Crystallizing Theory Evaluation: A Human-AI Collaborative Framework for Multi-Perspective Nursing Theory Analysis.

Medication errors, including dispensing errors, represent a substantial worldwide health risk with significant implications in terms of morbidity, mortality, and financial costs. Although pharmacists use methods like barcode scanning and double-checking for dispensing verification, these measures exhibit limitations. The application of artificial intelligence (AI) in pharmacy verification emerges as a potential solution, offering precision, rapid data analysis, and the ability to recognize medications through computer vision. For AI to be embraced, it must be designed with the end user in mind, fostering trust, clear communication, and seamless collaboration between AI and pharmacists. This study aimed to gather pharmacists' feedback in a focus group setting to help inform the initial design of the user interface and iterative designs of the AI prototype. A multidisciplinary research team engaged pharmacists in a 3-stage process to develop a human-centered AI system for medication dispensing verification. To design the AI model, we used a Bayesian neural network that predicts the dispensed pills' National Drug Code (NDC). Discussion scripts regarding how to design the system and feedback in focus groups were collected through audio recordings and professionally transcribed, followed by a content analysis guided by the Systems Engineering Initiative for Patient Safety and Human-Machine Teaming theoretical frameworks. A total of 8 pharmacists participated in 3 rounds of focus groups to identify current challenges in medication dispensing verification, brainstorm solutions, and provide feedback on our AI prototype. Participants considered several teaming scenarios, generally favoring a hybrid teaming model where the AI assists in the verification process and a pharmacist intervenes based on medication risk level and the AI's confidence level. Pharmacists highlighted the need for improving the interpretability of AI systems, such as adding stepwise checkmarks, probability scores, and details about drugs the AI model frequently confuses with the target drug. Pharmacists emphasized the need for simplicity and accessibility. They favored displaying only essential information to prevent overwhelming users with excessive data. Specific design features, such as juxtaposing pill images with their packaging for quick comparisons, were requested. Pharmacists preferred accept, reject, or unsure options. The final prototype interface included (1) checkmarks to compare pill characteristics between the AI-predicted NDC and the prescription's expected NDC, (2) a histogram showing predicted probabilities for the AI-identified NDC, (3) an image of an AI-provided "confused" pill, and (4) an NDC match status (ie, match, unmatched, or unsure). In partnership with pharmacists, we developed a human-centered AI prototype designed to enhance AI interpretability and foster trust. This initiative emphasized human-machine collaboration and positioned AI as an augmentative tool rather than a replacement. This study highlights the process of designing a human-centered AI for dispensing verification, emphasizing its interpretability, confidence visualization, and collaborative human-machine teaming styles.

Artificial intelligence (AI) is embedded in a wide variety of Smart City applications and infrastructures, often without the citizens being aware of the nature of their “intelligence”. AI can affect citizens’ lives concretely, and thus, there may be uncertainty, concerns, or even fears related to AI. To build acceptable futures of Smart Cities with AI-enabled functionalities, the Human-Centered AI (HCAI) approach offers a relevant framework for understanding citizen perceptions. However, only a few studies have focused on clarifying the citizen perceptions of AI in the context of smart city research. To address this gap, we conducted a two-phased study. In the pre-study, we explored citizen perceptions and experiences of AI with a short survey (N = 91). Second, scenario-based interviews (N = 7) were utilized to gain in-depth insights of citizen perceptions of AI in the Smart City context. Five central themes were recognized: (1) I don’t like them monitoring me, (2) I want maximum gain for minimum effort, (3) I don’t want AI to mimic people, (4) I’ll avoid using AI if I consider the risk too high, and (5) I don’t need to be concerned about AI. These offer an idea of human-centered requirements worth considering while designing AI applications for future Smart Cities.

