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Generative artificial intelligence (GenAI) and agentic systems are moving software engineering from code-centric production toward intent-centric human-agent work in which natural language, repository context, tools, tests, and governance…
Generative AI (GenAI) systems offer unprecedented opportunities for transforming professional and personal work, yet present challenges around prompting, evaluating and relying on outputs, and optimizing workflows. We argue that…
Generative Artificial Intelligence (GenAI) is rapidly reshaping higher education, yet barriers to its adoption across different disciplines and institutional roles remain underexplored. Existing literature frequently attributes adoption…
Adoption of state-of-art Generative Artificial Intelligence (GenAI) aims to revolutionize many industrial areas by reducing the amount of human intervention needed and effort for handling complex underlying processes. Automotive software…
Generative AI (GenAI) systems are inherently non-deterministic, producing varied outputs even for identical inputs. While this variability is central to their appeal, it challenges established HCI evaluation practices that typically assume…
The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency through automation of…
As organizations grapple with the rapid adoption of Generative AI (GenAI), this study synthesizes the state of knowledge through a systematic literature review of secondary studies and research agendas. Analyzing 28 papers published since…
Performance evaluation of Retrieval-Augmented Generation (RAG) systems within enterprise environments is governed by multi-dimensional and composite factors extending far beyond simple final accuracy checks. These factors include reasoning…
Although AI has significant potential to transform society, there are serious concerns about its ability to behave and make decisions responsibly. Many ethical regulations, principles, and guidelines for responsible AI have been issued…
Generative AI is reshaping software work, yet we lack clear guidance on where developers most need support and how to design it responsibly. We report a large-scale, mixed-methods study of N=860 developers examining where, why, and how they…
As the deployment of artificial intelligence (AI) is changing many fields and industries, there are concerns about AI systems making decisions and recommendations without adequately considering various ethical aspects, such as…
Generative AI is rapidly moving from research to deployment, elevating the need for responsible development, evaluation, and governance. We conduct a PRISMA guided review of 232 studies (November 2022 - December 2025), spanning large…
Context: Software engineering (SE) researchers increasingly study Generative AI (GenAI) while also incorporating it into their own research practices. Despite rapid adoption, there is limited empirical evidence on how GenAI is used in SE…
Generative AI is radically changing the creative arts, by fundamentally transforming the way we create and interact with cultural artefacts. While offering unprecedented opportunities for artistic expression and commercialisation, this…
Generative AI (genAI) tools promise productivity gains, yet miscalibrated trust and usage friction still hinder adoption. Moreover, genAI can be exclusionary, failing to adequately support diverse users. One such aspect of diversity is…
Incorporating responsible practices into software engineering (SE) for AI is essential to ensure ethical principles, societal impact, and accountability remain at the forefront of AI system design and deployment. This study investigates the…
Many guidelines for responsible AI have been suggested to help AI practitioners in the development of ethical and responsible AI systems. However, these guidelines are often neither grounded in regulation nor usable by different roles, from…
Generative Artificial Intelligence (GenAI) presents a governance challenge for STEM assessment. Unrestricted GenAI access enables task outsourcing that undermines the validity of traditional assessments; blanket prohibitions are difficult…
Model documentation plays a crucial role in promoting transparency and responsible development of AI systems. With the rise of Generative AI (GenAI), open-source platforms have increasingly become hubs for hosting and distributing these…
For AI technology to fulfill its full promises, we must have effective means to ensure Responsible AI behavior and curtail potential irresponsible use, e.g., in areas of privacy protection, human autonomy, robustness, and prevention of…