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Rethinking Programming Skills in the Age of Generative AI
Get PDFWhat does it mean to be skilled in a world where machines can now write computer code? We explore how generative AI is not only accelerating productivity, but reshaping the very meaning of programming expertise. Adopting a relational perspective, we focus on three interdependent skills that define effective human–AI collaboration: task framing, prompt design, and output interpretation. Drawing on research in programming skills development and human–AI interaction, we trace the emergence of hybrid forms of competence that blend technical reasoning with contextual judgment, skills like strategic prompting, critical debugging, and situated problem framing. These signal a broader shift in programming: from producing code to coordinating AI-assisted problem solving, requiring new forms of cognitive effort and evaluative thinking. As AI becomes an active collaborator, the focus is moving away from writing code line-by-line toward orchestrating adaptive systems. This transformation has deep implications for how technical skills are learned, applied, and socially valued in AI-mediated environments
Employees’ Sensemaking Processes in Service Robot Deployment and Use
Get PDFThe automation of tasks through AI-enabled technologies is of considerable practical and theoretical significance. Research into service robot-driven automation exposes a variety of interpretations from employees both within and across different workplaces. Understanding by users is crucial yet complex for the successful deployment and interaction with such technologies. In our study, we apply Weick’s enactment theory to examine how employees make sense of a service robot in their work environment. By analyzing 22 interviews, we constructed a process model illustrating how ambiguity triggers the sensemaking process before and during deployment, elucidating how cues are enacted and consolidated into shared understandings that are retained for future application. Our findings provide valuable insights for practitioners by underscoring how the anticipated operational benefits of task automation with service robots intersect with social dynamics and uncertainties. We contribute to the literature on IS and human-robot interaction by revealing how employees perceive service robots in everyday settings
AI-Enhanced Literature Reviews: Connecting Emerging Phenomena and Bodies of Knowledge
Get PDFBodies of Knowledge (BoKs) are structured knowledge collections that describe key concepts, terminology, and practices. Emerging research phenomena are rapidly evolving and novel domains that are attracting growing interest from researchers. The relationship between existing bodies of knowledge and emerging phenomena is complex, and offers exciting research opportunities. To address this challenge, we introduce a literature review framework called Contextualize, Visualize, and Interpret (CVI-AI), which leverages artificial intelligence and visualization capabilities to help researchers understand the relationship between emerging phenomena and existing BoKs. Our contributions are threefold. First, we illustrate how incorporating AI tools into the CVI-AI framework improves the literature review process and outcomes by examining the connections and semantics within the literature while formulating novel research questions. Second, our framework provides guidance for applying AI tools to research. Third, our study supports researchers in developing new research agendas by linking emerging phenomena to the existing BoKs
Supporting Informal Field-Based Learning in the AI Era: Key Antecedents in Human-AI Collaboration
Get PDFInformal field-based learning (IFBL) is essential for developing the current workforce in the age of AI as employees’ skills need to evolve with the introduction of these new technologies. Effective human-AI collaboration (HAIC) demands for considering factors such as individual needs, work practices and perspectives before the introduction of new technologies like generative artificial intelligence (genAI) systems. However, little is known of what factors exactly to consider toward informal fieldbased learning in HAIC and how these antecedents are related to each other. In this paper, we identify potential individual antecedents of IFBL in a HAIC context, examine their relationships and derive implications for managing the introduction of genAI into organizations
AutoTheme: A Multi-Agent Framework for Inductive Thematic Analysis with LLMs
Get PDFThematic analysis is a qualitative research method used to identify and interpret patterns in textual data. However, it can be time-consuming and challenging to replicate. While recent advancements in large language models (LLMs) and generative AI have enhanced thematic analysis, existing methods often rely on prompt-based interactions and require significant human intervention. This paper introduces an agentic AI framework comprising autonomous, goal-directed agents powered by LLMs to perform inductive thematic analysis with minimal human input. We evaluate the framework using a dataset from a Cognitive Behavioral Therapy (CBT) mobile app and compare the results with those from Latent Dirichlet Allocation (LDA), demonstrating improved efficiency, adaptability, and thematic depth. Overall, the approach has shown efficacy, especially for short texts, such as app reviews and social media posts