The integration of artificial intelligence (AI) and machine translation (MT) technologies into the language service industry is reshaping professional roles, workflows, and expectations. This study examines how AI is discursively constructed by two key groups within the translation profession: freelance translators and Language Service Providers (LSPs). Although both groups engage with similar AI tools, their perspectives differ due to varying professional priorities, constraints, and positionalities. Methodologically, the study uses a mixed-methods approach that combines sentiment analysis with qualitative linguistic and thematic analysis of online content, such as blog posts and social media discussions—to explore how these groups conceptualize AI’s impact on their work. Blogs, forums, and social media posts offer real-time reflections on technological change, making them valuable sources for understanding grassroots responses. The corpus is made up of a convenience sample of 45 blog and social media posts with comments discussing GenAI and new AI tools in the translation field. Findings reveal a clear divide in perceptions: LSPs tend to view AI as a beneficial tool that enhances efficiency, scalability, and competitiveness, while freelance translators often express concerns regarding translation quality, job insecurity, and diminishing professional standards. These concerns reflect broader anxieties about the technologization and platformization of the profession, with translators emphasizing the loss of control and autonomy in increasingly algorithm-driven workflows. An important insight from the study is the translators’ active resistance to anthropomorphizing MT, evident in their insistence that the designation of 'translator' applies exclusively to humans, and that translations are only those carried out by human translators. The research highlights the need for more inclusive, user-centered approaches in the design and implementation of AI tools. Specifically, it advocates for participatory design, usability testing, and greater engagement with diverse stakeholders to ensure AI technologies address both industry needs and the professional concerns of translators. By aligning with Human-Centered AI principles, future AI systems could better augment human capabilities, improve work conditions, and foster collaboration within the profession.

This study responds to the increasing call for thoughtful theological and ethical engagement with Artificial Intelligence (AI) by examining the role of personal theological reflection using Generative Artificial Intelligence (GenAI) content in Catholic theological education. It investigates how both educators and students might utilize AI-generated imagery as a pedagogical resource with which to enrich theological insight and foster ethical discernment, particularly through the lens of theological virtue ethics. AI is not a substitute for all human tasks. However, the use of AI holds potential for theology and catechetical religious education. Following Gläser-Zikuda’s model of Self-Reflecting Methods of Learning Research, this study systematically engages in reflective observation to examine how the use of GenAI in theology classrooms has influenced personal theological thinking, pedagogical practices, and ethical considerations. It documents experiences using common generative AI tools such as ChatGPT, Canva, Meta AI, Deep AI, and Gencraft in theology classes. The principles of virtue ethics and Human-Centered Artificial Intelligence (HCAI) offer a critical framework for ethical, pedagogical, and theological engagement. The findings contribute to the emerging interdisciplinary discourse on AI ethics and theology, and religious pedagogy in the digital age.

The growing use of artificial intelligence in healthcare, employment, and business has raised significant concerns regarding safety, ethics, and societal impact. While prior work offers ethical guidelines and technical solutions independently, a gap remains between ethical principles and practical system design. This study presents a unified framework that integrates ethical values, technical safeguards, and governance mechanisms to support safe and human-centered artificial intelligence. The framework maps principles such as fairness, accountability, transparency, and non-maleficence to engineering practices including safe reward design, explainable models, robustness testing, and privacy-preserving techniques. Governance and regulatory alignment are incorporated as continuous components of the AI lifecycle. Use-case analyses demonstrate how ethical objectives can be operationalized across real-world domains. The results emphasize that AI safety must be treated as a socio-technical process combining technical alignment with institutional oversight. This work contributes a practical approach for translating ethical commitments into trustworthy and resilient AI systems.

As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.

