TY - GEN
T1 - Students’ Perceptions and Use of Generative AI Tools for Programming Across Different Computing Courses
AU - Keuning, Hieke
AU - Alpizar-Chacon, Isaac
AU - Lykourentzou, Ioanna
AU - Beehler, Lauren
AU - Köppe, Christian
AU - de Jong, Imke
AU - Sosnovsky, Sergey
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/11/13
Y1 - 2024/11/13
N2 - Investigation of students’ perceptions and opinions on the use of generative artificial intelligence (GenAI) in education is a topic gaining much interest. Studies addressing this are typically conducted with large heterogeneous groups, at one moment in time. However, how students perceive and use GenAI tools can potentially depend on many factors, including their background knowledge, familiarity with the tools, and the learning goals and policies of the courses they are taking. In this study we explore how students following computing courses use GenAI for programming-related tasks across different programs and courses: Bachelor and Master, in courses in which learning programming is the learning goal, courses that require programming as a means to achieve another goal, and in courses in which programming is optional, but can be useful. We are also interested in changes over time, since GenAI capabilities are changing at a fast pace, and users are adopting GenAI increasingly. We conducted three consecutive surveys (fall ‘23, winter ‘23, and spring ‘24) among students of all computing programs of a large European research university. We asked questions on the use in education, ethics, and job prospects, and we included specific questions on the (dis)allowed use of GenAI tools in the courses they were taking at the time. We received 264 responses, which we quantitatively and qualitatively analyzed, to find out how students have employed GenAI tools across 59 different computing courses, and whether the opinion of an average student about these tools evolves over time. Our study contributes to the emerging discussion of how to differentiate GenAI use across different courses, and how to align its use with the learning goals of a computing course.
AB - Investigation of students’ perceptions and opinions on the use of generative artificial intelligence (GenAI) in education is a topic gaining much interest. Studies addressing this are typically conducted with large heterogeneous groups, at one moment in time. However, how students perceive and use GenAI tools can potentially depend on many factors, including their background knowledge, familiarity with the tools, and the learning goals and policies of the courses they are taking. In this study we explore how students following computing courses use GenAI for programming-related tasks across different programs and courses: Bachelor and Master, in courses in which learning programming is the learning goal, courses that require programming as a means to achieve another goal, and in courses in which programming is optional, but can be useful. We are also interested in changes over time, since GenAI capabilities are changing at a fast pace, and users are adopting GenAI increasingly. We conducted three consecutive surveys (fall ‘23, winter ‘23, and spring ‘24) among students of all computing programs of a large European research university. We asked questions on the use in education, ethics, and job prospects, and we included specific questions on the (dis)allowed use of GenAI tools in the courses they were taking at the time. We received 264 responses, which we quantitatively and qualitatively analyzed, to find out how students have employed GenAI tools across 59 different computing courses, and whether the opinion of an average student about these tools evolves over time. Our study contributes to the emerging discussion of how to differentiate GenAI use across different courses, and how to align its use with the learning goals of a computing course.
KW - Computing Education
KW - Generative AI
KW - Large Language Models
KW - Programming Courses
UR - http://www.scopus.com/inward/record.url?scp=85215534678&partnerID=8YFLogxK
U2 - 10.1145/3699538.3699546
DO - 10.1145/3699538.3699546
M3 - Contribución a la conferencia
AN - SCOPUS:85215534678
T3 - ACM International Conference Proceeding Series
BT - Proceedings of 24th International Conference on Computing Education Research, Koli Calling 2024
PB - Association for Computing Machinery
T2 - 24th International Conference on Computing Education Research, Koli Calling 2024
Y2 - 14 November 2024 through 17 November 2024
ER -