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Ismail Celik; Hanni Muukkonen; Signe Siklander – Policy Futures in Education, 2026
Despite the novel educational opportunities of chatbots, their integration into teaching and learning settings is still in the early stages. Understanding the interplay of teachers' perceptions, attitudes, and intentions to use chatbots can provide insight into the sustainable integration of chatbots in K-12 education. However, little is known…
Descriptors: Artificial Intelligence, Interaction, Trust (Psychology), Synchronous Communication
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Chukwuemeka Ihekweazu; Bing Zhou; Elizabeth Adepeju Adelowo – Information Systems Education Journal, 2024
This study delves into the opportunities and challenges associated with the deployment of AI tools in the education sector. It systematically explores the potential benefits and risks inherent in utilizing these tools while specifically addressing the complexities of identifying and preventing academic dishonesty. Recognizing the ethical…
Descriptors: Ethics, Artificial Intelligence, Responsibility, Technology Uses in Education
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Antara Mukherjee; Shashi Singh – Asian Journal of Distance Education, 2025
The digital age has witnessed a rapidly evolving educational landscape, with AI chatbots emerging as powerful tools supporting autonomous learning. This study investigates the acceptance level of AI chatbots among college students and evaluates the influence of factors such as gender, age, education level, learning styles, and major disciplines on…
Descriptors: Artificial Intelligence, College Students, Student Characteristics, Student Attitudes
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Jacqueline Zammit – Technology in Language Teaching & Learning, 2024
The Chat Generative Pretrained Transformer (ChatGPT) is a state-of-the-art artificial intelligence (AI) language model developed by OpenAI. It employs advanced deep-learning algorithms to generate text that mimics human language. ChatGPT, launched on November 30, 2022, has rapidly gained widespread recognition. Its influence on the future of…
Descriptors: Artificial Intelligence, Computer Software, Synchronous Communication, Second Language Learning
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David W. Brown; Dean Jensen – International Society for Technology, Education, and Science, 2023
The growth of Artificial Intelligence (AI) chatbots has created a great deal of discussion in the education community. While many have gravitated towards the ability of these bots to make learning more interactive, others have grave concerns that student created essays, long used as a means of assessing the subject comprehension of students, may…
Descriptors: Artificial Intelligence, Natural Language Processing, Computer Software, Writing (Composition)
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Younglong Kim; Katherine A. Curry; Ashlyn M. Fiegener – Journal of School Administration Research and Development, 2024
Educational leaders are faced with multi-faceted dilemmas that place decision-making at the heart of their day-to-day work. For support, they often turn to collaborative networks of experienced educators, such as Project ECHO, for solutions to address challenges they encounter while working in the field. The availability of generative AI…
Descriptors: Artificial Intelligence, Natural Language Processing, Barriers, Educational Practices
Ahmed Magooda; Diane Litman; Ahmed Ashraf; Muhsin Menekse – Grantee Submission, 2022
Having students write reflections has been shown to help teachers improve their instruction and students improve their learning outcomes. With the aid of Natural Language Processing (NLP), real-time educational applications that can assess and provide feedback on reflection quality can be deployed. In this work, we first evaluate various NLP…
Descriptors: Undergraduate Students, Writing Assignments, Reflection, Natural Language Processing
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Ranalli, Jim; Yamashita, Taichi – Language Learning & Technology, 2022
To the extent automated written corrective feedback (AWCF) tools such as Grammarly are based on sophisticated error-correction technologies, such as machine-learning techniques, they have the potential to find and correct more common L2 error types than simpler spelling and grammar checkers such as the one included in Microsoft Word (technically…
Descriptors: Error Correction, Feedback (Response), Computer Software, Second Language Learning
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Lämsä, Joni; Uribe, Pablo; Jiménez, Abelino; Caballero, Daniela; Hämäläinen, Raija; Araya, Roberto – Journal of Learning Analytics, 2021
Scholars have applied automatic content analysis to study computer-mediated communication in computer-supported collaborative learning (CSCL). Since CSCL also takes place in face-to-face interactions, we studied the automatic coding accuracy of manually transcribed face-to-face communication. We conducted our study in an authentic higher-education…
Descriptors: Cooperative Learning, Computer Assisted Instruction, Synchronous Communication, Learning Analytics
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Hernández-Lara, Ana Beatriz; Perera-Lluna, Alexandre; Serradell-López, Enric – Education & Training, 2021
Purpose: With the growth of digital education, students increasingly interact in a variety of ways. The potential effects of these interactions on their learning process are not fully understood and the outcomes may depend on the tool used. This study explores the communication patterns and learning effectiveness developed by students using two…
Descriptors: Game Based Learning, Learning Analytics, Computer Mediated Communication, Asynchronous Communication
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Rigo, Sandro José; da Rosa Alves, Isa Mara; Victória Barbosa, Jorge Luis – International Journal of Information and Communication Technology Education, 2015
The digital mediation resources used in Distance Education can hinder the teacher's perception about the student's state of mind. However, the textual expression in natural language is widely encouraged in most Distance Education courses, through the use of Virtual Learning Environments and other digital tools. This fact has motivated research…
Descriptors: Affective Behavior, Synchronous Communication, Telecommunications, Handheld Devices
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Kopp, Kristopher J.; Britt, M. Anne; Millis, Keith; Graesser, Arthur C. – Learning and Instruction, 2012
The current studies investigated the efficient use of dialogue in intelligent tutoring systems that use natural language interaction. Such dialogues can be relatively time-consuming. This work addresses the question of how much dialogue is needed to produce significant learning gains. In Experiment 1, a full dialogue condition and a read-only…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Computer Mediated Communication, Synchronous Communication
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Ann, Ong Chin; Theng, Lau Bee – Interactive Technology and Smart Education, 2012
Purpose: The purpose of this paper is to investigate an idea of producing an assistive and augmentative communication (AAC) tool that uses natural human computer interfacing to accommodate the disabilities of children with cerebral palsy (CP) and assist them in their daily communication. Design/methodology/approach: The authors developed a…
Descriptors: Foreign Countries, Developing Nations, Augmentative and Alternative Communication, Cerebral Palsy
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Vlugter, P.; Knott, A.; McDonald, J.; Hall, C. – Computer Assisted Language Learning, 2009
We describe a computer assisted language learning (CALL) system that uses human-machine dialogue as its medium of interaction. The system was developed to help students learn the basics of the Maori language and was designed to accompany the introductory course in Maori running at the University of Otago. The student engages in a task-based…
Descriptors: College Students, Introductory Courses, Malayo Polynesian Languages, Pretests Posttests