Human-centered approaches are vital to manage the rapid growth of artificial intelligence (AI) in higher education, where AI-driven applications can reshape teaching, research, and student engagement. This study presents the Human-Centered AI for Systematic Literature Reviews (HCAI-SLR) framework to guide educators and researchers in integrating AI tools effectively. The methodology combines AI augmentation with human oversight and ethical checkpoints at each review stage to balance automation and expertise. An illustrative example and experiments demonstrate how AI supports tasks such as searching, screening, extracting, and synthesizing large volumes of literature that lead to measurable gains in efficiency and comprehensiveness. Results show that HCAI-driven processes can reduce time costs while preserving rigor, transparency, and user control. By embedding human values through constant oversight, trust in AI-generated findings is bolstered and potential biases are mitigated. Overall, the framework promotes ethical, transparent, and robust approaches to AI integration in higher education without compromising academic standards. Future work will refine its adaptability across various research contexts and further validate its impact on scholarly practices.

As Artificial Intelligence (AI) transitions from military and industrial domains to environmental science, a fundamental shift toward data-driven methodologies is reshaping planetary protection. However, this transition frequently imports battlefield logic into conservation, utilizing autonomous systems-such as drones and machine learning algorithms-that introduce complex ethical and regulatory challenges. This paper presents a conceptual synthesis of Human-Centered AI (HCAI) frameworks and ecological security perspectives to address these risks. We identify critical friction points, including anthropocentric biases that neglect non-human wellbeing, a responsibility gap in autonomous decision-making, privacy infringements through surveillance, and the paradoxical environmental footprint of AI computing. To mitigate these risks, we propose three actionable recommendations: incorporating non-anthropocentric metrics into ethical AI standards; harmonizing transboundary regulatory frameworks to align with global standards like the EU AI Act; and mandating strictly defined human-in-the-loop protocols for all autonomous environmental interventions.

Researchers’ goals shape the questions they raise, collaborators they choose, methods they use, and outcomes of their work. This article offers a fresh vision of artificial intelligence (AI) research by suggesting a simplification to two goals: 1) emulation to understand human abilities to build systems that perform tasks as well as or better than humans and 2) application of AI methods to build widely used products and services. Researchers and developers for each goal can fruitfully work along their desired paths, but this article is intended to limit the problems that arise when assumptions from one goal are used to drive work on the other goal. For example, autonomous humanoid robots are prominent with emulation researchers, but application developers avoid them, in favor of tool-like appliances or teleoperated devices for widely used commercial products and services. This article covers four such mismatches in goals that affect AI-guided application development: 1) intelligent agent or powerful tool; 2) simulated teammate or teleoperated device; 3) autonomous system or supervisory control; and 4) humanoid robot or mechanoid appliance. This article clarifies these mismatches to facilitate the discovery of workable compromise designs that will accelerate human-centered AI applications research. A greater emphasis on human-centered AI could reduce AI’s existential threats and increase benefits for users and society, such as in business, education, healthcare, environmental preservation, and community safety.

A human-centered artificial intelligence (AI) approach is proposed as a theoretical foundation and a practical guideline to achieve green and sustainable AI. The goal of this study is to contribute to the discussion of AI as both more sustainable and greener.

The integration of chatbots in oncology underscores the pressing need for human-centered artificial intelligence (AI) that addresses patient and family concerns with empathy and precision. Human-centered AI emphasizes ethical principles, empathy, and user-centric approaches, ensuring technology aligns with human values and needs. This review critically examines the ethical implications of using large language models (LLMs) like GPT-3 and GPT-4 (OpenAI) in oncology chatbots. It examines how these models replicate human-like language patterns, impacting the design of ethical AI systems. The paper identifies key strategies for ethically developing oncology chatbots, focusing on potential biases arising from extensive datasets and neural networks. Specific datasets, such as those sourced from predominantly Western medical literature and patient interactions, may introduce biases by overrepresenting certain demographic groups. Moreover, the training methodologies of LLMs, including fine-tuning processes, can exacerbate these biases, leading to outputs that may disproportionately favor affluent or Western populations while neglecting marginalized communities. By providing examples of biased outputs in oncology chatbots, the review highlights the ethical challenges LLMs present and the need for mitigation strategies. The study emphasizes integrating human-centric values into AI to mitigate these biases, ultimately advocating for the development of oncology chatbots that are aligned with ethical principles and capable of serving diverse patient populations equitably.

