ref-check.org

Reference Check System

Academic Citation Verify Tool

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About ref-check.org

🔍 Fictitious References👨‍🔬 Developers❓ FAQ📚 Recent Research

ref-check.org is a Citation Check (Reference Check) System for Academic Papers (an Academic Paper Reference Verification Tool) designed to identify fictitious and erroneous citations (references) cited/listed in academic papers. The presence of such invalid references may stem from AI hallucinations when authors use generative AI in their writing process, or from serious oversights during manuscript preparation.

With ref-check.org, scholars can rapidly determine whether any citations (references) in a given manuscript are unverifiable or non-existent. This tool is an invaluable resource for scholars, researchers, graduate students, editors, and reviewers alike, saving significant time and effort that would otherwise be spent manually tracing citations (references). More importantly, it helps prevent the further propagation of false or inaccurate references, safeguarding the integrity of scholarly communication and supporting the continued advancement of scientific research.

The Growing Crisis of AI-Generated Fictitious References in Academia

Universities around the world have recently reported a surge of incidents involving suspected fabricated references in journal articles, doctoral dissertations, and master's theses, drawing widespread concern across the academic community. This problem has escalated to an international scale — even prestigious publishers such as Springer Nature have encountered similar cases. To help faculty and students address the academic integrity risks posed by AI hallucinations, the Automated Reference Verification System (ref-check.org) was developed.

The fundamental remedy for AI-fabricated references lies in researchers not over-relying on generative AI. Every cited reference should be personally read and verified for accuracy. As a guiding principle: "The appearance of fictitious references in a manuscript not only suggests that the content may have been AI-generated, but also directly undermines academic integrity." The authenticity of references serves as a critical quality signal — the discovery of fabricated citations should immediately raise concerns that the paper's content may have been largely AI-generated, warranting stricter scrutiny.

In the collaborative nature of academic research, the impact of AI misinformation is far-reaching. Academic papers are often co-authored by professors, research assistants, and students working together. If any team member misuses AI without rigorous oversight, and fabricated references end up in the final manuscript, it tarnishes not only the individual researcher's reputation but brings shame upon the entire research team. Verifying the authenticity of references has therefore become one of the most essential risk management practices in academia today.

Although verifying citations is a necessary task for researchers, the traditional manual approach — checking each reference one by one — could consume hours or even days, imposing enormous administrative and time costs. To enhance research efficiency, this system automatically checks each reference's DOI, queries journal article registries (Crossref), searches open academic databases including Semantic Scholar, OpenAlex, and PubMed, looks up Taiwan master's and doctoral theses, verifies reference URLs, searches library bibliographic databases, and queries Google Scholar — rapidly scanning and confirming the authenticity of each citation to help the academic community overcome the long-standing verification burden introduced by AI.

AI technology is a double-edged sword. While it brings remarkable gains in research efficiency, academia must also actively eliminate the false reasoning and fabricated references that AI can introduce. It is important to note that detecting fictitious references is only the starting point of a thorough review — the absence of fabricated references does not guarantee that a paper's content is entirely accurate or that it was not AI-generated. The academic community must continue to strive and improve in order to safeguard the rigor of knowledge and promote genuine scientific progress in the age of AI.

Developers of ref-check.org

The website is created and maintained by the following researchers:

Chih-Chien Wang (汪志堅)

Distinguished Professor, Graduate Institute of Information Management, National Taipei University, Taiwan

https://orcid.org/0000-0001-8940-3655

Cheng-Yu Lai (賴正育)

Assistant Professor, Department of Business Administration, Chung Yuan Christian University, Taiwan

Ting-Zhen Li (李庭甄)

Graduate Student, Graduate Institute of Information Management, National Taipei University, Taiwan

https://orcid.org/0009-0002-0577-1800

Hui-Yen Hou (侯惠晏)

Graduate Student, Graduate Institute of Information Management, National Taipei University, Taiwan

https://orcid.org/0009-0002-4555-6611

Shao-Chieh Peng (彭紹傑)

Graduate Student, Graduate Institute of Information Management, National Taipei University, Taiwan

https://orcid.org/0009-0001-6856-7156

Frequently Asked Questions (FAQ)

1. What types of files can be uploaded?

You can upload files in TXT, DOCX (Word), and PDF formats. Alternatively, you can paste your reference list or full manuscript text directly into the text input box without uploading any file.

2. Which is faster: pasting plain-text references or uploading a Word/PDF file?

Pasting plain text (references only) is significantly faster. When you paste the reference list directly, the system can begin verification almost immediately. Uploading a Word or PDF file requires additional processing time to extract and parse the text content before verification can begin. For the fastest results, we recommend copying your reference list and pasting it as plain text.

3. Is there a required citation format?

No. The system is designed to handle a wide variety of citation styles, including APA, IEEE, Chicago, MLA, Vancouver, and many journal-specific formats. As long as the reference retains a recognizable structure (e.g., author, year, title), the system can parse and verify it. Even references with minor formatting errors can still be processed successfully.

4. Does using standard APA format improve verification accuracy?

Yes, to some extent. Standard APA format places the title in a consistent, predictable position within the citation string, which helps the system extract it more reliably. When the title is clearly identifiable, the matching accuracy across databases improves. That said, the system performs well with other well-structured citation formats as well.

5. What should I do if the system fails to correctly extract the reference list from my uploaded Word/PDF file?

If the reference list is not correctly extracted from an uploaded file, we recommend copying the reference section from your document and pasting it directly as plain text using the 'Paste Text' tab. This bypasses the file parsing step entirely and typically yields more accurate extraction results. For best results, paste only the reference list rather than the full manuscript.

