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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 childs 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 childs 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. 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 ethicaland practicalquestions 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 childrens data. What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a childs 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 strugged 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. 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 is an associate professor of special education at the University of Iowa. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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E-Commerce
As Winter Storm Fern swept across the United States in late January 2026, bringing ice, snow, and freezing temperatures, it left more than a million people without power, mostly in the Southeast. Scrambling to meet higher than average demand, PJM, the nonprofit company that operates the grid serving much of the mid-Atlantic U.S., asked for federal permission to generate more power, even if it caused high levels of air pollution from burning relatively dirty fuels. Energy Secretary Chris Wright agreed and took another step, too. He authorized PJM and ERCOTthe company that manages the Texas power gridas well as Duke Energy, a major electricity supplier in the Southeast, to tell data centers and other large power-consuming businesses to turn on their backup generators. The goal was to make sure there was enough power available to serve customers as the storm hit. Generally, these facilities power themselves and do not send power back to the grid. But Wright explained that their industrial diesel generators could generate 35 gigawatts of power, or enough electricity to power many millions of homes. We are scholars of the electricity industry who live and work in the Southeast. In the wake of Winter Storm Fern, we see opportunities to power data centers with less pollution while helping communities prepare for, get through, and recover from winter storms. Data centers use enormous quantities of energy Before Wrights order, it was hard to say whether data centers would reduce the amount of electricity they take from the grid during storms or other emergencies. This is a pressing question, because data centers power demands to support generative artificial intelligence are already driving up electricity prices in congested grids like PJMs. And data centers are expected to need only more power. Estimates vary widely, but the Lawrence Berkeley National Lab anticipates that the share of electricity production in the U.S. used by data centers could spike from 4.4% in 2023 to between 6.7% and 12% by 2028. PJM expects a peak load growth of 32 gigawatts by 2030enough power to supply 30 million new homes, but nearly all going to new data centers. PJMs job is to coordinate that energyand figure out how much the public, or others, should pay to supply it. The race to build new data centers and find the electricity to power them has sparked enormous public backlash about how data centers will inflate household energy costs. Other concerns are that power-hungry data centers fed by natural gas generators can hurt air quality, consume water, and intensify climate damage. Many data centers are located, or proposed, in communities already burdened by high levels of pollution. Local ordinances, regulations created by state utility commissions, and proposed federal laws have tried to protect ratepayers from price hikes and require data centers to pay for the transmission and generation infrastructure they need. Always-on connections? In addition to placing an increasing burden on the grid, many data centers have asked utility companies for power connections that are active 99.999% of the time. But since the 1970s, utilities have encouraged demand response programs, in which large power users agree to reduce their demand during peak times like Winter Storm Fern. In return, utilities offer financial incentives such as bill credits for participation. Over the years, demand response programs have helped utility companies and power grid managers lower electricity demand at peak times in summer and winter. The proliferation of smart meters allows residential customers and smaller businesses to participate in these efforts as well. When aggregated with rooftop solar, batteries and electric vehicles, these distributed energy resources can be dispatched as virtual power plants. A different approach The terms of data center agreements with local governments and utilities often arent available to the public. That makes it hard to determine whether data centers could or would temporarily reduce their power use. In some cases, uninterrupted access to power is necessary to maintain critical data systems, such as medical records, bank accounts and airline reservation systems. Yet, data center demand has spiked with the AI boom, and developers have increasingly been willing to consider demand response. In August 2025, Google announced new agreements with Indiana Michigan Power and the Tennessee Valley Authority to provide data center demand response by targeting machine learning workloads, shifting non-urgent compute tasks away from times when the grid is strained. Several new companies have also been founded specifically to help AI data centers shift workloads and even use in-house battery storage to temporarily move data centers power use off the grid during power shortages. Flexibility for the future One study has found that if data centers would commit to using power flexibly, an additional 100 gigawatts of capacitythe amount that would power around 70 million householdscould be added to the grid without adding new generation and transmission. In another instance, researchers demonstrated how data centers could invest in offsite generation through virtual power plants to meet their generation needs. Installing solar panels with battery storage at businesses and homes can boost available electricity more quickly and cheaply than building a new full-size power plant. Virtual power plants also provide flexibility as grid operators can tap into batteries, shift thermostats or shut down appliances in periods of peak demand. These projects can also benefit the buildings where they are hosted. Distributed energy generation and