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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)
Weipeng Shen; Xiao-Fan Lin; Jiachun Liu; Xinxian Liang; Ruiqing Chen; Xiaoyun Lai; Xinwen Zheng – Journal of Computer Assisted Learning, 2025
Background: Generative artificial intelligence (GenAI) chatbots extend transformative impact in higher education. Current research requires more comprehensive evaluations of the collaborative learning fostered by students and GenAI chatbots. However, existing articles have rarely explored the dynamic process of student--AI collaboration in higher…
Descriptors: Undergraduate Students, Artificial Intelligence, Technology Uses in Education, Computer Mediated Communication
Jana Gonnermann-Müller; Jule M. Krüger – Journal of Computer Assisted Learning, 2025
Background: Despite the numerous positive effects of augmented reality (AR) on learning, previous research has shown ambiguous results regarding the cognitive demand on the learner arising from, for example, the overlay of virtual elements or novel interaction techniques. At the same time, the number of evidence-based guidelines on designing AR is…
Descriptors: Computer Simulation, Computer Assisted Design, Difficulty Level, Cognitive Processes
Tobias Kohn – Journal of Computer Assisted Learning, 2025
Background: The recent advent of powerful, exam-passing large language models (LLMs) in public awareness has led to concerns over students cheating, but has also given rise to calls for including or even focusing education on LLMs. There is a perceived urgency to react immediately, as well as claims that AI-based reforms of education will lead to…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Usability
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
Eunhye Shin – Journal of Computer Assisted Learning, 2025
Background: Analysing classroom dialogue is a widely used approach for understanding students' learning, often requiring team-based collaborative research. This presents a challenge for single researchers due to the labour-intensive nature of the process. Emerging advancements in large language models (LLMs) such as ChatGPT, enhance qualitative…
Descriptors: Artificial Intelligence, Technology Uses in Education, Science Education, Coding
Ahnaf Chowdhury Niloy; Salma Akter; Nayeema Sultana; Jakia Sultana; Sayed Imran Ur Rahman – Journal of Computer Assisted Learning, 2024
Background: The increasing prevalence of Artificial Intelligence (AI) language models, exemplified by ChatGPT, has sparked inquiries into their influence on creative writing skills in educational contexts. This study aims to quantitatively investigate whether ChatGPT's use negatively affects university students' creative writing abilities,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Creative Writing, Writing Ability
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
Flor, Michael; Andrews-Todd, Jessica – Journal of Computer Assisted Learning, 2022
Background: Collaborative problem solving (CPS) is important for success in the 21st century, especially for teamwork and communication in technology-enhanced environments. Measurement of CPS skills has emerged as an essential aspect in educational assessment. Modern research in CPS relies on theory-driven measurements that are usually carried out…
Descriptors: Automation, Documentation, Cooperative Learning, Teamwork
Di Mitri, Daniele; Schneider, Jan; Specht, Marcus; Drachsler, Hendrik – Journal of Computer Assisted Learning, 2018
Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is…
Descriptors: Educational Research, Data Collection, Data Analysis, Learning Modalities

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