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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
Andrew Millam; Christine Bakke – Journal of Information Technology Education: Innovations in Practice, 2024
Aim/Purpose: This paper is part of a multi-case study that aims to test whether generative AI makes an effective coding assistant. Particularly, this work evaluates the ability of two AI chatbots (ChatGPT and Bing Chat) to generate concise computer code, considers ethical issues related to generative AI, and offers suggestions for how to improve…
Descriptors: Coding, Artificial Intelligence, Natural Language Processing, Computer Software
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
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
Kenworthy, Jared B.; Doboli, Simona; Alsayed, Omar; Choudhary, Rishabh; Jaed, Abu; Minai, Ali A.; Paulus, Paul B. – Creativity Research Journal, 2023
We present the results of an ongoing collaboration between computer science and psychology researchers that employs Natural Language Processing (NLP) methods to examine the trajectory of semantic space used during group idea generation sessions. Specifically, we track and estimate the region of semantic space being used and the degree to which new…
Descriptors: Computer Science, Psychology, Researchers, Natural Language Processing
Anas Husain – Journal of Information Technology Education: Research, 2024
Aim/Purpose: This study aims to investigate the perceptions of programming instructors among the Information Technology faculty members at AL al-Bayt University regarding the effectiveness of ChatGPT in supporting the programming instructional process. This study also aims to explore their experiences concerning the potential benefits and adverse…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Programming
Dorottya Demszky; Jing Liu; Heather C. Hill; Dan Jurafsky; Chris Piech – Educational Evaluation and Policy Analysis, 2024
Providing consistent, individualized feedback to teachers is essential for improving instruction but can be prohibitively resource-intensive in most educational contexts. We develop M-Powering Teachers, an automated tool based on natural language processing to give teachers feedback on their uptake of student contributions, a high-leverage…
Descriptors: Online Courses, Automation, Feedback (Response), Large Group Instruction
Almotairi, Maram; Fkih, Fethi – Journal of Education and e-Learning Research, 2022
The Question answering (QA) system plays a basic role in the acquisition of information and the e-learning environment is considered to be the field that is most in need of the question-answering system to help learners ask questions in natural language and get answers in short periods of time. The main problem in this context is how to understand…
Descriptors: Semantics, Natural Language Processing, Intelligent Tutoring Systems, Ambiguity (Semantics)
Mitra, Reshmi; Schwieger, Dana; Lowe, Robert – Information Systems Education Journal, 2023
Many universities have, or are facing, the task of providing high quality essential customer services with fewer financial and human resources. The growing diversity of students, their needs and proficiencies, along with the increasing variety of university program offerings, make providing customized, ondemand, automated solutions crucial to…
Descriptors: Universities, Academic Advising, Artificial Intelligence, Faculty Workload
Abdur R. Shahid; Sushma Mishra – Journal of Information Systems Education, 2024
Due to the increasing demand for efficient, effective, and profitable applications of Artificial Intelligence (AI) in various industries, there is an immense need for professionals with the right skills to meet this demand. As a result, several institutions have started to offer AI programs. Yet, there is a notable gap in academia: the absence of…
Descriptors: Masters Programs, Information Systems, Computer Science Education, Artificial Intelligence
Ghadeer Sawalha; Imran Taj; Abdulhadi Shoufan – Cogent Education, 2024
Large language models present new opportunities for teaching and learning. The response accuracy of these models, however, is believed to depend on the prompt quality which can be a challenge for students. In this study, we aimed to explore how undergraduate students use ChatGPT for problem-solving, what prompting strategies they develop, the link…
Descriptors: Cues, Artificial Intelligence, Natural Language Processing, Technology Uses in Education
Mike Richards; Kevin Waugh; Mark A Slaymaker; Marian Petre; John Woodthorpe; Daniel Gooch – ACM Transactions on Computing Education, 2024
Cheating has been a long-standing issue in university assessments. However, the release of ChatGPT and other free-to-use generative AI tools has provided a new and distinct method for cheating. Students can run many assessment questions through the tool and generate a superficially compelling answer, which may or may not be accurate. We ran a…
Descriptors: Computer Science Education, Artificial Intelligence, Cheating, Student Evaluation
Nabor C. Mendonça – ACM Transactions on Computing Education, 2024
The recent integration of visual capabilities into Large Language Models (LLMs) has the potential to play a pivotal role in science and technology education, where visual elements such as diagrams, charts, and tables are commonly used to improve the learning experience. This study investigates the performance of ChatGPT-4 Vision, OpenAI's most…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Foreign Countries
Alexandra R. Costa; Natércia Lima; Clara Viegas; Amélia Caldeira – Cogent Education, 2024
The use of AI tools, particularly ChatGPT, has been widespread in recent years. Its application in education has been criticized by some and supported by others. In this article we present the case of a work carried out as part of a course unit in a computer science degree program in which the use of ChatGPT was not only encouraged but required.…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Computer Science Education