From Syllogism to Statistical Inference:Machine Learning as Epistemological Rupture and theFuture of Human Cognitive Evolution
DOI:
https://doi.org/10.65166/w8zvx652Keywords:
epistemological inversion, machine learning and human cognition, extended mind theory, evolutionary epistemology, cognitive offloading, epistemic identity, AI-integrated cognitionAbstract
For nearly three millennia, Western epistemology has framed human knowledge as a rational, intentional, and structurally ordered process — from Aristotle’s syllogistic logic through Kant’s constitutive categories of understanding (Bond, 2021; Russon, 2016). This paper argues that the emergence of machine learning constitutes a fundamental epistemological rupture with that tradition rather than an extension of it. Where classical knowing moves from principles to conclusions through intentional, phenomenologically grounded processes, machine learning moves from data to emergent pattern through procedures that are non-intentional, phenomenologically empty, and in significant measure opaque (Barelli et al., 2024; Lykhatskyi, 2025). This structural inversion is not a deficiency of machine learning — it is a philosophically significant divergence that the classical tradition lacks the conceptual vocabulary to evaluate and that demands new integrative frameworks to understand. Drawing on extended mind theory, cognitive offloading research, and evolutionary epistemology, this paper develops such a framework, arguing that machine learning represents the most consequential instance of cognitive extension in human intellectual history — and the first to introduce systemic opacity into the extended human cognitive system. This opacity extension generates specific behavioral vulnerabilities, including automation bias, skill atrophy, and epistemic dependence that are structural properties of the human-AI cognitive relationship rather than correctable traits of individual users (Natali et al., 2025; Zerilli et al., 2019). The paper further argues, through an evolutionary-epistemology lens, that the current moment of AI integration constitutes a transition point in the adaptive history of human cognitive ecology whose long-term consequences for epistemic identity, collective knowledge-making, and human cognitive development remain critically undertheorized. This paper employs a philosophical-psychological conceptual synthesis methodology, integrating philosophy of mind, cognitive science, behavioral research, and educational literature. The paper concludes with recommendations for research, educational design, and institutional governance, calibrated to the structural properties of AI-integrated cognition.
Downloads
References
Adams, F., & Aizawa, K. (2001). The bounds of cognition. Philosophical Psychology, 14(1), 43. https://doi.org/10.1080/09515080120033571
Agarwal, A. (2017). Knowing "knowledge" and "to know": An overview of concepts. International Journal of Research-GRANTHAALAYAH, 5(11), 86. https://doi.org/10.29121/granthaalayah.v5.i11.2017.2331
Aithal, P. S. (2023). Super-intelligent machines: Analysis of developmental challenges and predicted negative consequences. International Journal of Applied Engineering and Management Letters, 109. https://doi.org/10.47992/ijaeml.2581.7000.0191
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A. Q., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1). https://doi.org/10.1186/s40537-021-00444-8
Andrews, M. (2025). The immortal science of ML: Machine learning and the theory-free ideal. Erkenntnis. https://doi.org/10.1007/s10670-025-01010-x
Aristotle. (1994). Posterior analytics (J. Barnes, Trans.). Clarendon Press. (Original work published c. 350 BCE)
Atã, P., & Queiroz, J. (2016). Habit in semiosis: Two different perspectives based on hierarchical multi-level system modeling and niche construction theory. In Studies in applied philosophy, epistemology and rational ethics (p. 109). Springer Nature. https://doi.org/10.1007/978-3-319-45920-2_7
Atento, R. G. (2025). Exploring e-learning for sustainable development: Integrating SDGs in management education at Philippine higher education institutions. International Journal of Health & Business Analytics, 1(1). https://doi.org/10.65166/2qcjx561
