learning paths with AI and sequencing support around real workload constraints.

2) Compare AI and Human Tutoring on the Four Procurement Questions

Can it scale without diluting quality?

Scalability is one of the main reasons schools explore AI tutoring in the first place. A fixed-cost AI model can support large numbers of pupils without requiring the school to recruit, schedule, and quality-assure dozens of individual tutors. For multi-academy trusts, this can solve a recurring pain point: how to deliver tutoring consistently across sites with different timetables and staffing pressures. The source material’s example of Third Space Learning’s AI maths tutor, Skye, is instructive here because it offers unlimited one-to-one maths tutoring at a fixed annual price, starting from £3,500 per school per year.

Human tutoring can also scale, but usually through a different mechanism. It scales via supplier networks, scheduling systems, and tutor pools, which can work well in broad subject provision but may be more vulnerable to matching delays, tutor turnover, and variable delivery quality. This is why providers such as MyTutor, Fleet Tutors, and Spires emphasise vetting, matching, and reporting as part of the service. In other words, scalability in human tutoring is often operationally expensive, while scalability in AI tutoring is computationally cheap but pedagogically narrower.

Will it show measurable impact?

Impact is the second procurement question, and it should never be reduced to testimonials alone. Schools need evidence of learning gains through baseline data, progress tracking, attendance to sessions, completion rates, and where possible, internal assessment improvement. Human tutors can provide richer qualitative insight, particularly when they feed back on misconceptions, confidence, and learner behaviour. AI tutoring can provide highly consistent practice logs and near-real-time data, which can be valuable for leaders who need fast reporting and large-scale visibility.

The best decision framework asks not just whether a provider can improve attainment, but whether it can prove it in your context. If a platform can demonstrate usage but not learning transfer, that is not enough. Likewise, if a tutor can produce excellent one-off explanations but there is no monitoring data, schools may struggle to justify recurring spend. For leaders building a measurement system, the article on metrics that matter for scaled AI deployments offers a useful mindset: define leading indicators, lagging indicators, and decision thresholds before rollout begins.

Is the curriculum alignment strong enough?

Curriculum alignment is where many AI tutoring tools succeed or fail. A system that is not aligned to the school’s scheme of work can create fragmented learning, especially in maths where sequence matters. Human tutors can adapt more flexibly in the moment, but alignment still depends on the tutor’s familiarity with the curriculum, exam board, and intended progression. The safest procurement decision is one that explicitly asks how the provider maps to school content, not whether it claims to support “all learners.”

This is especially important for schools using tutoring as part of intervention catch-up rather than enrichment. A session that feels productive but does not reinforce the taught curriculum can lead to superficial confidence without durable attainment gains. That is why many leaders now inspect whether a provider can work from school-set materials, align to year-group objectives, or track against exam specifications. If you are thinking about the broader data and system questions behind AI adoption, our guide to choosing AI compute is a reminder that infrastructure decisions and educational outcomes are increasingly connected.

Does it meet safeguarding and equity expectations?

Safeguarding is not a checkbox; it is a product requirement. Schools need clear answers on identity verification, session monitoring, message controls, escalation routes, and data protection. Human tutoring providers typically highlight DBS checks, school DSL liaison, and tutor verification. AI tutoring platforms must show how they control inappropriate outputs, limit risky interactions, log usage, and protect pupil data. If a platform cannot describe these controls plainly, it should not be shortlisted.

Equity matters just as much. A tutoring model that only works well for pupils with quiet study space, reliable broadband, or strong self-regulation may increase gaps rather than close them. In schools where home access is uneven, in-school delivery with staff supervision may be preferable, even if the platform is technically online. For a broader lens on fairness and risk, see our related analysis of guardrails for AI tutors, which explains how to prevent over-reliance while building metacognition.

3) A Practical Decision Matrix for School Leaders

Use a weighted scorecard, not a vibes-based shortlist

A simple procurement scorecard helps avoid subjective decision-making. Assign weights to the factors that matter most to your school, then score each provider from 1 to 5. For many schools, safeguarding and curriculum alignment should carry more weight than headline price, because a cheap provider that is unsafe or misaligned is expensive in the long run. Likewise, if a trust needs rapid mass deployment, scalability may outweigh some of the personalised features associated with human tutoring.