6. How does this system detect AI-fabricated references? Which databases does it query?

The system verifies each reference by searching across multiple authoritative bibliographic databases. The verification pipeline includes: (1) DOI lookup via Crossref, (2) title search on Crossref, (3) Semantic Scholar, (4) OpenAlex, (5) PubMed, (6) the National Digital Library of Theses and Dissertations in Taiwan (for Chinese-language theses), (7) URL verification for web-based references, (8) Google Books, Open Library, and the Taiwan National Bibliographic Information Network (NBINet) for books, and (9) Google Scholar. A reference is considered verified when its title closely matches a record found in one of these databases. AI-fabricated references typically cannot be found in any of these databases because they do not actually exist.

7. Can book references be verified?

Yes. The system includes dedicated steps for verifying book references. It searches Google Books (by ISBN and by title), Open Library, and the Taiwan National Bibliographic Information Network (NBINet) maintained by the National Central Library of Taiwan. Both ISBN-based lookups and title-based matching are supported. Book chapters cited within edited volumes are also handled.

8. My reference cannot be found in any online database, including Google Scholar. Can this system verify it?

If a reference cannot be located in any of the nine verification steps, the system will report it as 'Not Found.' This may occur for several reasons: the reference is very old and not indexed in any modern database, it is a highly specialized or regional publication with limited online coverage, or the reference is fictitious. For references reported as not found, manual verification is strongly recommended.

9. If a reference is not found, does that mean it is a fictitious reference? Or is manual verification still needed?

A 'Not Found' result does not automatically mean the reference is fictitious. It means the system was unable to confirm its existence through the available databases. There are legitimate reasons a reference may not be found — for example, very old publications, obscure regional journals, conference proceedings with limited indexing, or references that are correct but use non-standard formatting that prevented accurate title extraction. However, a 'Not Found' result should be treated as a warning flag that warrants careful manual verification. We recommend checking the reference directly through library catalogs, publisher websites, or by contacting the original source.

10. Can the verification results be downloaded?

Yes. After verification is complete, the results can be downloaded in multiple formats: a Summary Report in PDF format, a Summary Report in TXT format, a detailed Not Found References report in TXT format, a complete list of all references in CSV format, and a list of unverified references in CSV format. These downloadable reports are useful for documentation, peer review, and further manual investigation.

Recent Research about AI Hallucination

Selected academic references related to AI hallucination in research and education.