Atento, R. G., Quinto, L., Espelita, C. A. M., & Castaneda, C. (2025). Integrating business and health analytics: A conceptual framework for dual outcomes in healthcare. International Journal of Health & Business Analytics, 1(1). https://doi.org/10.65166/04pdc866
Atento, R. G. O., Quinto, L. F., Espelita, C. A. M., & San Juan, F. M. (2025). Narrative health analytics: Integrating empathy, data, and ethics in patient-centered healthcare. International Journal of Health and Business Analytics, 1(2), 1-33. https://doi.org/10.65166/yxgx8e59
Avello, D., & Zurita, S. (2025). Exploring the nexus of academic integrity and artificial intelligence in higher education: A bibliometric analysis. International Journal for Educational Integrity, 21(1). https://doi.org/10.1007/s40979-025-00199-2
Barelli, E., Lodi, M., Branchetti, L., & Levrini, O. (2024). Epistemic insights as design principles for a teaching-learning module on artificial intelligence. Science & Education. https://doi.org/10.1007/s11191-024-00504-4
Bauer, K., Zahn, M. von, & Hinz, O. (2023). Please take over: XAI, delegation of authority, and domain knowledge. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4512594
Bendal, A., Sabasa, S. A., Espelita, C. A. M. H., & Atento, R. G. O. (2026). Artificial intelligence as disruptive technology in accounting: A qualitative study of practitioner perceptions on automation, judgment, and decision support. Journal of Enterprise Strategy & Management Innovation, 1(1). https://doi.org/10.65166/0sdayg70
Bengio, Y., Lodi, A., & Prouvost, A. (2018). Machine learning for combinatorial optimization: A methodological tour d’horizon. arXiv. https://doi.org/10.48550/arxiv.1811.06128
Berberian, B., Somon, B., Sahaï, A., & Gouraud, J. (2017). The out-of-the-loop brain: A neuroergonomic approach of the human automation interaction. Annual Reviews in Control, 44, 303. https://doi.org/10.1016/j.arcontrol.2017.09.010
Bermido, C. M., Quinto, L. F., & Atento, R. G. O. (2025). A qualitative thematic review of contemporary challenges affecting health professions education: Implications for higher education leadership. International Journal of Health and Business Analytics, 1(2). https://doi.org/10.65166/yfm5w791
Black, R. W., & Tomlinson, B. (2025). University students describe how they adopt AI for writing and research in a general education course. Scientific Reports, 15(1), 8799. https://doi.org/10.1038/s41598-025-92937-2
Block, J., & Kuckertz, A. (2024). What is the future of human-generated systematic literature reviews in an age of artificial intelligence? Management Review Quarterly. https://doi.org/10.1007/s11301-024-00471-8
Bond, E. (2021). Archaeology of human consciousness: An integrated narrative of cognitive evolution. Advances in Anthropology, 11(3), 201. https://doi.org/10.4236/aa.2021.113013
Botha, Griffiths, D., & Prozesky, M. (2021). Epistemological decolonization through a relational knowledge-making model. Africa Today, 67(4), 50. https://doi.org/10.2979/africatoday.67.4.04
Branda, F., & Ciccozzi, M. (2026). The comfort of automation: Why cognitive sovereignty matters in AI-driven life sciences. Artificial Intelligence in the Life Sciences, 9, 100158. https://doi.org/10.1016/j.ailsci.2026.100158
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512
Cassinadri, G., & Fasoli, M. (2023). Rejecting the extended cognition moral narrative: A critique of two normative arguments for extended cognition. Synthese, 202(5). https://doi.org/10.1007/s11229-023-04397-8
Chen, C., & de Beeck, H. O. (2021). Perceptual learning with complex objects: A comparison between full-practice training and memory reactivation. eNeuro, 8(2). https://doi.org/10.1523/eneuro.0008-19.2021
Chirayath, G., Premamalini, K., & Joseph, J. (2025). Cognitive offloading or cognitive overload? How AI alters the mental architecture of coping. Frontiers in Psychology, 16, 1699320. https://doi.org/10.3389/fpsyg.2025.1699320
Cibu, B., Crăciun, L., Molănescu, A. G., & Cotfas, L. (2025). Exploring the educational applications of large language models: A systematic review and topic analysis. Electronics, 14(23), 4683. https://doi.org/10.3390/electronics14234683
Clark, A. (2001). Natural-born cyborgs? In Lecture notes in computer science (p. 17). Springer. https://doi.org/10.1007/3-540-44617-6_2
Clark, A. (2003). Natural-born cyborgs: Minds, technologies, and the future of human intelligence. Oxford University Press.
