As more and more people turn to AI chatbots for quick, 24/7 medical advice, a new perspective paper co-authored by faculty at Binghamton University, State University of New York explores the risks this practice carries that are largely invisible to users – and healthcare systems.

Most AI language models never see the individual letters of a word directly. A new method changes that by retrofitting existing models at a fraction of the usual training cost.

Dr. Daniel Hajovsky, associate professor in the Department of Educational Psychology at Texas A&M University, studies human intelligence and the cognitive processes that affect learning. In a study published in the Journal of Intelligence, Hajovsky and his co-authors examined the relationship between cognitive abilities and mathematics skills. The findings point to fluid reasoning and comprehension knowledge as two of the most consistent cognitive predictors of math skills.

In this Policy Article, Alp Sungu and colleagues report on a large-scale randomized controlled trial evaluating the impacts of classroom phone restrictions on student outcomes in India. The findings show that restricting phone use can support learning, particularly among lower-performing students. Amidst growing concerns about smartphones’ effects on learning, mental health, and peer relationships, phone bans are being increasingly adopted in schools worldwide. However, large-scale experimental evidence supporting whether these bans improve learning or social outcomes is lacking. To address this gap, Sungu et al. performed a randomized controlled trial involving 16,683 students across 10 higher education institutions in Odisha, India – a state where smartphone ownership and use among youth aged 15 to 29 exceeds 70%. The authors measured academic performance, attitudes toward phone restrictions, well-being, motivation, digital use, and online harassment.

 

The findings show that banning smartphones in class improved academic performance, particularly among lower-performing students and first-year undergraduates. Effects were minimal among higher-performing, postgraduate, and STEM students. Removing phones also reduced disparities in GPA, suggesting that restrictions may help close gaps in learning outcomes. Importantly, attendance rates did not change, indicating that the improvement was likely driven by greater attention and more productive classroom dynamics, rather than students simply attending class more often. Students who experienced classroom phone bans became more supportive of the restrictions and more likely to view them as beneficial, while showing less preference for unrestricted phone use. Classroom observations also found less disruptive behavior and fewer off-topic conversations, along with greater teacher engagement. According to Sungu et al., these findings suggest that students may become more receptive to phone restrictions after experiencing a more focused, phone-free classroom environment.

A new large language model (LLM)-based artificial intelligence framework called AlphaProof Nexus can autonomously tackle select math problems, seeking proofs and using verification to ensure the resulting solutions are logically sound, researchers report. The system solved dozens of previously open problems across several mathematical fields, suggesting that artificial intelligence (AI) could become a useful tool for automated mathematical discovery and research. “[The authors report] that even unsuccessful proof attempts by AI could help them understand the problems better and make progress on solving them,” write Jeremy Avigad and Matthew Ballard in a related Perspective. “This underscores that an essential goal of developing AI for mathematics is to support mathematicians in the search for knowledge and understanding that lead to further advances.” Large language models (LLMs) have demonstrated growing ability to solve difficult mathematical problems, but their tendency to produce subtle logical errors or “hallucinations” makes them unreliable for research without extensive human expert review. One promising way to mitigate these issues is to have AI agents generate mathematical proofs in formal programming languages such as Lean, which automatically verifies each logical step and prevents errors from going unnoticed.  While this approach has been successfully applied to competition mathematics and in the human-aided formalization of natural language arguments, its potential in solving open research-level mathematical problems remains unknown.

 

To address this gap, George Tsoukalas and colleagues developed AlphaProof Nexus, a framework that uses multiple AI agents to search for mathematical proofs with feedback from the Lean compiler. Tsoukalas et al. also developed a more advanced, full-featured agent that coordinated subagents through an evolutionary algorithm to use AlphaProof as a specialized proof tool. In tests, the system was able to solve nine of the 353 attempted Erdős problems, including two that had remained unsolved for more than 50 years. It was also able to solve 44 of 492 open On-Line Encyclopedia of Integer Sequences (OEIS) conjectures as well as several other research-level problems in fields such as algebraic geometry, optimization, quantum optics, and graph theory.