This scorecard should be customised to your setting. A small primary school might prioritise cost and curriculum fit for maths catch-up, while a large secondary trust might prioritise scale, reporting, and timetable flexibility. The point is not to force every provider into the same box; it is to make trade-offs visible. Once they are visible, they are easier to govern.

Run a three-tier pilot before full adoption

Instead of committing a whole budget immediately, run a controlled pilot with three tiers: a high-need cohort, a mid-attainment cohort, and a control or business-as-usual comparison group where feasible. This lets you test whether the platform improves session completion, engagement, and assessment outcomes in your context. It also gives your safeguarding and SEND teams a chance to observe how the platform behaves with real users. A pilot is not just a technical trial; it is an operational rehearsal.

During the pilot, collect both quantitative and qualitative data. Quantitative data should include attendance, usage frequency, time-on-task, and attainment movement. Qualitative data should capture teacher observations, pupil confidence, and any access barriers. If you are looking for a useful procurement analogy, the discipline described in ROI modelling and scenario analysis can be borrowed here: compare best case, expected case, and downside case before scaling.

Set decision thresholds in advance

The most effective school procurement teams define “success” before the contract starts. For example, you may decide that a platform must achieve 80% weekly attendance, measurable improvement in unit tests within 8-10 weeks, and positive teacher usability feedback. If the provider misses those thresholds, you stop, adapt, or renegotiate. This prevents sunk-cost bias from keeping weak interventions alive longer than necessary.

Pro Tip: Don’t evaluate tutoring platforms only at renewal time. Put a termly review date in the contract and require a simple evidence pack: usage data, attainment snapshots, safeguarding incidents, and staff feedback.

This approach is especially valuable for schools managing multiple interventions at once. You can compare providers using one framework, rather than different anecdotes from different departments. To make this even more rigorous, align the pilot to your school improvement plan and use the same logic you might apply when evaluating business outcomes for scaled AI deployments: determine whether the intervention is merely active or actually effective.

4) How Safeguarding Changes the AI vs Human Decision

Why “safer by design” matters more than marketing language

Safeguarding is often described in broad terms, but leaders need operational detail. For human tutors, that means enhanced DBS checks, identity verification, professional boundaries, and a documented route for concerns. For AI tutors, it means content moderation, age-appropriate responses, restricted tools, session logging, and data governance. Both models can be safe when properly designed; both can also be risky when governance is weak. The difference is that AI risk is often invisible until something goes wrong, while human risk is more familiar but not automatically lower.

That is why school leaders should ask to see concrete safeguarding artefacts: incident logs, escalation procedures, staff training materials, and a named contact for DSL concerns. If the provider offers school-facing monitoring tools, ask whether they allow live oversight or only retrospective review. For schools exploring online tutoring platforms, the source guide’s emphasis on enhanced DBS checks, school DSL liaison, and clear progress reporting is the right benchmark to apply across the board. You should expect no less from any provider you commission.

Data privacy and auditability are part of safeguarding

For AI tutoring, safeguarding cannot be separated from data governance. If the system stores pupil conversations, produces adaptive profiles, or uses analytics to change content, leaders need to know where that data lives, who can access it, and how long it is retained. Audit trails matter because they allow schools to reconstruct what happened if a concern arises. Human tutoring platforms also need strong records, particularly around identity, session times, and communication with pupils or families.

In practice, the safest procurement decisions favor providers that can show chain-of-custody thinking: what was delivered, when, by whom, and with what content safeguards. That mindset is well explained in our guide to audit trail essentials, which is equally relevant in education settings where pupils’ work and interactions may be reviewed later. If the provider cannot explain logging and retention in plain English, that is a red flag.

Safeguarding should shape delivery model, not just supplier choice

Sometimes the right answer is not one platform over another, but one setting over another. A human tutor delivered in-school under staff supervision may be more appropriate for vulnerable pupils than unsupervised home access, even if the latter is cheaper. Conversely, a tightly controlled AI platform may provide better consistency for large numbers of pupils than a loosely supervised marketplace of tutors. Schools should therefore treat safeguarding as a design variable in the deployment model, not only as a contract clause.

