Artificial Intelligence Usage Research Articles

The ethical dimensions of utilizing Artificial Intelligence in palliative care

Palliative care aims to improve the quality of life for seriously ill individuals and their caregivers by addressing their holistic care needs through a person- and family-centered approach. While there have been growing efforts to integrate Artificial Intelligence (AI) into palliative care practice and research, it remains unclear whether the use of AI can facilitate the goals of palliative care. In this paper, we present three hypothetical case examples of using AI in the palliative care context, covering machine learning algorithms that predict patient mortality, natural language processing models that detect psychological symptoms, and AI chatbots addressing caregivers’ unmet needs. Using these cases, we examine the ethical dimensions of utilizing AI in palliative care by applying five widely accepted moral principles that guide ethical deliberations in AI: beneficence, nonmaleficence, autonomy, justice, and explicability. We address key ethical questions arising from these five core moral principles and analyze the potential impact the use of AI can have on palliative care stakeholders. Applying a critical lens, we assess whether AI can facilitate the primary aim of palliative care to support seriously ill individuals and their families. We conclude by discussing the gaps that need to be further addressed in order to promote ethical and responsible AI usage in palliative care.

Transformative Assessment Review

The rapid advancement of generative artificial intelligence (AI) in education and the workforce necessitates that students acquire critical competencies, including digital literacy, data integrity, and ethical AI use, to navigate an increasingly AI-driven world. Equally, academic staff must be upskilled to effectively guide students in the ethical and practical applications of AI. This project examines how a private higher education (HE) institute has modified its Quality Assessment Framework (QAF) to incorporate an AI competence dimension, improve HE assessment and foster students’ competencies. This new dimension complements the existing QAF elements—Intellectual Quality, Significance, and Student Support to ensure a holistic approach in preparing students for AI-integrated academic and professional environments. This project’s framework and innovative tools, such as the enhanced QAF and an AI-driven Generative Pre-trained Transformer (GPT) assessment review tool, support educators in adapting curriculum and assessment design, and empower them to integrate AI competencies seamlessly into the curriculum. The development of these tools drew upon established frameworks, particularly the QAF (Gore et al., 2009) and the AI Assessment Scale (AIAS) by Perkins et al. (2024), which categorises AI usage across five levels, guides assessment reviews and ensures consistent standards for AI integration in student work. A trial, conducted across selected disciplines at the institute, involved academic staff as assessment reviewers and utilised the GPT tool to streamline the assessment review process by aiding in feedback provision, coding, and suggestions for improvement. Evaluation of the tool includes qualitative and quantitative methodologies, gathering academic feedback on usability, clarity, and effectiveness, as well as comparative studies to assess review time and quality before and after GPT integration. Usability testing evaluates workflow compatibility, while academic performance data provide insights into the tool’s impact on student outcomes. Initial results on the tool’s success in enhancing assessment alignment with institutional goals and fostering a comprehensive understanding of AI competence were also examined. Developing and implementing these tools has presented challenges, including the complexity of aligning them with diverse curriculum needs across disciplines, the time investment required for staff training, and the necessity of continuous updates to keep pace with AI technology. Addressing these challenges has been crucial to ensuring the effectiveness and sustainability of these solutions. This digital poster provides insights into the project, covering its development process, trial outcomes, challenges encountered, and future directions for integrating AI competence in higher education assessments.

INTEGRATING ARTIFICIAL INTELLIGENCE INTO SUCCESSION PLANNING AND LEADERSHIP DEVELOPMENT IN HIGHER EDUCATION

This study evaluates and examines the use of artificial intelligence (AI) in succession planning and leadership development in higher education institutions. The primary focus is on how AI can enhance the effectiveness and efficiency in identifying and developing potential leadership candidates while addressing existing challenges. The review employs a comprehensive literature search strategy, involving four major databases: Scopus, Web of Science, ABI/INFORM Collection, and Google Scholar. Inclusion criteria ensure that relevant, up-to-date, and high-quality literature is gathered for analysis. The review findings indicate that the use of AI in succession planning and leadership development in higher education promises significant advancements. AI can enhance the effectiveness of succession planning by providing deeper analysis of talent and skills while addressing the digital transformation challenges faced by higher education institutions. The implementation of AI in this context has the potential not only to improve leadership quality but also to strengthen strong leadership continuity for the future. This review offers insights into best practices, challenges, and future potentials for AI usage in succession planning and leadership development in higher education institutions, along with recommendations for more effective implementation.

