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The Challenges of Balancing AI Compliance and Technological Innovations in Critical Sectors: A Systematic Literature Review
Get PDFThe rapid integration of artificial intelligence (AI) into critical infrastructure including healthcare, finance, energy, and defense, offers transformative benefits but also conflicts with evolving regulatory and governance frameworks. This paper presents a systematic literature review (SLR) to examine the challenges of balancing AI compliance and technological innovation across critical infrastructure sectors. The review follows established SLR guidelines to extract and synthesize insights from peer-reviewed articles, report, and institutional sources published between 2020–2025. The study identifies three interrelated challenges: fragmented regulations, excessive compliance burdens for smaller to medium enterprises (SMEs), and misaligned governance models. To address these challenges, the study highlights practical governance strategies, including risk-tiered regulation, compliance-by-design, and explainable AI, to support scalable and trustworthy AI deployment in critical sectors. Key contributions include a concise mapping of core AI-governance challenges and a conceptual diagram illustrating their overlap, as well as actionable strategies for policymakers and practitioners to harmonize oversight with innovation
Co-Designing a Virtual Reality System to Support Cognitive Behavior Therapy for Social Anxiety
Get PDFSocial anxiety disorder is one of the most common mental health disorders and is characterized by a fear of or avoidance of social situations. Standard treatment recommendations for this disorder include cognitive behavioral therapy (CBT). Virtual reality (VR) shows promise in improving CBT treatment, but it is necessary to understand how to design VR systems that can identify specific fears and effectively address safety seeking behaviours which play a critical role in maintaining the problem. This paper presents a co-design process and design evaluation of a VR system for this purpose, along with an evaluation of its treatment efficacy. Collaborative co-design sessions with experts in social anxiety, digital health, and game development were conducted and the design of the subsequently developed VR system was evaluated with ten participants. Based on the findings from the co-design activities and the design evaluation, this paper contributes system requirements for the development of a VR system to support CBT treatment for individuals with social anxiety disorder. The following therapeutic evaluation of the system with seven participants with reported levels of clinically relevant social anxiety symptoms showed promise that treatment in VR can potentially be used effectively to treat safety-seeking behaviors
Acceptability of Chatbot Support for Older Adolescents Involved in Cyberbullying
Get PDFChatbots may be effective tools to address cyberbullying among adolescents, but little research assesses their acceptability. To address this gap, we conducted 12 focus groups with U.S. adolescents (15-18 year-olds) to determine the acceptability of a hypothetical chatbot providing support for adolescents experiencing cyberbullying. We conducted qualitative content analysis using categories from the theoretical framework for acceptability. We find adolescents generally described the chatbot as acceptable, with the idea of such an intervention conjuring positive affect and expectations that it would be effective for perpetrators and victims and reduce the burden for seeking help. However, we also find evidence adolescents would hesitate to use such a chatbot due to ethical concerns, including whether the financial interests of the chatbot developers align with the wellbeing interests of adolescents. Chatbot-driven interventions for cyberbullying appear acceptable to adolescents, but it will be important that they be developed to prioritize wellbeing over other interests
Cybersecurity for Essential Services: Towards a Value-Based Approach
Get PDFThis paper argues that current cybersecurity theories are inadequate for protecting essential services. Existing approaches emphasize asset protection and infrastructure resilience, often overlooking the societal value of services and the complex, interdependent systems that sustain them. Rooted in computer security traditions, these models rarely account for the broader political, economic, and social consequences of service disruptions. We propose a value-informed theory of cybersecurity that centers on the services delivered, the users affected, and the dynamic relationships among actors. Based on interdisciplinary insights and real-world cases, we identify critical limitations in prevailing frameworks. We outline three key propositions: (1) cybersecurity should reflect the interests of all stakeholders in service provision; (2) it must assess value loss across societal, organizational, and individual levels; and (3) it should capture the interdependencies that shape service delivery ecosystems. This approach shifts the focus from protecting assets to sustaining essential services and the public value they create
Introduction to the Minitrack on Information Systems Research Methodology for the 2030’es.
No full textDesign Features for Explainable Generative AI (GenXAI) Systems in Knowledge-Intensive Service Work
No full textThe use of generative AI (GenAI) and large language models (LLMs) in knowledge-intensive fields like customer support is rapidly growing. While GenAI responses often appear persuasive, they carry the risk of inaccuracies and hallucinations. Hence, users must critically evaluate responses to reach appropriate reliance and knowledge utilization. Despite technological advancements, design knowledge for enhancing human-GenAI interaction from an explainable AI (XAI) perspective remains lacking. Thus, this study applies the design science research (DSR) approach to develop explanations that aid human interaction with GenAI systems. Drawing from XAI literature and human reasoning theories, we built and evaluated seven design features and instantiated a prototype that contributes to the development of reliable explainable GenAI (GenXAI)
Introduction to the Minitrack on Age and Generational Aspects in Technology Acceptance and Use
No full textA Survey of Social Media Users’ Decision-Making Processes when Sharing Information in Crisis Contexts
No full textSocial media platforms play a vital role in the rapid dissemination of essential information to the public during crisis events. They can, however, also amplify false or inaccurate information, posing life-threatening risks in such situations. As misinformation continues to spread during crises, it becomes increasingly important to understand how users consume and share crisis-related information on social media. The present study, based on a survey of 80 participants, seeks to better understand the factors that influence users’ motivations and decision-making processes when sharing content during crisis events. It explores participants’ understanding of social media algorithms, confidence in detecting false information, content reporting practices, fact-checking habits, and views on accountability of social media platforms. By examining how participants interact with information during crises, the study aims to identify ways to promote more responsible sharing practices to combat false information online
Evaluating and Improving Prompt Quality in LLM-Based Assistants: A Synthesis of Criteria and Indicators
No full textGenerative AI (GenAI) assistants, particularly large language models (LLMs), are gaining increasing relevance across domains. The quality of outputs generated by these systems is highly contingent on the input prompts, giving rise to new professional roles such as prompt engineers. In this study, we systematically examine evaluation criteria and optimization methods that can improve prompt quality. Drawing on a systematic literature review, we identify key criteria, including clarity, accuracy, and precision, and initial measurement techniques. In addition, we synthesize common optimization methods such as iterative refinement and shot-based prompting. Our work contributes to the growing efforts to standardize the evaluation and improvement of prompts in interactions with LLM-based assistants, thereby fostering a more rigorous and coherent understanding of the prompt quality construct
The Evaluator Age: Generative AI and the Future of Knowledge Work
Get PDFGenerative artificial intelligence is dramatically re-ordering knowledge work, propelling organizations from the Producer Age into what we term the Evaluator Age. In this emerging phase, human value lies not in generating content but in critically assessing, refining, and ethically stewarding machine outputs. We trace the progression from the Preserver Age—when humans safeguarded scarce knowledge—through the Producer Age of mass creation, to today’s evaluator imperative. Generative models now excel at first-draft production, shifting the strategic bottleneck to quality control, bias detection, and contextual fit—tasks uniquely served by professionals who blend deep domain expertise, AI literacy, and moral judgment. We argue that universities must redesign curricula accordingly, foregrounding evaluative competencies alongside technical fluency. By positioning graduates as skilled custodians of AI-generated insight, higher education can secure its relevance and ensure that organizations harness AI responsibly and effectively