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Hanshu Zhang; Ran Zhou; Cheng-You Cheng; Sheng-Hsu Huang; Ming-Hui Cheng; Cheng-Ta Yang – Cognitive Research: Principles and Implications, 2025
Although it is commonly believed that automation aids human decision-making, conflicting evidence raises questions about whether individuals would gain greater advantages from automation in difficult tasks. Our study examines the combined influence of task difficulty and automation reliability on aided decision-making. We assessed decision…
Descriptors: Task Analysis, Difficulty Level, Decision Making, Automation
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
Selcuk Acar; Peter Organisciak; Denis Dumas – Journal of Creative Behavior, 2025
In this three-study investigation, we applied various approaches to score drawings created in response to both Form A and Form B of the Torrance Tests of Creative Thinking-Figural (broadly TTCT-F) as well as the Multi-Trial Creative Ideation task (MTCI). We focused on TTCT-F in Study 1, and utilizing a random forest classifier, we achieved 79% and…
Descriptors: Scoring, Computer Assisted Testing, Models, Correlation
Peter Baldwin; Victoria Yaneva; Kai North; Le An Ha; Yiyun Zhou; Alex J. Mechaber; Brian E. Clauser – Journal of Educational Measurement, 2025
Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of…
Descriptors: Artificial Intelligence, Scoring, Computational Linguistics, Accuracy
Harikesh Singh; Li-Minn Ang; Dipak Paudyal; Mauricio Acuna; Prashant Kumar Srivastava; Sanjeev Kumar Srivastava – Technology, Knowledge and Learning, 2025
Wildfires pose significant environmental threats in Australia, impacting ecosystems, human lives, and property. This review article provides a comprehensive analysis of various empirical and dynamic wildfire simulators alongside machine learning (ML) techniques employed for wildfire prediction in Australia. The study examines the effectiveness of…
Descriptors: Artificial Intelligence, Computer Software, Computer Simulation, Prediction
Chi Hong Leung; Winslet Ting Yan Chan – Asian Journal of Contemporary Education, 2025
This paper explores the efficacy of ChatGPT, a generative artificial intelligence in educational contexts, particularly concerning its potential to assist students in overcoming academic challenges while highlighting its limitations. ChatGPT is suitable for solving general problems. When a student comes across academic challenges, ChatGPT may…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Error Patterns
Gary D. Fisk – Teaching of Psychology, 2025
Introduction: Recent innovations in generative artificial intelligence (AI) technologies have led to an educational environment in which human authorship cannot be assumed, thereby posing a significant challenge to upholding academic integrity. Statement of the problem: Both humans and AI detection technologies have difficulty distinguishing…
Descriptors: Technology Uses in Education, Writing (Composition), Plagiarism, Identification
Preet Chandan Kaur; Leena Ragha – Education and Information Technologies, 2025
Video summarization is a method of deducing the content of video content for generating a summary in video format. The generated summary should have the significant segments of raw video. Recently, the content of video has been rapidly increasing, thus automatic video summarization is beneficial for individuals who want to keep time and learn more…
Descriptors: Semantics, Video Technology, Audio Equipment, Linguistic Input
Orkun Kocak; Sahin Idil – Journal of Education in Science, Environment and Health, 2025
This study is developing a Deep Learning model automating the coding of drawings students provide about climate change phenomena in our world, as a learning contribution through formative assessment. We started first with ResNet50 architecture, but ultimately, we settled on MobileNetV2 reduced architecture for the sake of being able to integrate…
Descriptors: Climate, Artificial Intelligence, Accuracy, Environmental Education
Tamar Fuhrmann; Leah Rosenbaum; Aditi Wagh; Adelmo Eloy; Jacob Wolf; Paulo Blikstein; Michelle Wilkerson – Science Education, 2025
When learning about scientific phenomena, students are expected to "mechanistically" explain how underlying interactions produce the observable phenomenon and "conceptually" connect the observed phenomenon to canonical scientific knowledge. This paper investigates how the integration of the complementary processes of designing…
Descriptors: Mechanics (Physics), Thinking Skills, Scientific Concepts, Concept Formation
Guoyang Liu; Yueyuan Zheng; Michelle Hei Lam Tsang; Zhao Yazhou; Janet H. Hsiao – npj Science of Learning, 2025
Eye movement patterns and consistency during face recognition are both associated with recognition performance. We examined whether they reflect different mechanisms through EEG decoding. Eighty-four participants performed an old-new face recognition task with eye movement pattern and consistency quantified using eye movement analysis with hidden…
Descriptors: Eye Movements, Human Body, Recognition (Psychology), Diagnostic Tests
Rumeysa Demir; Metin Demir – Educational Process: International Journal, 2025
Background/purpose: This study aims to reveal in detail the extent to which the variables in The Primary and Secondary Education Institutions Scholarship Examination (PSEISE) predict the success of students on the scholarship exam with the help of artificial neural networks (ANN). In addition, in light of the findings obtained as a result of the…
Descriptors: Elementary Secondary Education, Foreign Countries, Artificial Intelligence, Computer Software
Hongfei Ye; Jian Xu; Danqing Huang; Meng Xie; Jinming Guo; Junrui Yang; Haiwei Bao; Mingzhi Zhang; Ce Zheng – Discover Education, 2025
This study evaluates Large language models (LLMs)' performance on Chinese Postgraduate Medical Entrance Examination (CPGMEE) as well as the hallucinations produced by LLMs and investigate their implications for medical education. We curated 10 trials of mock CPGMEE to evaluate the performances of 4 LLMs (GPT-4.0, ChatGPT, QWen 2.1 and Ernie 4.0).…
Descriptors: College Entrance Examinations, Foreign Countries, Computational Linguistics, Graduate Medical Education
Xieling Chen; Haoran Xie; Di Zou; Lingling Xu; Fu Lee Wang – Educational Technology & Society, 2025
In massive open online course (MOOC) environments, computer-based analysis of course reviews enables instructors and course designers to develop intervention strategies and improve instruction to support learners' learning. This study aimed to automatically and effectively identify learners' concerned topics within their written reviews. First, we…
Descriptors: Classification, MOOCs, Teaching Skills, Artificial Intelligence
Claude, ChatGPT, Copilot, and Gemini Performance versus Students in Different Topics of Neuroscience
Volodymyr Mavrych; Ahmed Yaqinuddin; Olena Bolgova – Advances in Physiology Education, 2025
Despite extensive studies on large language models and their capability to respond to questions from various licensed exams, there has been limited focus on employing chatbots for specific subjects within the medical curriculum, specifically medical neuroscience. This research compared the performances of Claude 3.5 Sonnet (Anthropic), GPT-3.5 and…
Descriptors: Artificial Intelligence, Computer Software, Neurosciences, Medical Education

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