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Peer reviewed
Direct linkMeng, Huijuan; Ma, Ye – Educational Measurement: Issues and Practice, 2023
In recent years, machine learning (ML) techniques have received more attention in detecting aberrant test-taking behaviors due to advantages when compared to traditional data forensics methods. However, defining "True Test Cheaters" is challenging--different than other fraud detection tasks such as flagging forged bank checks or credit…
Descriptors: Artificial Intelligence, Cheating, Testing, Information Technology
Hsu, Yu-Chang; Ching, Yu-Hui – TechTrends: Linking Research and Practice to Improve Learning, 2023
Generative artificial intelligence (GenAI), such as ChatGPT, has taken the world by storm. ChatGPT attracted 1 million users in 5 days and 100 million users in 2 months since its launch in November 2022. In this first article of a two-part series, we discuss the overall dynamic frontier of GenAI, its potential uses and benefits in education,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Benefits
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
Florent Vinchon; Todd Lubart; Sabrina Bartolotta; Valentin Gironnay; Marion Botella; Samira Bourgeois-Bougrine; Jean-Marie Burkhardt; Nathalie Bonnardel; Giovanni Emanuele Corazza; Vlad Glaveanu; Michael Hanchett Hanson; Zorana Ivcevic; Maciej Karwowski; James C. Kaufman; Takeshi Okada; Roni Reiter-Palmon; Andrea Gaggioli – Journal of Creative Behavior, 2023
With the advent of artificial intelligence (AI), the field of creativity faces new opportunities and challenges. This manifesto explores several scenarios of human--machine collaboration on creative tasks and proposes "fundamental laws of generative AI" to reinforce the responsible and ethical use of AI in the creativity field. Four…
Descriptors: Artificial Intelligence, Creativity, Man Machine Systems, Ethics
Tianjiao Wang; Xiaona Xia – SAGE Open, 2023
The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the…
Descriptors: Cluster Grouping, Mathematical Models, Artificial Intelligence, Learning Analytics
Hani Y. Ayyoub; Omar S. Al-Kadi – IEEE Transactions on Learning Technologies, 2024
Education is a dynamic field that must be adaptable to sudden changes and disruptions caused by events like pandemics, war, and natural disasters related to climate change. When these events occur, traditional classrooms with traditional or blended delivery can shift to fully online learning, which requires an efficient learning environment that…
Descriptors: Cognitive Style, Individualized Instruction, Learning Management Systems, Artificial Intelligence
Shemona Y. Rozario; Mahbub Sarkar; Melanie K. Farlie; Michelle D. Lazarus – Anatomical Sciences Education, 2024
Anatomical pathology (AP) is an anatomy-centric medical specialty devoted to tissue-based diagnosis of disease. The field faces a current and predicted workforce shortage, likely increasing diagnostic wait times and delaying patient access to urgent treatment. A lack of AP exposure is proposed to preclude recruitment to the field, as medical…
Descriptors: Anatomy, Pathology, Artificial Intelligence, Professional Identity
James Edward Hill; Catherine Harris; Andrew Clegg – Research Synthesis Methods, 2024
Data extraction is a time-consuming and resource-intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this…
Descriptors: Artificial Intelligence, Search Engines, Data Collection, Natural Language Processing
Christine Wusylko; Lauren Weisberg; Raymond A. Opoku; Brian Abramowitz; Jessica Williams; Wanli Xing; Teresa Vu; Michelle Vu – Journal of Research on Technology in Education, 2024
Social media has the unique capacity to expose many learners to media literacy instruction "via" targeted campaigns. Investigating learner engagement and reaction to these efforts may be a fruitful endeavor for researchers that can inform the design of future campaigns. However, the massive datasets associated with social media posts are…
Descriptors: Artificial Intelligence, Learner Engagement, Media Literacy, Social Media
Giora Alexandron; Aviram Berg; Jose A. Ruiperez-Valiente – IEEE Transactions on Learning Technologies, 2024
This article presents a general-purpose method for detecting cheating in online courses, which combines anomaly detection and supervised machine learning. Using features that are rooted in psychometrics and learning analytics literature, and capture anomalies in learner behavior and response patterns, we demonstrate that a classifier that is…
Descriptors: Cheating, Identification, Online Courses, Artificial Intelligence
Amanda Konet; Ian Thomas; Gerald Gartlehner; Leila Kahwati; Rainer Hilscher; Shannon Kugley; Karen Crotty; Meera Viswanathan; Robert Chew – Research Synthesis Methods, 2024
Accurate data extraction is a key component of evidence synthesis and critical to valid results. The advent of publicly available large language models (LLMs) has generated interest in these tools for evidence synthesis and created uncertainty about the choice of LLM. We compare the performance of two widely available LLMs (Claude 2 and GPT-4) for…
Descriptors: Data Collection, Artificial Intelligence, Computer Software, Computer System Design
Venera Nakhipova; Yerzhan Kerimbekov; Zhanat Umarova; Halil ibrahim Bulbul; Laura Suleimenova; Elvira Adylbekova – International Journal of Information and Communication Technology Education, 2024
This article introduces a novel method that integrates collaborative filtering into the naive Bayes model to enhance predicting student academic performance. The combined approach leverages collaborative user behavior analysis and probabilistic modeling, showing promising results in improved prediction precision. Collaborative Filtering explores…
Descriptors: Academic Achievement, Prediction, Cooperation, Behavior
Gizem Dilan Boztas; Muhammet Berigel; Fahriye Altinay – Education and Information Technologies, 2024
Educational Data Mining (EDM) is an interdisciplinary field that encapsulates different fields such as computer science, education, and statistics. It is crucial to make data mining in education to shape future trends in education for policymakers, researchers, and educators in terms of developments. To have an all-inclusive understanding of EDM…
Descriptors: Information Retrieval, Content Analysis, Artificial Intelligence, Educational Trends
Capability Assessment of Cultivating Innovative Talents for Higher Schools Based on Machine Learning
Rongjie Huang; Yusheng Sun; Zhifeng Zhang; Bo Wang; Junxia Ma; Yangyang Chu – International Journal of Information and Communication Technology Education, 2024
The innovation capability largely determines the initiative for future development of a region. Higher school is the main position for training innovative talents. Accurate and comprehensive assessment of innovation cultivation capability is an important basis of higher schools for continuous improvement. Thus, this paper focuses on assessing…
Descriptors: Models, Innovation, Higher Education, Evaluation
Bao Wang; Philippe J. Giabbanelli – International Journal of Artificial Intelligence in Education, 2024
Knowledge maps have been widely used in knowledge elicitation and representation to evaluate and guide students' learning. To effectively evaluate maps, instructors must select the most informative map features that capture students' knowledge constructs. However, there is currently no clear and consistent criteria to select such features, as…
Descriptors: Concept Mapping, Evaluation Methods, Student Evaluation, Algorithms