Implementasi Artificial Intelligence dalam Rekrutmen: Manfaat dan Tantangan di Industri 4.0

This study aims to explore the implementation of Artificial Intelligence (AI) in the recruitment process in the era of Industry 4.0, focusing on the benefits and challenges faced by companies. Utilizing a qualitative approach through in-depth interviews with recruitment teams, human resource managers, and candidates, the research finds that the adoption of AI brings several advantages, including enhanced efficiency in the selection process, improved screening accuracy, and an enriched candidate experience. However, significant challenges also arise, such as privacy and data security issues, a lack of transparency in algorithms, and regulatory uncertainties that may affect the adoption of this technology. The findings highlight the need for companies to address these challenges through the development of robust data protection policies, regular monitoring of algorithms, and educating both candidates and recruitment teams about AI usage. This research aims to provide insights for organizations in implementing AI ethically and effectively in the recruitment process.

Artificial Intelligence Usage in Higher Education: Influence on Online Learning

The conceptualization and implementation of artificial intelligence (AI) in online learning have shown the most effective ways to use AI pedagogically. Viewing AI as a tool for online learning is crucial. By integrating AI technologies into teaching practices, educators can create more engaging, effective, and personalized learning experiences for their students. However, it is essential to use AI responsibly, ensuring it complements and enhances the role of educators rather than replacing human interaction and support. This article begins by outlining its purpose and providing an overview of AI history. It will presents a comparative literature analysis, discussing the advantages and disadvantages of using AI in online learning. Finally, the article explores how to incorporate AI into online teaching practices and offers strategies to encourage students to utilize AI effectively.

A policy framework on AI usage in developing countries and its impact

The rapid growth of Artificial Intelligence (AI) presents both significant opportunities and challenges for developing countries. A well-structured policy framework is crucial to maximize the benefits of AI while mitigating its risks. This review proposes a comprehensive AI policy framework tailored to developing countries, emphasizing the need for robust infrastructure, capacity building, ethical governance, and economic incentives. Key elements include the development of digital infrastructure, education and training programs to enhance AI literacy, and ethical guidelines to ensure fairness and transparency in AI applications. Data governance and privacy protections are critical, particularly in countries where regulatory frameworks are underdeveloped. Furthermore, international cooperation is highlighted as a necessity for aligning local policies with global AI standards, facilitating cross-border data sharing, and ensuring equitable access to AI innovations. The potential impact of AI on economic growth, job creation, healthcare, education, and public service delivery is profound, yet challenges such as workforce displacement, increased inequality, and the digital divide must be carefully managed. The proposed framework addresses these challenges, providing strategies to overcome barriers to AI adoption, including financial constraints, governance issues, and unequal access to technology. Moreover, it stresses the importance of fostering public-private partnerships and ensuring that AI development is inclusive, benefiting all segments of society. By implementing a comprehensive AI policy framework, developing countries can harness AI’s transformative power to drive sustainable development, improve social outcomes, and strengthen their economic standing in the global landscape. This review concludes by recommending continuous policy evaluation and adaptation to keep pace with AI's rapid evolution.

Understanding AI Usage in Academics among Vocational High School Students

The purpose of this study is to gauge the degree of knowledge that high school students pursuing vocational training have about using artificial intelligence in the classroom and to pinpoint the variables that impact that knowledge. As artificial intelligence (AI) permeates more and more industrial sectors, it is imperative that vocational high school students understand AI and be prepared for its future. Using a mixed-methods approach, this study included 500 Indonesian students enrolled in vocational high schools who represented a range of academic levels through qualitative interviews and quantitative surveys. The study's conclusions show that students at vocational high schools have differing levels of comprehension about artificial intelligence (AI), with a variety of factors like socioeconomic status, school curriculum, and access to technology playing a major influence. The research's ramifications include suggestions for creating more efficient.