Clark, A. (2025). Extending minds with generative AI. Nature Communications, 16(1), 4627. https://doi.org/10.1038/s41467-025-59906-9
Clark, A., & Chalmers, D. J. (1998). The extended mind. Analysis, 58(1), 7. https://doi.org/10.1093/analys/58.1.7
Clark, A. J. (1986). Evolutionary epistemology and the scientific method. Philosophica, 37. https://doi.org/10.21825/philosophica.82528
Collins, H. (2024). Why artificial intelligence needs sociology of knowledge: Parts I and II. AI & Society, 40(3), 1249. https://doi.org/10.1007/s00146-024-01954-8
Cottingham, J. (Trans.). (2016). Meditations on first philosophy (R. Descartes). Routledge. https://doi.org/10.4324/9781315508818-8
Cummings, M. L. (2006). Automation and accountability in decision support system interface design. The Journal of Technology Studies, 32(1), 23. https://doi.org/10.21061/jots.v32i1.a.4
Dellsén, F. (2017). Certainty and explanation in Descartes’s philosophy of science. HOPOS: The Journal of the International Society for the History of Philosophy of Science, 7(2), 302. https://doi.org/10.1086/692013
Demerath, L. (2006). Epistemological identity theory: Reconceptualizing commitment as self-knowledge. Sociological Spectrum, 26(5), 491. https://doi.org/10.1080/02732170600786208
Dennett, D. C. (1995). Darwin’s dangerous idea: Evolution and the meanings of life. Simon & Schuster.
Deranty, J., & Corbin, T. (2022). Artificial intelligence and work: A critical review of recent research from the social sciences. AI & Society, 39(2), 675. https://doi.org/10.1007/s00146-022-01496-x
Dergaa, I., Saad, H. B., Glenn, J. M., Amamou, B., Aissa, M. B., Guelmami, N., Fekih-Romdhane, F., & Chamari, K. (2024). From tools to threats: A reflection on the impact of artificial-intelligence chatbots on cognitive health. Frontiers in Psychology, 15, 1259845. https://doi.org/10.3389/fpsyg.2024.1259845
Diržytė, A. (2025). Large language models and the enhancement of human cognition: Some theoretical insights. Filosofija Sociologija, 36(1). https://doi.org/10.6001/fil-soc.2025.36.1.2
Dreyfus, H. L. (1978). What computers can’t do: The limits of artificial intelligence. Harper & Row.
Embracing the ubiquity of machines. (2024). Nature Human Behaviour, 8(10), 1823. https://doi.org/10.1038/s41562-024-02049-6
Erk, C. (2010). Health, rights and dignity: Philosophical reflections on an alleged human right. https://doi.org/10.26530/oapen_626362
Floridi, L. (2023). The ethics of artificial intelligence. Oxford University Press. https://doi.org/10.1093/oso/9780198883098.001.0001
Ganuthula, V. R. R. (2024). The paradox of augmentation: A theoretical model of AI-induced skill atrophy. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4974044
Garzón, J., Patiño, E., & Marulanda, C. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), 84. https://doi.org/10.3390/mti9080084
George, A. S., Baskar, T., & Srikaanth, P. B. (2024). The erosion of cognitive skills in the technological age: How reliance on technology impacts critical thinking, problem-solving, and creativity. Zenodo. https://doi.org/10.5281/zenodo.11671150
Ghosh, A., & Choudhury, S. (2025). Understanding different types of review articles: A primer for early career researchers. Indian Journal of Psychiatry, 67(5), 535. https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_373_25
Giovanni, A. (2025). Cyber humanism in education: Reclaiming agency through AI and learning sciences. arXiv. https://doi.org/10.48550/arxiv.2512.16701
Gözütok, T. T. (2025). Yapay zekânın akademik yayın hakemliğindeki rolü üzerine epistemik ve etik bir sorgulama. DergiPark. https://doi.org/10.20981/kaygi.1704390
Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91. https://doi.org/10.1111/j.1471-1842.2009.00848.x
Grinschgl, S., Meyerhoff, H. S., Schwan, S., & Papenmeier, F. (2020). From metacognitive beliefs to strategy selection: Does fake performance feedback influence cognitive offloading? Psychological Research, 85(7), 2654. https://doi.org/10.1007/s00426-020-01435-9