Speculens: AI-Augmented Mixed Reality for Imaginative Knowledge Creation and Critical Futures Discourse
Get PDFImagination plays a vital role in generating knowledge, particularly when engaging with uncertain socio-technical futures. This paper positions imagination as a legitimate epistemic practice that enables individuals to formulate, critique, and reflect on conjectural possibilities. We introduce ‘Speculens’, an AI-augmented mixed reality system that supports knowledge creation through verbalized imagination and embodied interaction. By combining speech-to-image AI with haptic engagement, Speculens allows users to externalize future visions and encounter dynamically generated ‘artifacts-from-the-future.’ Rather than optimizing for visual fidelity, the system fosters critical discourse by prompting reflection on values, assumptions, and trajectories embedded in imagined scenarios. An exploratory study (n = 19) illustrates how such speculative interactions can support situated, affective, and interpretive knowledge work, expanding the epistemological scope of futures thinking and knowledge management
Cloud or On-Premise? A Strategic View of Large Language Model Deployment
Get PDFLarge language models (LLMs) have advanced rapidly in recent years. We examine a critical decision faced by an LLM provider: whether to provide a local (on-premise) service channel in addition to cloud services. We develop a game-theoretical queueing model to analyze the economic and welfare implications of introducing an on-premise model. Our results show that offering the localization option can reduce the provider's optimal profit due to market cannibalization, yet increase users' overall surplus. Such market outcomes can be reinforced by users' privacy concerns, but may reverse when users differ significantly in their service valuations, as localization enables the provider to extract users' surplus more effectively. When localization is offered through a third party, price discrimination can further increase surplus extraction; however, the double marginalization along the AI supply chain may offset these gains. Finally, in competitive markets, localization may prompt an entrant to lower the quality of their cloud services to limit cannibalization, thereby softening price competition with the incumbent to some extent. Overall, our analysis highlights the strategic trade-offs in LLM deployment and provides guidance on pricing and localization decisions
Beyond RAG: A LLM-Based FAQ Matching Framework for Real-Time Decision Support in Contact Centers
Get PDFIn customer contact centers, human agents often face long average handling times (AHT) due to the need to manually interpret queries and search large knowledge bases (KBs). While retrieval-augmented generation (RAG) systems using large language models (LLMs) are increasingly adopted to support these tasks, they face limitations in real-time conversations—particularly with poorly formulated queries and repeated retrieval of frequently asked questions (FAQs). To address these issues, we propose a decision support framework that extends beyond RAG by combining real-time question identification with a dual-threaded FAQ matching and generation system. If the query matches a FAQ, the answer is retrieved instantly; otherwise a well-formed query is generated and routed to a RAG model. Deployed within Minerva CQ’s human-agent assist platform, our solution delivers sub-2-second responses for matched queries, significantly reduces unnecessary RAG calls, and lowers operational costs. We also introduce an automated, LLM-agentic pipeline for mining FAQs from historical transcripts, enabling continuous improvement of the FAQ knowledge base in the absence of manually curated QA pairs
The Role of Flexible Connection in Accelerating Load Interconnection in Distribution Networks
Get PDFThis paper investigates the role of flexible connection in accelerating the interconnection of large loads amid rising electricity demand from data centers and electrification. Flexible connection allows new loads to defer or curtail consumption during rare, grid-constrained periods, enabling faster access without major infrastructure upgrades. To quantify how flexible connection unlocks load hosting capacity, we formulate a flexibility-aware hosting capacity analysis problem that explicitly limits the number of utility-controlled interventions per year, ensuring infrequent disruption. Efficient solution methods are developed for this nonconvex problem and applied to real load data and test feeders. Empirical results reveal that modest flexibility, i.e., few interventions with small curtailments or delays, can unlock substantial hosting capacity. Theoretical analysis further explains and generalizes these findings, highlighting the broad potential of flexible connection
The Trifecta of Knowledge Flow: Clarity, Relevance, and Experience
Get PDFKnowledge is the lifeblood of any organization. Its effective flow, from generation to application, determines innovation, efficiency, and ultimately, success. However, simply possessing knowledge isn't enough. For knowledge to truly fuel an organization, it must flow freely and efficiently. This paper explores three key factors—clarity, relevance, and experience—that significantly influence knowledge transfer and minimize Knowledge Friction, a concept introduced by Nissen (2017) to explore their relevance in enabling action using Artificial Intelligence (AI)-generated emails