Use of Generative AI by Higher Education Students DOI Open Access
Ana Elisa Sousa, Paula Cardoso

Electronics, Journal Year: 2025, Volume and Issue: 14(7), P. 1258 - 1258

Published: March 22, 2025

This research aims to explore the use, perceptions, and challenges associated with generative AI (GenAI) among higher education students. As GenAI technologies, such as language models, image generators, code assistants, become increasingly prevalent in academic settings, it is essential understand how students engage these tools their impact on learning process. The study investigates students’ awareness, adoption patterns, perceptions of AI’s role tasks, alongside benefits they identify face, including ethical concerns, reliability, accessibility. Through quantitative methods, provides a comprehensive analysis student experiences education. findings aim inform educators, technologists, institutions about opportunities barriers integrating technologies into educational practices guide development strategies that support effective responsible use academia.

Language: Английский

The Impact of Artificial Intelligence (AI) on Students’ Academic Development DOI Creative Commons

Aniella Mihaela Vieriu,

Gabriel Petrea

Education Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 343 - 343

Published: March 11, 2025

The integration of Artificial Intelligence (AI) in education has transformed academic learning, offering both opportunities and challenges for students’ development. This study investigates the impact AI technologies on learning processes performance, with a focus their perceptions associated adoption. Conducted at National University Science Technology POLITEHNICA Bucharest, this research involved second-year students who had direct experience AI-enhanced environments. Using purposive sampling, 85 participants were selected to ensure relevance. Data collected through structured questionnaire comprising 11 items as follows: seven closed-ended questions assessing perceptions, usage, effectiveness tools; four open-ended exploring experiences, expectations, concerns. Quantitative data analyzed using frequency percentage calculations, while qualitative responses subjected thematic analysis, incorporating vertical (individual responses) horizontal (cross-dataset) approaches comprehensive theme identification. findings reveal that offers significant benefits, including personalized improved outcomes, enhanced student engagement. However, such over-reliance AI, diminished critical thinking skills, privacy risks, dishonesty also identified. underscores necessity framework integration, supported by ethical guidelines, maximize benefits mitigating risks. In conclusion, holds immense potential enhance efficiency its successful implementation requires addressing concerns related accuracy, cognitive disengagement, implications. A balanced approach is essential equitable, effective, responsible experiences educational

Language: Английский

Citations

0
Automatic Knowledge Acquisition System with Large Language Model in Academic Domain DOI

Ahmad Julius Tarigan,

Kemas Rahmat Saleh Wiharja, Dade Nurjanah

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 206 - 217

Published: Jan. 1, 2025

Language: Английский

Citations

0
Use of Generative AI by Higher Education Students DOI Open Access
Ana Elisa Sousa, Paula Cardoso

Electronics, Journal Year: 2025, Volume and Issue: 14(7), P. 1258 - 1258

Published: March 22, 2025

This research aims to explore the use, perceptions, and challenges associated with generative AI (GenAI) among higher education students. As GenAI technologies, such as language models, image generators, code assistants, become increasingly prevalent in academic settings, it is essential understand how students engage these tools their impact on learning process. The study investigates students’ awareness, adoption patterns, perceptions of AI’s role tasks, alongside benefits they identify face, including ethical concerns, reliability, accessibility. Through quantitative methods, provides a comprehensive analysis student experiences education. findings aim inform educators, technologists, institutions about opportunities barriers integrating technologies into educational practices guide development strategies that support effective responsible use academia.

Language: Английский

Citations

0