storage, alongside winterizing power lines and using renewables, are key ways to help keep the lights on during and after winter storms. Those efforts can make a big difference in places like Nashville, Tennessee, where more than 230,000 customers were without power at the peak of outages during Fern, not because there wasnt enough electricity for their homes but because their power lines were down. The future of AI is uncertain. Analysts caution that the AI industry may prove to be a speculative bubble: If demand flatlines, they say, electricity customers may end up paying for grid improvements and new generation built to meet needs that would not actually exist. Onsite diesel generators are an emergency solution for large users such as data centers to reduce strain on the grid. Yet, this is not a long-term solution to winter storms. Instead, if data centers, utilities, regulators and grid operators are willing to also consider offsite distributed energy to meet electricity demand, then their investments could help keep energy prices down, reduce air pollution and harm to the climate, and help everyone stay powered up during summer heat and winter cold. Nikki Luke is an assistant professor of human geography at the University of Tennessee. Conor Harrison is an associate professor of economic geography at the University of South Carolina. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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E-Commerce
In 2024, the clean energy sector saw a job boom: The industry added nearly 100,000 new jobs throughout that year, meaning clean energy jobs grew more than three times faster than the rest of the workforce. Last year was a different story, however. It was a year of losses for the clean energy industry, in terms of projects, investments, and employment. Existing factories closed, like Natron Energys sodium-ion battery facilities in Michigan and California. Planned facilities were canceled, including a $3.2 billion Stellantis battery factory in Illinois. And multiple kinds of projects were scrapped, blocked, or downsized, from EV plants to wind farms. In total, the turbulent year meant that 38,000 jobsa mix of current and future positionswere erased from the clean energy industry, according to a new analysis by E2, a nonpartisan organization that tracks U.S. clean energy projects. A net loss of clean energy jobs The vast majority of those 38,000 lost jobs were in manufacturing (though some may have been counted in multiple categories, like energy generation or maintenance). For comparison, by the end of 2024, there were about 577,000 manufacturing jobs in the clean energy industry. These job losses are especially significant because theyre happening amid a general decline in manufacturing employment. In 2024, clean energy manufacturing had been a “bright spot,” says Michael Timberlake, E2 director of research and publications, helping bring back U.S. production. “When those projects are canceled, were not just losing jobs on paper; were losing a pathway that had been driving a new manufacturing resurgence,” he says. “And the investment doesnt disappear. It moves to other countries and U.S. competitors that are aggressively building clean energy supply chains and hiring the workers we cant afford to lose.” Even amid cancellations, some new clean energy projects and jobs were announced in 2025, like a $42 million Anthro Energy battery factory in Louisville, Kentucky, which will create 110 jobs. But the number of jobs eliminated outweighs those potential additions. Just 22,905 jobs were announced in 2025, meaning a net loss of more than 15,000 expected clean energy positions. No previous year tracked by E2 saw job losses on this scale, underscoring how quickly employment gains can evaporate when projects are abandoned, the analysis reads. New clean energy investments were also overshadowed by cancellations. Companies canceled, closed, or downsized $34.8 billion in clean energy projects, nearly three times the $12.3 billion in new investment announced throughout the year, a 3-to-1 imbalance. Republican-held districts hit harder Though the entire country was affected by these losses, Republican-held districts felt their impact a bit more than others. Republican districts lost $19.9 billion in investments that would have brought 24,500 jobs to those regions, compared to $10.6 billion and 12,600 jobs lost in Democratic-held districts. That makes sense because the Inflation Reduction Act (IRA) signed by then-President Joe Biden in 2022which spurred clean energy jobs and projectsbenefited many Republican-led districts, even though not a single Republican voted for the legislation and in fact House Republicans voted 42 times to repeal it. Nearly 200,000 of the 334,000 clean energy jobs that the IRA created in its first two years were in congressional districts represented by Republican House members. Still, clean energy is growing Despite attacks on clean energy by the current Trump administration, the sector is still growing in the United States. In 2025, nearly all of the new power added to the countrys grid came from solar, wind, and batteries. Even the U.S. Energy Information Administration has said that all net new generating capacity the country sees in 2026 will come from renewables. And clean energy experts say the industry will continue to groweven as the president tries to prop up coal, oil, and gasbecause electricity generated from renewables is cheaper than fossil fuels, and the projects are often faster to build than fossil fuel power plants. Still, economic losses that the clean energy sector saw in 2025 are devastating, and may not be fully recovered. And if clean energy job growth is at risk, that affects our entire economy. Clean energy jobs are present in every single state, and, as the World Resources Institute put it in November, movement toward clean energy will create opportunity for millions of Americans. E2’s data also doesn’t capture the “tens of thousands of additional jobs and projects” that likely would have been announced if the country’s policy and market certainty continued, Timberlake says. “Likely hundreds of projects that would have been announced, and hundreds more that could’ve been announced this year, cannot be recovered,” he adds, “and will instead benefiting workers and communities in other countries.”
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E-Commerce
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