Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We test whether this design scales by holding SkillsBe...
The paper investigates how to help users monitor their own and an AI system’s competence when using AI assistance. It identifies 30 interventions from experts and organizes them into a design space based on timing, target competence, and source of cue. A large experiment shows that reliability cards and contrasting replies reduce estimation error and overconfidence, though they do not improve task performance.
By Manuel A. D. Santos, Paul Thiesse, Steeven Villa, Daniela Fernandes, Albrecht Schmidt, Verena Distler, Robin Welsch
arXiv:2605. 15850v3 Announce Type: replace-cross Abstract: In recent years, generative AI (GenAI) in educational settings has become ubiquitous in university students' daily lives, despite its potential to induce over-reliance, metacognitive disengagement, and diminished learning when used unrestrictedly.
By Janne Rotter, Pau Benazet i Montobbio, Davinia Hern\'andez-Leo
arXiv:2610.18522v1 Announce Type: new
Abstract: Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We te...
By Jinlin Liu, Wei Chen, Xiaojin Zhang
arXiv:2606. 15601v1 Announce Type: cross Abstract: We introduce SCAN -- a human-centric decision-making framework to facilitate learners for effective task allocation with Generative Artificial Intelligence (GenAI) based on Vygotsky's Zone of Proximal Development and Metacognition.
By Fendi Tsim, Alina Gutoreva
As assistants move from reactive answering toward proactive intervention, memory changes role: it no longer only supplies the answer to a query, it determines whether and when to speak at all. Prior w...