Developers now have access to a growing array of increasingly autonomous AI tools for software development. While many studies examine copilots that provide chat assistance or code completions, evaluations of coding agents—which can automatically write files and run code—still rely on static benchmarks. We present the first controlled study of developer interactions with coding agents, characterizing how more autonomous AI tools affect productivity and experience. We evaluate two leading copilot and agentic coding assistants, recruiting participants who regularly use the former. Our results show agents can assist developers in ways that surpass copilots (e.g., completing tasks humans may not have accomplished) and reduce the effort required to finish tasks. Yet challenges remain for broader adoption, including ensuring users adequately understand agent behaviors. Our findings reveal how workflows shift with coding agents and how interactions differ from copilots, motivating recommendations for researchers and highlighting challenges in adopting agentic systems.

Integrated circuits used in modern, digital hearing aids are mixed-signal devices that combine analog and digital circuits on the same die. Due to the extreme size and power constraints encountered in hearing aids, the assignment of die area for such systems-on-chip (SOCs) must be carefully considered. The relative allocation between analog circuits, digital gates and on-chip memory has been relatively constant for traditional DSP-based systems. The precision and computational requirements for artificial intelligence (AI) based algorithms, however, are very different from traditional DSP. This is driving changes in die area allocation, starting with some recently launched devices. Despite these changes, projections based on recent growth trends show that future SOCs will only be able to support the smallest neural networks currently proposed for hearing aids. Minimizing data precision and pruning small-valued weights are shown to be essential to deploy deep-learning algorithms on hearing aid SOCs.

This is a research work-in progress paper. The hidden curriculum is the invisible norms, ideals, and values in engineering education that are not part of the formal curriculum. At the University of Calgary, a first-year program in mental wellbeing aims to counteract some of the hidden curriculum narratives by providing students with humanizing support and reflections throughout their curriculum. As part of this, the program delivered a module on hidden curriculum, where students were required to answer three reflective open-ended questions. This paper provides a preliminary qualitative content analysis of the student responses to a specific question: what are some of these hidden lessons taught about engineering? The results found three main themes: engineering is difficult, engineering requires teamwork and collaboration, and engineering should be independent. These early results provide a unique insight into the student perspective on the hidden curriculum of engineering.