Leveraging artificial intelligence: the machine/human agentic impact on evolving practices in learning & development

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.

The rapid advancement of artificial intelligence (AI) has significantly influenced human resource management (HRM), presenting both opportunities and challenges for individuals and organizations. This study provides a comprehensive analysis of the integration of AI in HRM, exploring its multifaceted implications on employee perceptions, engagement, and organizational practices. A systematic literature review was conducted, focusing on journal articles published between 2018 and 2024. The review includes studies examining AI’s role in enhancing HR processes, such as recruitment, employee monitoring, performance evaluation, and training, while emphasizing its ethical and legal considerations. Findings reveal that while AI has the potential to revolutionize HRM by streamlining processes and improving decision-making, its application raises concerns regarding employee trust, data privacy, and bias in algorithms. The study highlights diverse perspectives on the relationship between AI and HR, showcasing the dual role of AI as a tool for efficiency and a challenge to human-centric management approaches. A novel contribution of this paper is the development of an AI + HR model framework, specifically designed to assist HR managers in navigating the complexities of AI-driven systems. This framework provides practical insights for leveraging AI in HRM to enhance employee engagement and organizational outcomes. Despite its contributions, the study is limited to a specific timeframe and excludes certain types of literature, such as non-English articles and conference papers. Nevertheless, it offers valuable implications for HR practitioners and researchers by advancing the understanding of AI’s transformative role in HRM.

Artificial intelligence (AI) has the potential to change the world of work radically. Wherever information processing is involved, AI can be integrated into processes with added value. From the perspective of Human Resource (HR) management, this implies three things: first, business models and performance processes in the company will undergo change; second, employee requirements will change; and third, HR processes will change. While the literature describes various AI maturity models, there has been no dedicated consideration of HR management. This article, therefore, aims to identify relevant influencing factors for an AI-orientated approach to HR management and to describe these in more detail using maturity levels in a Human Resources Artificial Intelligence Maturity Model (HR-AIMM). The resulting HR-AIMM consists of eleven dimensions. These include anchoring the AI topic in the corporate and HR strategy, its use in selected HR processes, considering ethical, data-related, and infrastructural principles, and organisational, cultural, and competence-related anchoring. The characteristics of these factors enable the identification of four maturity levels for using AI in HR management: from a curious start to the level of holistic integration. Our framework supports researchers and companies in understanding and evaluating the factors influencing the professional application of AI in HR management.

This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) the drivers’ behaviors and (2) the situational criticality to manage in real time the Human-Machine Interactions (HMI). This Human-Centered AI (HCAI) approach was designed from real drivers’ needs, difficulties and errors observed at the wheel of an instrumented car. Then, the HCAI algorithm was integrated into demonstrators of Advanced Driving Aid Systems (ADAS) implemented on a driving simulator (dedicated to highway driving or to urban intersection crossing). Finally, user tests were carried out to support their evaluation from the end-users point of view. Thirty participants were invited to practically experience these ADAS supported by the HCAI algorithm. To increase the scope of this evaluation, driving simulator experiments were implemented among three groups of 10 participants, corresponding to three highly contrasted profiles of end-users, having respectively a positive, neutral or reluctant attitude towards vehicle automation. After having introduced the research context and presented the HCAI algorithm designed to contextually manage HMT with vehicle automation, the main results collected among these three profiles of future potential end users are presented. In brief, main findings confirm the efficiency and the effectiveness of the HCAI algorithm, its benefits regarding drivers’ satisfaction, and the high levels of acceptance, perceived utility, usability and attractiveness of this new type of “adaptive vehicle automation”.

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.

Today's businesses are operating in a complex environment, marked by the emergence of a digital culture that is transforming space and time, as well as relationships at work. These various transformations have given rise to a new concept: artificial intelligence. With the introduction of artificial intelligence, work is being transformed, creating new challenges for organizations and leading to new HR practices in terms of recruitment, training, compensation, talent management and so on.For all these reasons, the mobilization of collective intelligence is ultimately becoming a priority for HR, insofar as it makes it possible to support transformation within organizations by means of agile methods. Against this backdrop of changing organizational practices, a number of questions arise:-What is artificial intelligence and how does it impact organizations? -What role can collective intelligence and artificial intelligence play in the evolution of the HR function?The aim of this theoretical research is to underpin the various concepts linked to these notions. We will also try to understand what is at stake in collective intelligence for HR, and how artificial intelligence can impact organizational practices.

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.

