News

A large University of Maryland trial has produced a striking result for universities rolling AI into teaching: students offered a course-integrated AI tutor performed worse in matched classes and engaged substantially less with existing course materials.

The randomized study involved 2,379 undergraduate students and 30 instructors across multiple disciplines during fall 2025.

In the clearest comparison, between sections of the same course, students given access to the AI tutor finished around four percentage points lower in their final grades, equivalent to 0.37 standard deviations.

Concerns that suspensions disproportionately affect students of color have led many school districts to prioritize other forms of school discipline, including detention, loss of privileges, and restorative or behavioral interventions and supports. A new study by Youngsun Lee and Jing Liu at the University of Maryland, College Park, and Emily K. Penner at the University of California, Irvine finds that the strategy hasn’t closed at least one large district’s racial and ethnic discipline gaps.

Jing Liu, founding director of the Center for Educational Data Science Innovation and an associate professor in education policy at the University of Maryland, said issuing these guidelines are a good starting point, but it may be challenging to keep up as the technology rapidly develops.

“AI is very different compared to prior technology breakthroughs [because] they are evolving so fast and they can do things that are directly different this month compared to last month,” Liu said. “[The guidelines] cannot be a static thing. It was to be very adaptive.”

Accelerate today named Eedi and Edlight among its latest round of grantees under the Call for Effective Technology (CET) program, adding two new AI-enabled learning tools to EDSI's evaluation portfolio. With this award, EDSI will evaluate both platforms in public school classrooms during the 2026-27 school year.

This project uses AI-supported analysis of recorded classroom interactions to uncover the student skills embedded in real‑time instructional exchanges and assess how well these competencies—spanning both core mathematical reasoning tied to upward mobility and more durable skills such as agency, vocabulary use, and self‑management where disparities often emerge—serve as early precursors of long‑run mobility.

Jing Liu, an education policy associate professor at this university, said a rubric to evaluate these tools in schools is beneficial to ensure the selected AI platforms improve learning. This bill expands on the current education structure and allows Maryland to build on AI literacy momentum from an educational standpoint, he said. 

Liu, the director for this university’s educational data science and innovation center, also emphasized the need for professional training due to the lack of educators familiar with AI. Teachers can also benefit from using AI for lesson planning, providing feedback and evaluating students’ work, he added. 

As fears rise about the bridge between AI and the labor market, Liu said preparing students for an “AI–infused future” becomes more important.  

“AI is not just changing education,” he said. “It is changing everything, right, from the labor force, to higher education, to K-12 education, to our daily work.” 

One area with strong emerging evidence is AI-enabled instructional coaching. Emerging research, supported by Overdeck Family Foundation, on coaching tools like M-Powering Teachers suggests that AI-enabled coaching can improve teacher quality while reducing overall coaching time, with preliminary insights suggesting increased student engagement, deeper mathematical thinking, and positive experiences with personalized, practice-embedded feedback for participating students. While much of this research is just beginning, these initial descriptive data points offer an example of how an AI-powered intervention can measurably benefit educators and students.

Students, parents and school principals all instinctively know that some teachers are better than others. Education researchers have spent decades trying — with mixed success — to calculate exactly how much better.

What remains far more elusive is why.

A new study suggests that one surprisingly simple difference between stronger and weaker math teachers may be how often they use mathematical vocabulary, words such as “factors,” “denominators” and “multiples,” in class.

The International Workshop on Rethinking Children’s Automatic Speech Recognition for Education—held on September 10 at the University at Buffalo—brought together more than 50 researchers, practitioners, funders, developers, and educators to foster real conversations about moving the field forward, says Jing Liu, an associate professor of education policy at the University of Maryland.

“The data problem is real and urgent,” says Liu, who directs the Center for Educational Data Science and Innovation at UMD. “We're facing a massive shortage of children's speech datasets, which creates barriers for developing accurate, fair ASR systems.

Meanwhile, Liu adds, the potential applications in EdTech and educational assessment are mind-blowing—including uses like real-time reading support, authentic language assessment, and truly adaptive learning platforms.