67471 research outputs found
Sort by
Engineering prompts while navigating the tradeoffs of LLM integration in First Responder Software Application
Get PDFThis paper explores the implications and complications of incorporating LLM for software engineering or LLM4SE in conjunction with prompt engineering and vibe coding, a term coined by Andrej Karpathy, using tools such as Cursor and GitHub Copilot into the development lifecycle of an end-to-end software application. We present a case here in which we use these tools to implement a modular MVC architectural system with a SQLite-SQLAlchemy backend to support the import process, classification, and triage of large-scale case data. To support the large-volume data ingestion without compromising system stability, we implemented multithreaded chunked imports, which in turn enhanced UI responsiveness, minimizing memory overhead and reducing crashing risk. In this paper, we argue that although LLMs supported certain key logic components, in order to build a robust engineering pipeline, traditional software engineering practices had to co-exist, highlighting the importance of having an expert in the loop. We also examine the extent to which domain researchers, i.e., those without experience in software development, can leverage vibe coding and LLM-assisted workflows to build complete, production-level end-to-end software systems
The Architects of Narrative Evolution: Actor Interventions Across the SAGES Framework in Information Campaigns
Get PDFNarratives in digital spaces are not merely organic phenomena—they are strategically shaped by a range of actors to influence public perception, behavior, and sociopolitical outcomes. This paper offers an actor-oriented expansion of the SAGES Framework, a five-stage model that traces the evolution of narratives from digital inception to real-world impact: Seeding, Amplification, Galvanization, Expansion, and Stickiness. Unlike prior models that emphasize platform dynamics or automation, this framework maps how adversarial and constructive actors intervene at each stage to accelerate, redirect, or counter narrative trajectories. Through comparative case studies of the 2021 Myanmar military coup and the 2022 Russia–Ukraine war, we show how narrative manipulation campaigns unfold and how targeted interventions can mitigate their effects. The SAGES framework contributes a practical lens for analyzing influence operations and developing countermeasures in an era of contested information ecosystem
Bridging the Gap in Humanoid Robot Design Using Echeloned Design Science Research
Get PDFAmid digital transformation and AI-driven innovation, service systems demand adaptable solutions that integrate technical robustness with service delivery. This study investigates the design complexities of humanoid robots as service artifacts using echeloned Design Science Research (eDSR). By integrating a literature review with iterative stakeholder engagement, our research confronts fragmentation in design practices by bridging conceptual service and interaction design with technical modularity. Our contributions are threefold: 1) identifying critical research gaps through a review of 676 papers over the past decade, demonstrating the need for integrated service and technical design in humanoid robot development; 2) advancing DSR methods to conceptualize humanoid robots as versatile artifacts in digitalized environments; and 3) proposing validated design objectives and requirements via a mixed-method approach that synthesizes literature in-sights with expert perspectives from year-long collaboration. These contributions offer guidance for creating accessible, modular, and adaptable humanoid robots that enhance value creation in complex service ecosystems
Prompt Engineering as a Cognitive Interface: Reframing Human-AI Collaboration and Digital Literacy in the Age of Generative AI
Get PDFThe rapid evolution of generative artificial intelligence (AI), typified by systems such as GPT-4, Claude, and DALL-E, has introduced new paradigms for human-machine interaction. At the center of this transformation lies the practice of prompt engineering, the act of crafting natural language instructions to elicit specific, high-quality responses from AI models. Traditionally regarded as a technical workaround or hack, prompt engineering is increasingly recognized as a structured, strategic, and cognitively rich process. This paper reframes prompt engineering as a cognitive interface, a conceptual bridge through which humans externalize mental models, intentions, and iterative hypotheses into prompts that shape AI behavior. Drawing on a mixed-methods study that analyzed 300 real-world prompts and 15 interviews with expert prompt designers, we introduce the Prompt Cognition Loop (PCL). This novel framework describes prompting as a four-phase cycle: 1) mental modeling, 2) semantic projection, 3) dialogic feedback, and 4) intent refinement. This model aligns with key theories in cognitive science and human-computer interaction, including schema theory, mental models, and reflective practice. We further explore the pedagogical potential of prompt engineering in digital education, suggesting curricular and design strategies for developing prompt literacy