Artificial Intelligence Technology Embedded in High School Science Learning: A Study of Teacher Perception

This study examines teachers' perceptions and acceptance of using artificial intelligence (AI) technology in the teaching of Sciences (IPA) in Vocational High Schools (SMK). Based on quantitative and qualitative data, the results indicate that the understanding and use of AI in teaching are still limited. Quantitative data were collected through a structured questionnaire to assess teachers' knowledge, confidence, and AI usage frequency, while qualitative data were obtained through in-depth interviews exploring their views and challenges. Although some teachers see the great potential of AI in enhancing student engagement and learning outcomes, various obstacles such as lack of training and technological infrastructure hinder optimal implementation. This articles also offers strategic recommendations for integrating AI into the science curriculum in vocational schools. Although AI has significant potential in vocational science education, its effective implementation is hindered by limited teacher training and infrastructure, necessitating strategic improvements to fully leverage AI's benefits in schools.

ChatGPT adoption risks and cognitive achievement among tourism and hospitality college students: from faculty member perspective

PurposeDespite ChatGPT usage advantages to complete assignments as quickly as possible with detailed information, its adoption risks are crucial factors that may significantly influence student outcomes, like creative skills and achievement levels. Hence, this paper examines the potential effects of ChatGPT adoption risks on students’ cognitive achievement, underpinned by perceived risk theory (PRT). Further, this paper examines the mediating role of creative thinking levels among hospitality college students.Design/methodology/approachThis paper employed a quantitative approach to collect data. Using an online survey, simple random sampling was employed to ensure bias. Hence, 267 faculty members at tourism and hospitality colleges affiliated with Egyptian public universities responded, and their responses were tested using SmartPLS v.4.FindingsCreative thinking partially mediated five potential risks of students’ ChatGPT adoption effects on their cognitive achievement. More specifically, psychological risks of ChatGPT adoption among students are considered one of the most likely to reduce their levels of creative thinking and their ability to achieve achievement.Practical implicationsThis paper provides insights for policymakers aimed at enhancing higher education quality in tourism and hospitality colleges. Accordingly, this paper concludes by highlighting the importance of addressing ChatGPT adoption’s psychological risks, integrating strategies to promote creative thinking among students and fostering responsible artificial intelligence usage in higher education.Originality/valueThis paper underscores the critical role of faculty members in navigating and mitigating these risks, fostering a more conducive learning environment for responsible ChatGPT usage.

Conceptualizing the Implications of Artificial Intelligence (AI) Tools and Personalization Marketing on Consumer Purchase Intention: Insights from the Malaysian E-Commerce Market

Artificial intelligence (AI) has emerged as a powerful tool, enabling online retailers to offer highly personalized shopping experiences tailored to individual preferences and behaviors. As e-commerce continues to grow in Malaysia, understanding the influence of AI and personalization marketing on consumer purchase intentions has become increasingly important for businesses seeking to remain competitive. However, the rapid adoption of AI also raises concerns about data privacy, ethical AI usage, and compliance with emerging data protection regulations, such as Malaysia’s Personal Data Protection Act (PDPA). This study aims to explore the potential impact of artificial intelligence (AI) and personalization marketing on consumer purchase intention within the Malaysian e-commerce market. By reviewing previous literature and theoretical frameworks, the study explores how the use of AI tools including predictive analytics automation, and personalization experiences might influence consumer behavior in online shopping environments. The study adopts a quantitative approach, in which quota sampling will be used for the participant selection. A self-administered questionnaire with a five-point Likert scale will be employed to gather data from e-commerce users in Malaysia. The findings from this study have important implications for both e-commerce businesses and policymakers in Malaysia. For businesses, understanding which aspects of AI and personalization most influence consumer purchase intentions can help them strategically implement these technologies to enhance customer engagement and drive sales. For policymakers, the study highlights the need to consider ethical and legal issues, such as data privacy and policy issues, in the growing use of AI in the e-commerce market.

