Abstract
This study proposes and validates a pioneering psychometric instrument to measure the “Mandel-AI Effect,” a contemporary manifestation of the Mandela Effect enhanced by artificial intelligence (AI) in digital social networks. Following a rigorous theoretical design and expert judgment validation, the instrument was developed and tested through exploratory factor analysis using principal component extraction with oblique rotation (oblimin), complemented by confirmatory factor analysis. The final scale comprises three dimensions: AI Presence, Reality Distortion, and the Mandel-AI Effect itself. The study was conducted with a sample of 243 centennial-generation university students from southern Tamaulipas, Mexico, aged between 18 and 28 years, all active users of digital social networks. Statistical analyses confirmed the factorial structure of the model, with high levels of internal consistency, convergent validity, and discriminant validity. Cronbach’s alpha coefficients for each dimension were excellent, indicating strong reliability. The findings show that AI not only mediates content selection and viralisation processes but also structurally influences the configuration of cognitive and cultural frameworks underpinning the social construction of collective memory. This influence can facilitate the implantation of false memories, induce epistemic confusion, and blur the boundaries between authentic and artificial content. The validated instrument offers a robust tool for future research on the cognitive and social effects of AI, contributing to the emerging field of digital psychology and providing a methodological basis for the systematic study of one of the most pressing epistemic challenges of the digital age.
Adriaansen, R. -J., & Smit, R. (2025). Collective memory and social media. Current Opinion in Psychology, 65, 102077. https://doi.org/10.1016/j.copsyc.2025.102077
Agha, A. M. (2025). Artificial intelligence in social media: Opportunities and perspectives. Cihan University-Erbil Journal of Humanities and Social Sciences, 9(1), 125–132. https://doi.org/10.24086/cuejhss.v9n1y2025.pp125-132
Allal-Chérif, O., Aránega, A. Y., & Sánchez, R. C. (2021). Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technological Forecasting and Social Change, 169, 120822. https://doi.org/10.1016/j.techfore.2021.120822
Anantrasirichai, N., & Bull, D. (2021). Artificial intelligence in the creative industries: A review. Artificial Intelligence Review, 55(1), 589–656. https://doi.org/10.1007/s10462-021-10039-7
Bartlett, M. S. (1950). Tests of significance in factor analysis. British Journal of Statistical Psychology, 3(2), 77–85. https://doi.org/10.1111/j.2044-8317.1950.tb00285.x
Braidotti, R. (2019). Posthuman knowledge. Polity Press.
Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). The Guilford Press.
Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge.
Carmines, E. G., & Zeller, R. A. (1979). Reliability and validity assessment. SAGE Publications. https://doi.org/10.4135/9781412985642
Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. https://doi.org/10.1207/s15327906mbr0102_10
Chalmers, D. J. (2022). Reality+: Virtual worlds and the problems of philosophy. W. W. Norton & Company.
Chan, K. W., Septianto, F., Kwon, J., & Kamal, R. S. (2023). Color effects on AI influencers’ product recommendations. European Journal of Marketing, 57(9), 2290–2315. https://doi.org/10.1108/ejm-03-2022-0185
Clark, A. (2003). Natural-born cyborgs: Minds, technologies, and the future of human intelligence. Oxford University Press.
Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). Psychology Press. https://doi.org/10.4324/9781315827506
Cooke, D., Edwards, A., Barkoff, S., & Kelly, K. (2024). As good as a coin toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli. arXiv preprint arXiv:2403.16760. https://doi.org/10.48550/arxiv.2403.16760
Costello, A. B., & Osborne, J. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(1), 1–9. https://doi.org/10.7275/jyj1-4868
Couldry, N., & Mejias, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.
DeVellis, R. F. (20167). Scale development: Theory and applications (4th ed.). SAGE Publications.
Duan, J., Yu, S., Tan, H. L., Zhu, H., & Tan, C. (2022). A survey of embodied AI: From simulators to research tasks. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(2), 230–244. https://doi.org/10.1109/tetci.2022.3141105
Dunn, T. J., Baguley, T., & Brunsden, V. (2013). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412. https://doi.org/10.1111/bjop.12046
Erafy, A. N. E. (2023). Applications of Artificial Intelligence in the field of media. International Journal of Artificial Intelligence and Emerging Technology, 6(2), 19–41. https://doi.org/10.21608/ijaiet.2024.275179.1006
Essien, E. O. (2025). Climate change disinformation on social media: A meta-synthesis on epistemic welfare in the post-truth era. Social Sciences, 14(5), 304. https://doi.org/10.3390/socsci14050304
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989x.4.3.272
Farinella, F. (2023). Artificial intelligence and the right to memory. Revista quaestio iuris, 16(2), 976-996. https://doi.org/10.12957/rqi.2023.72636
Floridi, L. (2019). The logic of information: A theory of philosophy as conceptual design. Oxford University Press.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198237907.001.0001
Gerlich, M. (2023). Perceptions and acceptance of artificial intelligence: A multi-dimensional study. Social Sciences, 12(9), 502. https://doi.org/10.3390/socsci12090502
Ghiurău, D., & Popescu, D. E. (20254). Distinguishing reality from AI: Approaches for detecting synthetic content. Computers, 14(1), 1. https://doi.org/10.3390/computers14010001
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson Education.
