Beyond Artificial Intelligence Literacy: Conceptualising Digital Immunity against Hallucination Risks in Education

Authors

Nur Ashiela Abdul Manaf

Faculty of Education, National University of Malaysia, Selangor (Malaysia)

Mohd Effendi @ Ewan Mohd Matore

Faculty of Education, National University of Malaysia, Selangor (Malaysia)

Article Information

DOI: 10.47772/IJRISS.2026.100500039

Subject Category: Artificial Intelligence

Volume/Issue: 10/5 | Page No: 548-557

Publication Timeline

Submitted: 2026-04-26

Accepted: 2026-05-02

Published: 2026-05-22

Abstract

The rapid integration of generative Artificial Intelligence (AI) tools into students’ academic practices has brought not only benefits but also a hidden threat, namely AI hallucination. This phenomenon occurs when systems generate outputs that appear convincing but are in fact inaccurate, thus posing a critical challenge to academic integrity. This concept paper aims to elaborate and clarify the position of the concept of digital immunity against AI hallucination risk in educational contexts by developing a conceptual framework that integrates cognitive, behavioural and contextual dimensions of AI use. Its specific objectives are to explain the forms of AI hallucination risk in education, identify research gaps in AI literacy, propose a risk‑assessment instrument framework and discuss the implications of its implementation for learners, teachers and educational institutions. Methodologically, the paper employs document analysis and synthesis of recent literature, drawing on AI literacy frameworks, dual‑process theory, risk perception theory and information quality frameworks to shape the constructs of digital immunity and hallucination‑risk domains. The conceptual findings indicate the need for a valid and reliable psychometric instrument to assess risk awareness, the ability to detect false information and dependence on AI as a basis for planning pedagogical interventions. The main limitation of this study is the absence of empirical data to validate the proposed digital‑immunity framework and AI hallucination‑risk constructs. Hence, future studies may focus on developing and empirically validating a psychometric instrument to measure levels of digital immunity and AI hallucination risk among educational users. Fostering digital immunity is a crucial step to protect the cognitive safety of educators and learners, uphold academic integrity and align AI use with the aspirations of the national Digital Education Policy.

Keywords

Generative Artificial Intelligence; AI hallucination risk

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References

1. Ciubotaru, B. I. (2025). The hallucination problem in Generative Artificial Intelligence: accuracy and trust in digital learning. Proceedings of the International Conference on Virtual Learning, 20, 35–45. https://doi.org/10.58503/icvl-v20y202503 [Google Scholar] [Crossref]

2. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71(March). https://doi.org/10.1016/j.ijinfomgt.2023.102642 [Google Scholar] [Crossref]

3. Elsayed, H. (2024). The Impact of Hallucinated Information in Large Language Models on Student Learning Outcomes: A Critical Examination of Misinformation Risks in AI-Assisted Education. Northern Reviews on Algorithmic Research, Theoretical Computation, and Complexity, 9(8), 1–13. Retrieved from https://northernreviews.com/index.php/NRATCC/article/view/2024-08-07 [Google Scholar] [Crossref]

4. Erümit, A. K., and Sarıalioğlu, R. Ö. (2025). Artificial intelligence in science and chemistry education: a systematic review. Discover Education, 4(1). https://doi.org/10.1007/s44217-025-00622-3 [Google Scholar] [Crossref]

5. Fulsher, A., Pagkratidou, M., and Kendeou, P. (2025). GenAI and misinformation in education: a systematic scoping review of opportunities and challenges. AI and Society. Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s00146-025-02536-y [Google Scholar] [Crossref]

6. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., … Fung, P. (2023). Survey of Hallucination in Natural Language Generation. ACM Computing Surveys, 55(12). https://doi.org/10.1145/3571730 [Google Scholar] [Crossref]

7. Kahneman, D. (2011). Thinking, Fast and Slow (1st Editio). United States: Farrar, Straus and Giroux (FGS). [Google Scholar] [Crossref]

8. Kiili, C., Bråten, I., Strømsø, H. I., Hagerman, M. S., Räikkönen, E., and Jyrkiäinen, A. (2022). Adolescents’ credibility justifications when evaluating online texts. Education and Information Technologies, 27(6), 7421–7450. https://doi.org/10.1007/s10639-022-10907-x [Google Scholar] [Crossref]

9. Milmo, D., and Hern, A. (2024, March 8). ‘We definitely messed up’: why did Google AI tool make offensive historical images? The Guardian, p. 1. Retrieved from https://www.theguardian.com/technology/2024/mar/08/we-definitely-messed-up-why-did-google-ai-tool-make-offensive-historical-images [Google Scholar] [Crossref]

10. Ministry Of Education (MOE) Malaysia. (2023). Dasar Pendidikan Digital. Ministry Of Education (MOE) Malaysia, pp. 1–82. Kuala Lumpur: Ministry of Education, Malaysia (MoE). Retrieved from https://www.moe.gov.my/dasarmenu/dasar-pendidikan-digital [Google Scholar] [Crossref]

11. Slovic, P. (1987). Perception of Risk. Science, 236(4799), 280–285. https://doi.org/https://doi.org/10.1126/science.3563507 [Google Scholar] [Crossref]

12. Sparkes, M. (2023, February). Google Bard advert shows new AI search tool making a factual error. New Scientist, 1. Retrieved from https://www.newscientist.com/article/2358426-google-bard-advert-shows-new-ai-search-tool-making-a-factual-error/ [Google Scholar] [Crossref]

13. UNESCO. (2023). Guidance for generative AI in education and research. In Guidance for generative AI in education and research. UNESCO. https://doi.org/10.54675/ewzm9535 [Google Scholar] [Crossref]

14. Valeri, F., Nilsson, P., and Cederqvist, A. M. (2025). Exploring students’ experience of ChatGPT in STEM education. Computers and Education: Artificial Intelligence, 8, 1–15. https://doi.org/10.1016/j.caeai.2024.100360 [Google Scholar] [Crossref]

15. Zhang, H., Perry, A., and Lee, I. (2025). Developing and Validating the Artificial Intelligence Literacy Concept Inventory: an Instrument to Assess Artificial Intelligence Literacy among Middle School Students. International Journal of Artificial Intelligence in Education, 35(1), 398–438. https://doi.org/10.1007/s40593-024-00398-x [Google Scholar] [Crossref]

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