This is particularly important for schools supporting pupils with SEND, attendance concerns, or social-emotional fragility. For some learners, the predictability of an AI interaction can reduce anxiety and increase completion. For others, the relational quality of a human tutor is essential. There is no universal winner here; there is only a better fit for the learner and the risk profile.

5) Cost vs Impact: How to Judge Real Value for Money

Don’t compare hourly rates in isolation

Cost comparisons are often misleading because they ignore dosage, utilisation, and outcomes. A human tutor might cost more per hour but achieve better gains in fewer sessions, while an AI tutor might be cheaper per learner but require more time to reach mastery. What matters is not the headline rate but the cost per successful learner outcome. Schools should calculate cost against attainment movement, not just session delivery.

For example, a platform offering unlimited AI maths tutoring at a fixed annual cost may be excellent value if your school can deploy it widely and keep usage high. But if usage is low, the fixed fee can become inefficient. Conversely, a human-led platform may appear expensive until you account for targeted support that lifts exam readiness in a short burst. Procurement teams should therefore estimate both cost per pupil served and cost per pupil making expected progress.

Estimate opportunity cost as well as budget cost

Every tutoring pound spent on one model is a pound not spent elsewhere. That means the procurement question includes opportunity cost: what else could the school do with that money, and what would be foregone? If AI tutoring enables coverage of a whole cohort, it may outperform a handful of high-intensity human sessions in system-level impact. If a human tutor helps a borderline GCSE pupil pass a key subject, the attainment and life chances upside may justify the premium.

To think clearly about this, it helps to review how other sectors handle trade-offs between fixed costs and variable outcomes. Our analysis of total cost of ownership shows why maintenance, support, and replacement cycles matter as much as the initial price. The same logic applies to tutoring: onboarding time, staff workload, monitoring burden, and renewal friction all belong in the cost model.

Cost should be linked to dosage and frequency

One reason tutoring outcomes vary so widely is dosage. A model that is affordable but underused will not move attainment. A model that is more expensive but delivered regularly with strong attendance may produce far better results. Schools should therefore price tutoring by expected dose, not by list price alone. This is especially important in intervention planning, where inconsistent attendance can quietly sabotage even strong provision.

As a rule of thumb, AI tutoring is often more suitable when the goal is frequent practice at scale, while human tutoring is often better when the goal is concentrated support in a limited window. Neither model wins on cost alone. The better question is which model gives your cohort the highest probability of gaining the learning that matters most.

6) Curriculum Alignment: The Hidden Variable That Decides Success

Why alignment beats generic personalization

Many providers advertise personalization, but not all personalization is educationally useful. A system that adapts to a pupil’s responses but drifts away from your curriculum can create mismatched learning experiences. Schools need tutoring that reinforces what teachers are teaching, when they are teaching it, in the sequence that supports retention and transfer. That is why curriculum alignment should be treated as a top-tier criterion in procurement.

Human tutors can tailor explanations and pacing naturally, but schools still need assurance that tutors understand the curriculum, assessment objectives, and common misconceptions. AI tutors can be highly consistent, but only if the underlying content model reflects the right pedagogy and curriculum structure. In maths especially, weak sequencing can produce false confidence. If you are comparing platform options, review the subject-by-subject architecture carefully rather than assuming all “maths tutoring” is equivalent.

Ask for mapping, not just claims

When evaluating providers, request curriculum maps, sample lesson flows, and example progress reports aligned to your key objectives. Ask how the provider handles mixed-age classes, foundation versus higher tiers, and exam board specificity. If the provider cannot show a transparent mapping, then alignment is probably an aspiration rather than a feature. Schools should not buy aspirations when they need delivery.

It can be helpful to compare this to how content systems are built in other sectors: a coherent workflow needs integration before optimization. That principle is clearly articulated in our guide to building a seamless content workflow, and the same logic applies to tutoring programs. First integrate the provider with the curriculum. Only then optimize for scale or automation.

Alignment supports teacher trust

One of the fastest ways to kill a tutoring rollout is to make classroom teachers feel the intervention is disconnected from teaching. If teachers do not trust the provider, they will not refer the right pupils, reinforce the right content, or use the data effectively. The procurement process should therefore include teacher feedback on sample materials, reporting format, and alignment with current schemes of work. When teachers see tutoring as part of the instructional system, implementation quality rises.

