Publication Date
In 2025 | 6 |
Since 2024 | 9 |
Descriptor
Source
Journal of Computer Assisted… | 9 |
Author
Abhishek Chugh | 1 |
Anique de Bruin | 1 |
Areej ElSayary | 1 |
Chia-Ju Lin | 1 |
Dabae Lee | 1 |
Dennis Müller | 1 |
Detlef Urhahne | 1 |
Dominic Lohr | 1 |
Elisabeth Bauer | 1 |
Frank Fischer | 1 |
Frank Niklas | 1 |
More ▼ |
Publication Type
Journal Articles | 9 |
Reports - Research | 9 |
Education Level
Higher Education | 5 |
Postsecondary Education | 5 |
Middle Schools | 2 |
Elementary Education | 1 |
Grade 6 | 1 |
High Schools | 1 |
Intermediate Grades | 1 |
Junior High Schools | 1 |
Secondary Education | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Suping Yi; Wayan Sintawati; Yibing Zhang – Journal of Computer Assisted Learning, 2025
Background: Natural language processing (NLP) and machine learning technologies offer significant advantages, such as facilitating the delivery of reflective feedback in collaborative learning environments while minimising technical constraints for educators related to time and location. Recently, scholars' interest in reflective feedback has…
Descriptors: Reflection, Feedback (Response), Cooperative Learning, Natural Language Processing
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
Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning Objects
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
Safaa M. Abdelhalim – Journal of Computer Assisted Learning, 2024
Background: Introducing new technologies in education sparks debates, disrupting traditional practices, and requiring teacher adaptation. ChatGPT is an example. Research explores its benefits and concerns in education, with recommendations for classroom use. Nevertheless, limited evidence supports ChatGPT as a tool for supporting English as a…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Second Language Learning
Dabae Lee; Taekwon Son; Sheunghyun Yeo – Journal of Computer Assisted Learning, 2025
Background: Artificial Intelligence (AI) technologies offer unique capabilities for preservice teachers (PSTs) to engage in authentic and real-time interactions using natural language. However, the impact of AI technology on PSTs' responsive teaching skills remains uncertain. Objectives: The primary objective of this study is to examine whether…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Preservice Teachers
Xin Tang; Zhiqiang Yuan; Shaojun Qu – Journal of Computer Assisted Learning, 2025
Background: Generative artificial intelligence (AI) represents a significant technological leap, with platforms like OpenAI's ChatGPT and Baidu's Ernie Bot at the forefront of innovation. This technology has seen widespread adoption across various sectors of society and is anticipated to revolutionise the educational landscape, especially in the…
Descriptors: Influences, College Students, Student Behavior, Intention
Héctor J. Pijeira-Díaz; Shashank Subramanya; Janneke van de Pol; Anique de Bruin – Journal of Computer Assisted Learning, 2024
Background: When learning causal relations, completing causal diagrams enhances students' comprehension judgements to some extent. To potentially boost this effect, advances in natural language processing (NLP) enable real-time formative feedback based on the automated assessment of students' diagrams, which can involve the correctness of both the…
Descriptors: Learning Analytics, Automation, Student Evaluation, Causal Models
Ting-Ting Wu; Hsin-Yu Lee; Pei-Hua Chen; Chia-Ju Lin; Yueh-Min Huang – Journal of Computer Assisted Learning, 2025
Background: Science, Technology, Engineering, and Mathematics (STEM) education in Asian universities struggles to integrate Knowledge, Skills, and Attitudes (KSA) due to large classes and student reluctance. While ChatGPT offers solutions, its conventional use may hinder independent critical thinking. Objectives: This study introduces PA-GPT,…
Descriptors: Peer Evaluation, Artificial Intelligence, Natural Language Processing, Technology Uses in Education
Areej ElSayary – Journal of Computer Assisted Learning, 2024
Background: The widespread use of information and communication technology (ICT) has led to significant changes in societal aspects, resulting in the emergence of a "knowledge society." However, students and teachers have faced challenges in adapting to this digitalization. In the United Arab Emirates (UAE), transitioning to a…
Descriptors: Teacher Attitudes, Artificial Intelligence, Information Technology, Barriers