This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA, suggesting positive academic outcomes. Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified, emphasizing the need for AI tools to complement conventional educational strategies rather than replace them. Data were collected through a survey with a Likert scale and follow-up interviews, providing both quantitative and qualitative insights. The analysis involved descriptive statistics to summarize demographic data, AI usage patterns, and perceived effectiveness, as well as inferential statistics (T-tests, ANOVA) to examine the impact of demographic factors on AI adoption. Regression analysis identified predictors of AI adoption, and qualitative responses were thematically analyzed to understand students' perspectives on the future of AI in education. This mixed-methods approach provided a comprehensive view of AI's role in education and highlighted the importance of privacy, transparency, and continuous refinement of AI features to maximize their educational benefits.
Profiles of AI Dependency: A Latent Class Analysis of Filipino Students' Academic Competencies
College of Education（教育学院）；College of Hospitality and Tourism（旅游与 hospitality 学院）；Pampanga State University（帕玛潘加州立大学）；College of Education Management（教育管理学院）；College of Computing Studies（计算机科学学院）
The impact of generative artificial intelligence on academic development of Chinese students in humanities and social sciences
Design School, Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学设计学院）；School of Computer Science and Informatics, University of Liverpool（利物浦大学计算机科学与信息学院）
Evaluation of Machine Learning Models in Student Academic Performance Prediction
Dept. of Computer Engineering General Sir John Kotelawala Defence University（计算机工程系 加普尔·约翰·科特拉瓦拉国防大学）；Dept. of Computer Science Sri Lanka Institute of Information Technology（计算机科学系 斯里兰卡信息技术研究所）
Impact, Causation and Prediction of Socio-Academic and Economic Factors in Exam-centric Student Evaluation Measures using Machine Learning and Causal Analysis
Institute of Information Technology（信息科技研究所）；Jahangirnagar University（贾汗吉尔纳加尔大学）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Erik Jonsson School of Engineering and Computer Science（埃里克·乔纳森工程与计算机科学学院）；University of Texas（德克萨斯大学）
AI Slop or AI-enhancement? Student perceptions of AI-generated media for an English for Academic Purposes course
Everwrite Limited（Everwrite有限公司）；Faculty of Education, The University of Hong Kong（香港大学教育学院）；Faculty of Education, The Chinese University of Hong Kong（香港中文大学教育学院）
Protecting Student Mental Health with a Context-Aware Machine Learning Framework for Stress Monitoring
Dept. of Computer Science, George Mason University, Virginia, USA（计算机科学系，乔治·马歇尔大学，弗吉尼亚州，美国）；Dept. of Computer Science and Engineering, University of Rajshahi, Bangladesh（计算机科学与工程系，拉贾沙希大学，孟加拉国）；Dept. of Computer Science and Engineering, Green University of Bangladesh, Bangladesh（计算机科学与工程系，孟加拉国绿色大学，孟加拉国）
Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；University at Buffalo（布法罗大学）
