Celeste Kidd, University of California, Berkeley

I’m in a coffee shop when a young child dumps out his mother’s bag in search of fruit snacks. The contents spill onto the table, bench and floor. It’s a chaotic – but functional – solution to the problem.

Children have a penchant for unconventional thinking that, at first glance, can look disordered. This kind of apparently chaotic behavior served as the inspiration for developmental psychologist Jean Piaget’s best-known theory: that children construct their knowledge through experience and must pass through four sequential stages, the first two of which lack the ability to use structured logic.

Piaget remains the GOAT of developmental psychology. He fundamentally and forever changed the world’s view of children by showing that kids do not enter the world with the same conceptual building blocks as adults, but must construct them through experience. No one before or since has amassed such a catalog of quirky child behaviors that researchers even today can replicate within individual children.

While Piaget was certainly correct in observing that children engage in a host of unusual behaviors, my lab recently uncovered evidence that upends some long-standing assumptions about the limits of children’s logical capabilities that originated with his work. Our new paper in the journal Nature Human Behaviour describes how young children are capable of finding systematic solutions to complex problems without any instruction. https://www.youtube.com/embed/Qb4TPj1pxzQ?wmode=transparent&start=0 Jean Piaget describes how children of different ages tackle a sorting task, with varying success.

Putting things in order

Throughout the 1960s, Piaget observed that young children rely on clunky trial-and-error methods rather than systematic strategies when attempting to order objects according to some continuous quantitative dimension, like length. For instance, a 4-year-old child asked to organize sticks from shortest to longest will move them around randomly and usually not achieve the desired final order.

Psychologists have interpreted young children’s inefficient behavior in this kind of ordering task – what we call a seriation task – as an indicator that kids can’t use systematic strategies in problem-solving until at least age 7.

Somewhat counterintuitively, my colleagues and I found that increasing the difficulty and cognitive demands of the seriation task actually prompted young children to discover and use algorithmic solutions to solve it.

Piaget’s classic study asked children to put some visible items like wooden sticks in order by height. Huiwen Alex Yang, a psychology Ph.D. candidate who works on computational models of learning in my lab, cranked up the difficulty for our version of the task. With advice from our collaborator Bill Thompson, Yang designed a computer game that required children to use feedback clues to infer the height order of items hidden behind a wall, .

The game asked children to order bunnylike creatures from shortest to tallest by clicking on their sneakers to swap their places. The creatures only changed places if they were in the wrong order; otherwise they stayed put. Because they could only see the bunnies’ shoes and not their heights, children had to rely on logical inference rather than direct observation to solve the task. Yang tested 123 children between the ages of 4 and 10. https://www.youtube.com/embed/GlsbcE6nOxk?wmode=transparent&start=0 Researcher Huiwen Alex Yang tests 8-year-old Miro on the bunny sorting task. The bunnies are hidden behind a wall with only their sneakers visible. Miro’s selections exemplify use of selection sort, a classic efficient sorting algorithm from computer science. Kidd Lab at UC Berkeley.

Figuring out a strategy

We found that children independently discovered and applied at least two well-known sorting algorithms. These strategies – called selection sort and shaker sort – are typically studied in computer science.

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More than half the children we tested demonstrated evidence of structured algorithmic thinking, and at ages as young as 4 years old. While older kids were more likely to use algorithmic strategies, our finding contrasts with Piaget’s belief that children were incapable of this kind of systematic strategizing before 7 years of age. He thought kids needed to reach what he called the concrete operational stage of development first.

Our results suggest that children are actually capable of spontaneous logical strategy discovery much earlier when circumstances require it. In our task, a trial-and-error strategy could not work because the objects to be ordered were not directly observable; children could not rely on perceptual feedback.

Explaining our results requires a more nuanced interpretation of Piaget’s original data. While children may still favor apparently less logical solutions to problems during the first two Piagetian stages, it’s not because they are incapable of doing otherwise if the situation requires it.

A systematic approach to life

Algorithmic thinking is crucial not only in high-level math classes, but also in everyday life. Imagine that you need to bake two dozen cookies, but your go-to recipe yields only one. You could go through all the steps of making the recipe twice, washing the bowl in between, but you’d never do that because you know that would be inefficient. Instead, you’d double the ingredients and perform each step only once. Algorithmic thinking allows you to identify a systematic way of approaching the need for twice as many cookies that improves the efficiency of your baking.

Algorithmic thinking is an important capacity that’s useful to children as they learn to move and operate in the world – and we now know they have access to these abilities far earlier than psychologists had believed.

That children can engage with algorithmic thinking before formal instruction has important implications for STEM – science, technology, engineering and math –education. Caregivers and educators now need to reconsider when and how they give children the opportunity to tackle more abstract problems and concepts. Knowing that children’s minds are ready for structured problems as early as preschool means we can nurture these abilities earlier in support of stronger math and computational skills.

And have some patience next time you encounter children interacting with the world in ways that are perhaps not super convenient. As you pick up your belongings from a café floor, remember that it’s all part of how children construct their knowledge. Those seemingly chaotic kids are on their way to more obviously logical behavior soon.

