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Birks, Daniel; Clare, Joseph – International Journal for Educational Integrity, 2023
This paper connects the problem of artificial intelligence (AI)-facilitated academic misconduct with crime-prevention based recommendations about the prevention of academic misconduct in more traditional forms. Given that academic misconduct is not a new phenomenon, there are lessons to learn from established information relating to misconduct…
Descriptors: Artificial Intelligence, Cheating, Student Behavior, Prevention
Duraisamy Akila; Harish Garg; Souvik Pal; Sundaram Jeyalaksshmi – Education and Information Technologies, 2024
Online education has been expected to be the future of learning; it will never replace the value of traditional classroom experiences fully. Technical problems have less impact on offline education, which gives students more freedom to plan their time and stick to it. In addition, teachers cannot observe their students' behavior and activities…
Descriptors: In Person Learning, Student Behavior, Attention, Artificial Intelligence
Shunan Zhang; Xiangying Zhao; Tong Zhou; Jang Hyun Kim – International Journal of Educational Technology in Higher Education, 2024
Although previous studies have highlighted the problematic artificial intelligence (AI) usage behaviors in educational contexts, such as overreliance on AI, no study has explored the antecedents and potential consequences that contribute to this problem. Therefore, this study investigates the causes and consequences of AI dependency using ChatGPT…
Descriptors: Artificial Intelligence, Self Efficacy, Anxiety, Expectation
Rico-Juan, Juan Ramon; Sanchez-Cartagena, Victor M.; Valero-Mas, Jose J.; Gallego, Antonio Javier – IEEE Transactions on Learning Technologies, 2023
Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an…
Descriptors: Artificial Intelligence, Models, Student Behavior, Feedback (Response)
Jan Gunis; L'ubomir Snajder; L'ubomir Antoni; Peter Elias; Ondrej Kridlo; Stanislav Krajci – IEEE Transactions on Education, 2025
Contribution: We present a framework for teachers to investigate the relationships between attributes of students' solutions in the process of problem solving or computational thinking. We provide visualization and evaluation techniques to find hidden patterns in the students' solutions which allow teachers to predict the specific behavior of…
Descriptors: Artificial Intelligence, Educational Games, Game Based Learning, Problem Solving
Sarah W. Beck; Sarah Levine – Reading Research Quarterly, 2024
In "Parable of the Sower," Octavia Butler (1993) wrote: "Any Change may bear seeds of benefit. Seek them out. Any Change may bear seeds of harm. Beware" (p. 116). In this paper, we apply this command to a speculative examination of the consequences of text-based generative AI (GAI) for adolescent writers, framing this…
Descriptors: Artificial Intelligence, Writing (Composition), Student Behavior, Writing Processes
Yoon Lee; Gosia Migut; Marcus Specht – British Journal of Educational Technology, 2025
Learner behaviours often provide critical clues about learners' cognitive processes. However, the capacity of human intelligence to comprehend and intervene in learners' cognitive processes is often constrained by the subjective nature of human evaluation and the challenges of maintaining consistency and scalability. The recent widespread AI…
Descriptors: Artificial Intelligence, Cognitive Processes, Student Behavior, Cues
Xiao Wen; Hu Juan – Interactive Learning Environments, 2024
To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows:…
Descriptors: MOOCs, Dropouts, Dropout Characteristics, Dropout Research
Yang Zhen; Xiaoyan Zhu – Educational and Psychological Measurement, 2024
The pervasive issue of cheating in educational tests has emerged as a paramount concern within the realm of education, prompting scholars to explore diverse methodologies for identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep…
Descriptors: Artificial Intelligence, Models, Cheating, Identification
Anass Bayaga – Education and Information Technologies, 2025
This study examines the influence of AI-powered and emerging technologies on pedagogical practices in higher education, focusing on their role on behavioural intention (BI) and actual usage among educators and students. The research hypothesises that the relationship between each Unified Theory of Acceptance and Use of Technology (UTAUT)…
Descriptors: Artificial Intelligence, Educational Technology, Teaching Methods, Educational Innovation
Sarah Levine; Sarah W. Beck; Chris Mah; Lena Phalen; Jaylen PIttman – Journal of Adolescent & Adult Literacy, 2025
Educators and researchers are interested in ways that ChatGPT and other generative AI tools might move beyond the role of "cheatbot" and become part of the network of resources students use for writing. We studied how high school students used ChatGPT as a writing support while writing arguments about topics like school mascots. We…
Descriptors: Natural Language Processing, Artificial Intelligence, Technology Uses in Education, Writing (Composition)
Ching Sing Chai; Ding Yu; Ronnel B. King; Ying Zhou – SAGE Open, 2024
As artificial intelligence (AI) permeates almost all aspects of our lives, university students need to acquire relevant knowledge, skills, and attitudes to adapt to the challenges it poses. This study reports the development and validation of a scale called the Artificial Intelligence Learning Intention Scale (AILIS). AILIS was designed to measure…
Descriptors: Artificial Intelligence, Intention, Measures (Individuals), Development
Ghadah Al Murshidi; Galina Shulgina; Anastasiia Kapuza; Jamie Costley – Smart Learning Environments, 2024
Generative Artificial Intelligence (GAI) holds promise for enhancing the educational experience by providing personalized feedback and interactive simulations. While its integration into classrooms would improve education, concerns about how students may use AI in the class has prompted research on the perceptions related to the intention to…
Descriptors: Artificial Intelligence, Educational Experience, Feedback (Response), Interaction
Orji, Fidelia A.; Vassileva, Julita – Journal of Educational Computing Research, 2023
There is a dearth of knowledge on how persuasiveness of influence strategies affects students' behaviours when using online educational systems. Persuasiveness is a term used in describing a system's capability to motivate desired behaviour. Most existing approaches for assessing the persuasiveness of a system are based on subjective measures…
Descriptors: Influences, Student Behavior, Artificial Intelligence, Electronic Learning
Nye, Benjamin D.; Core, Mark G.; Jaiswa, Shikhar; Ghosal, Aviroop; Auerbach, Daniel – International Educational Data Mining Society, 2021
Engaged and disengaged behaviors have been studied across a variety of educational contexts. However, tools to analyze engagement typically require custom-coding and calibration for a system. This limits engagement detection to systems where experts are available to study patterns and build detectors. This work studies a new approach to classify…
Descriptors: Learner Engagement, Profiles, Artificial Intelligence, Student Behavior