  1. Abdaoui, H., Barbaria, S., Dergaa, I., Ceylan, H. İ., Bragazzi, N. L., de Giorgio, A., Salah, R. B., & Rahmouni, H. B. (2026). MedFusionT5: Cross-Modal Attention Boosts Semantic Quality and Reduces Hallucinations in Dental AI [Article]. International Dental Journal, 76(3), Article 109404. https://doi.org/10.1016/j.identj.2025.109404
  2. Adejumo, A. A., Oyelere, S. S., Sanusi, I. T., & Suhonen, J. (2026). A systematic review of the impact of GenAI on learning performance, AI hallucinations, and problem-solving in computer science education [Review]. Computers and Education: Artificial Intelligence, 10, Article 100570. https://doi.org/10.1016/j.caeai.2026.100570
  3. Adel, A., & Alani, N. (2025). Can generative AI reliably synthesise literature? exploring hallucination issues in ChatGPT [Review]. AI and Society, 40(8), 6799–6812. https://doi.org/10.1007/s00146-025-02406-7
  4. Al Umri, N., Karnyoto, A. S., & Pardamean, B. (2025). The Impact of AI-Generated Hallucinations in Educational Settings: Trends, Gaps, and Future Directions. Proceedings - 2025 9th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2025.
  5. Anghelescu, A., Ciobanu, I., Munteanu, C., Anghelescu, L. A. M., & Onose, G. (2023). Chatgpt: "to be or not to be"… in academic research. the human mind's analytical rigor and capacity to discriminate between ai bots' truths and hallucinations [Article]. Balneo and PRM Research Journal, 14(4), Article 614. https://doi.org/10.12680/balneo.2023.614
  6. Ariso, J. M., & Bannister, P. (2025). 'AI lost the prompt!' Replacing 'AI hallucination' to distinguish between mere errors and irregularities [Article]. AI and Society. https://doi.org/10.1007/s00146-025-02757-1
  7. Ashktorab, Z., Desmond, M., Pan, Q., Johnson, J., Brachman, M., Dugan, C., Danilevsky, M., & Geyer, W. (2025). Emerging Reliance Behaviors in Human-AI Content Grounded Data Generation: The Role of Cognitive Forcing Functions and Hallucinations. CHIWORK 2025 - Proceedings of the 4th Annual Symposium on Human-Computer Interaction for Work.
  8. Ashwin, M., Jha, S., Prasad, G., & Kumar, S. (2025). Fake it till you make it? AI hallucinations and ethical dilemmas in Anesthesia research and practice [Editorial]. Journal of Anaesthesiology Clinical Pharmacology, 41(3), 381–383. https://doi.org/10.4103/joacp.joacp_56_25
  9. Bassi, E., & Pagallo, U. (2026). Just Hallucinations? The Problem of AI Literacy with a New Digital Divide. Lecture Notes in Computer Science.
  10. Blanchard, E. G., Callahan, J. C., Akretche, I., Asswiel, N., Schmitt, L., Kessemtini, A., & Khan, I. S. A. (2025). Making Generative AI Hallucinations Useful by Reassessing the Troublemaker Agent Strategy. Communications in Computer and Information Science.
  11. Boretti, A. (2026). Hallucinations in generative AI: A threat to scholarly integrity and the urgent need for publisher-led academically supervised verification [Short survey]. Energy Research and Social Science, 136, Article 104720. https://doi.org/10.1016/j.erss.2026.104720
  12. Brunetta, C. M., Ferraz, T. S., & Alencar, A. C. D. (2025). BETWEEN HALLUCINATIONS AND INNOVATIONS: THE CHALLENGE OF RESPONSIBLE IMPLEMENTATION OF GENERATIVE AI IN THE JUSTICE SYSTEM [Article]. Revista Opiniao Juridica, 23(44), 1–26. https://doi.org/10.12662/2447-6641oj.v23i44.p1-26.2025
  13. Chakraborty, T., & Masud, S. (2024). The Promethean Dilemma of AI at the Intersection of Hallucination and Creativity [Note]. Communications of the ACM, 67(10), 26–28. https://doi.org/10.1145/3652102
  14. Cheah, B. C. J. (2026). Task type and instructional context shape the influence of generative AI on hallucinations of learning [Letter]. Medical Teacher, 48(1), 164. https://doi.org/10.1080/0142159X.2025.2535420
  15. Chen, J., & Yang, W. (2025). Prudence and Integration: Decision-Tree-Based Strategies for Enhancing Teacher TPACK in the Context of Generative AI Hallucinations. Proceedings of 2025 2nd International Symposium on Artificial Intelligence for Education, ISAIE 2025.
  16. Cheng, A., Nagesh, V., Eller, S., Grant, V., & Lin, Y. (2025). Exploring AI Hallucinations of ChatGPT [Article]. Simulation in Healthcare, 20(6), 413–418. https://doi.org/10.1097/SIH.0000000000000877
  17. Cheng, Q., Dai, Y., Liu, X., & Peng, S. (2026). The trust crisis in artificial intelligence: AI hallucinations and human-AI collaboration [Article]. Technology in Society, 86, Article 103286. https://doi.org/10.1016/j.techsoc.2026.103286
  18. Chi, Z., & Liang, W. (2025). Soundscape Thresholds: AI Hallucinations Reimagining the Sacred Experience of Ming Rituals. C and C 2025 - Proceedings of the 2025 Conference on Creativity and Cognition.
  19. Christensen, J., Hansen, J. M., & Wilson, P. (2025). Understanding the role and impact of Generative Artificial Intelligence (AI) hallucination within consumers' tourism decision-making processes [Article]. Current Issues in Tourism, 28(4), 545–560. https://doi.org/10.1080/13683500.2023.2300032
  20. Devenot, N. (2023). TESCREAL hallucinations: Psychedelic and AI hype as inequality engines [Article]. Journal of Psychedelic Studies, 7, 22–39. https://doi.org/10.1556/2054.2023.00292
  21. Diaz Campo, M. P., Ghafoori, A., & Gupta, M. (2024). AI HALLUCINATION IN THE WAKE OF GENAI [Article]. Cutter business technology journal, 37(5), 33–37.
  22. Diers-Lawson, A., & Lawson, S. J. (2025). Hallucination or vision? Establishing reliability and validity of AI-based qualitative analysis of the unified model of activism in the Scottish independence Twitter debate [Article]. Corporate Communications, 30(1), 162–183. https://doi.org/10.1108/CCIJ-08-2024-0139
