AI Hallucination in the Context of Education: Exploring College Students’ Use of Generative AI for Academic Tasks
Abstract
Artificial intelligence (AI) tools are becoming increasingly prevalent in higher education, offering students new opportunities for learning and research. However, concerns about the potential for AI to produce inaccurate or misleading information—commonly referred to as "hallucinations"—have raised questions about the impact of these tools on student learning and academic integrity. This research paper examines how students' awareness of AI tools, the types of academic tasks they are assigned, faculty support, and institutional policies influence their perceptions and attitudes toward the use of generative AI for academic purposes. The study utilized a descriptive-correlational design, guided by the Technology Acceptance Model (TAM), which provides insights into how users accept and effectively utilize technology. Structural Equation Modeling (SEM) was employed as a robust statistical technique to analyze the impact of various factors on college students' perceptions and attitudes regarding the use of generative AI for academic work. The study's constructs were assessed for reliability and validity using Cronbach's Alpha, Average Variance Extracted (AVE), and the Heterotrait-Monotrait (HTMT) ratio. The findings indicate that the complexity and intensity of academic tasks, students' engagement with generative AI platforms, and the support they receive from educational stakeholders significantly influence their attitudes toward using these platforms for academic assignments. Conversely, students' perceptions of generative AI are heavily shaped by the support systems available to them and their understanding of the potential risks associated with using such technologies for their assignments and projects. This research contributes to the ongoing discussion about the ethical and practical implications of AI in education, providing valuable insights into best practices for integrating AI technologies to enhance learning while upholding academic integrity.
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Some universities have introduced mandatory AI disclosure policies, requiring students to declare their use of AI in assignments to promote transparency and accountability.
“To address this challenge, some universities have introduced mandatory AI disclosure poli -cies, requiring students to declare their use of AI in assign -ments to promote transparency and accountability [ 56, 69].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [56]
Generative AI tools allow students to automatically generate essays, reports, and coding solutions, increasing the risk of plagiarism and contract cheating.
“These tools allow students to automatically gen -erate essays, reports, and coding solutions, increasing the risk of plagiarism and contract cheating [ 55, 56, 79-81].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [56]
AI-generated content can be factually inaccurate, biased, or otherwise inappropriate.
“In addition, concerns have been raised about the authenticity and reliability of AI-generated context, as tools like Chat -GPT can produce information that is factually inaccurate, biased, or otherwise inappropriate [ 56, 65, 88, 92, 94, 99].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [56]
Institutions have shifted toward alternative assessment methods, such as oral exams (viva voce), real-world reflective tasks, scaffolded assignments, reflective journals, Bloom's Taxonomy higher-order thinking questions, and interactive problem-solving tasks, which require direct student engagement and cannot be easily outsourced to AI.
“To achieve this, institutions have shifted toward alternative assessment methods, such as oral exams (viva voce), real-world reflective tasks, scaffolded assignments, reflective journals, use of Bloom's Taxonomy higher-order thinking questions, and interactive problemsolving tasks, which require direct student engagement and cannot be easily outsourced to AI [ 56, 65, 67, 70, 73, 79, 89, 94, 97-99”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [56]
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“[11 , 12] This research paper examined how students' awareness, the use of AI tools, the nature of academic tasks, faculty support, and institutional policies affect students' perceptions and attitudes toward using generative AI for academic purposes.”
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“The way students interact with AI platforms also affects their perceptions, with straightforward tasks being well-received but more complex assignments causing frustration due to accuracy issues [19.”
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Ncube, P. D. N., Dzvapatsva, G. P., Matobobo, C., & Ranga, M. M. (2026) · AI and Ethics · doi:10.1007/s43681-025-00871-w · Cited by 31 · see citing works
“To address this challenge, some universities have introduced mandatory AI disclosure poli - cies, requiring students to declare their use of AI in assign - ments to promote transparency and accountability [ 56, 69].”
Influential
- Authors
- Torres, R. C.
- Affiliations
- Malayan Colleges Laguna (PH)
- Corresponding author
- Torres, Ramachandra C.
- Venue
- 2025 16th International Conference on E-Education, E-Business, E-Management and E-Learning (IC4e)
- Publisher
- IEEE
- Published
- 2025-04-26
- Pages
- 445-449
- OpenAlex
- W4412482316
- Cited by
- 10
- References
- 12
- Citations by year
- 2026: 9 · 2025: 1
- FWCI
- 11.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on e-Education, e-Business, e-Management and e-Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- AI hallucination, generative AI, university students, academic integrity, student perceptions, attitudes toward AI, student assignments, faculty support, institutional policy, technology acceptance model, artificial intelligence in higher education
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