Investigation of Generative AI Adoption in IT-Focused Vocational Secondary School Programming Education

Αποθηκεύτηκε σε:
Λεπτομέρειες βιβλιογραφικής εγγραφής
Εκδόθηκε σε:Education Sciences vol. 15, no. 9 (2025), p. 1152-1178
Κύριος συγγραφέας: Annuš Norbert
Έκδοση:
MDPI AG
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100 1 |a Annuš Norbert 
245 1 |a Investigation of Generative AI Adoption in IT-Focused Vocational Secondary School Programming Education 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a The application of artificial intelligence in education, particularly in learning programming, is gaining increasing significance. However, research on secondary school students specializing in IT at an early stage has received relatively little attention in this field. The aim of this study is to assess how vocational secondary school IT students utilize Generative artificial intelligence in learning programming. The study employed a survey-based methodology, where students with varying levels of knowledge were surveyed to understand their AI usage patterns. The sample consisted of students from vocational IT schools, and data were analyzed using descriptive statistics and independent samples t-tests. The results indicate that students with different levels of knowledge use AI tools differently, with ChatGPT being the most popular tool. The study further highlights that AI usage brings significant benefits, such as providing a personalized learning experience and enabling quick error correction. However, excessive reliance on AI tools may hinder students from acquiring fundamental programming skills. The findings support the idea that while AI can effectively complement teachers’ explanations, overdependence on it can be risky, potentially reducing students’ creativity and problem-solving abilities. The study emphasizes the crucial role of educators in teaching the responsible and ethical use of artificial intelligence. The results of this research offer new perspectives on the effective integration of Generative artificial intelligence into vocational secondary school programming education and suggest further studies to compare its applications at the university level. However, the study acknowledges certain limitations, such as the potential bias of self-reported data, which may affect the generalizability of the results. Unlike other studies, the age groups we surveyed, and the cohorts formed from them are nearly evenly distributed, making our sample representative of the region in question. 
653 |a Problem solving 
653 |a Teaching 
653 |a Software 
653 |a Network topologies 
653 |a Computer science 
653 |a Ethics 
653 |a Secondary school students 
653 |a Generative artificial intelligence 
653 |a Teachers 
653 |a Chatbots 
653 |a Cognition & reasoning 
653 |a Use statistics 
653 |a Programming languages 
653 |a Perceptions 
653 |a Vocational schools 
653 |a Personalized learning 
653 |a Technology Acceptance Model 
653 |a Secondary schools 
653 |a Education 
653 |a Secondary School Science 
653 |a High School Students 
653 |a Information Retrieval 
653 |a Control Groups 
653 |a Influence of Technology 
653 |a Learning Strategies 
653 |a Educational Methods 
653 |a Learning Processes 
653 |a Researchers 
653 |a Computer Science Education 
653 |a Learning Experience 
653 |a Educational Technology 
653 |a Instructional Materials 
653 |a Cooperative Learning 
653 |a College Students 
653 |a Artificial Intelligence 
653 |a Comparative Education 
653 |a Comparative Analysis 
653 |a Educational Environment 
653 |a Database Management Systems 
653 |a College Science 
653 |a Logical Thinking 
653 |a Algorithms 
773 0 |t Education Sciences  |g vol. 15, no. 9 (2025), p. 1152-1178 
786 0 |d ProQuest  |t Education Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3254506371/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
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