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.

More than half of new articles on the internet are being written by AI – is human writing headed for extinction?

Francesco Agnellini, Binghamton University, State University of New York The line between human and machine authorship is blurring, particularly as it’s become increasingly difficult to tell whether something was written by a person or AI. Now, in what may seem like a tipping point, the digital marketing firm Graphite recently published a study showing that more than 50% of articles on the web are being generated by artificial intelligence. As a scholar who explores how AI is built, how people are using it in their everyday lives, and how it’s affecting culture, I’ve thought a lot about what this technology can do and where it falls short. If you’re more likely to read something written by AI than by a human on the internet, is it only a matter of time before human writing becomes obsolete? Or is this simply another technological development that humans will adapt to?

It isn’t all or nothing

Thinking about these questions reminded me of Umberto Eco’s essay “Apocalyptic and Integrated,” which was originally written in the early 1960s. Parts of it were later included in an anthology titled “Apocalypse Postponed,” which I first read as a college student in Italy. In it, Eco draws a contrast between two attitudes toward mass media. There are the “apocalyptics” who fear cultural degradation and moral collapse. Then there are the “integrated” who champion new media technologies as a democratizing force for culture.

Back then, Eco was writing about the proliferation of TV and radio. Today, you’ll often see similar reactions to AI. Yet Eco argued that both positions were too extreme. It isn’t helpful, he wrote, to see new media as either a dire threat or a miracle. Instead, he urged readers to look at how people and communities use these new tools, what risks and opportunities they create, and how they shape – and sometimes reinforce – power structures. While I was teaching a course on deepfakes during the 2024 election, Eco’s lesson also came back to me. Those were days when some scholars and media outlets were regularly warning of an imminent “deepfake apocalypse.” Would deepfakes be used to mimic major political figures and push targeted disinformation? What if, on the eve of an election, generative AI was used to mimic the voice of a candidate on a robocall telling voters to stay home? Those fears weren’t groundless: Research shows that people aren’t especially good at identifying deepfakes. At the same time, they consistently overestimate their ability to do so. In the end, though, the apocalypse was postponed. Post-election analyses found that deepfakes did seem to intensify some ongoing political trends, such as the erosion of trust and polarization, but there’s no evidence that they affected the final outcome of the election.

Listicles, news updates and how-to guides

Of course, the fears that AI raises for supporters of democracy are not the same as those it creates for writers and artists. For them, the core concerns are about authorship: How can one person compete with a system trained on millions of voices that can produce text at hyper-speed? And if this becomes the norm, what will it do to creative work, both as an occupation and as a source of meaning? It’s important to clarify what’s meant by “online content,” the phrase used in the Graphite study, which analyzed over 65,000 randomly selected articles of at least 100 words on the web. These can include anything from peer-reviewed research to promotional copy for miracle supplements. A closer reading of the Graphite study shows that the AI-generated articles consist largely of general-interest writing: news updates, how-to guides, lifestyle posts, reviews and product explainers. https://stmdailynews.com/wp-admin/post-new.php#visibility The primary economic purpose of this content is to persuade or inform, not to express originality or creativity. Put differently, AI appears to be most useful when the writing in question is low-stakes and formulaic: the weekend-in-Rome listicle, the standard cover letter, the text produced to market a business. A whole industry of writers – mostly freelance, including many translators – has relied on precisely this kind of work, producing blog posts, how-to material, search engine optimization text and social media copy. The rapid adoption of large language models has already displaced many of the gigs that once sustained them.

Collaborating with AI

The dramatic loss of this work points toward another issue raised by the Graphite study: the question of authenticity, not only in identifying who or what produced a text, but also in understanding the value that humans attach to creative activity. How can you distinguish a human-written article from a machine-generated one? And does that ability even matter? Over time, that distinction is likely to grow less significant, particularly as more writing emerges from interactions between humans and AI. A writer might draft a few lines, let an AI expand them and then reshape that output into the final text. This article is no exception. As a non-native English speaker, I often rely on AI to refine my language before sending drafts to an editor. At times the system attempts to reshape what I mean. But once its stylistic tendencies become familiar, it becomes possible to avoid them and maintain a personal tone. Also, artificial intelligence is not entirely artificial, since it is trained on human-made material. It’s worth noting that even before AI, human writing has never been entirely human, either. Every technology, from parchment and stylus paper to the typewriter and now AI, has shaped how people write and how readers make sense of it. Another important point: AI models are increasingly trained on datasets that include not only human writing but also AI-generated and human–AI co-produced text. This has raised concerns about their ability to continue improving over time. Some commentators have already described a sense of disillusionment following the release of newer large models, with companies struggling to deliver on their promises.

