Published: April 24, 2025
Language: Английский
Published: May 11, 2024
Among creative professionals, Generative Artificial Intelligence (GenAI) has sparked excitement over its capabilities and fear unanticipated consequences. How does GenAI impact User Experience Design (UXD) practice, are fears warranted? We interviewed 20 UX Designers, with diverse experience across companies (startups to large enterprises). probed them characterize their practices, sample attitudes, concerns, expectations. found that experienced designers confident in originality, creativity, empathic skills, find GenAI's role as assistive. They emphasized the unique human factors of "enjoyment" "agency", where humans remain arbiters "AI alignment''. However, skill degradation, job replacement, creativity exhaustion can adversely junior designers. discuss implications for human-GenAI collaboration, specifically copyright ownership, agency, AI literacy access. Through lens responsible participatory AI, we contribute a deeper understanding opportunities UXD.
Language: Английский
Citations
31
Published: May 11, 2024
Data storytelling is powerful for communicating data insights, but it requires diverse skills and considerable effort from human creators. Recent research has widely explored the potential artificial intelligence (AI) to support augment humans in storytelling. However, there lacks a systematic review understand tools perspective of human-AI collaboration, which hinders researchers reflecting on existing collaborative tool designs that promote humans' AI's advantages mitigate their shortcomings. This paper investigated with framework two perspectives: stages workflow where serves, including analysis, planning, implementation, communication, roles AI each stage, such as creators, assistants, optimizers, reviewers. Through our we recognize common collaboration patterns tools, summarize lessons learned these patterns, further illustrate opportunities
Language: Английский
Citations
17
Published: June 8, 2023
Human-Centered Artificial Intelligence (AI) focuses on AI systems prioritizing user empowerment and ethical considerations. We explore the importance of usercentric design principles guidelines in creating technologies that enhance experiences align with human values. It emphasizes through personalized explainable AI, fostering trust agency. Ethical considerations, including fairness, transparency, accountability, privacy protection, are addressed to ensure respect rights avoid biases. Effective collaboration is emphasized, promoting shared decision-making control. By involving interdisciplinary collaboration, this research contributes advancing human-centered providing practical recommendations for designing experiences, promote empowerment, adhere standards. harmonious coexistence between humans enhancing well-being autonomy a future where benefit humanity. Overall, highlights significance positive impact. centering users' needs values, can be designed empower individuals their experiences. considerations crucial fairness transparency. With effective we harness potential create aligns aspirations promotes societal well-being.
Language: Английский
Citations
38
ACM Transactions on Computer-Human Interaction, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 5, 2025
Despite recent advancements, real-world use of Artificial Intelligence (AI) in radiology remains low, often due to the mismatch between AI offerings and situated challenges faced by healthcare professionals. To bridge this gap, we conducted a field study at nine medical sites Denmark Kenya with two goals: (1) understand radiologists during chest X-ray practice; (2) envision alternative futures that align collaborative clinical work. This uniquely grounds design insights comprehensive characterisation diagnostic work across multiple geographical institutional contexts. Building on ideas articulated interviewed (N=18), conceptualised five visions transcend traditional notions support. These emphasise usefulness AI-based systems depends their configurability flexibility three dimensions: type site, expertise professionals, situational patient Addressing these dependencies requires expanding space envisioning rooted realities practice rather than solely following trajectory development.
Language: Английский
Citations
1
Behaviour and Information Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 40
Published: Feb. 16, 2025
Language: Английский
Citations
1
Published: May 11, 2024
Landscape renderings are realistic images of landscape sites, allowing stakeholders to perceive better and evaluate design ideas. While recent advances in Generative Artificial Intelligence (GAI) enable automated generation renderings, the end-to-end methods not compatible with common processes, leading insufficient alignment idealizations limited cohesion iterative design. Informed by a formative study for comprehending requirements, we present PlantoGraphy, an system that allows interactive configuration GAI models accommodate human-centered practice. A two-stage pipeline is incorporated: first, concretization module transforms conceptual ideas into concrete scene layouts domain-oriented large language model; second, illustration converts using fine-tuned low-rank adaptation diffusion model. PlantoGraphy has undergone series performance evaluations user studies, demonstrating its effectiveness rendering high recognition functionality.