Hassan, A., & Barber, S. J. (2021). The effects of repetition frequency on the illusory truth effect. Cognitive Research Principles and Implications, 6(1), 1-12. https://doi.org/10.1186/s41235-021-00301-5
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction, 7(2), 174–196. https://doi.org/10.1145/353485.353487
Horkheimer, M., & Adorno, T. W. (2002). Dialectic of enlightenment: Philosophical fragments (E. Jephcott, Trans.). Stanford University Press.
Hu, L. -T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Hu, T. (2024). Analysis of the Mandela effect phenomenon and its propagation mechanism in the we-media era. Creativity and Innovation, 8(6), 165–170. https://doi.org/10.47297/wspciwsp2516-252726.20240806
Hussain, K., Khan, M. L., & Malik, A. (2023). Exploring audience engagement with ChatGPT-related content on YouTube: Implications for content creators and AI tool developers. Digital Business, 4(1), 100071. https://doi.org/10.1016/j.digbus.2023.100071
Hussein, N. EI. S. (2025). The spread of misinformation via digital platforms and its role in falsifying collective memories (Mandela Effect). The Egyptian Journal of Media Research, 2025(90), 405-475. https://doi.org/doi: 10.21608/ejsc.2025.405911
IBM Corp. (2013). IBM SPSS Statistics for Windows (Version 22). IBM Corp.
IBM Corp. (2016). IBM SPSS AMOS for Windows (Version 24). IBM Corp.
Ienca, M. (2023). On artificial intelligence and manipulation. Topoi, 42(3), 833–842. https://doi.org/10.1007/s11245-023-09940-3
Jang, E., Lee, H. M., Lee, S., Jung, Y., & Sundar, S. S. (2025). Too good to be false: How photorealism promotes susceptibility to misinformation. CHI EA '25: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, Japan, 531, 1-8. https://doi.org/10.1145/3706599.3719796
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/bf02291575
Knell, M. (2021). The digital revolution and digitalized network society. Review of Evolutionary Political Economy, 2(1), 9–25. https://doi.org/10.1007/s43253-021-00037-4
Lewandowsky, S., Ecker, U. K. H., & Cook, J. (2017). Beyond misinformation: Understanding and coping with the “post-truth” era. Journal of Applied Research in Memory and Cognition, 6(4), 353–369. https://doi.org/10.1016/j.jarmac.2017.07.008
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22 140, 55.
Lu, W. (2024). Inevitable challenges of autonomy: ethical concerns in personalized algorithmic decision-making. Humanities and Social Sciences Communications, 11(1), 1-9. https://doi.org/10.1057/s41599-024-03864-y
MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84–99. https://doi.org/10.1037/1082-989X.4.1.84
MacLin, M. K. (2023). Mandela Effect. In M. K. MacLin (Eds.), Experimental design in psychology: A case approach (pp. 267–288). Routledge. https://doi.org/10.4324/9781003378044-20
Makhortykh, M., Zucker, E. M., Simon, D. J., Bultmann, D., & Ulloa, R. (2023). Shall androids dream of genocides? How generative AI can change the future of memorialization of mass atrocities. Discover Artificial Intelligence, 3(1), 1-17. https://doi.org/10.1007/s44163-023-00072-6
Matei, S. (2024). Generative artificial intelligence and collective remembering. The technological mediation of mnemotechnic values. Journal of Human-Technology Relations, 2(1), 1-22. https://doi.org/10.59490/jhtr.2024.2.7405
McAvoy, E. N., & Kidd, J. (2024). Synthetic hHeritage: Online platforms, deceptive genealogy and the ethics of algorithmically generated memory. Memory Mind & Media, 3, e12. https://doi.org/10.1017/mem.2024.10
McDonald, R. P. (1999). Test theory: A unified treatment. Lawrence Erlbaum Associates Publishers.
Milfont, T. L., & Fischer, R. (2010). Testing measurement invariance across groups: Applications in cross-cultural research. International Journal of Psychological Research, 3(1), 111–130. https://doi.org/10.21500/20112084.857
Momeni, M. (2024). Artificial intelligence and political deepfakes: Shaping citizen perceptions through misinformation. Journal of Creative Communications, 20(1), 41-56. https://doi.org/10.1177/09732586241277335
Muralidhar, A., & Lakkanna, Y. (2024). From clicks to conversions: Analysis of traffic sources in E-Commerce. arXiv preprint arXiv:2403.16115. https://doi.org/10.48550/arxiv.2403.16115
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Origgi, G. (2018). Reputation: What it is and why it matters. Princeton University Press.
