How can computer science educators teach students to calibrate their trust in GenAI programming tools?
Reports and Proceedings
Updates every hour. Last Updated: 18-Jan-2026 18:11 ET (18-Jan-2026 23:11 GMT/UTC)
How much do undergraduate computer science students trust chatbots powered by large language models like GitHub CoPilot and ChatGPT? And how should computer science educators modify their teaching based on these levels of trust? These were the questions that a group of U.S. computer scientists set out to answer in a study that will be presented at the Koli Calling conference Nov. 11 to 16 in Finland. In the course of the study’s few weeks, researchers found that trust in generative AI tools increased in the short run for a majority of students. But in the long run, students said they realized they needed to be competent programmers without the help of AI tools.
When writing program code, software developers often work in pairs—a practice that reduces errors and encourages knowledge sharing. Increasingly, AI assistants are now being used for this role. But this shift in working practice isn’t without its drawbacks, as a new empirical study by computer scientists in Saarbrücken reveals. Developers tend to scrutinize AI-generated code less critically and they learn less from it. These findings will be presented at a major scientific conference in Seoul.
Air pollution is a major environmental challenge of this century. In a recent Journal of Environmental Sciences review paper, scientists from the Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, have highlighted potential technologies for direct purification of air pollutants in the environment, including photocatalysis and ambient non-photocatalytic approaches. They also propose the novel concept of an ‘Environmental Catalytic City.’
In an effort to create simpler next-generation lighting solutions, researchers from the University of Turku in Finland have developed a colour-tunable white OLED. It is a top light-emitting device that produces white light from a single organic layer and two standard aluminum electrodes, eliminating the need for scarce indium tin oxide. This streamlined design replaces the need for complex organic stacks with smart optics, which promises to lower manufacturing costs and reduce reliance on scarce materials.
A team of researchers from the National Institutes for Quantum Science and Technology (QST) and Tokyo Metropolitan University has developed a protein-based gel that replicates the softness and fibrous structure of native skeletal muscle tissue. This innovation enables the cultivation of muscle cells with slow-twitch characteristics, offering new possibilities for treating muscle loss, enhancing metabolic function, and developing next-generation biomedical devices.