  23. Domingues-dos-Santos, L., Maltarollo, T. F. H., & Skelton-Macedo, M. C. (2026). AI hallucination risks and mitigation strategies [Letter]. British Dental Journal, 240(3), 131. https://doi.org/10.1038/s41415-026-9583-0
  24. Dumit, J., & Roepstorff, A. (2025). AI hallucinations are a feature of LLM design, not a bug [Note]. Nature, 639(8053), 38. https://doi.org/10.1038/d41586-025-00662-7
  25. Ehsan, U., Watkins, E. A., Wintersberger, P., Manger, C., Hubig, N., Savage, S., Weisz, J. D., & Riener, A. (2025). New Frontiers of Human-centered Explainable AI (HCXAI): Participatory Civic AI, Benchmarking LLMs, XAI Hallucinations, and Responsible AI Audits. Conference on Human Factors in Computing Systems - Proceedings.
  26. El Ganadi, A., Aftar, S., Gagliardelli, L., & Ruozzi, F. (2025). Generative AI for Islamic Texts: The EMAN Framework for Mitigating GPT Hallucinations. International Conference on Agents and Artificial Intelligence.
  27. Erdem, O., Hassett, K., & Egriboyun, F. (2025). Hallucination in AI-generated financial literature reviews: evaluating bibliographic accuracy [Article]. International Journal of Data Science and Analytics, 20(5), 4501–4510. https://doi.org/10.1007/s41060-025-00731-0
  28. Förster, S., & Skop, Y. (2026). Between fact and fairy: tracing the hallucination metaphor in AI discourse [Article]. AI and Society, 41(3), 1685–1698. https://doi.org/10.1007/s00146-025-02392-w
  29. Fortier, L. A. (2026a). Fabricated citations: the quiet crisis of AI hallucinations in scientific publishing [Editorial]. Journal of the American Veterinary Medical Association, 264(4), 382. https://doi.org/10.2460/javma.264.4.382
  30. Fortier, L. A. (2026b). Fabricated citations: the quiet crisis of AI hallucinations in scientific publishing [Editorial]. American Journal of Veterinary Research, 87(4). https://doi.org/10.2460/ajvr.87.04.editorial
  31. Frampton, J. R., & Smith, W. R. (2026). AI use for cybervetting: exploring hallucination disclaimers and applicants' race in applicant evaluations and hiring decisions [Article]. Journal of Applied Communication Research, 54(1), 21–41. https://doi.org/10.1080/00909882.2025.2602653
  32. Gao, Y., & Jia, Q. (2025). Understanding AI Hallucination Effects on Human-AI Symbiosis Quality based on Extended Cognition Theory. Proceedings of the International Conference on Electronic Business (ICEB).
  33. Gao, Y., Wang, J., Lin, Z., & Sang, J. (2024). AIGCs Confuse AI Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Models. MM 2024 - Proceedings of the 32nd ACM International Conference on Multimedia.
  34. Ge, L. (2025). Spectral imaginings and sympoietic creativity: AI hallucinations and the ethics of posthuman creativity [Article]. Big Data and Society, 12(4). https://doi.org/10.1177/20539517251400696
  35. Georgieva, I., & Georgiev, G. V. (2026). Kaleidoscopic AI: Hallucinations on the Verge of Creativity. Lecture Notes in Computer Science.
  36. Geroimenko, V. (2025). Generative AI Hallucinations in Healthcare: A Challenge for Prompt Engineering and Creativity. In Springer Series on Cultural Computing (Vol. Part F561, pp. 321–335). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-86551-0_17
  37. Gnanajeyaraman, R., & Reetha, L. (2025). Neurobiologically Informed AI for Schizophrenia Diagnosis: A Study on Verbal Auditory Hallucinations Using Graph Learning. 2025 10th International Conference on Applying New Technology in Green Buildings, ATiGB 2025.
  38. Gupta, S., Agrawal, V., Negi, Y., Karunanithi, S., & Balakrishnan, A. (2026). Reducing Hallucinations in Legal AI: A Retrieval Augmented Generation-Based Model for Accurate Legal Guidance [Article]. IEEE Access, 14, 44451–44463. https://doi.org/10.1109/ACCESS.2026.3675624
  39. Halperin, B. A., & Lukin, S. M. (2024). Artificial Dreams: Surreal Visual Storytelling as Inquiry Into AI 'Hallucination'. Proceedings of the 2024 ACM Designing Interactive Systems Conference, DIS 2024.
  40. Hamid, O. H. (2024). Beyond Probabilities: Unveiling the Delicate Dance of Large Language Models (LLMs) and AI-Hallucination. 2024 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2024.
  41. Haque, M. A., Siddique, S., Rahman, M. M., Hasan, A. R., Das, L. R., Kamal, M., Sujaee, K., Gupta, K. D., & George, R. (2025). SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment. 2025 6th International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2025.
  42. Hardack, R. (2025). THEY WILL REPLACE U.S.: AI AND THE HALLUCINATIONS OF CORPORATIONS [Article]. Revista de Estudios Norteamericanos, 29, 161–195. https://doi.org/10.12795/REN.2025.i29.13
  43. Hardy, R., Kim, S. E., Ro, D. H., & Rajpurkar, P. (2025). ReXTrust: A Model for Fine-Grained Hallucination Detection in AI-Generated Radiology Reports. Proceedings of Machine Learning Research.
  44. Hasnain, M., Aurangzeb, K., Alhussein, M., Ghani, I., & Mahmood, M. H. (2025). AI in conjunctivitis research: assessing ChatGPT and DeepSeek for etiology, intervention, and citation integrity via hallucination rate analysis [Article]. Frontiers in Artificial Intelligence, 8, Article 1579375. https://doi.org/10.3389/frai.2025.1579375
  45. Iyidogan, E., & Ozkes, A. I. (2025). Agentic AI and hallucinations [Article]. Economics Letters, 255, Article 112520. https://doi.org/10.1016/j.econlet.2025.112520
  46. Jacob, C., Kerrigan, P., & Bastos, M. (2025). The chat-chamber effect: Trusting the AI hallucination [Article]. Big Data and Society, 12(1). https://doi.org/10.1177/20539517241306345