Artificial Intelligence (AI), a fundamental aspect of the Fourth Industrial Revolution, has become a pivotal concern across various industries, including business management. It is increasingly applied in fields such as marketing, human resource management (HRM), and manufacturing. Within HRM, AI and machine learning technologies are utilized to enhance employee experience, provide personalized training, and analyze HR data for informed decision-making. This integration of technology into HRM, traditionally a people-focused domain, represents a significant shift. AI technology adoption in HRM has surged in prominent settings. For instance, IBM has leveraged AI solutions like Blue Matching, CogniPay, and Watson Career Coach to streamline HR operations. Blue Matching predicts mobile tasks based on employee profiles, while CogniPay aids managers in decision-making by evaluating performance. Watson Career Coach offers personalized career guidance. However, notable cases like Amazon’s AI Recruitment Program have highlighted concerns regarding biases. Artificial intelligence is reshaping various aspects of our lives, including recruitment practices. As more businesses harness AI for candidate selection and evaluation, there’s a growing interest among HR managers in adopting AI solutions. However, this technological advancement also brings challenges, such as the displacement of existing job roles and the need for additional professionals to manage the evolving landscape. Understanding the potential benefits and challenges of AI in recruitment is essential for informed decision-making in HRM.

Examining the impact of artificial intelligence on employee performance in the digital era: An analysis and future research direction

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.

Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Ancient civilizations contained tales of intelligent robots, such as Greek and Chinese cultures. AI as a modern phenomenon which took shape in the mid-20th century with the advent of digital computers. The first breakthrough, published in 1950, was Alan Turing's paper, ‘Computing Machinery and Intelligence. AI can make provide detailed solution to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car). Many sectors like finance, healthcare, to human resource management (HRM), rely on AI technologies. AI's integration in HRM has brought about great change in conventional HR practices used to measure efficiency, accuracy, strategic decision-making, etc. AI technologies help to provide solution to automation of repetitive tasks, analysis of sprawling volumes of data and insights facilitating decision-making. Through this research paper we try to understand the AI's effects on HR processes, in terms of recruitment, onboarding, performance appraisal, training and development, employee engagement, workforce planning; understanding the benefits and cons of implementing AI in HRM; ethical considerations and potential biases in AI algorithms relevant to HR practices; analysis of emergent AI trends with respect to HRM. Trends in AI in HR tend towards a most promising future, presenting several innovations. Accordingly, HR professionals will be expected to acquire competency with regards to new roles and responsibilities to avail AI in enhancing strategic planning, ethical oversight, and data-driven decision-making.

PurposeThis paper aims to specifically focus on the challenges that human resource management (HRM) leaders and departments in contemporary organisations face due to close interaction between artificial intelligence (AI) (primarily robots) and human workers especially at the team level. It further discusses important potential strategies, which can be useful to overcome these challenges based on a conceptual review of extant research.Design/methodology/approachThe current paper undertakes a conceptual work where multiple streams of literature are integrated to present a rather holistic yet critical overview of the relationship between AI (particularly robots) and HRM in contemporary organisations.FindingsWe highlight that interaction and collaboration between human workers and robots is visible in a range of industries and organisational functions, where both are working as team members. This gives rise to unique challenges for HRM function in contemporary organisations where they need to address workers' fear of working with AI, especially in relation to future job loss and difficult dynamics associated with building trust between human workers and AI-enabled robots as team members. Along with these, human workers' task fulfilment expectations with their AI-enabled robot colleagues need to be carefully communicated and managed by HRM staff to maintain the collaborative spirit, as well as future performance evaluations of employees. The authors found that organisational support mechanisms such as facilitating environment, training opportunities and ensuring a viable technological competence level before organising human workers in teams with robots are important. Finally, we found that one of the toughest challenges for HRM relates to performance evaluation in teams where both humans and AI (including robots) work side by side. We referred to the lack of existing frameworks to guide HRM managers in this concern and stressed the possibility of taking insights from the computer gaming literature, where performance evaluation models have been developed to analyse humans and AI interactions while keeping the context and limitations of both in view.Originality/valueOur paper is one of the few studies that go beyond a rather general or functional analysis of AI in the HRM context. It specifically focusses on the teamwork dimension, where human workers and AI-powered machines (robots) work together and offer insights and suggestions for such teams' smooth functioning.

This study examines the factors that influence integrating artificial intelligence (AI) into human resources management (HRM) in Jordanian banks, a sector that has not yet extensively adopted AI. This study uses a mixed-method technique that includes both qualitative and quantitative approaches, the research consists of a sample of 130 HR directors and other stakeholders from seven commercial banks in Jordan. The main data-gathering instruments were surveys and interviews programmed to meet the research objectives and to confirm the research hypotheses. The major findings indicate some substantial barriers and challenges that prevent AI’s integration into the HRM in Jordanian banks. Such barriers include a lack of knowledge, poor skillful personnel, employee resistance to change, and privacy and security concerns about data. Despite many challenges such as high costs of AI implementation, and difficulties in AI implementation, and regulatory and compliance issues the study demonstrates that AI can positively affect HRM by enhancing talent acquisition, automating routine tasks, and other HR functions. According to the experimental validation of our proposition, AI can be used in HRM to improve productivity and reduce time wastage on administrative tasks with enough provision of financial and material resources. It concludes by giving practical advice on how the given barriers and challenges may be overcome and how AI could be optimally employed to boost HRM services in Jordan’s banking sector. If these barriers and challenges are addressed and the potential benefits of its implementation are showcased, then banks can enhance their HRM practices which will eventually lead to improved organizational performance as well as competitiveness.