This is another reason human tutors are not automatically superior. A well-designed AI tutoring platform that gives teachers clear visibility into what pupils are practising can be easier to align than a loosely managed tutor pool. The final decision should come down to how well the platform connects with the school’s teaching sequence, not whether it is fashionable or familiar.

7) Equity and Access: Choosing a Provider That Works for All Pupils

Not every learner benefits equally from the same format

Equity is often discussed as if it means “free access,” but in schools it also means suitable access. Some pupils thrive with independent online practice; others need adult mediation, structured routines, or high emotional support. AI tutoring can be a powerful equaliser when it provides consistent scaffolding to large groups, but it can also widen gaps if pupils lack digital confidence or reliable devices. Human tutoring can provide the relational support some pupils need, but it may be harder to distribute fairly across all learners who need help.

School leaders should ask which pupils the model is least likely to serve well, not only which pupils it helps most. That question often reveals hidden access barriers. For example, if the platform relies on homework-style engagement, pupils with crowded homes or poor connectivity may be disadvantaged. If the model requires synchronous scheduling, pupils with attendance issues may miss the support most.

Build access supports into the rollout

The solution is often not to reject a model entirely, but to build the right scaffolding around it. Schools can deliver AI tutoring during the school day with supervision, provide devices, or schedule human support for pupils who need additional mediation. Likewise, human tutoring can be made more equitable with careful timetable design, transport awareness, and fair referral criteria. The platform is only part of the access story.

This is where school leaders can borrow a lesson from operational planning in other sectors: keep the service simple, reliable, and easy to adopt. In our guide to family-friendly broadband upgrades, the key lesson is that good infrastructure matters, but usability matters just as much. The same is true for tutoring: if the learning journey is cumbersome, equity will suffer.

Measure participation by subgroup

Equity should be measured, not assumed. Track engagement, progress, and satisfaction by pupil premium, SEND, EAL, year group, and attendance profile. If one subgroup is systematically underperforming, ask whether the problem lies in the platform, the scheduling, or the support wrapper around delivery. A good provider should welcome this analysis because it makes impact more visible.

Schools that take subgroup analysis seriously often uncover very practical fixes. For instance, attendance may improve when sessions are moved in-school, or progress may rise when teachers pre-teach key vocabulary. The point is not that AI or human tutoring is inherently more equitable. The point is that equity depends on the design of delivery, the quality of implementation, and the fit with your learner population.

8) When to Choose AI Tutoring, Human Tutors, or a Hybrid Model

Choose AI tutoring when the task is narrow, repeatable, and scale-sensitive

AI tutoring is often the strongest option for schools that need consistent practice at scale, especially in subjects like maths where fluency, repetition, and rapid feedback matter. It is also attractive when budgets are tight and the school needs to serve many pupils without adding scheduling complexity. AI-led provision can be particularly effective for primary and lower secondary catch-up, routine homework support, and structured reinforcement of taught content. The fixed-price model can make budgeting simpler and reduce administrative overhead.

That said, schools should be cautious about overestimating AI’s ability to replace pedagogically rich instruction. AI tutoring is usually best viewed as a high-throughput intervention, not a complete substitute for expert teaching. If the learner needs emotional reassurance, complex explanation, or extended reasoning support, the AI model may need to be supplemented.

Choose human tutors when relational, adaptive teaching is the priority

Human tutors are generally best when the goal is exam preparation, nuanced conceptual teaching, or support for pupils who need encouragement as much as explanation. They are especially valuable in GCSE and A level contexts, where subject depth and responsiveness can materially affect outcomes. Human providers often bring richer judgment to unexpected misconceptions and can flex more naturally around pupil confidence, pace, and questioning. This is why platforms such as Tutorful and Tutor House emphasize tutor profiles, verification, and subject breadth.

Human tutoring is also preferable when the school needs strong pastoral sensitivity or when learners require sustained adult presence. The trade-off is cost and complexity. If you need dozens of tutors across multiple subjects, sourcing, scheduling, and quality assurance can become a substantial management task.

Choose a hybrid model when your priorities are mixed

In many schools, the best answer is neither purely AI nor purely human. A hybrid model can use AI tutoring for baseline practice and human tutoring for targeted deep dives, exam clinics, or vulnerable learners. This lets the school match resource intensity to need. It also reduces the risk of over-committing to a single delivery philosophy.

