42,245 research outputs found
AI-PRISM Project Launch Press Release
No full textThe consortium of AI-PRISM (AI-Powered human-centred Robot Interactions for Smart Manufacturing) is pleased to announce the launch of this joint initiative, granted by the European Commission under the Horizon Europe programme
Human-AI Collaboration in Academic Writing: towards a Synergy Model and A Case to Include AI as a Co-Author
No full textAs generative AI systems such as ChatGPT and Gemini 2.5 become increasingly integrated into academic workflows, the question of their legitimacy, limitations, and potential in scholarly writing has become urgent. This paper presents a reflexive case study of a sustained collaboration between a domain expert in consciousness studies and Gemini 2.5, culminating in the co-authorship of a peer-reviewed research article. By analyzing exactly 37,440 words of recorded interactions, we identify patterns of synergy, including recursive refinement, conceptual amplification, and accelerated manuscript development. We argue that when guided by a knowledgeable human author, AI can act as a cognitive partner rather than a passive tool—amplifying scholarly creativity and improving efficiency without compromising academic rigor. The case supports a '1+1=3' synergy model for co-authorship, in which human steering and AI fluency converge to produce novel insights and polished output faster and more effectively than either could achieve alone. The findings advocate for a paradigm shift from prohibitive policies to the responsible, expert-guided integration of AI in academic research and writing, grounded in transparency and accountability, and present arguments for why the AI tool should be listed as a co-author despite current injunctions against such practice
AI-PRISM 1st Newsletter
No full textOur first newsletter including information about our identity and our main achievements during this first project perio
D1.1 Technology benchmarking and Project Vision
No full textD1.1 “Technology benchmarking and project vision” is the main output of tasks 1.1 and 1.2 and its main aim is to serve as a guidance for partners to stay focused on the main goals and objectives of AI-PRISM. It gathers the common vision of the AI-PRISM project agreed among all partners. In particular, D1.1 provides information about the general positioning and vision of AI-PRISM, the project business and research/technological opportunities, stakeholders, and also use cases. Project innovations and vision enablers for AI-PRISM will also be considered
Meaningful human control: actionable properties for AI system development
No full textHow can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.Interactive IntelligenceDesign AestheticsCyber SecurityHuman-Robot InteractionEthics & Philosophy of TechnologyHuman Information Communication DesignWeb Information System
A Two-Dimensional Explanation Framework to Classify AI as Incomprehensible, Interpretable, or Understandable
No full textBecause of recent and rapid developments in Artificial Intelligence (AI), humans and AI-systems increasingly work together in human-agent teams. However, in order to effectively leverage the capabilities of both, AI-systems need to be understandable to their human teammates. The branch of eXplainable AI (XAI) aspires to make AI-systems more understandable to humans, potentially improving human-agent teamwork. Unfortunately, XAI literature suffers from a lack of agreement regarding the definitions of and relations between the four key XAI-concepts: transparency, interpretability, explainability, and understandability. Inspired by both XAI and social sciences literature, we present a two-dimensional framework that defines and relates these concepts in a concise and coherent way, yielding a classification of three types of AI-systems: incomprehensible, interpretable, and understandable. We also discuss how the established relationships can be used to guide future research into XAI, and how the framework could be used during the development of AI-systems as part of human-AI teams.Accepted author manuscriptInteractive Intelligenc
D1.2 Use cases scenarios and requirements analysis (I)
No full textD1.2 presents the use case scenarios and requirement analysis of the AI-PRISM system design. It includes the initial description of the five different use cases of the AI-PRISM project and the related key performance indicators (KPIs). Furthermore, the deliverable contains the initial set of hierarchical requirements for subsequent system development: the high-level requirements identified so far by the stakeholders and the system requirements (related to Hardware, Human-Robot Collaboration aspects, the AI Enhancing Tools and Social Collaboration aspects) resulting from the analysis of the stakeholder requirements
Using Generative AI in Research
No full textThe slides accompany a workshop that is intended for graduate students to learn more about generative AI in the context of the research lifecycle. This work is licensed under a Creative Commons license so that others may share and adapt the content for other purposes as long as appropriate credit is provided to the author of the work. To access the Google slides, click here: https://bit.ly/Library_AI_Research
Learning Objectives
At the end of the session participants will be able to:
Demonstrate a basic understanding of how AI tools work
Differentiate between grounded and ungrounded AI tools
Identify key considerations for grad students/researchers
Identify ways AI tools can be used to support the phases of the research lifecycle
Identify main areas of concern with using AI tools
Outline the steps and potential resources for evaluating and citing AI outpu
Impact of AI (ChatGPT) in Education and Assessment Strategies and Policy Recommendations for the HE Sectors
No full textThe integration of artificial intelligence (AI) ChatGPT has changed the way of Higher Education (HE) and assessment processes through auto-generated study content, easily accessible information, and brainstorming exercises. The use of ChatGPT helped learners to create personalised learning plans along with generating educational content. For instance, ChatGPT is effectively used by educators in reviewing and grading student essays through analysing the content, structure, and critical insight of writing
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