Sep 2026· Language Policy· Vol 25· 0 citations· 30 references
Abstract
Universities have been rapidly developing generative AI policies to govern teaching, learning, research, and administration. Although these policies are typically framed around innovation, academic integrity, and risk management, they also shape language practices and participation in multilingual institutional contexts. Drawing on language policy scholarship that conceptualizes policy as discourse-in-action, this study examines AI policy discourse at two Hispanic-Serving Institutions (HSIs) in the United States. Using critical discourse analysis of AI guidance documents, syllabus templates, faculty guidelines, task force materials, and administrative communications, we identify two interrelated patterns across both institutions. First, AI governance constructs an implicitly English-dominant user while leaving multilingual practices and perspectives largely unaddressed. Second, responsible AI use is framed through procedural compliance and individualized responsibility, creating uneven terrain for multilingual users to navigate. Together, these patterns suggest that institutional AI policies function as de facto language policies that reproduce monolingual assumptions through both what they explicitly address and what they leave unsaid. Read against each institution’s broader commitments to multilingual communities, these findings indicate that AI governance and institutional linguistic priorities have not yet been fully integrated. We conclude by proposing a five-dimension continuum for more linguistically responsive and participatory AI governance across domains of governance, language orientation, ethics, responsibility, and innovation.
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
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026