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Nermin Punar Özçelik; Gonca Yangin Eksi – Smart Learning Environments, 2024
Artificial intelligence (AI) has garnered considerable interest in the field of language education in recent times; however, limited research has focused on the role of AI in the specific context of register knowledge learning during English language writing. This study aims to address this research gap by examining the impact of ChatGPT, an…
Descriptors: Artificial Intelligence, Synchronous Communication, Technology Uses in Education, Writing Skills
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Santosh Mahapatra – Smart Learning Environments, 2024
This paper presents a study on the impact of ChatGPT as a formative feedback tool on the writing skills of undergraduate ESL students. Since artificial intelligence-driven automated writing evaluation tools positively impact students' writing, ChatGPT, a generative artificial intelligence-propelled tool, can be expected to have a more substantial…
Descriptors: Artificial Intelligence, English (Second Language), Writing Skills, Academic Language
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Tlili, Ahmed; Shehata, Boulus; Adarkwah, Michael Agyemang; Bozkurt, Aras; Hickey, Daniel T.; Huang, Ronghuai; Agyemang, Brighter – Smart Learning Environments, 2023
Artificial Intelligence (AI) technologies have been progressing constantly and being more visible in different aspects of our lives. One recent phenomenon is ChatGPT, a chatbot with a conversational artificial intelligence interface that was developed by OpenAI. As one of the most advanced artificial intelligence applications, ChatGPT has drawn…
Descriptors: Computer Mediated Communication, Educational Technology, Technology Uses in Education, Artificial Intelligence
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Nja, Cecilia Obi; Idiege, Kimson Joseph; Uwe, Uduak Edet; Meremikwu, Anne Ndidi; Ekon, Esther Etop; Erim, Costly Manyo; Ukah, Julius Ukah; Eyo, Eneyo Okon; Anari, Mary Ideba; Cornelius-Ukpepi, Bernedette Umalili – Smart Learning Environments, 2023
This study investigated the factors influencing science teachers' 'Artificial Intelligence' (AI) utilization by using the 'Technology Acceptance Model' (TAM). The factors investigated alongside TAM variables were teachers' data like; age, sex, and residence type. TAM items that were correlated in this study included; self-esteem, stress and…
Descriptors: Science Teachers, Educational Technology, Technology Integration, Artificial Intelligence
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Lin, Chien-Chang; Huang, Anna Y. Q.; Lu, Owen H. T. – Smart Learning Environments, 2023
Sustainable education is a crucial aspect of creating a sustainable future, yet it faces several key challenges, including inadequate infrastructure, limited resources, and a lack of awareness and engagement. Artificial intelligence (AI) has the potential to address these challenges and enhance sustainable education by improving access to quality…
Descriptors: Artificial Intelligence, Educational Technology, Sustainability, Technology Uses in Education
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Venigalla, Akhila Sri Manasa; Chimalakonda, Sridhar – Smart Learning Environments, 2023
E-textbooks are one of the commonly used sources to learn programming, in the domain of computer science and engineering. Programming related textbooks provide examples related to syntax, but the number of examples are often limited. Thus, beginners who use e-textbooks often visit other sources on the internet for examples and other information.…
Descriptors: Electronic Publishing, Textbooks, Documentation, Programming
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Dimitriadou, Eleni; Lanitis, Andreas – Smart Learning Environments, 2023
The term "Smart Classroom" has evolved over time and nowadays reflects the technological advancements incorporated in educational spaces. The rapid advances in technology, and the need to create more efficient and creative classes that support both in-class and remote activities, have led to the integration of Artificial Intelligence and…
Descriptors: Program Evaluation, Artificial Intelligence, Technology Uses in Education, Educational Technology
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Yamauchi, Taisei; Flanagan, Brendan; Nakamoto, Ryosuke; Dai, Yiling; Takami, Kyosuke; Ogata, Hiroaki – Smart Learning Environments, 2023
In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and…
Descriptors: Mathematics Instruction, Classification, Algorithms, Barriers
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Liang, Yicong; Zou, Di; Xie, Haoran; Wang, Fu Lee – Smart Learning Environments, 2023
The pretrained large language models have been widely tested for their performance on some challenging tasks including arithmetic, commonsense, and symbolic reasoning. Recently how to combine LLMs with prompting techniques has attracted lots of researchers to propose their models to automatically solve math word problems. However, most research…
Descriptors: Science Instruction, Physics, Artificial Intelligence, Computer Mediated Communication
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Shiao, Yi-Tzone; Chen, Cheng-Huan; Wu, Ke-Fei; Chen, Bae-Ling; Chou, Yu-Hui; Wu, Trong-Neng – Smart Learning Environments, 2023
In recent years, initiatives and the resulting application of precision education have been applied with increasing frequency in Taiwan; the accompanying discourse has focused on identifying potential applications for artificial intelligence and how to use learning analytics to improve teaching quality and learning outcomes. This study used the…
Descriptors: Foreign Countries, Dropout Prevention, Models, Sustainability
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Lee, Alwyn Vwen Yen; Tan, Seng Chee; Teo, Chew Lee – Smart Learning Environments, 2023
Utilizing generative artificial intelligence, especially the more popularly used Generative Pre-trained Transformer (GPT) architecture, has made it possible to employ AI in ways that were previously not possible with conventional assessment and evaluation technologies for learning. As educational use cases and academic studies become increasingly…
Descriptors: Artificial Intelligence, Technology Integration, Sustainability, Knowledge Level
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Pellas, Nikolaos – Smart Learning Environments, 2023
Artificial Intelligence (AI) and Machine Learning (ML) technologies offer the potential to support digital content creation and media production, providing opportunities for individuals from diverse sociodemographic backgrounds to engage in creative activities and enhance their multimedia video content. However, less attention has been paid to…
Descriptors: Artificial Intelligence, Video Technology, Creative Activities, Undergraduate Students
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Chan, Cecilia Ka Yuk; Lee, Katherine K. W. – Smart Learning Environments, 2023
This study aimed to explore the experiences, perceptions, knowledge, concerns, and intentions of Generation Z (Gen Z) students with Generation X (Gen X) and Generation Y (Gen Y) teachers regarding the use of generative AI (GenAI) in higher education. A sample of students and teachers were recruited to investigate the above using a survey…
Descriptors: Age Groups, Generational Differences, Artificial Intelligence, College Faculty
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Toyokawa, Yuko; Horikoshi, Izumi; Majumdar, Rwitajit; Ogata, Hiroaki – Smart Learning Environments, 2023
In inclusive education, students with different needs learn in the same context. With the advancement of artificial intelligence (AI) technologies, it is expected that they will contribute further to an inclusive learning environment that meets the individual needs of diverse learners. However, in Japan, we did not find any studies exploring…
Descriptors: Barriers, Affordances, Artificial Intelligence, Inclusion
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Chan, Cecilia Ka Yuk; Zhou, Wenxin – Smart Learning Environments, 2023
This study examines the relationship between student perceptions and their intention to use generative artificial intelligence (GenAI) in higher education. With a sample of 405 students participating in the study, their knowledge, perceived value, and perceived cost of using the technology were measured by an Expectancy-Value Theory (EVT)…
Descriptors: Student Attitudes, College Students, Artificial Intelligence, Technology Uses in Education
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