Community College Students' Perceptions and Use of Generative Artificial Intelligence: Self-Reported AI Understanding, Accuracy Concerns, and Institutional Support Needs
DOI:
https://doi.org/10.70088/27vb8056Keywords:
Generative Artificial Intelligence, Community College Students, AI Literacy, Accuracy Concerns, Trust in AI, Responsible AI UseAbstract
Generative artificial intelligence (GenAI) is increasingly used in higher education, offering new learning opportunities while raising concerns about accuracy, reliability, and responsible use. This study examines community college students' perceptions and use of GenAI, focusing on self-reported AI understanding, accuracy concerns, and institutional support needs, with implications for trust and continued use. This study used a descriptive quantitative approach to analyze survey responses from 81 students at a community college in the southwestern United States. Data were summarized using frequencies and percentages. The findings showed that 76.5% of respondents had previously used AI tools, with the most frequently reported academic applications including understanding course concepts, reviewing learning materials, and obtaining grammar assistance. Additionally, 77.8% reported understanding basic AI concepts, while 44.4% identified inaccurate AI-generated information as a concern and 33.3% expressed a need for clearer institutional guidelines. These findings highlight the importance of strengthening AI literacy, encouraging critical evaluation of AI-generated content, and providing institutional guidance for responsible AI use. Although the study did not directly measure trust and continuance intention, it identifies student perceptions that may inform future research on these constructs.References
M. I. Baig and E. Yadegaridehkordi, "ChatGPT in the higher education: A systematic literature review and research challenges," Int. J. Educ. Res., vol. 127, Art. no. 102411, 2024, doi: 10.1016/j.ijer.2024.102411.
E. Kasneci et al., "ChatGPT for good? On opportunities and challenges of large language models for education," Learn. Individ. Differ., vol. 103, Art. no. 102274, 2023, doi: 10.1016/j.lindif.2023.102274.
D. R. E. Cotton, P. A. Cotton, and J. R. Shipway, "Chatting and cheating: Ensuring academic integrity in the era of ChatGPT," Innov. Educ. Teach. Int., vol. 61, no. 2, pp. 228–239, 2024, doi: 10.1080/14703297.2023.2190148.
D. T. K. Ng, J. K. L. Leung, S. K. W. Chu, and M. S. Qiao, "Conceptualizing AI literacy: An exploratory review," Comput. Educ. Artif. Intell., vol. 2, Art. no. 100041, 2021, doi: 10.1016/j.caeai.2021.100041.
G. Hoachlander, A. C. Sikora, and L. Horn, Community College Students: Goals, Academic Preparation, and Outcomes, Postsecondary Education Descriptive Analysis Reports, NCES 2003-164. Washington, DC, USA: National Center for Education Statistics, 2003.
C. K. Lo, "What is the impact of ChatGPT on education? A rapid review of the literature," Educ. Sci., vol. 13, no. 4, Art. no. 410, 2023, doi: 10.3390/educsci13040410.
D. Shin, "The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI," Int. J. Hum.-Comput. Stud., vol. 146, Art. no. 102551, 2021, doi: 10.1016/j.ijhcs.2020.102551.
C. Baek, T. Tate, and M. Warschauer, "'ChatGPT seems too good to be true': College students' use and perceptions of generative AI," Comput. Educ. Artif. Intell., vol. 7, Art. no. 100294, 2024, doi: 10.1016/j.caeai.2024.100294.
J. E. Ha, D. Kim, J. Tae, and H.-J. So, "Community college students' use of ChatGPT for computational problem-solving tasks: Perceptions and interaction patterns," Educ. Technol. Int., vol. 26, no. 2, pp. 393–421, 2025, doi: 10.23095/ETI.2025.26.2.393.
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Copyright (c) 2026 Ye Huo (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