Grinschgl, S., & Neubauer, A. C. (2022). Supporting cognition with modern technology: Distributed cognition today and in an AI-enhanced future. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.908261
Gurevich, Y., & Blass, A. (2024). On logic and generative AI. arXiv. https://doi.org/10.48550/arxiv.2409.14465
Hannig, L., Bush, A., Aksoy, M., Trappen, T., Becker, S., & Ontrup, G. (2025). Campus AI vs. commercial AI: How customizations shape trust and usage of LLM as-a-service chatbots. arXiv. https://doi.org/10.48550/arxiv.2509.15826
Harman, G. (2020). The only exit from modern philosophy. Open Philosophy, 3(1), 132. https://doi.org/10.1515/opphil-2020-0009
Hatfield, G., & Goldman, A. I. (1989). Epistemology and cognition. The Philosophical Review, 98(3), 386. https://doi.org/10.2307/2185025
Hayes, P., van de Poel, I., & Steen, M. (2022). Moral transparency of and concerning algorithmic tools. AI and Ethics, 3(2), 585. https://doi.org/10.1007/s43681-022-00190-4
Heersmink, R. (2020). Narrative niche construction: Memory ecologies and distributed narrative identities. Biology & Philosophy, 35(5). https://doi.org/10.1007/s10539-020-09770-2
Heersmink, R., & Knight, S. (2018). Distributed learning: Educating and assessing extended cognitive systems. Philosophical Psychology, 31(6), 969. https://doi.org/10.1080/09515089.2018.1469122
Heidegger, M. (1962). Being and time (J. Macquarrie & E. Robinson, Trans.). Harper & Row. (Original work published 1927)
Hosseini, M., & Horbach, S. P. J. M. (2023). Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other large language models in scholarly peer review. Research Square. https://doi.org/10.21203/rs.3.rs-2587766/v1
Husserl, E. (1970). Logical investigations (J. N. Findlay, Trans.). Routledge. (Original work published 1900–1901)
Jan, S. (2022). Music in evolution and evolution in music. Open Book Publishers. https://doi.org/10.11647/obp.0301
Jensen, L. X., Buhl, A., Sharma, A. V. N. L., & Bearman, M. (2024). Generative AI and higher education: A review of claims from the first months of ChatGPT. Higher Education. https://doi.org/10.1007/s10734-024-01265-3
Jussupow, E., Spohrer, K., & Heinzl, A. (2022). Identity threats as a reason for resistance to artificial intelligence: Survey study with medical students and professionals. JMIR Formative Research, 6(3). https://doi.org/10.2196/28750
Kabashkin, I. (2025). Cognitive atrophy paradox of AI–human interaction: From cognitive growth and atrophy to balance. Information, 16(11), 1009. https://doi.org/10.3390/info16111009
Kant, I. (1998). Critique of pure reason (P. Guyer & A. W. Wood, Trans.). Cambridge University Press. (Original work published 1781) https://doi.org/10.1017/cbo9780511804649
Karaca, K. (2021). Values and inductive risk in machine learning modelling: The case of binary classification models. European Journal for Philosophy of Science, 11(4). https://doi.org/10.1007/s13194-021-00405-1
Kheokao, D., Kheokao, J., Nopsuwam, R., & Boonwattanopas, D. (2025). AI, sovereignty, and the reshaping of knowledge production and public opinion. Asian Journal of Political Science. https://doi.org/10.15206/ajpor.2025.13.4.513
Koskinen, I. (2023). We have no satisfactory social epistemology of AI-based science. Social Epistemology, 38(4), 458. https://doi.org/10.1080/02691728.2023.2286253
Krishnan, G., Singh, S., Pathania, M., Gosavi, S., Abhishek, S., Parchani, A., & Dhar, M. (2023). Artificial intelligence in clinical medicine: Catalyzing a sustainable global healthcare paradigm. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1227091
Langlois, R. N. (2003). Cognitive comparative advantage and the organization of work: Lessons from Herbert Simon’s vision of the future. Journal of Economic Psychology, 24(2), 167. https://doi.org/10.1016/s0167-4870(02)00201-5
Larsen, B. C. (2022). Governing artificial intelligence: Lessons from the United States and China. Danish Institute for International Studies.