The Effect of Artificial Intelligence (AI) on Students' Learning

Various studies have been conducted to identify factors that contribute to student engagement, personalized learning experience, and student academic performance. The evolution of technology offers various benefits including in the education sector. To date, the use of Artificial Intelligence (AI) in education has been seen to provide various benefits. This study aims to identify the relationship between the usage of AI with student engagement, personalized learning experience, and student academic performance. Data was collected from 110 undergraduate students from the Faculty of Business and Management, UiTM Puncak Alam Campus using a questionnaire. 106 data were analyzed using SPSS version 29. The findings show that AI usage for study purposes significantly influences student’s engagement and academic performance. On the other hand, the usage of AI and personalized learning experience show no significant influence. This study not only provides a deeper understanding of the context of AI usage for better student engagement and academic performance but also gives valuable insight for UiTM and faculty specifically to develop strategies and modules that enhance the implementation and usage of AI in their learning activities.

Discourse analysis on academic integrity generative AI: Perspectives from science and mathematics students in higher education

Artificial Intelligence (AI) has significantly influenced the current educational system, offering various benefits to teachers and students. However, the integration of AI in education also poses challenges, particularly in maintaining academic integrity. This study investigates the perspectives of 24 science and mathematics students on the impact of Generative AI (GAI) on academic integrity, a core principle in higher education. Using an exploratory design, the findings reveal a dual perspective: GAI can enhance learning and research by providing advanced tools and resources, yet it also risks undermining ethical standards if misused. The study highlights the importance of balancing technological advancements with the maintenance of academic integrity, emphasizing the need for responsible AI usage and the development of policies to mitigate potential negative impacts. Further research is needed to explore the impact of GAI on academic integrity across different disciplines and educational contexts.

Digital and AI transformation in the contemporary art industry in China

This study examines the digital and artificial intelligence (AI) transformation in the contemporary art industry in China. This industry in China is undergoing a distinct digital transition and is “ahead” of other countries, having fully integrated digital technologies and AI (digiAI) into policies, regulations, organizations, and professional practices. A systematic, large-scale national integration of digiAI has led to its widespread adoption by artists and arts professionals. However, little is known about how or when this rapid and extensive integration and subsequent adoption occurred or about its impacts on professional practices. This study draws on research conducted between 2023 and 2024, including 30 interviews with contemporary Chinese visual artists, 23 interviews with arts professionals, a survey of 110 professional contemporary visual artists, and a systematic review of government policy. Findings indicate that the government began integrating digital technology into the contemporary art industry in 2016, further promoted digital technology integration in 2021, and introduced regulations to support AI usage in 2023. The data reveal a significant spike in the adoption of digital technologies by professionals between 2019 and 2020, followed by a rise in AI adoption in 2023. DigiAI has been accepted and now used across different kinds of arts professions, various types of visual artists, and several age groups. Digital and AI tools are now being applied in both creative and non-creative aspects of arts practices.

The Impact of AI Usage on University Students' Willingness for Autonomous Learning.

As artificial intelligence (AI) technology becomes increasingly integrated into education, understanding the theoretical mechanisms that drive university students to adopt new learning behaviors through these tools is essential. This study extends the Expectation-Confirmation Model (ECM) by incorporating both cognitive and affective variables to examine students' current AI usage and their future expectations. The model includes intrinsic and extrinsic motivations, focusing on three key factors: positive emotions, digital efficacy, and willingness for autonomous learning. A survey of 721 valid responses revealed that positive emotions, digital efficacy, and satisfaction significantly influence continued AI usage, with positive emotions being particularly critical. Digital efficacy and perceived usefulness also impact satisfaction, but long-term usage intentions are more effectively driven by positive emotions. Furthermore, digital efficacy strongly affects the willingness for autonomous learning. Therefore, higher education institutions should promote AI technology, enhance students' expectation-confirmation levels, and emphasize positive emotional experiences during AI use. Adopting a "human-machine symbiosis" model can foster active learning, personalized learning pathways, and the development of students' digital efficacy and innovation capabilities.

