AIMC Topic: Students
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Apriori algorithm based prediction of students' mental health risks in the context of artificial intelligence.
Frontiers in public health
Feb 12, 2025
INTRODUCTION: The increasing prevalence of mental health challenges among college students necessitates innovative approaches to early identification and intervention. This study investigates the application of artificial intelligence (AI) techniques...
Predictors of depression among Chinese college students: a machine learning approach.
BMC public health
Feb 5, 2025
BACKGROUND: Depression is highly prevalent among college students, posing a significant public health challenge. Identifying key predictors of depression is essential for developing effective interventions. This study aimed to analyze potential depre...
Exploring AI-Driven Feedback as a Cultural Tool: A Cultural-Historical Perspective on Design of AI Environments to Support Students' Writing Process.
Integrative psychological & behavioral science
Feb 1, 2025
This study draws on the cultural-historical perspectives of Vygotsky and Galperin to examine the role of AI-generated feedback within the Assessment for Learning (AfL) process in fostering students' development as learners. By leveraging Galperin's c...
On the relationship between music students' negative emotions, artificial intelligence readiness, and their engagement.
Acta psychologica
Jan 30, 2025
This study explored the relationship between negative emotions, engagement, and artificial intelligence (AI) readiness among 323 music students. The researchers employed SPSS (version 27) and AMOS (version 24) for analysis using the Emotion Beliefs Q...
Predicting bullying victimization among adolescents using the risk and protective factor framework: a large-scale machine learning approach.
BMC public health
Jan 25, 2025
BACKGROUND: Bullying, encompassing physical, psychological, social, or educational harm, affects approximately 1 in 20 United States teens aged 12-18. The prevalence and impact of bullying, including online bullying, necessitate a deeper understandin...
Optimizing multi label student performance prediction with GNN-TINet: A contextual multidimensional deep learning framework.
PloS one
Jan 22, 2025
As education increasingly relies on data-driven methodologies, accurately predicting student performance is essential for implementing timely and effective interventions. The California Student Performance Dataset offers a distinctive basis for analy...
The ChatGPT Fact-Check: exploiting the limitations of generative AI to develop evidence-based reasoning skills in college science courses.
Advances in physiology education
Jan 17, 2025
Generative large language models (LLMs) like ChatGPT can quickly produce informative essays on various topics. However, the information generated cannot be fully trusted, as artificial intelligence (AI) can make factual mistakes. This poses challenge...
Machine learning approach to student performance prediction of online learning.
PloS one
Jan 14, 2025
Student performance is crucial for addressing learning process problems and is also an important factor in measuring learning outcomes. The ability to improve educational systems using data knowledge has driven the development of the field of educati...
Generative AI in Higher Education: Balancing Innovation and Integrity.
British journal of biomedical science
Jan 9, 2025
Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI's integ...
What is the influence of psychosocial factors on artificial intelligence appropriation in college students?
BMC psychology
Jan 4, 2025
BACKGROUND: In recent years, the adoption of artificial intelligence (AI) has become increasingly relevant in various sectors, including higher education. This study investigates the psychosocial factors influencing AI adoption among Peruvian univers...
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