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Tolsgaard, Martin G.; Boscardin, Christy K.; Park, Yoon Soo; Cuddy, Monica M.; Sebok-Syer, Stefanie S. – Advances in Health Sciences Education, 2020
Data science is an inter-disciplinary field that uses computer-based algorithms and methods to gain insights from large and often complex datasets. Data science, which includes Artificial Intelligence techniques such as Machine Learning (ML), has been credited with the promise to transform Health Professions Education (HPE) by offering approaches…
Descriptors: Allied Health Occupations Education, Data Analysis, Artificial Intelligence, Theories
Pang, Bo; Nijkamp, Erik; Wu, Ying Nian – Journal of Educational and Behavioral Statistics, 2020
This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models…
Descriptors: Artificial Intelligence, Regression (Statistics), Models, Classification
Wilkens, Uta – International Journal of Information and Learning Technology, 2020
Purpose: The aim of this paper is to outline how artificial intelligence (AI) can augment learning process in the workplace and where there are limitations. Design/methodology/approach: The paper is a theoretical-based outline with reference to individual and organizational learning theory, which are related to machine learning methods as they are…
Descriptors: Artificial Intelligence, Work Environment, Organizational Learning, Theories
Hu, Qian; Rangwala, Huzefa – International Educational Data Mining Society, 2020
Over the past decade, machine learning has become an integral part of educational technologies. With more and more applications such as students' performance prediction, course recommendation, dropout prediction and knowledge tracing relying upon machine learning models, there is increasing evidence and concerns about bias and unfairness of these…
Descriptors: Artificial Intelligence, Bias, Learning Analytics, Statistical Analysis
Xiao, Yunkai; Zingle, Gabriel; Jia, Qinjin; Akbar, Shoaib; Song, Yang; Dong, Muyao; Qi, Li; Gehringer, Edward – International Educational Data Mining Society, 2020
Peer assessment adds value when students provide "helpful" feedback to their peers. But, this begs the question of how we determine "helpfulness." One important aspect is whether the review detects problems in the submitted work. To recognize problem detection, researchers have employed NLP and machine-learning text…
Descriptors: Peer Evaluation, Problems, Identification, Natural Language Processing
Abdalkader, Shireen Mostafa Ahmed – Online Submission, 2023
This study aimed to investigate the effect of using some proposed artificial intelligence activities on enhancing EFL writing fluency and self-regulation for the preparatory stage students in Distinguished Governmental Language Schools. Participants of the study were 33 students in preparatory three from Hassan Abu Bakr governmental language…
Descriptors: Foreign Countries, State Schools, Second Language Instruction, English Language Learners
Yang, Qi-Fan; Lian, Li-Wen; Zhao, Jia-Hua – International Journal of Educational Technology in Higher Education, 2023
According to previous studies, traditional laboratory safety courses are delivered in a classroom setting where the instructor teaches and the students listen and read the course materials passively. The course content is also uninspiring and dull. Additionally, the teaching period is spread out, which adds to the instructor's workload. As a…
Descriptors: Undergraduate Students, Gamification, Artificial Intelligence, Robotics
Mangera, Elisabet; Supratno, Haris; Suyatno – Pegem Journal of Education and Instruction, 2023
This studied focus on the relationship between transhumanist and artificial intelligence in the Education Context; Particularly Teaching and Learning Process at private university in Makassar, South Sulawesi, Indonesia. Anchored by a qualitative analysis and participated by five teachers, the data were analyzed in-depth interview. It was designed…
Descriptors: Humanism, Artificial Intelligence, Learning Processes, Postsecondary Education
Mitra, Reshmi; Schwieger, Dana; Lowe, Robert – Information Systems Education Journal, 2023
Many universities have, or are facing, the task of providing high quality essential customer services with fewer financial and human resources. The growing diversity of students, their needs and proficiencies, along with the increasing variety of university program offerings, make providing customized, ondemand, automated solutions crucial to…
Descriptors: Universities, Academic Advising, Artificial Intelligence, Faculty Workload
Wan, Haipeng; Yu, Shengquan – Interactive Learning Environments, 2023
Most online learning researchers use resource recommendation and retrieve based on learning performance and learning style to provide accurate learning resources, but it is a closed and passive adaptive way. Learners always do not know the recommendation rationale and just receive the result-oriented recommended resources without having a chance…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Artificial Intelligence, Cognitive Mapping
Sun, Junmei; Ma, Hongliang; Zeng, Yu; Han, Dong; Jin, Yunbo – Education and Information Technologies, 2023
With the rapid development of artificial intelligence (AI), the demand for K-12 computer science (CS) education continues to grow. However, there has long been a lack of trained CS teachers. To promote the AI teaching competency of CS teachers, a professional development (PD) program based on the technological pedagogical content knowledge (TPACK)…
Descriptors: Artificial Intelligence, Elementary Secondary Education, Computer Science Education, Teacher Competencies
Xia, Qi; Chiu, Thomas K. F.; Chai, Ching Sing; Xie, Kui – British Journal of Educational Technology, 2023
The anthropomorphic characteristics of artificial intelligence (AI) can provide a positive environment for self-regulated learning (SRL). The factors affecting adolescents' SRL through AI technologies remain unclear. Limited AI and disciplinary knowledge may affect the students' motivations, as explained by self-determination theory (SDT). In this…
Descriptors: Secondary School Students, Grade 9, Needs, Satisfaction
He, Jie; Ma, Tingjuan; Zhang, Yongliang – Education and Information Technologies, 2023
This study implemented a blended learning mode with the aid of artificial intelligence teaching platform in the English language course. In this study, 110 students majoring in computer network were randomly selected as subjects, 55 of whom was the experimental group, the rest of 55 was the control group. Before the experiment, this study carried…
Descriptors: Blended Learning, Communities of Practice, Artificial Intelligence, College Students
Woo, David James; Wang, Yanzhi; Susanto, Hengky; Guo, Kai – Journal of Educational Computing Research, 2023
Natural language generation (NLG) is a process within artificial intelligence where computer systems produce human-comprehensible language texts from information. English as a foreign language (EFL) students' use of NLG tools might facilitate their idea generation, which is fundamental to creative writing. However, little is known about how EFL…
Descriptors: Natural Language Processing, Artificial Intelligence, English (Second Language), Second Language Learning
Hsu, Ting-Chia; Huang, Hsiu-Ling; Hwang, Gwo-Jen; Chen, Mu-Sheng – Educational Technology & Society, 2023
In traditional instruction, teachers generally deliver the content of textbooks to students via lectures, making teaching activities lack vibrancy. Moreover, in such a one-to-many teaching mode, the teacher is usually unable to check on individual students' learning status or to provide immediate feedback to resolve their learning problems.…
Descriptors: High School Students, Expertise, Decision Making, Artificial Intelligence

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