Generative Artificial Intelligence in Higher Education
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Item Highlighting Competency Gaps in Health Informatics Education Using Advanced Text-Embedding Models(2025-01-07) El-Gayar, Omar; Wahbeh, Abdullah; Al-Ramahi, Mohammad; Elnoshokaty, AhmedThe healthcare sector anticipates substantial growth, with 125,000 job openings by 2026, including 69,000 middle-skilled positions. Despite this growth, data use and integration inefficiencies cost the sector $750 billion annually. Health informatics, an interdisciplinary field blending healthcare, computer, information, and cognitive sciences, can address these challenges through enhanced healthcare management via information technology. However, there is a critical disparity between competencies taught in educational programs and those demanded by the job market. This study examines competencies from job postings on Indeed.com and accredited health informatics programs, comparing them with the Health Information Technology Competencies (HITComp) database. Utilizing advanced text-embedding models, the study found that while educational programs focus on foundational competencies, the job market demands practical applications. Forty-six specific competencies, particularly in administrative, direct patient care, informatics engineering, and research domains, are identified as gaps. Addressing these gaps is essential to prepare the workforce for the evolving healthcare industry.Item Simulating Market Equilibrium with Large Language Models(2025-01-07) Junqué De Fortuny, EnricLarge Language Models (LLMs) have the potential to simulate complex human decision-making and economic behavior, making them well-suited for training simulations. This study explores LLMs' ability to simulate a market equilibrium game commonly used in MBA classrooms to train future business leaders. We test three simulation architectures: prompt-based, Retrieval Augmented Generation-based, and controller-based. The prompt-based approach struggled with limited context, while the RAG-based system improved information retrieval but occasionally veered off course. To address these challenges, we developed a controller-based simulation integrating multiple LLM agents and custom tools, which enhanced both control and accuracy. Our results show that this approach not only improves the fidelity of economic simulation, but also enriches the learning experience for students by providing more accurate, engaging, and realistic business case environments.Item Investigating the Factors Influencing ChatGPT Adoption Intention among Chinese Higher Education Personnel: An Empirical Study Based on the Extended UTAUT2 Model(2025-01-07) Long, Chunlan; Wu, Junjie; Ye, Jianmei; Wang, WeijunThis study explores the factors influencing the intent of Chinese higher education professionals to use ChatGPT, expanding our understanding to contexts where the technology is not officially accessible. We conducted an online survey involving 317 Chinese students and higher education staff, drawing insights from the UTAUT2 model. We established a structural equation model for data analysis. Our findings reveal direct influences of performance expectancy, social influence, price value, personal innovativeness, and technology fear on usage intention. We also observed indirect effects stemming from effort expectancy, context awareness, and perceived risk on usage intention. Furthermore, our results highlight that personal innovativeness moderates the impact of technology fear on usage intention. This study deepens our understanding of attitudes toward ChatGPT in higher education, especially in situations where official access is restricted, and extends the UTAUT2 model by establishing a connection between the psychological traits of higher education personnel and their technology acceptance.Item The BIG Activity: Using Generative AI for Hybrid Strategic Decision-Making with Business Students(2025-01-07) Bass, Erin; Pleggenkuhle-Miles, ErinArtificial intelligence (AI) is an increasingly important tool used for decision-making across business careers. AI-driven decision-making can identify new approaches to efficiency, innovation, and value creation. Despite its promise, AI has not been widely integrated into business school curricula. Thus, there is a growing gap between the skills that students gain in business school and the AI skills that are needed once they move into their professional careers. This paper aims to bridge some of this gap by introducing the Business Idea Generation (BIG) Activity, a hybrid decision-making activity that combines Generative AI (GenAI) with human intuition for strategic decision-making. Through this experiential activity, students gain practical experience leveraging GenAI for cognitively complex tasks typical for strategic management. The study contributes by demonstrating hybrid decision-making in strategic management education, providing insights into the post-search process of decision-making, and showcasing GenAI’s potential in handling complex cognitive tasks.Item Generative AI Agents in Language Learning: A Randomized Field Experiment(2025-01-07) Cheon, Gyeombi; Choi, Yunmin; Lee, Dongwon; Baek, JiyeArtificial Intelligence (AI) is revolutionizing education, particularly with advancements in generative AI conversational agents. Our study investigates the effectiveness of these generative AI agents in enhancing English-speaking skills compared to human agents. Through a randomized field experiment involving 363 participants, we found that unrestricted use of AI tools led to a 5.90% improvement in lexical diversity, as measured by the Type-Token Ratio (TTR), highlighting the benefits of self-paced learning. Notably, learners with below-average proficiency experienced a 9.53% improvement in TTR, suggesting AI's potential to bridge educational equity gaps. Moreover, AI tools significantly reduced evaluation apprehension, further enhancing learning outcomes. These