A hybrid model works best when there is a clear logic for which pupils go where. For example, you might place pupils who need daily maths practice into AI tutoring, while directing pupils with low confidence or complex gaps into small-group human support. If you want a broader framework for blending methods, our article on designing learning paths with AI is a helpful model for sequencing support rather than treating all interventions as interchangeable.

9) The Procurement Checklist Schools Should Use Before Signing

Request proof, not promises

Before procurement, ask providers for evidence across five areas: safeguarding, curriculum mapping, implementation support, reporting, and case studies from schools similar to yours. Request sample reports, sample sessions, and a clear explanation of data handling. If the provider is human-led, ask about tutor vetting, DBS, training, and supervision. If the provider is AI-led, ask about model restrictions, content filters, escalation protocols, and how hallucinations or inappropriate outputs are prevented.

It is also sensible to ask how the provider handles service continuity if staffing or technical issues arise. Schools need reliable provision that will not collapse mid-term. This matters because tutoring is not just a content service; it is a timetable dependency. If a provider cannot deliver consistently, any theoretical benefits quickly disappear.

Score implementation support separately from the tutoring itself

Strong tutoring providers help with onboarding, scheduling, data review, and teacher communication. Weak ones sell sessions and disappear. In school settings, implementation support can be as important as the tutoring method itself because it determines uptake and fidelity. A platform that is technically excellent but operationally opaque will underperform.

This is where procurement teams should distinguish product quality from service quality. The same provider can score differently on each. If you are comparing vendors, use a separate line item for implementation support, because it often predicts whether a programme survives beyond the pilot stage. The analogy to enterprise systems is useful here: even the most powerful tool fails if deployment is poor.

Plan for renewal, exit, and continuity

Every contract should include renewal criteria and an exit plan. Schools should know how to terminate, export data, and transition pupils if the provider is discontinued or underperforms. This is especially important for AI tutoring, where dependence can build quickly if the system becomes embedded in daily practice. Good governance means the school remains in control.

As a final procurement habit, keep a short “vendor decision memo” for governors or trust boards that records the rationale, scoring, risks, and review date. That way, the decision is explainable, revisit-able, and aligned to strategic priorities rather than memory or anecdote. For a broader perspective on how organisations justify and monetise high-value editorial and service decisions, our piece on value signals is a reminder that clarity of proposition matters when budgets are under pressure.

10) A School Leader’s Bottom-Line Recommendation

Use AI for scale, humans for complexity, and governance for both

The best practical framework is simple: use AI tutoring where the need is large, structured, and repeatable; use human tutors where the need is nuanced, relational, or exam-critical; and use a shared governance layer to ensure safeguarding, curriculum alignment, and equity. This is not an ideological choice between technology and teaching. It is a resource allocation decision shaped by the school’s real-world constraints and learner needs. The right answer may change by subject, year group, or term.

For many schools, AI tutoring will be the most cost-effective way to extend provision without increasing staff burden. For others, human tutors will remain indispensable because the need is too complex to automate. The strongest leaders will not ask which model is superior in theory; they will ask which model produces the highest educational return for the pupils in front of them.

Make the decision transparent to staff and governors

Finally, the framework should be visible. Share the criteria, the scores, and the rationale with staff and governors so that everyone understands why a provider was chosen. Transparency builds trust, and trust improves implementation. When the school community sees that the choice was made around measurable impact, curriculum fit, safeguarding, and equity, procurement becomes part of the improvement strategy rather than just an expenditure line.

If you need a short rule of thumb, use this: buy AI tutoring for breadth, buy human tutoring for depth, and buy neither until you can explain how it advances the curriculum and protects learners. That is the standard school leaders should expect from modern tutoring procurement in 2026.

Pro Tip: The “best” tutoring provider is the one that your school can implement well, measure properly, and defend to governors six months later.

FAQ

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• 7 Best Online Tutoring Websites For UK Schools: 2026 - Compare leading school-focused tutoring providers, pricing, and safeguarding standards.
• Guardrails for AI Tutors: Preventing Over-Reliance and Building Metacognition - Learn how schools can use AI without weakening independent thinking.
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