AbstractGenAI is increasingly capable of performing activities central to Informatics learning, includinggenerating, explaining, modifying, and debugging code, raising questions about what studentsneed to learn and how teachers' roles may change. This study explored Informatics teachers'perceptions of the changing purposes and practices of Informatics education in the GenAI era.An exploratory qualitative design was employed with 12 Informatics teachers from a collegespecializing in Informatics education. Data were collected through semi-structured interviewsand analysed using reflexive thematic analysis. Three interconnected themes were identified.First, teachers perceived a shift in emphasis from producing correct computational outputstoward understanding, explaining, evaluating, and modifying both independently produced andAI-generated solutions, while continuing to regard foundational programming knowledge asessential. Second, teachers described their roles as increasingly involving the evaluation,contextualization, and mediation of AI-supported learning rather than primarily providinginformation and solutions. Third, participants negotiated a contextual boundary between AI asassistance and AI as substitution, particularly when evaluating whether successful taskperformance represented genuine student competence. The findings suggest that GenAI does notsimply introduce a new instructional tool but challenges established assumptions aboutcomputational competence, pedagogical expertise, and evidence of learning. Informaticseducation may therefore need to balance independent computational competence with criticaljudgement and purposeful human-AI collaboration.
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
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.
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