  47. Jain, A., Nimonkar, P., & Jadhav, P. (2025). Citation integrity in the age of AI: evaluating the risks of reference hallucination in maxillofacial literature [Article]. Journal of Cranio-Maxillofacial Surgery, 53(10), 1871–1872. https://doi.org/10.1016/j.jcms.2025.08.004
  48. Jančařík, A., & Dušek, O. (2024). The Problem of AI Hallucination and How to Solve It. Proceedings of the European Conference on e-Learning, ECEL.
  49. Jesson, A., Beltran-Velez, N., Chu, Q., Karlekar, S., Kossen, J., Gal, Y., Cunningham, J. P., & Blei, D. (2024). Estimating the Hallucination Rate of Generative AI. Advances in Neural Information Processing Systems.
  50. Jho, H. (2024). Leveraging Generative AI in Physics Education: Addressing Hallucination Issues in Large Language Models [Review]. New Physics: Sae Mulli, 74(8), 812–823. https://doi.org/10.3938/NPSM.74.812
  51. Jin, L., Shen, Z., Alhur, A. A., & Naeem, S. B. (2025). Exploring the determinants and effects of artificial intelligence (AI) hallucination exposure on generative AI adoption in healthcare [Article]. Information Development, Article 02666669251340954. https://doi.org/10.1177/02666669251340954
  52. Jones, N. (2025). AI hallucinations can't be stopped - but these techniques can limit their damage [Note]. Nature, 637(8047), 778–780. https://doi.org/10.1038/d41586-025-00068-5
  53. Kaate, I., Salminen, J., Jung, S. G., Xuan, T. T. T., Häyhänen, E., Azem, J. Y., & Jansen, B. J. (2025). "You Always Get an Answer": Analyzing Users' Interaction with AI-Generated Personas Given Unanswerable Questions and Risk of Hallucination. International Conference on Intelligent User Interfaces, Proceedings IUI.
  54. Kadian, A., Merhi, M. I., Gupta, M., Ghafoori, A., & Dennehy, D. (2025). I Can't Take This Anymore! Understanding the Relationship Between Personality Traits and Tolerance of Generative AI Hallucinations [Article]. IEEE Transactions on Engineering Management, 72, 3864–3876. https://doi.org/10.1109/TEM.2025.3606352
  55. Katrak, M., & Effoduh, J. O. (2025). A Few Critical People to Handle the Truth: AI, Hallucinations, and the Labour of Law Clerks. In Artificial Intelligence for Legal System: Jurisprudence in the Digital Age (pp. 121–133). CRC Press. https://doi.org/10.1201/9781003491903-10
  56. Khawaldeh, A. M. (2025). Generative AI Hallucinations and Legal Liability in Jordanian Civil Courts: Promoting the Responsible Use of Conversational Chat Bots [Article]. International Journal for the Semiotics of Law, 38(2), 381–401. https://doi.org/10.1007/s11196-024-10199-z
  57. Kim, H., & Lee, S. W. (2026). Sorry, it's my fault: Politeness, attribution, and anthropomorphism in managing generative AI hallucinations [Article]. International Journal of Information Management, 86, Article 102996. https://doi.org/10.1016/j.ijinfomgt.2025.102996
  58. Kim, H., Seo, S., & Lee, S. W. (2025). When generative AI messes up: How politeness and attribution shape user reactions to hallucinations [Article]. International Journal of Information Management, 85, Article 102958. https://doi.org/10.1016/j.ijinfomgt.2025.102958
  59. Kim, S., Park, C., Jeon, G., Kim, S., & Kim, J. H. (2025). Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI [Article]. Bioengineering, 12(1), Article 81. https://doi.org/10.3390/bioengineering12010081
  60. King, M. R. (2025). An Update on AI Hallucinations: Not as Bad as You Remember or as You've Been Told [Note]. Cellular and Molecular Bioengineering, 18(6), 543–548. https://doi.org/10.1007/s12195-025-00874-x
  61. Kokkodis, M., Mahendran, P., & Boatwright, G. (2025). Taming the Unicorn: Turning Generative AI Into a Workhorse How to Draw Boundaries, Handle Hallucinations, and Make AI Behave in Product. CIKM 2025 - Proceedings of the 34th ACM International Conference on Information and Knowledge Management.
  62. Król, K. (2025). Between Truth and Hallucinations: Evaluation of the Performance of Large Language Model-Based AI Plugins in Website Quality Analysis [Article]. Applied Sciences (Switzerland), 15(5), Article 2292. https://doi.org/10.3390/app15052292
  63. Kundu, A., Malhotra, A., & Malhotra, V. (2026). AI Hallucination Prediction: A Novel Approach for Preventing False AI Outputs. Lecture Notes in Networks and Systems.
  64. Kushwah, S., & Dave, N. (2025). AI Hallucination and Strategies to Overcome: Enhancing Human-AI Interaction. 2025 International Conference on Artificial Intelligence and Machine Vision, AIMV 2025.
  65. Lane, R. (2025). Mitigating generative AI hallucinations in geographical education [Article]. International Research in Geographical and Environmental Education. https://doi.org/10.1080/10382046.2025.2555185
  66. Larsen, A. G., Skjuve, M. B., Følstad, A., & van As, N. (2025). LLM Hallucinations in Conversational AI for Customer Service: Framework and End-User Perceptions [Article]. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2025.2580540
  67. Leach, N. (2022). Architectural Hallucinations: What Can AI Tell Us About the Mind of an Architect? [Article]. Architectural Design, 92(3), 66–71. https://doi.org/10.1002/ad.2815
  68. Lecu, A., Groza, A., & Hawizy, L. (2025). Reducing Hallucinations in Medical AI: A Knowledge Graph-Augmented Retrieval System for Evidence-Based Age-Related Macular Degeneration Information [Article]. IEEE Access, 13, 210624–210639. https://doi.org/10.1109/ACCESS.2025.3643370
  69. Lee, C., Kim, J., Lim, J. S., & Shin, D. (2025). Generative AI risks and resilience: How users adapt to hallucination and privacy challenges [Article]. Telematics and Informatics Reports, 19, Article 100221. https://doi.org/10.1016/j.teler.2025.100221