Laurent, B. (2023). Institutions of expert judgment: The production and use of objectivity in public expertise. In Oxford handbook of expertise (p. 214). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190848927.013.10
LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep learning. Nature, 521(7553), 436. https://doi.org/10.1038/nature14539
Liu, D., Fan, G. F., & Pan, L. (2026). Tool, tutor, or crutch? A grounded theory of cognitive scaffolding and offloading in AI-assisted programming education. International Journal of STEM Education, 13(1). https://doi.org/10.1186/s40594-025-00592-w
Lucio‐Arias, D., & Leydesdorff, L. (2009). The dynamics of exchanges and references among scientific texts, and the autopoiesis of discursive knowledge. Journal of Informetrics, 3(3), 261. https://doi.org/10.1016/j.joi.2009.03.003
Lykhatskyi, A. (2025). Hybrid epistemology: Emergent knowledge forms in the age of human-AI cognitive integration. The Bulletin of Yaroslav Mudryi National Law University, 3(66). https://doi.org/10.21564/2663-5704.66.337968
Maclure, J. (2021). AI, explainability and public reason: The argument from the limitations of the human mind. Minds and Machines, 31(3), 421. https://doi.org/10.1007/s11023-021-09570-x
Macnamara, B. N., Berber, I., Çavuşoğlu, M. C., Krupinski, E. A., Nallapareddy, N., Nelson, N. E., Smith, P. J., Wilson-Delfosse, A. L., & Ray, S. (2024). Does using artificial intelligence assistance accelerate skill decay and hinder skill development without performers’ awareness? Cognitive Research: Principles and Implications, 9(1). https://doi.org/10.1186/s41235-024-00572-8
Mai, X., Zeng, T., Lin, J., Wang, H., Chang, Y., Kang, Y., Wang, Y., & Zhang, W. (2024). From efficient multimodal models to world models: A survey. arXiv. https://doi.org/10.48550/arxiv.2407.00118
Mann, S. P., Aboy, M., Seah, J. J., Lin, Z., Luo, X., Rodger, D., Zohny, H., Minssen, T., Savulescu, J., & Earp, B. D. (2025). AI and the future of academic peer review. arXiv. https://doi.org/10.48550/arxiv.2509.14189
Marcus, G., & Davis, E. (2019). Rebooting AI: Building artificial intelligence we can trust. Pantheon Books.
Margondai, A., Willox, S., & Mouloua, M. (2025). Autonomy in transition: AI, self-identity, and the evolution of human agency. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 69(1), 1403. https://doi.org/10.1177/10711813251364790
Meacham, D. (2017). Introduction: Critiquing technologies of the mind: Enhancement, alteration, and anthropotechnology. Phenomenology and the Cognitive Sciences, 16(1), 1. https://doi.org/10.1007/s11097-017-9505-3
Misfeldt, M., Barbin, É., Jankvist, U. T., & Kjeldsen, T. H. (2015). Panel debate: Technics and technology in mathematics and mathematics education. Research Portal Denmark.