How can we improve AI competencies for tomorrow's leaders: Insights from multi-stakeholders’ interaction

This study investigates factors influencing artificial intelligence (AI) competencies in higher education for sustainable development using learning theories. Utilizing a mixed-method approach, it analyzes responses from 525 students and interviews with 35 faculty members from five Indian universities. The study employs confirmatory factor analysis (CFA) and structural equation modeling (CB-SEM) to explore relationships between various learning approaches and AI competencies. Findings indicate that collaborative learning, problem-solving, and cognitive competence significantly contribute to AI proficiency. Human-tool collaboration and self-learning also enhance students' understanding of AI concepts, fostering strategic decision-making in business contexts. Additionally, thematic analysis from qualitative interviews identifies critical themes for developing an AI curriculum framework, including curriculum design, pedagogical strategies, ethical considerations, and global perspectives. The research provides an integrated theoretical model and offers practical recommendations for enhancing AI education, emphasizing the importance of interdisciplinary collaboration and ethical AI usage in management education.

Use of AI in Mental Health Care: Community and Mental Health Professionals Survey.

Artificial intelligence (AI) has been increasingly recognized as a potential solution to address mental health service challenges by automating tasks and providing new forms of support. This study is the first in a series which aims to estimate the current rates of AI technology use as well as perceived benefits, harms, and risks experienced by community members (CMs) and mental health professionals (MHPs). This study involved 2 web-based surveys conducted in Australia. The surveys collected data on demographics, technology comfort, attitudes toward AI, specific AI use cases, and experiences of benefits and harms from AI use. Descriptive statistics were calculated, and thematic analysis of open-ended responses were conducted. The final sample consisted of 107 CMs and 86 MHPs. General attitudes toward AI varied, with CMs reporting neutral and MHPs reporting more positive attitudes. Regarding AI usage, 28% (30/108) of CMs used AI, primarily for quick support (18/30, 60%) and as a personal therapist (14/30, 47%). Among MHPs, 43% (37/86) used AI; mostly for research (24/37, 65%) and report writing (20/37, 54%). While the majority found AI to be generally beneficial (23/30, 77% of CMs and 34/37, 92% of MHPs), specific harms and concerns were experienced by 47% (14/30) of CMs and 51% (19/37) of MHPs. There was an equal mix of positive and negative sentiment toward the future of AI in mental health care in open feedback. Commercial AI tools are increasingly being used by CMs and MHPs. Respondents believe AI will offer future advantages for mental health care in terms of accessibility, cost reduction, personalization, and work efficiency. However, they were equally concerned about reducing human connection, ethics, privacy and regulation, medical errors, potential for misuse, and data security. Despite the immense potential, integration into mental health systems must be approached with caution, addressing legal and ethical concerns while developing safeguards to mitigate potential harms. Future surveys are planned to track use and acceptability of AI and associated issues over time.

Exploring the Influence of Artificial Intelligence Usage on Ethical Decision Making Among Public Sector Employees: Insights into Moral Identity and Service Motivation

This study constructs a moderating mediation model to link public sector employees’ Artificial Intelligence (AI) usage with employees’ moral norms and ethical decision-making behaviors. Based on the theory of public service motivation, this study hypothesizes that the impact of AI usage on employees’ ethical decision-making behaviors acts through the mediating effects of employees’ service motivation, employees’ moral norms, and employees’ ethical perceptions and that the relationship between AI usage and employees’ service motivation, employees’ ethical norms, and employees’ ethical perceptions is moderated by the culture of the public organization. The selected data from 417 public sector employees in China supported most of the research hypotheses. The findings show that employee service motivation, employee moral norms, and employee moral cognition mediate the relationship between AI usage and employee ethical decision-making behavior. Public organization culture moderated the relationship between AI usage and employee service motivation, as well as AI usage and employee ethics. This study reveals the complex mediating and moderating relationships between AI usage and employees’ ethical decision-making behaviors in the public sector. It provides important theoretical and practical insights for further understanding and promoting public sector employees’ ethical behaviors in the era of AI.

How and when artificial intelligence usage facilitates task performance

This study investigated how the increasing usage of artificial intelligence (AI) in diverse business applications has affected task performance. Building on the job demands–resources model, we employed moderated mediation modeling to investigate the association between AI usage and the task performance of delivery drivers. Participants comprised 251 delivery drivers who completed online surveys over a 1-month period. Hierarchical regression analysis revealed that work engagement played a mediating role in the association between AI usage and the task performance of delivery drivers. Moreover, the mediating role of work engagement was moderated by an individual's concept of their future work self. When an individual had a stronger sense of their future work self, the mediating role of work engagement became more pronounced. This study has introduced a new mediating mechanism linking artificial intelligence with employee performance and has identified boundary conditions that increase its beneficial impact on employee performance, thus contributing theoretically to the extant literatures. The study findings can help companies improve employee engagement and performance.