  70. Leventi-Peetz, A. M., & Zacharis, N. (2025). Knowledge Evolution and Scientific Breakthroughs Triggered by AI Hallucinations. A Paradigm Shift? In Applied Human Factors and Ergonomics International (Vol. 196, pp. 76–87). AHFE International. https://doi.org/10.54941/ahfe1005956
  71. Li, Z., Yi, W., & Chen, J. (2026). Accuracy paradox: Addressing epistemic, manipulative, and societal risks of hallucination in AI governance [Article]. Computer Law and Security Review, 61, Article 106311. https://doi.org/10.1016/j.clsr.2026.106311
  72. Lim, J. S., Shin, D., Lee, C., Kim, J., & Zhang, J. (2025). The Role of User Empowerment, AI Hallucination, and Privacy Concerns in Continued Use and Premium Subscription Intentions: An Extended Technology Acceptance Model for Generative AI [Article]. Journal of Broadcasting and Electronic Media, 69(3), 183–199. https://doi.org/10.1080/08838151.2025.2487679
  73. Liu, C., & Lin, C. (2026). Are AI hallucinations the same as error information? A qualitative exploration of hallucination tolerance and resistance strategies [Article]. Online Information Review, 50(2), 390–408. https://doi.org/10.1108/OIR-04-2025-0233
  74. Liu, Z., Kou, J., Zhang, W., Gu, C., Fang, X., Huang, Z., Yuan, H., Li, H., Lu, X., Yin, A., Liu, C., Zhao, C., Lin, W., Zhang, L., Wang, Z., Huang, H., Duan, X., & Fang, Y. (2024). Comprehensive Evaluation of AI Hallucination and Novel UV-Oriented Framework toward Safe and Trustworthy AI. 7th International Conference on Universal Village, UV 2024.
  75. Magalhães, B., & Matos, F. (2025). FALSE TRUTHS: THE IMPACT OF AI HALLUCINATIONS ON ADMINISTRATIVE JUDICIAL PROCESSES [Article]. Revista Eletronica de Direito Processual, 26(2), 73–95. https://doi.org/10.12957/redp.2025.90706
  76. Magesh, V., Surani, F., Dahl, M., Suzgun, M., Manning, C. D., & Ho, D. E. (2025). Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools [Article]. Journal of Empirical Legal Studies, 22(2), 216–242. https://doi.org/10.1111/jels.12413
  77. Maleki, N., Padmanabhan, B., & Dutta, K. (2024). AI Hallucinations: A Misnomer Worth Clarifying. Proceedings - 2024 IEEE Conference on Artificial Intelligence, CAI 2024.
  78. Man, X. (2025). A Study of Suppression Strategies for AI Hallucinations in Personal Learning Scenarios. Proceedings of 2025 2nd International Conference on Image Processing, Intelligent Control and Computer Engineering, IPICE 2025.
  79. Massenon, R., Gambo, I., Khan, J. A., Agbonkhese, C., & Alwadain, A. (2025). "My AI is Lying to Me": User-reported LLM hallucinations in AI mobile apps reviews [Article]. Scientific Reports, 15(1), Article 30397. https://doi.org/10.1038/s41598-025-15416-8
  80. Mastropaolo, A. (2026). The Virtue of Hallucination: When AI Mistakes Make Software Safer [Article]. Computer, 59(4), 9–11. https://doi.org/10.1109/MC.2026.3659286
  81. Matharaarachchi, A., Moraliyage, H., Mills, N., Gamage, G., De Silva, D., & Manic, M. (2026). Addressing hallucinations in generative AI agents using observability and dual memory knowledge graphs [Article]. Knowledge-Based Systems, 338, Article 115469. https://doi.org/10.1016/j.knosys.2026.115469
  82. Matsui, M. (2026). Large Language Models as Inadvertent Models of Dementia with Lewy Bodies: How a Disorder of Reality Construction Illuminates AI Hallucination [Article]. Integrative Psychological and Behavioral Science, 60(2), Article 34. https://doi.org/10.1007/s12124-026-09997-w
  83. Mohamed, Y., & Foo, Y. L. (2025). Countering AI Hallucination by Utilizing a Concept-Aware Model. 2025 Multimedia University Engineering Conference, MECON 2025.
  84. Mohammed, M. N., Al Dallal, A., Emad, M., Emran, A. Q., & Al Qaidoom, M. (2025). A Comparative Analysis of Artificial Hallucinations in GPT-3.5 and GPT-4: Insights into AI Progress and Challenges. In Studies in Systems, Decision and Control (Vol. 566, pp. 197–203). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-71318-7_18
  85. Mohammed, M. Y., Ali, S. A., Ali, S. K., Majeed, A. A., & Mohamed, E. H. (2025). Aftina: enhancing stability and preventing hallucination in AI-based Islamic fatwa generation using LLMs and RAG [Article]. Neural Computing and Applications, 37(25), 20957–20982. https://doi.org/10.1007/s00521-025-11229-y
  86. Monti, M., & Magaña Vsevolodovna, R. I. (2025). Transforming Hallucinations into Discovery: A Neurosymbolic Framework for Human-AI Collaborative Knowledge Evolution. Frontiers in Artificial Intelligence and Applications.
  87. Monti, M., & Vsevolodovna, R. I. M. (2025). The Second Life of Hallucinations: A Neurosymbolic Framework for Transforming Errors into Insights Through Human–AI Collaboration. Frontiers in Artificial Intelligence and Applications.
  88. Moussaoui, M. E. (2026). Architecture and AI: Controlling the Hallucination [Book]. CRC Press. https://doi.org/10.1201/9781003637691
  89. Munir, B., Abbasi, M. Z., Blake Wilson, W., & Colombo, A. (2025). Evaluating AI in Legal Operations: A Comparative Analysis of Accuracy, Completeness, and Hallucinations in ChatGPT-4, Copilot, DeepSeek, Lexis+ AI, and Llama 3 [Article]. International Journal of Legal Information, 53(2), 103–114. https://doi.org/10.1017/jli.2025.10052
  90. Nalawati, R. E., Huzaifa, M., Muharram, A. T., Marcheta, N., & Rasyiidin, M. Y. B. (2025). A Vision-Language Career Chatbot with LLAMA and Explainable AI for Transparent Job Matching under Hallucination Mitigation Approaches. Proceedings - 2025 8th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2025.
