Sep 2026· Culture, Medicine and Psychiatry· Vol 50· 0 citations· 73 references
Medicine
TL;DR
This case situates AI-mediated listening within culturally specific moral relations of obligation and responsibility within culturally specific moral relations of obligation and responsibility, and reframes therapeutic automation as an index of the erosion of listening as a shared social good.
Abstract
The increasing use of generative AI for emotional support has prompted growing debate about the future of mental health care. This article suggests that such reliance is better understood not simply as a technological shift, but as symptomatic of a wider crisis in the politics of listening. This cultural case study examines the experiences of Lily, a 24-year-old Chinese migrant woman navigating emotional distress across uneven care infrastructures in China and the UK. Drawing on person-centred ethnography, it shows how AI chatbots come to function as provisional sites of care under conditions of precarity, gendered obligation, and moralised endurance. The analysis conceptualises these chatbot interactions as a care-patch: a temporary form of digital holding that emerges where human listening is scarce, delayed, or experienced as burdensome. Rather than treating AI use as a matter of technological adoption, the case situates AI-mediated listening within culturally specific moral relations of obligation and responsibility. In doing so, it reframes therapeutic automation as an index of the erosion of listening as a shared social good and redirects attention to the political and ethical challenge of rebuilding infrastructures of human listening capable of absorbing distress without extracting it, outsourcing it, or returning it as blame.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026