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Showing 1 to 15 of 68 results Save | Export
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Shang Shanshan; Geng Sen – Journal of Computer Assisted Learning, 2024
Background: Artificial intelligence-generated content (AIGC) has stepped into the spotlight with the emergence of ChatGPT, making effective use of AIGC for education a hot topic. Objectives: This study seeks to explore the effectiveness of integrating AIGC into programming learning through debugging. First, the study presents three levels of AIGC…
Descriptors: Artificial Intelligence, Educational Technology, Technology Integration, Programming
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Chengliang Wang; Xiaojiao Chen; Zhebing Hu; Sheng Jin; Xiaoqing Gu – Journal of Computer Assisted Learning, 2025
Background: ChatGPT, as a cutting-edge technology in education, is set to significantly transform the educational landscape, raising concerns about technological ethics and educational equity. Existing studies have not fully explored learners' intentions to adopt artificial intelligence generated content (AIGC) technology, highlighting the need…
Descriptors: College Students, Student Attitudes, Computer Attitudes, Computer Uses in Education
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Bryan Abendschein; Xialing Lin; Chad Edwards; Autumn Edwards; Varun Rijhwani – Journal of Computer Assisted Learning, 2024
Background: Education is often the primary arena for exploring and integrating new technologies. AI and human-machine communication (HMC) are prevalent in the classroom, yet we are still learning how student perceptions of these tools will impact education. Objectives: We sought to understand student perceptions of credibility related to written…
Descriptors: Students, Student Attitudes, Feedback (Response), Writing (Composition)
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Dominic Lohr; Hieke Keuning; Natalie Kiesler – Journal of Computer Assisted Learning, 2025
Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially large…
Descriptors: College Students, Programming, Artificial Intelligence, Feedback (Response)
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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
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Ho Young Yoon; Seokmin Kang; Sungyeun Kim – Journal of Computer Assisted Learning, 2024
Background: Research into enhancing the effectiveness of information delivery in asynchronous video lectures remains sparse. This study analyzes the nonverbal teaching behaviours in asynchronous online videos, drawing comparisons between pre-service and in-service teachers (ITs). Objectives: This research primarily aims to juxtapose the nonverbal…
Descriptors: Asynchronous Communication, Video Technology, Lecture Method, Nonverbal Communication
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Umar Alkafaween; Ibrahim Albluwi; Paul Denny – Journal of Computer Assisted Learning, 2025
Background: Automatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some…
Descriptors: Automation, Grading, Introductory Courses, Programming
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Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
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Gulnur Tyulepberdinova; Madina Mansurova; Talshyn Sarsembayeva; Sulu Issabayeva; Darazha Issabayeva – Journal of Computer Assisted Learning, 2024
Background: This study aims to assess how well several machine learning (ML) algorithms predict the physical, social, and mental health condition of university students. Objectives: The physical health measurements used in the study include BMI (Body Mass Index), %BF (percentage of Body Fat), BSC (Blood Serum Cholesterol), SBP (Systolic Blood…
Descriptors: Artificial Intelligence, Algorithms, Predictor Variables, Physical Health
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Ley, Tobias; Tammets, Kairit; Pishtari, Gerti; Chejara, Pankaj; Kasepalu, Reet; Khalil, Mohammad; Saar, Merike; Tuvi, Iiris; Väljataga, Terje; Wasson, Barbara – Journal of Computer Assisted Learning, 2023
Background: With increased use of artificial intelligence in the classroom, there is now a need to better understand the complementarity of intelligent learning technology and teachers to produce effective instruction. Objective: The paper reviews the current research on intelligent learning technology designed to make models of student learning…
Descriptors: Artificial Intelligence, Technology Uses in Education, Learning Analytics, Instructional Effectiveness
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Dominic Lohr; Marc Berges; Abhishek Chugh; Michael Kohlhase; Dennis Müller – Journal of Computer Assisted Learning, 2025
Background: Over the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models…
Descriptors: Resource Units, Semantics, Automation, Questioning Techniques
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Rakovic, Mladen; Iqbal, Sehrish; Li, Tongguang; Fan, Yizhou; Singh, Shaveen; Surendrannair, Surya; Kilgour, Jonathan; Graaf, Joep; Lim, Lyn; Molenaar, Inge; Bannert, Maria; Moore, Johanna; Gaševic, Dragan – Journal of Computer Assisted Learning, 2023
Background: Assignments that involve writing based on several texts are challenging to many learners. Formative feedback supporting learners in these tasks should be informed by the characteristics of evolving written product and by the characteristics of learning processes learners enacted while developing the product. However, formative feedback…
Descriptors: Artificial Intelligence, Essays, High Achievement, Writing Achievement
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Shilpi Harnal; Gaurav Sharma; Anupriya; Anand Muni Mishra; Deepak Bagga; Nikhil Saini; Pankaj Kumar Goley; Kumar Anupam – Journal of Computer Assisted Learning, 2024
Background: An innovative and interactive real-world environment can be presented with augmented reality (AR) that comprises digital visual elements, audio, or other sensory information delivered via technology to enhance one's experience. AR has numerous potential applications in various everyday fields. The education sector is one such arena…
Descriptors: Bibliometrics, Computer Simulation, Artificial Intelligence, Educational Technology
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Robert O. Davis; Yong-Jik Lee; Joseph Vincent; Lili Wan – Journal of Computer Assisted Learning, 2024
Background: Gestures are an integral component in human-to-human communication when the speaker is visually present to the listener. In the past several years, research has examined how computer-generated pedagogical agents can be designed to perform the four main gesture types and what this means for agent persona and learning outcomes. The…
Descriptors: Nonverbal Communication, Interpersonal Communication, Multimedia Instruction, Imagery
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Robert F. Siegle; Scotty D. Craig – Journal of Computer Assisted Learning, 2024
Background: The voices virtual on-screen characters use has been shown to impact learning and perception outcomes. Recent replication research on these voices showed that synthetic voices were not a detriment if produced by a high-quality engine with clear articulation. The current manuscript examines previous accent research that utilized now…
Descriptors: Acoustics, Artificial Intelligence, Electronic Learning, Quality Assurance
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