  91. Namazi, H., & Radfar, M. M. (2025). Philosophy of medicine meets AI hallucination and AI drift: moving toward a more gentle medicine [Article]. Journal of Medical Ethics and History of Medicine, 18, Article 2. https://doi.org/10.18502/jmehm.v18i2.18812
  92. Nambiar, J. P., & Sreedevi, A. G. (2023). Orchestrating Consensus Strategies to Counter AI Hallucination in Generative Chatbots. Proceedings - 2023 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2023.
  93. Neves, J., Pereira, M. C., & Bacalhau, L. M. (2026). AI Hallucinations on Marketing: A Systematic Review of Risks, Impacts, and Mitigation Strategies and Future Directions. In Studies in Systems, Decision and Control (Vol. 238, pp. 499–509). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-85398-2_44
  94. Nishisako, S., Higashi, T., & Wakao, F. (2025). Reducing Hallucinations and Trade-Offs in Responses in Generative AI Chatbots for Cancer Information: Development and Evaluation Study [Article]. JMIR Cancer, 11, Article e70176. https://doi.org/10.2196/70176
  95. Ojala, M. (2025). Citations Gone Wild: AI Hallucinations Persist [Article]. Computers in Libraries, 45(3), 31.
  96. Olagoke, A., Jacobson, L. T., Babajide, O., & Qi, Z. (2026). Building Safe AI Chatbots for Rural Mothers Seeking Breastfeeding Support: Understanding Hallucinations and How to Mitigate Them [Note]. Social Sciences, 15(2), Article 119. https://doi.org/10.3390/socsci15020119
  97. Olson, M. (2025). Error as insight: AI hallucinations and the pedagogical possibilities of generative visual misrepresentation [Article]. Journal of Visual Literacy, 44(4), 454–470. https://doi.org/10.1080/1051144X.2025.2569015
  98. Onder, I., & McCabe, S. (2025). How AI hallucinations threaten research integrity in tourism [Note]. Annals of Tourism Research, 111, Article 103900. https://doi.org/10.1016/j.annals.2025.103900
  99. Ozturk, D. (2026). Teaching Middle School Learners AI Hallucinations Through Chatbot Design. SIGCSE TS 2026 - Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.2.
  100. Pak, R., Rovira, E., & McLaughlin, A. (2025). Polite AI mitigates user susceptibility to AI hallucinations [Article]. Ergonomics, 68(10), 1735–1745. https://doi.org/10.1080/00140139.2024.2434604
  101. Patil, A. D., & Singh, S. (2026). AI Hallucination, Research Integrity and the Future of Homeopathic Research: Navigating Promise and Peril [Article]. International Journal of High Dilution Research, 26(CF), 01–08. https://doi.org/10.51910/ijhdr.v26icf.1819
  102. Pawlik, L., & Deniziak, S. (2026). Reducing Hallucinations in Medical AI Through Citation Enforced Prompting in RAG Systems [Article]. Applied Sciences (Switzerland), 16(6), Article 3013. https://doi.org/10.3390/app16063013
  103. Perov, V., & Perova, N. (2024). AI Hallucinations: Is 'Artificial Evil' Possible? Proceedings - 2024 IEEE Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2024.
  104. Qian, K., Liu, S., Li, T., Raković, M., Li, X., Guan, R., Molenaar, I., Nawaz, S., Swiecki, Z., Yan, L., & Gašević, D. (2026). Towards reliable generative AI-driven scaffolding: Reducing hallucinations and enhancing quality in self-regulated learning support [Article]. Computers and Education, 240, Article 105448. https://doi.org/10.1016/j.compedu.2025.105448
  105. Qiu, W., & Legrady, G. (2023). Fencing Hallucination: An Interactive Installation for Fencing with AI and Synthesizing Chronophotographs. Conference on Human Factors in Computing Systems - Proceedings.
  106. Rajkumar, K., Devarajan, D., Kavitha, S. J., & Usharani, S. (2025). Towards Reliable AI-Based Assessment: Modular Scoring and Hallucination Control. 2025 4th International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2025.
  107. Rapp, A., Di Lodovico, C., & Di Caro, L. (2025). How do people react to ChatGPT's unpredictable behavior? Anthropomorphism, uncanniness, and fear of AI: A qualitative study on individuals' perceptions and understandings of LLMs' nonsensical hallucinations [Article]. International Journal of Human Computer Studies, 198, Article 103471. https://doi.org/10.1016/j.ijhcs.2025.103471
  108. Rejón-Guardia, F., Molinillo, S., & Anaya-Sánchez, R. (2026). AI Hallucinations in Tourism: How Errors Impact Consumer Trust and Recommendation Acceptance [Article]. Journal of Consumer Behaviour, 25(2), 923–938. https://doi.org/10.1002/cb.70105
  109. Roychowdhury, S. (2024). Journey of Hallucination-minimized Generative AI Solutions for Financial Decision Makers. WSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining.
  110. Sakib, S. M. N. (2024). Bane and boon of hallucinations in the context of generative AI. In Cases on AI Ethics in Business (pp. 276–299). IGI Global. https://doi.org/10.4018/9798369326435.ch016
  111. Salehi, S., Singh, Y., Horst, K. K., Hathaway, Q. A., & Erickson, B. J. (2025). Agentic AI and Large Language Models in Radiology: Opportunities and Hallucination Challenges [Review]. Bioengineering, 12(12), Article 1303. https://doi.org/10.3390/bioengineering12121303
  112. Salem, M. A. (2026). Infusing Gen Z's Pro-Ecological Intentions: From AI Hallucinations to the Ethical Governance of Green Digital Footprints [Article]. Education Sciences, 16(3), Article 431. https://doi.org/10.3390/educsci16030431
  113. Samanta, S. K., & Chakraborty, A. (2026). Trust me, I'm wrong: The perils of AI hallucinations, a silent killer [Article]. Journal of Information Technology Teaching Cases. https://doi.org/10.1177/20438869261423221
