Assessing the Impact of Prior Coding and Artificial Intelligence Learning on Non-Computing Majors’ Perception of AI in a University Context
Abstract
:1. Introduction
2. Literature Review
2.1. Block-Based and Text-Based Coding
2.2. AI Literacy
2.3. Software Education in Korea
3. Research Questions
- RQ1: To what extent does prior coding experience influence AI literacy in university students enrolled in non-computer-based majors?
- RQ2: How does the age at which students are first exposed to AI components affect their AI literacy in university non-computer-based majors?
4. Methods
4.1. Study Participants
4.2. Data Collection
4.2.1. Instruments
4.2.2. Interviews
5. Results
5.1. RQ1: To What Extent Does Prior Coding Experience Influence AI Literacy in University Students Enrolled in Non-Computer-Based Majors?
5.2. AI Awareness
5.3. Social Impact of AI
5.4. Self-Impact of AI
5.5. Need for AI
5.6. RQ2: How Does the Age at Which Students Are First Exposed to AI Components Affect Their AI Literacy in University Non-Computer-Based Majors?
5.7. AI Awareness
5.8. Social Impact of AI
5.9. Self-Impact of AI
5.10. Need for AI
5.11. Interview Findings
Currently, everyone in the media is talking about AI and recognizing its importance. I am not in a computer-related major, so I do not directly experience the importance or necessity of class due to my major. It will be helpful to get a job if I have basic skills related to AI. It is one of the good skills to put in my resume (Student C, interview transcript).
Looking around my friends, I see many people who want to get a job as a software developer by learning computer coding or advanced technology skills. However, the basic AI education classes universities provide are insufficient, so they sometimes go to private academies to obtain certificates related to this AI field. While talking with them, I also need to be ready for AI applications. However, my major is humanities, so I still need to improve in AI (Student A, interview transcript).
Before entering this university, my high school was designated a SW (Software) leading school, so I took a few classes related to basic computer coding skills or using the Entry apps. Of course, it was not an advanced AI course, but these prior AI learning experiences greatly helped me take courses at the university level. Those courses I took in high school influenced the classes we are taking now at the university (Student B, interview transcript).
I agree with the above comment. I went to local/regional schools, so I did not have a chance to learn about AI-related education in middle and high school. Therefore, when I was admitted to this university and tried to take AI classes as one of the mandatory courses, I had many difficulties. I tried to teach myself while watching YouTube tutorial videos, but it took work (Student D, interview transcript).
AI classes in liberal arts are being conducted targeting a general average level that does not fit the individual students’ majors and AI application abilities. For example, some students may want to receive more advanced courses because they are already good at computer coding. In contrast, others may want very basic AI or SW education. I hope that various AI classes are provided so that students can find suitable courses for their interests and AI application skills (Student E, interview transcript).
I feel the same way. Before opening new AI-related courses, I hope the university can survey to determine students’ needs and interests. Particularly for students who are not computer majors, the requirements can vary because they are not experts in AI applications (Student B, interview transcript).
6. Discussion
6.1. RQ1: To What Extent Does Prior Coding Experience Influence AI Literacy in University Students Enrolled in Non-Computer-Based Majors?
6.2. RQ2: How Does the Age at Which Students Are First Exposed to AI Components Affect Their AI Literacy in University Non-Computer-Based Majors?
7. Limitations
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Frequency (N = 222) | Percentage (%) | |
---|---|---|---|
Gender | Male | 76 | 34.23 |
Female | 146 | 65.77 | |
Major | Science | 74 | 33.33 |
Humanities | 10 | 4.51 | |
Arts | 114 | 51.35 | |
Music | 8 | 3.60 | |
PE | 1 | 0.45 | |
Communications | 11 | 4.96 | |
Social Science | 4 | 1.80 | |
Grade/Year | Freshman | 35 | 15.77 |
Sophomore | 115 | 51.80 | |
Junior | 40 | 18.02 | |
Senior | 32 | 14.41 | |
Coding Experience | Text-based coding | 81 | 36.49 |
Block-based coding | 45 | 20.27 | |
No coding experience | 96 | 43.24 | |
Age of Learning about AI | No learning experience | 65 | 29.28 |
Elementary school | 12 | 5.41 | |
Middle School | 21 | 9.46 | |
High School | 39 | 17.57 | |
University | 85 | 38.28 |
Category | Student A | Student B | Student C | Student D | Student E |
---|---|---|---|---|---|
Gender | Male | Male | Female | Female | Female |
Major | Science | Science | Humanities | Social studies | Art |
Prior AI experience | O | O | X | X | O |
AI course taken at university | O | O | X | X | O |
AI Awareness | AI Social Impact | AI Self Impact | Need for AI | ||
---|---|---|---|---|---|
N | M (SD) | M (SD) | M (SD) | M (SD) | |
Text-based coding | 81 | 6.40 (1.53) | 32.15 (3.50) | 7.17 (1.71) | 8.04 (1.54) |
Block-based coding | 45 | 6.20 (1.50) | 31.40 (3.33) | 7.31 (1.31) | 8.09 (1.61) |
No coding | 96 | 5.49 (1.31) | 29.95 (4.51) | (6.46 (1.70) | 6.93 (1.53) |
AI Awareness | AI Social Impact | AI Self Impact | Need for AI | ||
---|---|---|---|---|---|
N | M (SD) | M (SD) | M (SD) | M (SD) | |
No Learning | 65 | 5.35 (1.38) | 30.12 (4.84) | 6.49 (1.59) | 7.08 (1.47) |
Elementary School | 12 | 5.50 (1.17) | 30.25 (4.60) | 6.42 (1.44) | 6.67 (2.31) |
Middle School | 21 | 6.619 (1.02) | 32.52 (3.92) | 7.29 (1.59) | 8.14 (1.65) |
High School | 39 | 6.718 (1.10) | 31.69 (2.77) | 7.26 (1.43) | 7.64 (1.39) |
University | 85 | 5.988 (1.64) | 31.200 (3.72) | 7.00 (1.84) | 7.89 (1.65) |
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Lee, Y.-J.; Davis, R.O. Assessing the Impact of Prior Coding and Artificial Intelligence Learning on Non-Computing Majors’ Perception of AI in a University Context. Information 2025, 16, 277. https://doi.org/10.3390/info16040277
Lee Y-J, Davis RO. Assessing the Impact of Prior Coding and Artificial Intelligence Learning on Non-Computing Majors’ Perception of AI in a University Context. Information. 2025; 16(4):277. https://doi.org/10.3390/info16040277
Chicago/Turabian StyleLee, Yong-Jik, and Robert O. Davis. 2025. "Assessing the Impact of Prior Coding and Artificial Intelligence Learning on Non-Computing Majors’ Perception of AI in a University Context" Information 16, no. 4: 277. https://doi.org/10.3390/info16040277
APA StyleLee, Y.-J., & Davis, R. O. (2025). Assessing the Impact of Prior Coding and Artificial Intelligence Learning on Non-Computing Majors’ Perception of AI in a University Context. Information, 16(4), 277. https://doi.org/10.3390/info16040277