Pataranutaporn, P., Archiwaranguprok, C., Chan, S. W. T., Loftus, E., & Maes, P. (2025). Synthetic human memories: AI-edited images and videos can implant false memories and distort recollection. CHI '25: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Japan, 538, 1-20. https://doi.org/10.1145/3706598.3713697
Pataranutaporn, P., Danry, V., Leong, J., Punpongsanon, P., Novy, D., Maes, P., & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Nature Machine Intelligence, 3(12), 1013–1022. https://doi.org/10.1038/s42256-021-00417-9
Penfield, R. D., & Giacobbi, P. R., Jr. (2004). Applying a score confidence interval to Aiken’s item Content-Relevance Index. Measurement in Physical Education and Exercise Science, 8(4), 213–225. https://doi.org/10.1207/s15327841mpee0804_3
Prasad, D., & Bainbridge, W. A. (2022). The visual Mandela effect as evidence for shared and specific false memories across people. Psychological Science, 33(12), 1971–1988. https://doi.org/10.1177/09567976221108944
Purnama, Y., & Asdlori, A. (2023). The role of social media in students’ social perception and interaction: Implications for learning and education. Technology and Society Perspectives (TACIT), 1(2), 45–55. https://doi.org/10.61100/tacit.v1i2.50
Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004
Rigdon, E. E. (1996). CFI versus RMSEA: A comparison of two fit indexes for structural equation modeling. Structural Equation Modeling a Multidisciplinary Journal, 3(4), 369–379. https://doi.org/10.1080/10705519609540052
Rodilosso, E. (2024). Filter bubbles and the unfeeling: How AI for social media can foster extremism and polarization. Philosophy & Technology, 37(2), 1-21. https://doi.org/10.1007/s13347-024-00758-4
Rüther, M. (2024). Why care about sustainable AI? Some thoughts from the debate on meaning in life. Philosophy & Technology, 37(1), 1-19. https://doi.org/10.1007/s13347-024-00717-z
Salazar-Altamirano, M. A., Martínez-Arvizu, O. J., Galván-Vela, E., Ravina-Ripoll, R., Hernández-Arteaga, L. G., & Sánchez, D. G. (2025). AI as a facilitator of creativity and wellbeing in business students: A multigroup approach between public and private universities. Encontros Bibli Revista Eletrônica De Biblioteconomia E Ciência Da Informação, 30, 1–30. https://doi.org/10.5007/1518-2924.2025.e103485
Shanmugasundaram, M., & Tamilarasu, A. (2023). The impact of digital technology, social media, and artificial intelligence on cognitive functions: A review. Frontiers in Cognition, 2, 1203077. https://doi.org/10.3389/fcogn.2023.1203077
Sireli, O., Dayi, A., & Colak, M. (2023). The mediating role of cognitive distortions in the relationship between problematic social media use and self-esteem in youth. Cognitive Processing, 24(4), 575–584. https://doi.org/10.1007/s10339-023-01155-z
Spring, M., Faulconbridge, J., & Sarwar, A. (2022). How information technology automates and augments processes: Insights from Artificial‐Intelligence‐based systems in professional service operations. Journal of Operations Management, 68(6–7), 592–618. https://doi.org/10.1002/joom.1215
Sun, Y., Sheng, D., Zhou, Z., & Wu, Y. (2024). AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content. Humanities and Social Sciences Communications, 11(1), 1-13. https://doi.org/10.1057/s41599-024-03811-x
Swart, J. (2021). Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media. Social Media + Society, 7(2). https://doi.org/10.1177/20563051211008828
Tabachnick, B. G., & Fidell, L. S. (20189). Using multivariate statistics (7th ed.). Pearson.
Theodorakopoulos, L., Theodoropoulou, A., & Klavdianos, C. (2025). Interactive viral marketing through big data analytics, influencer networks, AI integration, and ethical dimensions. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 115. https://doi.org/10.3390/jtaer20020115
Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt‐Jepsen, M., Whelan, P., Carvalho, A. F., Keshavan, M., Linardon, J., & Firth, J. (2021). The growing field of digital psychiatry: Current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry, 20(3), 318–335. https://doi.org/10.1002/wps.20883
Trigka, M., & Dritsas, E. (2025). The evolution of Generative AI: Trends and applications. IEEE Access, 13, 98504-98529. https://doi.org/10.1109/access.2025.3574660
Trizano-Hermosilla, I., & Alvarado, J. M. (2016). Best alternatives to Cronbach’s alpha reliability in realistic conditions: Congeneric and asymmetrical measurements. Frontiers in Psychology, 7, 769. https://doi.org/10.3389/fpsyg.2016.00769
Wilcox, R. R. (1980). Some results and comments pn using latent structure models to measure achievement. Educational and Psychological Measurement, 40(3), 645–658. https://doi.org/10.1177/001316448004000308
Williamson, S. M., & Prybutok, V. (2024). The era of Artificial Intelligence deception: unraveling the complexities of false realities and emerging threats of misinformation. Information, 15(6), 299. https://doi.org/10.3390/info15060299
Wu, X., Zhou, Z., & Chen, S. (2024). A mixed-methods investigation of the factors affecting the use of facial recognition as a threatening AI application. Internet Research, 34(5), 1872–1897. https://doi.org/10.1108/intr-11-2022-0894
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Profile Books.

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