  114. Schiller, C. A., & Maier, C. (2025). User Resistance Towards Artificial Intelligence: A Study Focusing on AI-Hallucinations and Ethics. Americas Conference on Information Systems, AMCIS 2025.
  115. Shao, A. (2025). New sources of inaccuracy? A conceptual framework for studying AI hallucinations [Note]. Harvard Kennedy School Misinformation Review, 6(4). https://doi.org/10.37016/mr-2020-182
  116. Slater, J., & Humphries, J. (2025). Another reason to call bullshit on AI "hallucinations" [Note]. AI and Society, 40(7), 5631–5632. https://doi.org/10.1007/s00146-025-02346-2
  117. Somaini, A. (2023). Dreams, Visions, Hallucinations AI-Generated Images and the Long History of Surrealism [Article]. Paradigmi, 2023(3), 541–556. https://doi.org/10.30460/112741
  118. Song, Y., Cui, M., Wan, F., Yu, Z., & Jiang, J. (2025). AI Hallucination in Crisis Self-Rescue Scenarios: The Impact on AI Service Evaluation and the Mitigating Effect of Human Expert Advice [Article]. International Journal of Human-Computer Interaction, 41(22), 14419–14439. https://doi.org/10.1080/10447318.2025.2483858
  119. Sood, A. K., Zeadally, S., & Hong, E. (2025). The Paradigm of Hallucinations in AI-driven cybersecurity systems: Understanding taxonomy, classification outcomes, and mitigations [Article]. Computers and Electrical Engineering, 124, Article 110307. https://doi.org/10.1016/j.compeleceng.2025.110307
  120. Sovrano, F., Ashley, K., & Bacchelli, A. (2023). Toward Eliminating Hallucinations: GPT-based Explanatory AI for Intelligent Textbooks and Documentation. CEUR Workshop Proceedings.
  121. Sun, Y., Sheng, D., Zhou, Z., & Wu, Y. (2024). AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content [Article]. Humanities and Social Sciences Communications, 11(1), Article 1278. https://doi.org/10.1057/s41599-024-03811-x
  122. Tao, Y., Yoo, C., & Animesh, A. (2024). Detection of AI Hallucinations: The Impact of Information Characteristics. 45th International Conference on Information Systems, ICIS 2024.
  123. Tlili, A., & Burgos, D. (2025). AI Hallucinations? What About Human Hallucination?! Addressing Human Imperfection Is Needed for an Ethical AI [Article]. International Journal of Interactive Multimedia and Artificial Intelligence, 9(2), 68–71. https://doi.org/10.9781/ijimai.2025.02.010
  124. Torres, R. C. (2025). AI Hallucination in the Context of Education: Exploring College Students' Use of Generative AI for Academic Tasks. 2025 16th International Conference on E-Education, E-Business, E-Management and E-Learning, IC4e 2025.
  125. Tripathi, Y., Kohli, V., & Misra, S. (2025). Confident but Incorrect: Mitigating Hallucination and Overconfidence in Agentic AI Coders. Proceedings of the IEEE International Conference Image Information Processing.
  126. Virvou, M., Tsihrintzis, G. A., & Tsichrintzi, E. A. (2025). Hallucinations in Generative AI: Epistemic Risks for Learners in Educational Applications. 16th International Conference on Information, Intelligence, Systems and Applications, IISA 2025.
  127. Wang, X., Wang, J., Feng, L., Niyato, D., Zhang, R., Kang, J., Xiong, Z., Du, H., & Mao, S. (2026). Hallucination in Generative AI-Enabled Wireless Communications: Concepts, Issues, and Solutions [Article]. IEEE Communications Magazine. https://doi.org/10.1109/MCOM.001.2500124
  128. Wang, Y., & Guo, R. (2026). The bright side of fictional information: Positive impacts of AI hallucination on tourists' cultural contact [Article]. Journal of Retailing and Consumer Services, 89, Article 104588. https://doi.org/10.1016/j.jretconser.2025.104588
  129. Wang, Z., Li, L., Chen, L., Xu, X., Li, M., & Sue, Y. (2025). A Domain-specific Hallucination Evaluation Framework for AI Agents in Substation Operation and Maintenance Scenarios. 2025 5th International Conference on Wireless Communication, Networking and Internet of Things, WCNIoT 2025.
  130. Wen, Z., Wang, J., Yang, W., Yang, H., Ma, X., & Liu, Y. (2026). Construction and efficacy verification of an intelligent pharmaceutical Q&A platform based on AI hallucination-suppression [Article]. China Pharmacy, 37(2), 226–231. https://doi.org/10.6039/j.issn.1001-0408.2026.02.16
  131. Xiao, W., Huang, Z., Gan, L., He, W., Li, H., Yu, Z., Shu, F., Jiang, H., & Zhu, L. (2025). Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback. Proceedings of the AAAI Conference on Artificial Intelligence.
  132. Yao, X., & Xi, Y. (2026). Where Risk Travels: A Dual-Path Model of Media and Governance in the Perception of AI Hallucination [Article]. Policy and Internet, 18(1), Article e70032. https://doi.org/10.1002/poi3.70032
  133. Zhou, J., Zhang, J., Wan, R., Cui, X., Liu, Q., Guo, H., Shi, X., Fu, B., Meng, J., Yue, B., Zhang, Y., & Zhang, Z. (2025). Integrating AI into clinical education: evaluating general practice trainees' proficiency in distinguishing AI-generated hallucinations and impacting factors [Article]. BMC Medical Education, 25(1), Article 406. https://doi.org/10.1186/s12909-025-06916-2
  134. Zhu, Z., Zhang, Y., Zhuang, X., Zhang, F., Wan, Z., Chen, Y., Long, Q., Zheng, Y., & Wu, X. (2025). Can We Trust AI Doctors? A Survey of Medical Hallucination in Large Language and Large Vision-Language Models. Proceedings of the Annual Meeting of the Association for Computational Linguistics.
  135. Zouaghi, I., Beldjoudi, S., Gunasekaran, A., Fosso Wamba, S., & Sehrane, S. (2025). Balancing performance and innovation in AI-driven supply chains through temperature-scaled hallucination control [Article]. International Journal of Production Research. https://doi.org/10.1080/00207543.2025.2601264

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