Children with autism have difficulty sustaining their selective attention during therapy sessions. Attention management techniques involve the use of verbal and visual prompting, annotated on top of the physical objects used during therapies. Here, the authors explore how augmented reality helps integrate the physical and digital worlds, mimicking current strategies for attention management in autism. They describe their design decisions when developing the Mobile Object Identification System (Mobis), a mobile augmented reality application that lets teachers superimpose digital content on top of physical objects. The results of a five-week deployment study demonstrate that Mobis is useful and easy to use, increases the sustained and selective attention of children with autism, and elicits positive emotions during therapies. This article is part of a special issue on managing attention.

This work presents a case study of a Large Language Model based system for automated classification of student survey responses. The system processes 22,286 open-text responses collected from 2062 students across 12 academic programs and 21 nationalities spanning the years 2010–2025. The system architecture has been deployed on institutional servers for security, while integrating databases, an asynchronous task queue for processing, a web-based service layer, and distributed background workers that interact with remote LLM inference services. This work provides a practical reference framework for educational institutions aiming to responsibly and effectively operationalize LLMs in real-world applications.