Celeste Kidd, Professor of Psychology, University of California, Berkeley

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Discord is rolling out a major shift in how its platform handles teen safety: teen-appropriate settings will become the default experience for all new and existing users worldwide, with age verification required to unlock certain settings and access sensitive or age-gated spaces.

The update is set to begin as a phased global rollout in early March, and Discord says the goal is to strengthen age-appropriate protections while still preserving the privacy, community, and meaningful connection that have made the platform a go-to for gaming and interest-based groups.

Teen-by-default, globally (starting in March)

Discord says the new defaults will apply to all users, not just new signups. In practice, that means accounts will start with a more protective baseline, and verified adults will have more flexibility to adjust settings or access age-restricted content.

Discord is also introducing an age-verification (age assurance) step that may be required to:

• Change certain communication settings
• Access sensitive content
• Enter age-restricted channels, servers, or commands
• Use select message request features

“Nowhere is our safety work more important than when it comes to teen users,” said Savannah Badalich, Head of Product Policy at Discord, adding that the company is building on its existing safety architecture with teen safety principles at the core.

Privacy-forward age assurance: how Discord says it will work

A big part of the announcement is Discord’s attempt to thread the needle between safety and privacy.

Users will be able to choose from multiple methods, including:

• Facial age estimation (video selfie)
• Submitting identification to vendor partners

Discord also says it will implement its age inference model, a background system designed to help determine whether an account belongs to an adult without always requiring users to verify their age. Some users may be asked to use multiple methods if more information is needed to assign an age group.

Discord highlighted several privacy protections in its approach:

• On-device processing: Video selfies for facial age estimation never leave a user’s device.
• Quick deletion: Identity documents submitted to vendor partners are deleted quickly (in most cases, immediately after age confirmation).
• Straightforward verification: In most cases, users complete the process once and their Discord experience adapts to their verified age group.
• Private status: A user’s age verification status cannot be seen by other users.

After completing a chosen method, Discord says users will receive confirmation via a direct message from Discord’s official account. A user’s assigned age group can also be viewed in My Account settings, and users can appeal by retrying the process.

Discord also notes it prompts users to age-assure only within Discord and currently does not send emails or text messages about its age assurance process or results.

What’s changing in the default safety settings

Starting in early March, Discord says it will assign new default settings designed to support age-appropriate experiences while keeping privacy front and center. Highlights include:

• Content filters: Users must be age-assured as adults to unblur sensitive content or turn the setting off.
• Age-gated spaces: Only age-assured adults can access age-restricted channels, servers, and app commands.
• Message Request Inbox: DMs from people a user may not know are routed to a separate inbox by default; only age-assured adults can modify this setting.
• Friend request alerts: People will receive warning prompts for friend requests from users they may not know.
• Stage restrictions: Only age-assured adults may speak on stage in servers.

Discord notes it previously launched a teen-by-default experience in the UK and Australia last year, and says this global rollout builds on that approach to deliver consistent protections worldwide.

Giving teens a seat at the table: Discord Teen Council

Along with the safety updates, Discord also announced recruitment for its inaugural Discord Teen Council, a teen advisory body intended to bring authentic teen perspectives into how Discord shapes their experience.

Discord says the Teen Council will consist of 10–12 teens and will help inform future product features, policies, and educational resources.

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• Who can apply: Teens ages 13–17
• Apply by: May 1, 2026

The bigger picture

Discord says these updates build on its broader safety ecosystem, including tools and resources such as Family Center, Teen Safety Assist, a Warning System, and more.

Whether you’re a parent, a teen user, or an adult who uses Discord for gaming communities and group chats, the headline is simple: the default experience is becoming more restrictive, and adult access will increasingly depend on age assurance.

Source: PRNewswire

Dive into “The Knowledge,” where curiosity meets clarity. This playlist, in collaboration with STMDailyNews.com, is designed for viewers who value historical accuracy and insightful learning. Our short videos, ranging from 30 seconds to a minute and a half, make complex subjects easy to grasp in no time. Covering everything from historical events to contemporary processes and entertainment, “The Knowledge” bridges the past with the present. In a world where information is abundant yet often misused, our series aims to guide you through the noise, preserving vital knowledge and truths that shape our lives today. Perfect for curious minds eager to discover the ‘why’ and ‘how’ of everything around us. Subscribe and join in as we explore the facts that matter.  https://stmdailynews.com/the-knowledge/

Seth King, University of Iowa

In special education in the U.S., funding is scarce and personnel shortages are pervasive, leaving many school districts struggling to hire qualified and willing practitioners.

Amid these long-standing challenges, there is rising interest in using artificial intelligence tools to help close some of the gaps that districts currently face and lower labor costs.

Over 7 million children receive federally funded entitlements under the Individuals with Disabilities Education Act, which guarantees students access to instruction tailored to their unique physical and psychological needs, as well as legal processes that allow families to negotiate support. Special education involves a range of professionals, including rehabilitation specialists, speech-language pathologists and classroom teaching assistants. But these specialists are in short supply, despite the proven need for their services.