Human voices may matter even more

But what happens when people become overly reliant on AI in their writing? Some studies show that writers may feel more creative when they use artificial intelligence for brainstorming, yet the range of ideas often becomes narrower. This uniformity affects style as well: These systems tend to pull users toward similar patterns of wording, which reduces the differences that usually mark an individual voice. Researchers also note a shift toward Western – and especially English-speaking – norms in the writing of people from other cultures, raising concerns about a new form of AI colonialism. In this context, texts that display originality, voice and stylistic intention are likely to become even more meaningful within the media landscape, and they may play a crucial role in training the next generations of models. If you set aside the more apocalyptic scenarios and assume that AI will continue to advance – perhaps at a slower pace than in the recent past – it’s quite possible that thoughtful, original, human-generated writing will become even more valuable. Put another way: The work of writers, journalists and intellectuals will not become superfluous simply because much of the web is no longer written by humans. Francesco Agnellini, Lecturer in Digital and Data Studies, Binghamton University, State University of New York This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Learning with AI falls short compared to old-fashioned web search

Shiri Melumad, University of Pennsylvania Since the release of ChatGPT in late 2022, millions of people have started using large language models to access knowledge. And it’s easy to understand their appeal: Ask a question, get a polished synthesis and move on – it feels like effortless learning. However, a new paper I co-authored offers experimental evidence that this ease may come at a cost: When people rely on large language models to summarize information on a topic for them, they tend to develop shallower knowledge about it compared to learning through a standard Google search. Co-author Jin Ho Yun and I, both professors of marketing, reported this finding in a paper based on seven studies with more than 10,000 participants. Most of the studies used the same basic paradigm: Participants were asked to learn about a topic – such as how to grow a vegetable garden – and were randomly assigned to do so by using either an LLM like ChatGPT or the “old-fashioned way,” by navigating links using a standard Google search. No restrictions were put on how they used the tools; they could search on Google as long as they wanted and could continue to prompt ChatGPT if they felt they wanted more information. Once they completed their research, they were then asked to write advice to a friend on the topic based on what they learned. The data revealed a consistent pattern: People who learned about a topic through an LLM versus web search felt that they learned less, invested less effort in subsequently writing their advice, and ultimately wrote advice that was shorter, less factual and more generic. In turn, when this advice was presented to an independent sample of readers, who were unaware of which tool had been used to learn about the topic, they found the advice to be less informative, less helpful, and they were less likely to adopt it. We found these differences to be robust across a variety of contexts. For example, one possible reason LLM users wrote briefer and more generic advice is simply that the LLM results exposed users to less eclectic information than the Google results. To control for this possibility, we conducted an experiment where participants were exposed to an identical set of facts in the results of their Google and ChatGPT searches. Likewise, in another experiment we held constant the search platform – Google – and varied whether participants learned from standard Google results or Google’s AI Overview feature. The findings confirmed that, even when holding the facts and platform constant, learning from synthesized LLM responses led to shallower knowledge compared to gathering, interpreting and synthesizing information for oneself via standard web links.

Why it matters

Why did the use of LLMs appear to diminish learning? One of the most fundamental principles of skill development is that people learn best when they are actively engaged with the material they are trying to learn. When we learn about a topic through Google search, we face much more “friction”: We must navigate different web links, read informational sources, and interpret and synthesize them ourselves. While more challenging, this friction leads to the development of a deeper, more original mental representation of the topic at hand. But with LLMs, this entire process is done on the user’s behalf, transforming learning from a more active to passive process.

What’s next?

To be clear, we do not believe the solution to these issues is to avoid using LLMs, especially given the undeniable benefits they offer in many contexts. Rather, our message is that people simply need to become smarter or more strategic users of LLMs – which starts by understanding the domains wherein LLMs are beneficial versus harmful to their goals. Need a quick, factual answer to a question? Feel free to use your favorite AI co-pilot. But if your aim is to develop deep and generalizable knowledge in an area, relying on LLM syntheses alone will be less helpful. As part of my research on the psychology of new technology and new media, I am also interested in whether it’s possible to make LLM learning a more active process. In another experiment we tested this by having participants engage with a specialized GPT model that offered real-time web links alongside its synthesized responses. There, however, we found that once participants received an LLM summary, they weren’t motivated to dig deeper into the original sources. The result was that the participants still developed shallower knowledge compared to those who used standard Google. Building on this, in my future research I plan to study generative AI tools that impose healthy frictions for learning tasks – specifically, examining which types of guardrails or speed bumps most successfully motivate users to actively learn more beyond easy, synthesized answers. Such tools would seem particularly critical in secondary education, where a major challenge for educators is how best to equip students to develop foundational reading, writing and math skills while also preparing for a real world where LLMs are likely to be an integral part of their daily lives. The Research Brief is a short take on interesting academic work. Shiri Melumad, Associate Professor of Marketing, University of Pennsylvania This article is republished from The Conversation under a Creative Commons license. Read the original article.

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/