NotesFAQContact Us
Collection
Advanced
Search Tips
Publication Date
In 202526
Audience
Researchers1
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Showing 1 to 15 of 26 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Guangrui Fan; Dandan Liu; Rui Zhang; Lihu Pan – International Journal of STEM Education, 2025
Purpose: This study investigates the impact of AI-assisted pair programming on undergraduate students' intrinsic motivation, programming anxiety, and performance, relative to both human-human pair programming and individual programming approaches. Methods: A quasi-experimental design was conducted over two academic years (2023-2024) with 234…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Programming
Peer reviewed Peer reviewed
Direct linkDirect link
Jinshui Wang; Shuguang Chen; Zhengyi Tang; Pengchen Lin; Yupeng Wang – Education and Information Technologies, 2025
Mastering SQL programming skills is fundamental in computer science education, and Online Judging Systems (OJS) play a critical role in automatically assessing SQL codes, improving the accuracy and efficiency of evaluations. However, these systems are vulnerable to manipulation by students who can submit "cheating codes" that pass the…
Descriptors: Programming, Computer Science Education, Cheating, Computer Assisted Testing
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Guido Lang; Tan Gürpinar – Information Systems Education Journal, 2025
This study investigates the effectiveness of a Retrieval-Augmented Generation (RAG) chatbot to enhance learning and engagement in a self-paced, asynchronous online R programming course. To contextualize the development and potential of RAG chatbots, we conducted a literature review on existing approaches and their use in educational settings.…
Descriptors: Artificial Intelligence, Technology Uses in Education, Program Effectiveness, Online Courses
Peer reviewed Peer reviewed
Direct linkDirect link
Aimei Yang – Journalism and Mass Communication Educator, 2025
At the forefront of industries profoundly influenced by artificial intelligence (AI), public relations (PRs) are undergoing a transformative revolution. The increasing applications of AI in PRs are driving a demand for proficient practitioners. Recognizing this, PR educational institutions must adapt by delivering tailored AI education. Despite…
Descriptors: Artificial Intelligence, Public Relations, Programming, Coding
Peer reviewed Peer reviewed
Direct linkDirect link
Ishaya Gambo; Faith-Jane Abegunde; Omobola Gambo; Roseline Oluwaseun Ogundokun; Akinbowale Natheniel Babatunde; Cheng-Chi Lee – Education and Information Technologies, 2025
The current educational system relies heavily on manual grading, posing challenges such as delayed feedback and grading inaccuracies. Automated grading tools (AGTs) offer solutions but come with limitations. To address this, "GRAD-AI" is introduced, an advanced AGT that combines automation with teacher involvement for precise grading,…
Descriptors: Automation, Grading, Artificial Intelligence, Computer Assisted Testing
Peer reviewed Peer reviewed
Direct linkDirect link
Harry Barton Essel; Dimitrios Vlachopoulos; Henry Nunoo-Mensah; John Opuni Amankwa – British Journal of Educational Technology, 2025
Conversational user interfaces (CUI), including voice interfaces, which allow users to converse with computers via voice, are gaining wide popularity. VoiceBots allow users to receive a response in real-time, regardless of the communication device. VoiceBots have been explored in fields such as customer service to automate repetitive queries and…
Descriptors: Foreign Countries, Artificial Intelligence, Program Effectiveness, Undergraduate Students
Peer reviewed Peer reviewed
Direct linkDirect link
Austin Wyman; Zhiyong Zhang – Grantee Submission, 2025
Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and…
Descriptors: Artificial Intelligence, Algorithms, Computer Software, Identification
Peer reviewed Peer reviewed
Direct linkDirect link
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)
Peer reviewed Peer reviewed
Direct linkDirect link
Umar Alkafaween; Ibrahim Albluwi; Paul Denny – Journal of Computer Assisted Learning, 2025
Background: Automatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some…
Descriptors: Automation, Grading, Introductory Courses, Programming
Peer reviewed Peer reviewed
Direct linkDirect link
Seongyune Choi; Hyeoncheol Kim – Education and Information Technologies, 2025
Attention to programming education from K-12 to higher education has been growing with the aim of fostering students' programming ability. This ability involves employing appropriate algorithms and computer codes to solve problems and can be enhanced through practical learning. However, in a formal educational setting, it is challenging to provide…
Descriptors: Foreign Countries, High School Freshmen, Programming, Artificial Intelligence
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Mark Frydenberg; Anqi Xu; Jennifer Xu – Information Systems Education Journal, 2025
This study explores student perceptions of learning to code by evaluating AI-generated Python code. In an experimental exercise given to students in an introductory Python course at a business university, students wrote their own solutions to a Python program and then compared their solutions with AI-generated code. They evaluated both solutions…
Descriptors: Student Attitudes, Programming, Computer Software, Quality Assurance
Peer reviewed Peer reviewed
Direct linkDirect link
Shu-Jie Chen; Xiaofen Shan; Ze-Min Liu; Chuang-Qi Chen – Educational Technology & Society, 2025
The introduction of programming education in K-12 schools to promote computational thinking has attracted a great deal of attention from scholars and educators. Debugging code is a central skill for students, but is also a considerable challenge when learning to program. Learners at the K-12 level often lack confidence in programming debugging due…
Descriptors: Programming, Coding, Elementary School Students, Secondary School Students
Peer reviewed Peer reviewed
Direct linkDirect link
Erkan Er; Gökhan Akçapinar; Alper Bayazit; Omid Noroozi; Seyyed Kazem Banihashem – British Journal of Educational Technology, 2025
Despite the growing research interest in the use of large language models for feedback provision, it still remains unknown how students perceive and use AI-generated feedback compared to instructor feedback in authentic settings. To address this gap, this study compared instructor and AI-generated feedback in a Java programming course through an…
Descriptors: Student Evaluation, Student Attitudes, Feedback (Response), Artificial Intelligence
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Ernst Bekkering; Patrick Harrington – Information Systems Education Journal, 2025
Generative AI has recently gained the ability to generate computer code. This development is bound to affect how computer programming is taught in higher education. We used past programming assignments and solutions for textbook exercises in our introductory programming class to analyze how accurately one of the leading models, ChatGPT, generates…
Descriptors: Higher Education, Artificial Intelligence, Programming, Textbook Evaluation
Peer reviewed Peer reviewed
Direct linkDirect link
Hao-Chiang Koong Lin; Chun-Hsiung Tseng; Nian-Shing Chen – Educational Technology & Society, 2025
In recent years, learning programming has been a challenge for both learners and educators. How to enhance student engagement and learning outcomes has been a significant concern for researchers. This study examines the effects of AI-based pedagogical agents on students' learning experiences in programming courses, focusing on web game development…
Descriptors: Programming, Learner Engagement, Self Efficacy, Artificial Intelligence
Previous Page | Next Page »
Pages: 1  |  2