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AI is already used for work such as adjusting traffic signals, supporting clinical tasks, and assisting with educational activities. But its value is not automatic: results depend on the task, data, infrastructure, human oversight, and who can access the system. Here is what is established in each field—and where claims about future benefits still need evidence.
How is AI changing education?
UNESCO describes AI as a potential way to support personalized learning, broaden access, and make some education management more efficient. Those are opportunities, not proof that every AI tool improves learning or that it suits every classroom. UNESCO’s guidance emphasizes a human-centered approach that protects inclusion, equity, and learners’ rights.
Access is a basic constraint. UNESCO reported in 2025 that around 2.6 billion people lacked internet access as of 2024—roughly one-third of the world’s population. When learning tools depend on reliable connectivity or devices that students do not have, adoption can widen an existing divide rather than close it.
The Tool Desk
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Can AI reduce traffic congestion?
One established traffic-management application is adaptive signal control. Unlike a fixed timing plan, an adaptive system detects traffic conditions and uses algorithms to adjust signal timings as demand changes. The Federal Highway Administration (FHWA) describes the approach as a way to respond to changing traffic patterns, not as a guaranteed solution to congestion.
Performance depends on local conditions and the system being compared against. FHWA materials report improvements in some settings, while noting that an adaptive system may add little when existing signal timings are already well tuned. A result from one intersection or network should not be treated as a universal forecast.
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What a deployment needs
- Reliable detection: The system needs accurate traffic inputs; FHWA stresses that detection equipment must be maintained.
- Operational support: Agencies need the people and processes to monitor and maintain the equipment and signal system.
- A relevant baseline: Evaluations should compare results with the local system’s prior performance and operating conditions.
The U.S. Department of Transportation discusses AI across transportation, including traffic-management operations and the safe integration of AI into transport systems. That broad scope should not be confused with evidence about any one signal system: claims about a particular outcome need to match the specific application and setting.
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The World Health Organization (WHO) identifies AI applications in diagnosis and clinical care, drug development, disease surveillance, outbreak response, and health-systems management. These are areas where AI may support work; they do not mean a system can independently make safe decisions in every clinical or public-health context.
Healthcare adoption calls for attention to evidence, safety, equity, trust, and governance. A tool’s output needs to be assessed in the setting where it will be used, with clear accountability for decisions and safeguards for the people affected. Access matters too: benefits that are available only to some communities can deepen inequities.
Generative and multimodal AI need careful qualification
In 2025 guidance on large multimodal models, WHO discussed anticipated applications across health care, research, public health, and drug development. It also cautioned that broad general-purpose capability across a wide range of tasks had not been proven. A model’s ability to produce fluent text or handle multiple input types is not, by itself, evidence that it is reliable for a specific health task.
WHO Director-General Tedros Adhanom Ghebreyesus said AI already plays a role in several health areas, including diagnosis, disease surveillance, and health-systems management, and called for universal access so these innovations do not become another driver of inequity. That framing makes access and governance part of responsible deployment, alongside technical performance.
Do AI coding assistants make developers more productive?
AI coding assistants are designed to generate or explain code, but those capabilities alone do not establish that they make developers more productive, improve software quality, or reduce bugs. The evidence summarized here does not establish measured outcomes for those claims, so no general performance verdict is justified.
Best Value
For a team considering an assistant, the useful question is not simply whether it can produce code. The team should decide how generated code will be reviewed, tested, and maintained; what information may be shared with the tool; and how to evaluate results against its own work. Any productivity or quality claim should be tied to a defined task and a credible measurement, rather than inferred from a demonstration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What determines whether AI helps in any of these fields?
The applications differ, but the decision points are similar. A system should be judged against the actual task and intended outcome, with evidence suited to the setting—not against a broad promise about what AI can do.
| Field | Established application or opportunity | What evidence or conditions matter | Human responsibility |
|---|---|---|---|
| Education | Personalized learning, access, and education management are opportunities identified by UNESCO. | Whether the tool supports learning in the intended context; whether students can access it; and how learner rights, privacy, safety, and equity are protected. | Teachers and decision-makers should keep learning goals and learner welfare central. |
| Traffic management | Adaptive signals adjust timing based on detected traffic conditions. | Reliable, maintained detection and a comparison with the local baseline; gains vary by operating conditions. | Transport agencies remain responsible for system operation, maintenance, and evaluation. |
| Healthcare | WHO lists applications from clinical care to surveillance and health-systems management. | Evidence for the specific task, safety, equity, trust, and governance; broad multimodal capability is not proof of general-purpose health competence. | Health professionals and institutions need accountable review and safeguards. |
| Coding | Assistants can be considered for code generation or explanation. | The available evidence summarized here does not establish productivity or quality effects. | Developers and teams need to review, test, and maintain code and assess results in context. |
Across all four fields, ask who benefits, who may be excluded, what data the system relies on, what happens when its output is wrong, and who is answerable for the outcome. Those questions turn an AI capability into a decision about whether—and how—to use it.
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