The practices and processes enabling learning within organisations have emanated from the metacognitive abilities of humans that have been ‘agentic’ in creating action plans, adopting goals, visualising outcomes, and providing direction and coherence. Rapid technological advancement has increased the adoption of emerging technologies, such as artificial intelligence (AI), in various Human Resource Management (HRM) domains, which demonstrate agentic behaviour. A perspective of human-centered AI (HCAI) is needed to understand better the development of structures and protocols for learning and development (L&D) practices in organisations with human-centered design, which is poorly understood. Our study adopts a practice lens to examine human and technology agents’ embodying structures and routines in L&D functions within organisations. We adopt an exploratory qualitative approach and analyze 24 HR & L&D leaders’ interviews by deploying Gioia's methodology. We find that L&D practitioners currently use and plan to use AI in various applications, which change protocols and activities of human and technology agents. Based on our findings, we propose a conceptual framework for leveraging AI in L&D, operating with different structures, and encompassing various conjoined agencies.

This 3-hour tutorial proposes a new synthesis, in which Artificial Intelligence (AI) algorithms are combined with human-centered thinking to make Human-Centered AI (HCAI). This approach combines research on AI algorithms with user experience design methods to shape technologies that amplify, augment, empower, and enhance human performance. Researchers and developers for HCAI systems value meaningful human control, putting people first by serving human needs, values, and goals.

In recent years, ‘human-centered artificial intelligence’ (HCAI) has emerged as a prominent framing device in the societal debate on the implications of AI. By adopting this phrase, AI ethics discourses make an appeal to the notion of the ‘human,’ while failing to critically reflect on its meaning. Against this background, we pose the question as to what ‘human’ is taken to mean in the context of HCAI. We apply a critical hermeneutic approach to analyze prominent HCAI literature and identify five key characteristics that shape the meaning given to the concept of the ‘human’ in HCAI: universalism, individualism, instrumentalism, psychologism, and exceptionalism. Following this, we introduce Hannah Arendt’s philosophical anthropology to provide an understanding of human existence as essentially political life, and argue that the HCAI discourse would benefit from considering the political dimension of human existence. We conclude the paper by proposing a research program for developing a reconceptualization of HCAI informed by philosophical anthropology.

Purpose Artificial intelligence service quality (AI-SQ) comprises both technical and functional quality. In this study, we investigate how AI-SQ affects trust and commitment in customer engagement, including customer purchases, referrals, social influence and knowledge sharing. Additionally, this study examines the role of AI marketing specialists (AIMSs) in moderating the relationship between AI-SQ and customer engagement. Design/methodology/approach Partial least squares structural equation modeling was applied to the data to evaluate composite reliability, convergent and discriminant validity, effect sizes (f2 and Q2) and the model’s predictive accuracy. Findings First, technical quality has a significant and direct impact on customer social influence and knowledge sharing, whereas functional quality significantly and directly affects customer purchases, referrals and knowledge sharing. Second, both trust and commitment significantly mediate the relationship between AI-SQ and customer engagement. Third, AIMSs significantly moderate the impact of technical quality on customer knowledge sharing. Originality/value This study contributes by developing and validating a human-centered AI-SQ framework that integrates AIMSs to enhance personalization while addressing the limitations of service quality dimensions in the context of AI-driven standardization and customization. These findings provide a foundation for developing human-centered AI marketing strategies, allowing firms to conduct comprehensive SWOT (strengths, weaknesses, opportunities and threats) analyses of AIMSs. Firms can leverage the positive moderating effects of AIMSs while mitigating potential challenges. Therefore, as the synergy between AI-SQ and AIMSs intensifies, the 5Ps of the marketing mix (i.e. product, price, place, promotion and professional) will increasingly serve as a practical framework for human-centered AI marketing strategies.