Language: Английский
Citations
7
Published: May 11, 2024
Maximizing educational impacts with learning technologies is one of the areas that researchers and practitioners are concerned about in field Human-Computer Interaction (HCI) human-centered artificial intelligence (HCAI). In this case study, we report user experiences lessons learned Enactive Virtual Environment for teaching practice (EVETeach) AI-powered virtual student agents called Evelyn. We conducted a study research design. collected multiple sources data from 24 teachers, including participatory observations, notes, semi-structured interviews, computer-based conversation logs, audio-, video-, screen-recordings, cognitive walkthrough. identified following salient emerging findings as learned: 1) Student teachers value relate to practices reality simulation conversational agents, 2) inject humor facilitate situational social practice, 3) maintain authentic discourse promote teachers' pedagogical reasoning.
Language: Английский
Citations
6
Journal of Geovisualization and Spatial Analysis, Journal Year: 2024, Volume and Issue: 8(2)
Published: June 26, 2024
Abstract Artificial intelligence (AI) has increasingly been integrated into various domains, significantly impacting geospatial applications. Machine learning (ML) and computer vision (CV) are critical in urban decision-making. However, AI implementation faces unique challenges. Academic literature on responsible largely focuses general principles, with limited emphasis the domain. This important gap scholarly work could hinder effective integration Our study employs a multi-method approach, including systematic academic review, word frequency analysis insights from grey literature, to examine potential challenges propose strategies for (GeoAI) integration. We identify range of practices relevant complexities using planning its implementation. The review provides comprehensive actionable framework adoption domain, offering roadmap researchers practitioners. It highlights ways optimise benefits while minimising negative consequences, contributing sustainability equity.
Language: Английский
Citations
6
International Journal of Human-Computer Interaction, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 32
Published: March 27, 2024
This paper describes the motivation, framework, concepts, method, implementation, and results of HCII2023 Design Café, a dedicated participatory highly interactive design workshop held in Copenhagen July 2023. Motivated by ubiquitous challenges our world is facing, this initiative had goal to explore six main issues from an interdisciplinary perspective with focus on UN SDG 11: "Sustainable Cities Communities" HCI Grand Challenge 2 "Human-Environment Interactions." The were formulated as questions presented participants working small groups. (1) How create inclusive ethical smart cities? (2) establish trust between people environments? (3) address privacy concerns environments that adopt "disappearing computer" paradigm? (4) promote explainability transparency policies measures citizens cities or general? (5) incentives rewards for engagement sustainable behavior at personal well collective/corporate level? (6) measure success impact city projects? method approach Café tailored composition guided, structured format combined inspired processes informal communication exchange knowledge ideas. Aligned moderation, minimum set rules, relevant topics group motivated rotating formations provides structure achieving results. show are not independent each other, but require holistic view, considering various dependencies synergies when exploring solutions. Nevertheless, importance first establishing higher-level goals based approaches fostering human dignity rights key. Acceptance overall goals, processes, regulations was considered fundamental pre-requisite change towards declared goal. needs measures. role twofold. Offering must include planning how their impact. Effective measurement depends heavily institutions privacy. Explainability should become one guidelines steer control decision making, implementations all activities. scale these societal still be recognized those responsible. It essential educate makers psychological effects sense comprehensibility, finally acceptance well-being moving humanity-centered design.
Language: Английский
Citations
5
Published: May 11, 2024
The ability to make appropriate delegation decisions is an important prerequisite of effective human-AI collaboration. Recent work, however, has shown that people struggle evaluate AI systems in the presence forecasting errors, falling well short relying on appropriately. We use a pre-registered crowdsourcing study (N = 611) extend this literature by two underexplored crucial features human decision-making: choice independence and error type. Subjects our repeatedly complete prediction tasks choose which predictions they want delegate system. For one task, subjects receive decision heuristic allows them informed relatively accurate predictions. second task substantially harder solve, must come up with their own rule. systematically vary system's performance such it either provides best possible for both or only two. Our results demonstrate violate taking AI's unrelated into account. Humans who superior expertise domain significantly reduce reliance when model makes systematic errors complementary domain. In contrast, humans increase errs Furthermore, we show differentiate between types effect conditional considered This first empirical exploration context have broad implications future design, deployment, application systems.
Language: Английский
Citations
5