The Pivotal Role of Libraries in Sustainable AI Development

Artificial Intelligence (AI) has gained exceptional public and media coverage since the launch of the ChatGPT platform, a generative conversational intelligence, in November 2022. Nevertheless, AI is already an integral part of our digital daily lives, as we navigate through social networks, use our GPS, or consult recommendations on e-commerce websites. Due to its pervasive influence across all sectors of our societies, AI is gradually becoming a pivotal subject in terms of regulation, societal direction, and legislation. As early as 2021, UNESCO published a report presenting avenues for ethical considerations in AI. In June 2023, the European Union also established a regulatory framework outlining requirements and obligations for AI usage. As a digital manifestation and given the "new ways in which its use influences human thinking, interaction and decision-making and affects education, human, social and natural sciences, culture, and communication and information" (UNESCO. General Conference, 41st, 2021), public libraries have a role to play in enabling residents within their communities to grasp this technology. Their role is all the more significant as AI generates concerns and distrust (Gillath et al., 2021) among populations when "libraries also continue to enjoy a high level of trust and appreciation in most of their communities" (Arlitsch \& Newell, 2017). Understanding AI thus constitutes a new cornerstone for accessing the necessary information to advance sustainable development, as outlined in the Lyon Declaration (2014). Moreover, comprehension of AI aligns with the ethical concerns articulated by UNESCO in terms of explainability and transparency and aligns with several Sustainable Development Goals (SDGs) of the 2030 Agenda. These goals include quality education (4), industry, innovation and infrastructure (9), reduced inequalities (10), sustainable cities and communities (11), as well as responsible consumption and production (12). The role of public libraries in advancing the goals of the 2030 Agenda is beyond dispute (IFLA, 2016), and various digital literacies are already integral to their actions. As AI is predominantly developed by global economic giants and permeates all of our practices, "Shouldn’t [libraries] be the bastions of information literacy and information privacy in an AI world?" (Cox et al., 2018). Thus, to what extent can public libraries take on this subject to promote and offer relevant literacy? For this study, we will conduct a cross-analysis among three European countries—Spain, France, and Italy—to provide insights into the diverse ways in which AI influences professional practices. Through a literature review and semi-structured interviews, the objective is to delineate the challenges of Artificial Intelligence within the framework of the Agenda 2030 program. Subsequently, we will delve into the specificity of AI Literacy in comparison to Information Literacy, a practice already adopted by libraries. Finally, we will analyze the current and prospective role of AI in libraries to propose avenues for implementing concrete actions.

Generative artificial intelligence usage by researchers at work: Effects of gender, career stage, type of workplace, and perceived barriers

The integration of generative artificial intelligence technology into research environments has become increasingly common in recent years, representing a significant shift in the way researchers approach their work. This paper seeks to explore the factors underlying the frequency of use of generative AI amongst researchers in their professional environments. As survey data may be influenced by a bias towards scientists interested in AI, potentially skewing the results towards the perspectives of these researchers, this study uses a regression model to isolate the impact of specific factors such as gender, career stage, type of workplace, and perceived barriers to using AI technology on the frequency of use of generative AI. It also controls for other relevant variables such as direct involvement in AI research or development, collaboration with AI companies, geographic location, and scientific discipline. Our results show that researchers who face barriers to AI adoption experience an 11 % increase in tool use, while those who cite insufficient training resources experience an 8 % decrease. Female researchers experience a 7 % decrease in AI tool usage compared to men, while advanced career researchers experience a significant 19 % decrease. Researchers associated with government advisory groups are 45 % more likely to use AI tools frequently than those in government roles. Researchers in for-profit companies show an increase of 19 %, while those in medical research institutions and hospitals show an increase of 16 % and 15 %, respectively. This paper contributes to a deeper understanding of the mechanisms driving the use of generative AI tools amongst researchers, with valuable implications for both academia and industry.