Reno, Nev. (January 27, 2206) – DRI’s STEM Education Program was recently awarded a $2.7 million grant from the U.S. Department of Education, Fund for the Improvement of Postsecondary Education. The four-year project will address the need to advance artificial intelligence (AI) and computer science classroom education in grades K-12. To accomplish this, training and resources will be provided to undergraduate preservice educators and those already in the classroom, with a focus on Nevada’s rural communities.

As artificial intelligence and software systems increasingly operate in physical environments — from medical devices and vehicles to public infrastructure — the consequences of failure extend beyond data loss to real-world harm. In this feature, University of Virginia School of Data Science Associate Professor Daniel Graham explains why cybersecurity should be understood as a form of quality assurance and safety, akin to engineering standards used for bridges or power systems. Drawing on his research in secure embedded systems and his work on system evaluation and penetration testing, Graham outlines the need for stronger accountability, oversight, and professional norms to ensure that intelligent systems are not only powerful, but also trustworthy.

Generative artificial intelligence is rapidly reshaping education, prompting many schools to restrict or ban its use. In this interview, University of Virginia School of Data Science lecturer Bryan Christ argues that prohibiting AI in classrooms may do more harm than good by denying students opportunities to learn responsible, ethical use in low-stakes learning environments. Drawing on research and classroom experience, Christ outlines how AI can support learning, accessibility, and creativity when thoughtfully integrated into education and why students should have a voice in shaping AI policies that affect their academic experience.

Can you imagine anticipating the struggle between the immune system and a tumor using mathematical tools that understand each patient's uncertainty? Researchers at ESPOL developed an innovative model based on Type-3 Fuzzy Logic that simulates precisely that confrontation. Unlike traditional models, this proposal integrates individual biological variability and delays in the immune response, allowing highly accurate prediction of chaotic patterns and possible tumor relapses. In addition, the study generates clear and interpretable risk maps that facilitate the design of personalized and explainable therapies, aligning with the principles of Explainable Artificial Intelligence (XAI) to support clinical decision-making.