The use of artificial intelligence to score statewide standardized tests resulted in errors that affected hundreds of exams, the NBC10 Investigators have learned.

The issue with the Massachusetts Comprehensive Assessment System (MCAS) surfaced over the summer, when preliminary results for the exams were distributed to districts.

The state’s testing contractor, Cognia, found roughly 1,400 essays did not receive the correct scores, according to a spokesperson with the Department of Elementary and Secondary Education.

DESE told NBC10 Boston all the essays were rescored, affected districts received notification, and all their data was corrected in August.

So how did humans detect the problem?

We found one example in Lowell. Turns out an alert teacher at Reilly Elementary School was reading through her third-grade students’ essays over the summer. When the instructor looked up the scores some of the students received, something did not add up.

The teacher notified the school principal, who then flagged the issue with district leaders.

“We were on alert that there could be a learning curve with AI,” said Wendy Crocker-Roberge, an assistant superintendent in the Lowell school district.

AI essay scoring works by using human-scored exemplars of what essays at each score point look like, according to DESE.

The AI tool uses that information to score the essays. In addition, humans give 10% of the AI-scored essays a second read and compare their scores with the AI score to make sure there aren’t discrepancies. AI scoring was used for the same amount of essays in 2025 as in 2024, DESE said.

Crocker-Roberge said she decided to read about 1,000 essays in Lowell, but it was tough to pinpoint the exact reason some students did not receive proper credit.

However, it was clear the AI technology was deducting points without justification. For instance, Crocker-Roberge said she noticed that some essays lost a point when they did not use quotation marks when referencing a passage from the reading excerpt.

“We could not understand why an individual score was scored a zero when it should have gotten six out of seven points,” Crocker-Roberge said. “There just wasn’t any rhyme or reason to that.”

District leaders notified DESE about the problem, which resulted in approximately 1,400 essays being rescored. The state agency says the scoring problem was the result of a “temporary technical issue in the process.”

According to DESE, 145 districts were notified that had at least one student essay that was not scored correctly.

“As one way of checking that MCAS scores are accurate, DESE releases preliminary MCAS results to districts and gives them time to report any issues during a discrepancy period each year,” a DESE spokesperson wrote in a statement.

Mary Tamer, the executive director of MassPotential, an organization that advocates for educational improvement, said there are a lot of positives to using AI and returning scores back to school districts faster so appropriate action can be taken. For instance, test results can help identify a child in need of intervention or highlight a lesson plan for a teacher that did not seem to resonate with students.

“I think there’s a lot of benefits that outweigh the risks,” said Tamer. “But again, no system is perfect and that’s true for AI. The work always has to be doublechecked.”

DESE pointed out the affected exams represent a small percentage of the roughly 750,000 MCAS essays statewide.

However, in districts like Lowell, there are certain schools tracked by DESE to ensure progress is being made and performance standards are met.

That’s why Crocker-Roberge said every score counts.

With MCAS results expected to be released to parents in the coming weeks, the assistant superintendent is encouraging other districts to do a deep dive on their student essays to make sure they don’t notice any scoring discrepancies.

“I think we have to always proceed with caution when we’re introducing new tools and techniques,” Crocker-Roberge said. “Artificial intelligence is just a really new learning curve for everyone, so proceed with caution.”

There’s a new major push for AI training in the Bay State, where educators are getting savvier by the second. NBC10 Boston education reporter Lauren Melendez has the full story.

As higher education adapts to artificial intelligence’s impact, colleges and universities face the challenge of affording the computing power necessary to implement AI changes. The National Research Platform (NRP), a federally funded pilot program, is trying to solve that by pooling infrastructure across institutions.

Running large language models or training machine learning systems requires powerful graphics processing units (GPUs) and maintenance by skilled staff, Frank Würthwein, NRP’s executive director and director of the San Diego Supercomputer Center, said. The demand has left institutions either reliant on temporary donations and collaborations with tech companies, or unable to participate at all.

“The moment Google no longer gives it for free, they’re basically stuck,” Würthwein said.

Cloud services like Amazon Web Services and Azure offer these tools, he said, but at a price not every school can afford.

Traditionally, universities have tried to own their own research computing resources, like the supercomputer center at the University of California, San Diego (UCSD). But individual universities are not large enough to make the cost of obtaining and maintaining those resources cost-effective.

“Almost nobody has the scale to amortize the staff appropriately,” he said.

Even UCSD has struggled to keep its campus cluster affordable. For Würthwein, scaling up is the answer.

“If I serve a million students, I can provide [AI] services for no more than $10 a year per student,” he said. “To me, that’s free, because if you think about in San Diego, $10 is about a beer.”

A NATIONAL APPROACH

NRP adds another option for acquiring AI computing resources through cross-institutional pooling. Built on the earlier Pacific Research Platform, the NRP organizes a distributed computing system called the Nautilus Hypercluster, in which participating institutions contribute access to servers and GPUs they already own.

Würthwein said that while not every college has spare high-end hardware, many research institutions do, and even smaller campuses often have at least a few machines purchased through grants. These can be federated into NRP’s pool, with NRP providing system management, training and support. He said NRP employs a small, skilled staff that automates basic operations, monitors security and provides example curricula to partner institutions so that campuses don’t need local teams for those tasks.