findings underscore AI's capacity to provide personalized, anxiety-free learning environments, particularly for students with lower proficiency, and offer valuable insights for integrating AI into educational strategies to foster more inclusive learning experiences.Item Exploring the Affordances of Generative AI in Academic Writing for Disabled Student(2025-01-07) Zhao, Xin; Chen, Xuanning; Cox, AndrewThis study explores the use and attitudes towards generative AI technology among disabled students in higher education, addressing a gap in existing research on accessibility and inclusivity challenges for marginalized groups. Informed by a prior study and affordance theory, we surveyed 124 students with various disabilities (e.g., neurodiversity, dyslexia and social/communication impairment) about their use of and attitudes toward generative AI during academic writing. We identified three key affordances provided by generative AI—explainability, expressibility, and plannability—that positively affect disabled students' writing processes. However, our study also highlights significant areas where generative AI remains insufficient in addressing barriers faced by disabled students, such as concerns about hallucination, loss own voices, and academic integrity. Our findings offer practical implications for both developers and educational practitioners. These include the need to design more inclusive generative AI technologies and to promote AI literacy, along with providing guidance and training for both students and staff in higher education institutions.Item Students’ Use and Attitudes Toward Generative Artificial Intelligence: A Comparative Study Between the UK and China(2025-01-07) Zhao, Xin; Chen, Xuanning; Huang , Vincent; Rollins, Minna; Carratù, Marco; Shallari, IridaGenerative Artificial Intelligence (AI) has become a focal point in higher education worldwide. Research has demonstrated its potential to enhance student learning, particularly for those disadvantaged in traditional educational contexts. However, concerns regarding fairness, transparency, and the ethical use of technology underscore the importance of developing evidence-based guidelines and policies for governing generative AI. While global efforts have been made to create policies and support the ethical use of these tools, there remains a lack of data on student usage and attitudes toward generative AI tools to inform such policy-making. Specifically, the absence of cross-national perspectives on student use poses risks of widening the existing digital divide when implementing guidelines established by leading organizations, which often lack the voices of Global South contexts. Our study aims to use a cross-national survey to examine the current practices of GenAI in various national contexts. We are collecting data from several countries. So far, we have collected data from China (Hong Kong and Macau) and the UK and this paper reports the results from two countries. Our findings highlight three main themes comparing students' use and attitudes toward generative AI in learning between the UK and China: types of tools used, students’ primary uses of the tools and associated concerns, and students’ attitudes toward using the tools. Our results offer valuable insights into student use of generative AI worldwide and conclude with practical recommendations for policymakers and educational practitioners in the field of generative AI in higher education.Item Reskilling Me Softly: Perceived Changes in Students’ Skilling When Using GenAI in Academic Research Projects(2025-01-07) Passlack, Nina; Gerholz, Karl-Heinz; Schlottmann, PhilippThe increasing use of Generative Artificial Intelligence (GenAI) in education for academic research projects is reshaping how we acquire information and generate knowledge. Generative AI is defined as computational techniques generating content, such as text from training data, which drives significant skill changes. This raises the question of how GenAI prompts reskilling, involving both deskilling (reduced skill development) and upskilling (acquisition of new skills). The balance between these processes is crucial, as overreliance on GenAI in academic research projects could lead to a performance gap due to, for instance, diminished critical thinking. Semi-structured interviews with students using GenAI for their academic research projects provide insights into shifts in skill requirements and the implications for future academic competence levels. The paper outlines a potential skill gap and concludes that individuals’ motivational factors impact reskilling.Item ChatGPT in the Engineering Classroom: A Pre-Study on Students’ Perceptions and Experience(2025-01-07) Hussain, Mazhar; Pietrosanto, Antonio; Liguori, Consolatina; Paciello, Vincenzo; De Santo, MassimoEngineering education constantly evolves to meet engineering fields' demands and keep up with the latest technological advances. Generative AI is considered one of the most significant technological developments in recent decades, impacting numerous fields. It has excellent potential as a versatile and valuable tool for information systems education inside and outside universities. ChatGPT significantly enhances personalized learning in education, providing quick access to information. However, this comes with the duality where, from one side, it can serve as a means for students’ access to tailored knowledge at any time. Conversely, it can hinder students’ education, leading them to loss of interest, lack of critical thinking, or even acts of plagiarism. Focusing on four categories of cognitive depth in the form of several assignments on the subject of Digital Systems Design, we present a pre-study on the perceived usefulness of ChatGPT from the student’s perspective. We also provide ideas for the future research.Item Introduction to the Minitrack on Generative Artificial Intelligence in Higher Education(2025-01-07) Zhao, Xin; Carratù, Marco; Shallari, Irida; Rollins, Minna