Adoption of Power Balancing Technologies
Get PDFThis article examines the influence of sustainability orientation and knowledge of power balancing mechanisms on the adoption of technologies that support power balancing among photovoltaic (PV) prosumers. Prosumers are individuals who both generate and consume electricity. The findings are derived from a survey of 1054 respondents, conducted among Polish prosumers who own photovoltaic installations generating electricity for household consumption. The survey was designed based on a decision model constructed by the authors incorporating five steps of technology adoption model: knowledge acquisition, knowledge development, technology acquisition, technology implementation, and system development. Statistical analyses confirm that sustainability orientation and knowledge of power balancing mechanisms significantly influence the perceived ease-of-use and the extent of achieved benefits. This research highlights the importance of behavioral factors in shaping adoption of power balancing technologies. These insights offer practical implications for designing policies, business models, and educational strategies that promote engagement with smart energy systems among prosumers
Toward Effective AIGC for Marketing: A Theory-Driven System Design and Empirical Evaluation
Get PDFThis paper introduces a theory-driven AIGC (AI-generated content) system for marketing image generation, grounded in visual marketing theory and implemented through structured prompt engineering, AI-based image generation, and a LLM-guided evaluation and selection process. The system employs a multi-agent architecture—comprising prompting, generation, and evaluation agents—to ensure content diversity, product authenticity, and theoretical alignment. Empirical evaluations across Meta Ads and Prolific show that the system significantly outperforms baseline AIGC—which lack theoretical grounding—in marketing effectiveness, and performs competitively with professionally generated content (PGC)—exceeding it in ad engagement while trailing in perceived effectiveness. The system also supports scalable theory validation through automated, controlled image generation. This work offers a practical and theoretically grounded framework for enhancing the reliability, adaptability, and research utility of generative AI in both commercial and academic contexts
From Access to Orchestration: The Role of Data Commons in Digital Innovation
Get PDFDigital innovation increasingly relies on shared data environments that promote collaboration, experimentation, and co-creation of novel solutions. Data commons have emerged within digital ecosystems, enabling stakeholders to pool, access, and recombine data across organizational boundaries. However, despite the growing interest, we know little about how data commons facilitate digital innovation by building on shared data. Existing research often emphasizes factors such as trust, governance, or regulation, but ignores the actors and their innovation practices within data commons. This study employs an exploratory research design to investigate how exactly data commons facilitate digital innovation. We focus on the actors, practices, and sociotechnical configurations that arise within data commons and identify key mechanisms related to data that contribute to digital innovation outcomes. This enhances our understanding of data commons as enablers of digital innovation, highlights the critical role of data, and offers insights for organizations seeking to leverage shared data resources
Introduction to the Minitrack on Generative AI for Organizational, Societal, and Emotional Relationships and Partnerships
No full textResponsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
Get PDFDespite Artificial Intelligence (AI) transformative potential for public sector services, decision-making, and administrative efficiency, adoption remains uneven due to complex technical, organizational, and institutional challenges. Responsible AI frameworks emphasize fairness, accountability, and transparency, aligning with principles of trustworthy AI and fair AI, yet remain largely aspirational, overlooking technical and institutional realities, especially foundational data and governance. This study addresses this gap by developing a taxonomy of data-related challenges to responsible AI adoption in government. Based on a systematic review of 43 studies and 21 expert evaluations, the taxonomy identifies 13 key challenges across technological, organizational, and environmental dimensions, including poor data quality, limited AI-ready infrastructure, weak governance, misalignment in human-AI decision-making, economic and environmental sustainability concerns. Annotated with institutional pressures, the taxonomy serves as a diagnostic tool to surface “symptoms” of high-risk AI deployment and guides policymakers in building the institutional and data governance conditions necessary for responsible AI adoption