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Wei Zhang; Mingxuan Cai; Hong Joo Lee; Richard Evans; Chengyan Zhu; Chenghan Ming – Education and Information Technologies, 2024
Artificial Intelligence (AI) is transforming healthcare and shows considerable promise for the delivery of medical education. This systematic review provides a comprehensive analysis of the global situation, effects, and challenges associated with applying AI at the different stages of medical education. This review followed the PRISMA guidelines,…
Descriptors: Artificial Intelligence, Medical Education, Content Analysis, Teaching Methods
Bambang Sulistio; Arief Ramadhan; Edi Abdurachman; Muhammad Zarlis; Agung Trisetyarso – Education and Information Technologies, 2024
Computer science development, especially machine learning, is a thriving innovation essential for education. It makes the process of teaching and learning more accessible and manageable and also promotes equality. The positive influence of machine learning can also be felt in Islamic studies, particularly in Hadith studies. This literature review…
Descriptors: Electronic Learning, Artificial Intelligence, Computer Uses in Education, Islam
Xiao-Fan Lin; Yue Zhou; Weipeng Shen; Guoyu Luo; Xiaoqing Xian; Bo Pang – Education and Information Technologies, 2024
K-12 artificial intelligence (AI) education requires cultivating students' computational thinking in the school curriculum so as to transfer their computational thinking to diverse problems and authentic contexts. However, students may be limited by traditional computational thinking development activities because they may have a lower degree of…
Descriptors: Secondary School Students, Artificial Intelligence, Foreign Countries, Computation
Jiahong Su; Weipeng Yang – Journal of Computer Assisted Learning, 2024
Background: The number of artificial intelligence (AI) literacy studies in K-12 education has recently increased, with most research focusing on primary and secondary education contexts. Little research focuses on AI literacy programs in early childhood education. Objectives: The aim of this mixed-methods study is to examine the feasibility of an…
Descriptors: Foreign Countries, Artificial Intelligence, Kindergarten, Young Children
Chih-Hsuan Chen; Chia-Ru Chung; Hsuan-Yu Yang; Shih-Ching Yeh; Eric Hsiao-Kuang Wu; Hsin-Jung Ting – IEEE Transactions on Learning Technologies, 2024
Possible symptoms of intellectual disability (ID) include delayed physical development that becomes more pronounced as the disability progresses, delayed development of gross and fine motor skills, sensory perception problems, and difficulty grasping the integrity of objects. Although there is no cure or reversal, research has shown that extensive…
Descriptors: Intellectual Disability, Disability Identification, Simulated Environment, Computer Simulation
Felipe de Morais; Patricia A. Jaques – IEEE Transactions on Learning Technologies, 2024
Emotion detection through sensors is intrusive and expensive, making it impractical for many educational settings. As an alternative, sensor-free affect detection, which relies solely on interaction log data for machine learning models, has been explored. However, sensor-free emotion detectors have not significantly improved performance when…
Descriptors: Psychological Patterns, Personality Traits, Artificial Intelligence, Models
Danial Hooshyar – Education and Information Technologies, 2024
Neural and symbolic architectures are key techniques in AI for learner modelling, enhancing adaptive educational services. Symbolic models offer explanation and reasoning for decisions but require significant human effort. On the other hand, neural architectures demand less human input and yield better predictions, yet lack interpretability. Given…
Descriptors: Artificial Intelligence, Modeling (Psychology), Learner Engagement, Achievement
Stephanie Moore; Amir Hedayati-Mehdiabadi; Victor Law; Sung Pil Kang – TechTrends: Linking Research and Practice to Improve Learning, 2024
Early hype cycles surrounding new technologies may promote simplistic binary options of either adoption or rejection, but socio-historical analyses of technologies illuminate how they are worked into shape by human actors. Humans enact agency through many choices that result in adaptations and contextual variations. In this piece, we argue that…
Descriptors: Artificial Intelligence, Natural Language Processing, Man Machine Systems, Ethics
Sue Lim; Ralf Schmälzle – Communication Teacher, 2024
Courses: Health Communication, Public Communication Campaigns, Public Relations, Introduction to Communication. Objectives: By the end of this workshop, students will be able to: (1) understand how artificial intelligence--based large language learning models work and be able to explain core concepts such as word embeddings, neural networks, and…
Descriptors: Artificial Intelligence, Communication Skills, Introductory Courses, Workshops
Heng Zhang; Minhong Wang – Knowledge Management & E-Learning, 2024
With the fast development of artificial intelligence and emerging technologies, automatic recognition of students' facial expressions has received increased attention. Facial expressions are a kind of external manifestation of emotional states. It is important for teachers to assess students' emotional states and adjust teaching activities…
Descriptors: Artificial Intelligence, Models, Recognition (Psychology), Nonverbal Communication
Reem Jalal Eddine; Claudio Mulatti; Francesco N. Biondi – Cognitive Research: Principles and Implications, 2024
The use of partially-automated systems require drivers to supervise the system functioning and resume manual control whenever necessary. Yet literature on vehicle automation show that drivers may spend more time looking away from the road when the partially-automated system is operational. In this study we answer the question of whether this…
Descriptors: Motor Vehicles, Attention Control, Artificial Intelligence, Eye Movements
Michael E. Ellis; K. Mike Casey; Geoffrey Hill – Decision Sciences Journal of Innovative Education, 2024
Large Language Model (LLM) artificial intelligence tools present a unique challenge for educators who teach programming languages. While LLMs like ChatGPT have been well documented for their ability to complete exams and create prose, there is a noticeable lack of research into their ability to solve problems using high-level programming…
Descriptors: Artificial Intelligence, Programming Languages, Programming, Homework
Vasiliki Mollaki – Research Ethics, 2024
Peer review facilitates quality control and integrity of scientific research. Although publishing policies have adapted to include the use of Artificial Intelligence (AI) tools, such as Chat Generative Pre-trained Transformer (ChatGPT), in the preparation of manuscripts by authors, there is a lack of guidelines or policies on whether peer…
Descriptors: Peer Evaluation, Writing for Publication, Ethics, Artificial Intelligence
Garry Vanz V. Blancia; Eddie G. Fetalvero; Philip R. Baldera; Merian C. Mani – Problems of Education in the 21st Century, 2024
These days' educational landscape forces teachers to adapt to changing demands and embrace innovations. In this study, Artificial Intelligence (AI) literacy was analyzed as how it mediates the association between Computational Thinking Skills (CTS) and Organizational Agility (OA) among secondary teachers. A quantitative causal mediation analysis…
Descriptors: Artificial Intelligence, Technological Literacy, Mental Computation, Secondary School Teachers
Dana-Kristin Mah; Nele Groß – International Journal of Educational Technology in Higher Education, 2024
Faculty perspectives on the use of artificial intelligence (AI) in higher education are crucial for AI's meaningful integration into teaching and learning, yet research is scarce. This paper presents a study designed to gain insight into faculty members' (N = 122) AI self-efficacy and distinct latent profiles, perceived benefits, challenges, use,…
Descriptors: Artificial Intelligence, College Faculty, Technology Uses in Education, Self Efficacy

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