The result is a distributed cloud supercomputer running on community contributions. According to a March 2025 slide presentation by Seungmin Kim, a researcher from the Yonsei University College of Medicine in Korea, the cluster now includes more than 1,400 GPUs, quadruple the initial National Science Foundation-funded purchase, thanks to contributions from participating campuses.

Since the project’s official launch in March 2023, NRP has onboarded more than 50 colleges and 84 geographic sites, according to Würthwein. NRP’s pilot goal is to reach 100 institutions, but he is already planning for 1,000 colleges after that, which would provide AI access to 1 million students.

To reach these goals, Würthwein said, NRP tries to reach both IT staff who manage infrastructure and faculty who manage curriculum. Regional research and education networks, such as California’s CENIC, connect NRP with campus CIOs, while the Academic Data Science Alliance connects with leaders on the teaching side.

WHAT STUDENTS AND FACULTY SEE

From the user side, the system looks like a one-stop cloud environment. Platforms like JupyterHub and GitLab are preconfigured and ready to use. The platform also hosts collaboration tools for storage, chats and video meetings that are similar to commercial offerings.

Würthwein said the infrastructure is designed so students can log in and run assignments and personalized learning tools that would normally require expensive computing resources.

“At some point … education will be considered subpar if it doesn’t provide that,” he said. “Institutions who have not transitioned to provide education like this, in this individualized fashion for every student, will fundamentally offer a worse product.”

For faculty, the same infrastructure supports research. Classroom usage tends to leave servers idle outside of peak times, leaving capacity for faculty projects. NRP’s model expects institutions to own enough resources to cover classroom needs, but anything unused can be pooled nationally. This could allow even teaching-focused colleges with modest resources to offer AI research experiences previously out of reach.

According to Kim’s presentation, researchers have used the platform to predict the efficiency of gene editing without lab experimentation and to map and detect wildfire patterns.

The system has already enabled collaboration beyond its San Diego campus. At Sonoma State University, faculty are working with a local vineyard to pair the system with drones, robotics and AI to enable vineyard management, Würthwein said. Making AI for classroom applications, enhancing research and enabling industry collaboration at more higher-education institutions is the overall goal.

“To me, that is the perfect trifecta of positive effects,” he said. “This is ultimately what we’re trying to achieve.”

Research from Lenovo reveals that 96% of retail sector AI deployments are meeting or exceeding expectations – outpacing other industries. While finance and healthcare are investing heavily, their results show mixed returns, highlighting sharp differences in how AI is being applied across sectors.

The CIO Playbook 2025, Lenovo’s study of EMEA IT leaders in partnership with IDC, uncovers sharply different attitudes, investment strategies, and outcomes across the Healthcare, Retail, and, Banking, Financial Services & Insurance (BFSI) industries.

Caution Pays Off for EMEA BFSI and Retail sectors

Of all the sectors analysed, BFSI stands out for its caution. Potentially reflecting the highly regulated nature of the industry, only 7% of organisations have adopted AI, and just 38% of AI budgets allocated to Generative AI (GenAI) in 2025 – the lowest across all sectors surveyed.

While the industry is taking a necessarily measured approach to innovation, the strategy appears to be paying dividends: BFSI companies reported the highest rate of AI projects exceeding expectations (33%), suggesting that when AI is deployed, it’s well-aligned with specific needs and workloads.

A similar pattern is visible in Retail, where 61% of organisations are still in the pilot phase. Despite below-average projected spending growth (97%), the sector reported a remarkable 96% of AI deployments to date either meeting or exceeding expectations, the highest combined satisfaction score among all industries surveyed.

Healthcare: Rapid Investment, Uneven Results

In contrast, the healthcare sector is moving quickly to catch up, planning a 169% increase in AI spending over 2025, the largest increase of any industry. But spend doesn’t directly translate to success. Healthcare currently has the lowest AI adoption rate and the highest proportion of organisations reporting that AI fell short of expectations.

This disconnect suggests that, while the industry is investing heavily, it may lack the internal expertise or strategy needed to implement AI effectively and may require stronger external support and guidance to ensure success.

One Technology, Many Journeys

“These findings confirm that there’s no one-size-fits-all approach to AI,” said Simone Larsson, Head of Enterprise AI, Lenovo. “Whether businesses are looking to take a bold leap with AI, or a more measured step-by-step approach, every industry faces unique challenges and opportunities. Regardless of these factors, identification of business challenges and opportunity areas followed by the development of a robust plan provides a foundation on which to build a successful AI deployment.”

The CIO Playbook 2025 is designed to help IT leaders benchmark their progress and learn from peers across industries and geographies. The report provides actionable insights on AI strategy, infrastructure, and transformation priorities in 2025 and beyond. The full CIO Playbook 2025 report for EMEA can be downloaded here.

Europe and Middle East CIO Playbook 2025, It’s Time for AI-nomics features research from IDC, commissioned by Lenovo, which surveyed 620 IT decision-makers in nine markets, [Denmark, Eastern Europe, France, Germany, Italy, Middle East, Netherlands, Spain and United Kingdom]. Fieldwork was conducted in November 2024.

Explore the full EMEA Lenovo AInomics Report here.