As an associate professor in special education who works with AI, I see its potential and its pitfalls. While AI systems may be able to reduce administrative burdens, deliver expert guidance and help overwhelmed professionals manage their caseloads, they can also present ethical challenges – ranging from machine bias to broader issues of trust in automated systems. They also risk amplifying existing problems with how special ed services are delivered.

Yet some in the field are opting to test out AI tools, rather than waiting for a perfect solution.

A faster IEP, but how individualized?

AI is already shaping special education planning, personnel preparation and assessment.

One example is the individualized education program, or IEP, the primary instrument for guiding which services a child receives. An IEP draws on a range of assessments and other data to describe a child’s strengths, determine their needs and set measurable goals. Every part of this process depends on trained professionals.

But persistent workforce shortages mean districts often struggle to complete assessments, update plans and integrate input from parents. Most districts develop IEPs using software that requires practitioners to choose from a generalized set of rote responses or options, leading to a level of standardization that can fail to meet a child’s true individual needs.

Preliminary research has shown that large language models such as ChatGPT can be adept at generating key special education documents such as IEPs by drawing on multiple data sources, including information from students and families. Chatbots that can quickly craft IEPs could potentially help special education practitioners better meet the needs of individual children and their families. Some professional organizations in special education have even encouraged educators to use AI for documents such as lesson plans.

Training and diagnosing disabilities

There is also potential for AI systems to help support professional training and development. My own work on personnel development combines several AI applications with virtual reality to enable practitioners to rehearse instructional routines before working directly with children. Here, AI can function as a practical extension of existing training models, offering repeated practice and structured support in ways that are difficult to sustain with limited personnel.

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Some districts have begun using AI for assessments, which can involve a range of academic, cognitive and medical evaluations. AI applications that pair automatic speech recognition and language processing are now being employed in computer-mediated oral reading assessments to score tests of student reading ability.

Practitioners often struggle to make sense of the volume of data that schools collect. AI-driven machine learning tools also can help here, by identifying patterns that may not be immediately visible to educators for evaluation or instructional decision-making. Such support may be especially useful in diagnosing disabilities such as autism or learning disabilities, where masking, variable presentation and incomplete histories can make interpretation difficult. My ongoing research shows that current AI can make predictions based on data likely to be available in some districts.

Privacy and trust concerns

There are serious ethical – and practical – questions about these AI-supported interventions, ranging from risks to students’ privacy to machine bias and deeper issues tied to family trust. Some hinge on the question of whether or not AI systems can deliver services that truly comply with existing law.

The Individuals with Disabilities Education Act requires nondiscriminatory methods of evaluating disabilities to avoid inappropriately identifying students for services or neglecting to serve those who qualify. And the Family Educational Rights and Privacy Act explicitly protects students’ data privacy and the rights of parents to access and hold their children’s data.

What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a child’s data is misused or leaked by an AI system? Using AI systems to perform some of the functions described above puts families in a position where they are expected to put their faith not only in their school district and its special education personnel, but also in commercial AI systems, the inner workings of which are largely inscrutable.

These ethical qualms are hardly unique to special ed; many have been raised in other fields and addressed by early-adopters. For example, while automatic speech recognition, or ASR, systems have struggled to accurately assess accented English, many vendors now train their systems to accommodate specific ethnic and regional accents.

But ongoing research work suggests that some ASR systems are limited in their capacity to accommodate speech differences associated with disabilities, account for classroom noise, and distinguish between different voices. While these issues may be addressed through technical improvement in the future, they are consequential at present.

Embedded bias

At first glance, machine learning models might appear to improve on traditional clinical decision-making. Yet AI models must be trained on existing data, meaning their decisions may continue to reflect long-standing biases in how disabilities have been identified.

Indeed, research has shown that AI systems are routinely hobbled by biases within both training data and system design. AI models can also introduce new biases, either by missing subtle information revealed during in-person evaluations or by overrepresenting characteristics of groups included in the training data.

Such concerns, defenders might argue, are addressed by safeguards already embedded in federal law. Families have considerable latitude in what they agree to, and can opt for alternatives, provided they are aware they can direct the IEP process.

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By a similar token, using AI tools to build IEPs or lessons may seem like an obvious improvement over underdeveloped or perfunctory plans. Yet true individualization would require feeding protected data into large language models, which could violate privacy regulations. And while AI applications can readily produce better-looking IEPs and other paperwork, this does not necessarily result in improved services.

Filling the gap

Indeed, it is not yet clear whether AI provides a standard of care equivalent to the high-quality, conventional treatment to which children with disabilities are entitled under federal law.

The Supreme Court in 2017 rejected the notion that the Individuals with Disabilities Education Act merely entitles students to trivial, “de minimis” progress, which weakens one of the primary rationales for pursuing AI – that it can meet a minimum standard of care and practice. And since AI really has not been empirically evaluated at scale, it has not been proved that it adequately meets the low bar of simply improving beyond the flawed status quo.

But this does not change the reality of limited resources. For better or worse, AI is already being used to fill the gap between what the law requires and what the system actually provides.

Seth King, Associate Profess of Special Education, University of Iowa

This article is republished from The Conversation under a Creative Commons license. Read the original article.