A Framework for the Responsible Integration of Generative AI Tools in Learning DOI
Stephen Ko, Simon C. H. Chan

Advances in educational technologies and instructional design book series, Год журнала: 2024, Номер unknown, С. 163 - 194

Опубликована: Дек. 13, 2024

Generative artificial intelligence (Gen-AI) tools are increasingly utilized in educational settings for tasks ranging from content creation to personalizing learning experiences. While these offer considerable potential transform education, their integration brings challenges, including biases, dependency, and ethical dilemmas. Addressing challenges concerns is essential fully leveraging Gen-AI promote equitable effective learning. This chapter presents a framework the responsible use of environments, offering guidance educators, technologists, policymakers, students, other stakeholders. The provides proactive guidelines navigate complexities Gen-AI, ensuring employed ethically effectively enhance student outcomes.

Язык: Английский

Transforming marketing landscapes: a systematic literature review of generative AI using the TCCM model framework DOI

Akshara Prasanna,

Bijay Prasad Kushwaha

Management Review Quarterly, Год журнала: 2025, Номер unknown

Опубликована: Янв. 29, 2025

Язык: Английский

Процитировано

0

Anxiety among Medical Students Regarding Generative Artificial Intelligence Models: A Pilot Descriptive Study DOI Open Access
Malik Sallam,

Kholoud Al-Mahzoum,

Yousef Mubrik N. Almutairi

и другие.

Опубликована: Авг. 16, 2024

Despite the potential benefits of generative Artificial Intelligence (genAI), concerns about its psy-chological impact on medical students, especially with regard to job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed examine specific fears, anxieties, mistrust, and ethical students could harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 studying across various academic years, employing structured self-administered questionnaire an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, Ethics comprising 12 items, three items for each construct. The results indicated variable levels anxiety genAI among participating students: 34.1% reported no role their future careers (n = 56), while 41.5% slightly anxious 61), 22.0% somewhat 36), 2.4% extremely 4). Among constructs, Mistrust was most agreed upon (mean: 12.35±2.78), followed by construct 10.86±2.90), Fear 9.49±3.53), Anxiety 8.91±3.68). Sex, level, Grade Point Average (GPA) did not significantly affect students’ perceptions However, there notable direct association between general elevated scores constructs scale. Prior exposure previous use modify These findings highlighted critical need refined educational strategies address integration training. demonstrated pervasive anxiety, fear, regarding deployment healthcare, indicating necessity curriculum modifi-cations that focus specifically these areas. Interventions should be tailored increase familiarity competency, which would alleviate apprehension equip physicians engage this inevitable technology effectively. study also importance incorporating discussions into courses mistrust human-centered aspects Conclusively, calls proactive evolution education prepare AI-driven healthcare practices shortly ensure well-prepared, confident, ethically informed professional interactions technologies.

Язык: Английский

Процитировано

4

Artificial intelligence in higher education institutions: review of innovations, opportunities and challenges DOI Creative Commons

Samuel Ocen,

Joseph Elasu, Sylvia Manjeri Aarakit

и другие.

Frontiers in Education, Год журнала: 2025, Номер 10

Опубликована: Март 3, 2025

Artificial intelligence is revolutionizing industries including institutions of higher learning as it enhances teaching and processes, streamline administrative tasks drive innovations. Despite the unprecedented opportunities, AI tools if not used correctly, can be challenging in education institutions. The purpose this study was to comprehensively review innovations, opportunities challenges associated with use Education learning. A systematic literature methodology adopted locate select existing studies, analyze synthesize evidence arrive at clear conclusion about current debate area study. Following PRISMA, analyzed a total 54 documents that met inclusion exclusion criteria set for selection documents. unveiled many enhanced research capabilities, automation among others. Intelligence are found refine different units include ethical concerns, integrity issues data fabrication issues. With notwithstanding, benefits cannot over emphasized. remains powerful tool research, tasked, personalized learning, inclusivity accessibility educational content all. Emphasis should put regulatory frameworks detailing how such while maintaining level standards required.

Язык: Английский

Процитировано

0

Generative AI Techniques and Models DOI
Rajan T. Gupta, Sanju Tiwari, Poonam Chaudhary

и другие.

Lecture notes on data engineering and communications technologies, Год журнала: 2025, Номер unknown, С. 45 - 64

Опубликована: Янв. 1, 2025

Процитировано

0

Anxiety among Medical Students Regarding Generative Artificial Intelligence Models: A Pilot Descriptive Study DOI Creative Commons
Malik Sallam,

Kholoud Al-Mahzoum,

Yousef Almutairi

и другие.

International Medical Education, Год журнала: 2024, Номер 3(4), С. 406 - 425

Опубликована: Окт. 9, 2024

Despite the potential benefits of generative artificial intelligence (genAI), concerns about its psychological impact on medical students, especially job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine specific fears, anxieties, mistrust, and ethical students harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 studying across various academic years, employing structured self-administered questionnaire with an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, Ethics—comprising 12 items, 3 items for each construct. Exploratory confirmatory factors analyses assess construct validity scale. The results indicated variable levels anxiety genAI among participating students: 34.1% reported no genAI‘s role their future careers (n = 56), while 41.5% slightly anxious 61), 22.0% somewhat 36), 2.4% extremely 4). Among constructs, Mistrust was most agreed upon (mean: 12.35 ± 2.78), followed by Ethics 10.86 2.90), Fear 9.49 3.53), Anxiety 8.91 3.68). Their sex, level, Grade Point Average (GPA) did not significantly affect students’ perceptions However, there notable direct association between general elevated scores constructs Prior exposure previous use modify These findings highlight critical need refined educational strategies address integration into training. demonstrate anxiety, fear, regarding deployment healthcare, indicating necessity curriculum modifications that focus specifically these areas. Interventions should be tailored increase familiarity competency genAI, which would alleviate apprehensions equip physicians engage this inevitable technology effectively. study also highlights importance incorporating discussions courses mistrust human-centered aspects In conclusion, calls proactive evolution education prepare new AI-driven healthcare practices ensure well prepared, confident, ethically informed professional interactions technologies.

Язык: Английский

Процитировано

2

Marketing Is a Transdisciplinary Body of Knowledge DOI Creative Commons
Robert A. Peterson

Journal of Marketing Education, Год журнала: 2024, Номер unknown

Опубликована: Окт. 30, 2024

“What ‘is’ marketing?” Perusal of the marketing literature reveals that “marketing” has been defined and characterized in multiple, often inconsistent but typically ambiguous, ways have evolved over time. The present essay argues characterizing as a transdisciplinary body knowledge formally captures its essence possesses numerous implications for education well practice. Following brief review how conceptualized past, benefits challenges conceptualizing are discussed from paradigmatic perspective.

Язык: Английский

Процитировано

0

A Framework for the Responsible Integration of Generative AI Tools in Learning DOI
Stephen Ko, Simon C. H. Chan

Advances in educational technologies and instructional design book series, Год журнала: 2024, Номер unknown, С. 163 - 194

Опубликована: Дек. 13, 2024

Generative artificial intelligence (Gen-AI) tools are increasingly utilized in educational settings for tasks ranging from content creation to personalizing learning experiences. While these offer considerable potential transform education, their integration brings challenges, including biases, dependency, and ethical dilemmas. Addressing challenges concerns is essential fully leveraging Gen-AI promote equitable effective learning. This chapter presents a framework the responsible use of environments, offering guidance educators, technologists, policymakers, students, other stakeholders. The provides proactive guidelines navigate complexities Gen-AI, ensuring employed ethically effectively enhance student outcomes.

Язык: Английский

Процитировано

0