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Atharva Naik; Jessica Ruhan Yin; Anusha Kamath; Qianou Ma; Sherry Tongshuang Wu; R. Charles Murray; Christopher Bogart; Majd Sakr; Carolyn P. Rose – British Journal of Educational Technology, 2025
The relative effectiveness of reflection either through student generation of contrasting cases or through provided contrasting cases is not well-established for adult learners. This paper presents a classroom study to investigate this comparison in a college level Computer Science (CS) course where groups of students worked collaboratively to…
Descriptors: Cooperative Learning, Reflection, College Students, Computer Science Education

Clayton Cohn; Surya Rayala; Caitlin Snyder; Joyce Horn Fonteles; Shruti Jain; Naveeduddin Mohammed; Umesh Timalsina; Sarah K. Burriss; Ashwin T. S.; Namrata Srivastava; Menton Deweese; Angela Eeds; Gautam Biswas – Grantee Submission, 2025
Collaborative dialogue offers rich insights into students' learning and critical thinking. This is essential for adapting pedagogical agents to students' learning and problem-solving skills in STEM+C settings. While large language models (LLMs) facilitate dynamic pedagogical interactions, potential hallucinations can undermine confidence, trust,…
Descriptors: STEM Education, Computer Science Education, Artificial Intelligence, Natural Language Processing
Rui Wang; Haili Ling; Jie Chen; Huijuan Fu – International Journal of Distance Education Technologies, 2025
This study adopted the Latent Dirichlet Allocation (LDA) to extract learners' needs based on 70,145 reviews from online course designed for software design and development in China and then applied Quality Function Deployment (QFD) to map learners' differentiated needs into quality attributes. Taking national first-class courses as the…
Descriptors: Educational Improvement, Student Needs, Computer Science Education, Foreign Countries
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
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
Xieling Chen; Haoran Xie; S. Joe Qin; Fu Lee Wang; Yinan Hou – European Journal of Education, 2025
Artificial intelligence (AI) is increasingly exploited to promote student engagement. This study combined topic modelling, keyword analysis, trend test and systematic analysis methodologies to analyse AI-supported student engagement (AIsE) studies regarding research keywords and topics, AI roles, AI systems and algorithms, methods and domains,…
Descriptors: Artificial Intelligence, Learner Engagement, Technology Uses in Education, Electronic Learning
Sina Rismanchian; Eesha Tur Razia Babar; Shayan Doroudi – Annenberg Institute for School Reform at Brown University, 2025
In November 2022, OpenAI released ChatGPT, a groundbreaking generative AI chatbot backed by large language models (LLMs). Since then, these models have seen various applications in education, from Socratic tutoring and writing assistance to teacher training and essay scoring. Despite their widespread use among high school and college students in…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Undergraduate Students
Ibrahim Abba Mohammed; Ahmed Bello; Bala Ayuba – Education and Information Technologies, 2025
In spite of the emergence of studies seeking to integrate chatbot into education, there is a wide literature gap in the Nigerian contexts. While most studies focus on the design and development of chatbots, there exists a very scarce literature on the effect of ChatGPT chatbot on students' achievement. To address this gap, this study checked the…
Descriptors: Natural Language Processing, Artificial Intelligence, Academic Achievement, Computer Science Education
Zhao Wanli; Tang Youjun; Ma Xiaomei – SAGE Open, 2025
Deeper learning (DL) is firmly rooted in learning science and computer science. However, a dearth of review studies has probed its trajectory in DL in foreign languages (DLFL). Utilizing SSCI from the Web of Science Core Collection, we employ Citespace and Vosviewer to analyze the scientific knowledge graph of DLFL literature. Our analysis…
Descriptors: Bibliometrics, Second Language Learning, Computer Science, Educational Research
Alexander Tobias Neumann; Yue Yin; Sulayman Sowe; Stefan Decker; Matthias Jarke – IEEE Transactions on Education, 2025
Contribution: This research explores the benefits and challenges of developing, deploying, and evaluating a large language model (LLM) chatbot, MoodleBot, in computer science classroom settings. It highlights the potential of integrating LLMs into LMSs like Moodle to support self-regulated learning (SRL) and help-seeking behavior. Background:…
Descriptors: Computer Science Education, Databases, Information Systems, Classroom Environment
Dorottya Demszky; Heather C. Hill; Eric S. Taylor; Ashlee Kupor; Deepak Varuvel Dennison; Chris Piech – Annenberg Institute for School Reform at Brown University, 2025
The role of teacher agency in professional learning has been the subject of several qualitative studies but has not yet been tested in an experimental setting. To provide causal evidence of the impact of teacher agency on the effectiveness of professional learning, we conducted a preregistered randomized controlled trial in an online computer…
Descriptors: Professional Autonomy, Faculty Development, Attribution Theory, Online Courses