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Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning Objects
Peer reviewed
Direct linkDominic 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
Aidan Doyle; Pragnya Sridhar; Arav Agarwal; Jaromir Savelka; Majd Sakr – Journal of Computer Assisted Learning, 2025
Background: In computing education, educators are constantly faced with the challenge of developing new curricula, including learning objectives (LOs), while ensuring that existing courses remain relevant. Large language models (LLMs) were shown to successfully generate a wide spectrum of natural language artefacts in computing education.…
Descriptors: Computer Science Education, Artificial Intelligence, Learning Objectives, Curriculum Development
Meina Zhu – Journal of Computer Assisted Learning, 2025
Background: Computer programming learning and education play a critical role in preparing a workforce equipped with the necessary skills for diverse fields. ChatGPT and YouTube are technologies that support self-directed programming learning. Objectives: This study aims to examine the sentiments and primary topics discussed in YouTube comments…
Descriptors: Computer Science Education, Programming, Social Media, Video Technology
Hui-Tzu Chang; Chia-Yu Lin – Journal of Computer Assisted Learning, 2024
Background: Numerous higher education institutions worldwide have adopted English-language-medium computer science courses and integrated online problem-solving competitions to bridge gaps in theory and practice (Alhamami "Education and Information Technologies," 2021; 26: 6549-6562). Objectives: This study aimed to investigate the…
Descriptors: Artificial Intelligence, Instructional Improvement, Problem Solving, Competition
Esmaeil Jafari – Journal of Computer Assisted Learning, 2024
Background: Artificial intelligence (AI) has created new opportunities, challenges, and potentials in teaching; however, issues related to the philosophy of using AI technology in learners' learning have not been addressed and have caused some issues and concerns. This issue is due to the research gap in addressing issues related to ethical and…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, College Faculty
Zhan, Zehui; He, Guoqing; Li, Tingting; He, Luyao; Xiang, Siyu – Journal of Computer Assisted Learning, 2022
Background: Group size is one of the important factors that affect collaborative learning, however, there is no consensus in the literature on how many students should the groups be composed of during the problem-solving process. Objectives: This study investigated the effect of group size in a K-12 introductory Artificial Intelligence course by…
Descriptors: Cognitive Ability, High School Students, Cooperative Learning, Artificial Intelligence
Carpio Cañada, J.; Mateo Sanguino, T. J.; Merelo Guervós, J. J.; Rivas Santos, V. M. – Journal of Computer Assisted Learning, 2015
Limitations of formal learning (e.g., one-way communication, rigid methodology, results-oriented approach) can significantly influence the motivation and expectation of students, thus resulting in an academic progress reduction. In order to make learning processes more playful and motivating, this paper presents a new educational experience…
Descriptors: Foreign Countries, Open Education, Computer Science Education, Artificial Intelligence