Mollick, E., & Mollick, L. (2023). Assigning AI: Seven approaches for students, with prompts. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4475995
Morais, J., & Kolinsky, R. (2020). Seeing thought: A cultural cognitive tool. Journal of Cultural Cognitive Science, 5(2), 181. https://doi.org/10.1007/s41809-020-00059-0
Naeem, H., & Hauser, J. (2024). Should we discourage AI extension? Epistemic responsibility and AI. Philosophy & Technology, 37(3). https://doi.org/10.1007/s13347-024-00774-4
Natali, C., Marconi, L., Duran, L. D. D., & Cabitza, F. (2025). AI-induced deskilling in medicine: A mixed-method review and research agenda for healthcare and beyond. Artificial Intelligence Review, 58(11). https://doi.org/10.1007/s10462-025-11352-1
Ninos, G. (2024). Hegel’s theory of finite cognition and Marx’s critique of political economy. Hegel Bulletin, 1. https://doi.org/10.1017/hgl.2024.22
Oermann, M. H., Owens, J. K., Carter-Templeton, H., Peterson, G. M., & Bailey, H. (2025). Using artificial intelligence for scholarly writing. AJN American Journal of Nursing, 125(11), 52. https://doi.org/10.1097/ajn.0000000000000179
O’Halloran, K. L., Tan, S., & E, M. K. L. (2015). Multimodal analysis for critical thinking. Learning, Media and Technology, 42(2), 147. https://doi.org/10.1080/17439884.2016.1101003
Orbik, Z. (2024). Husserl’s concept of transcendental consciousness and the problem of AI consciousness. Phenomenology and the Cognitive Sciences, 23(5), 1151. https://doi.org/10.1007/s11097-024-09993-8
Osbeck, L. M., & Nersessian, N. J. (2017). Epistemic identities in interdisciplinary science. Perspectives on Science, 25(2), 226. https://doi.org/10.1162/posc_a_00242
Page, S. E., & Kallapur, A. (2025). Replace, augment, disrupt: AI & organizational decision-making. Journal of Organization Design. https://doi.org/10.1007/s41469-025-00194-4
Paradice, D. (2010). Emerging systems approaches in information technologies. IGI Global. https://doi.org/10.4018/978-1-60566-976-2
Pasquale, F. (2015). The black box society. Harvard University Press. https://doi.org/10.4159/harvard.9780674736061
Pellegrain, V. (2023). Harnessing the power of multimodal and textual data in industry 4.0 [Doctoral dissertation]. HAL. https://theses.hal.science/tel-04280319
Peters, U. (2022). Explainable AI lacks regulative reasons: Why AI and human decision-making are not equally opaque. AI and Ethics, 3(3), 963. https://doi.org/10.1007/s43681-022-00217-w
Pinker, S. (2010). The cognitive niche: Coevolution of intelligence, sociality, and language. Proceedings of the National Academy of Sciences, 107, 8993. https://doi.org/10.1073/pnas.0914630107
Piredda, G., & Francesco, M. D. (2020). Overcoming the past-endorsement criterion: Toward a transparency-based mark of the mental. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.01278
Plotkin, H. (1995). Darwin machines and the nature of knowledge: Concerning adaptations, instinct and the evolution of intelligence. Penguin Books.
Qamar, T., & Bawany, N. Z. (2023). Understanding the black-box: Towards interpretable and reliable deep learning models. PeerJ Computer Science, 9. https://doi.org/10.7717/peerj-cs.1629
Raab, J. (2018). Aristotle, logic, and QUARC. History and Philosophy of Logic, 39(4), 305. https://doi.org/10.1080/01445340.2018.1467198
Ramos, G., Suh, J., Ghorashi, S., Meek, C., Banks, R., Amershi, S., Fiebrink, R., Smith,
A., & Bansal, G. (2019). Emerging perspectives in human-centered machine learning. https://doi.org/10.1145/3290607.3299014
Rao, L., Tian, Y., & Atento, R. G. O. (2025). Adoption and perceived effectiveness of AI in education: Personalization, outcomes, and equity. International Journal of Health & Business Analytics, 1(1). https://doi.org/10.65166/qgq89291
Rao, S. (2025). The impact of artificial intelligence tools on human cognitive abilities: A comprehensive review. INNOVAPATH, 1(10), 7. https://doi.org/10.63501/hsdq5611
Richter, J., & Schaller, R. (2025). AI identity threats and reinforcement in organizations: A theoretical model of professional role identity implications. Proceedings of the Annual Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2025.023
Riley, C., Alrefai, O., Reyes, Y. C., & Hammad, E. (2025). Human-AI interactions: Cognitive, behavioral, and emotional impacts. arXiv. https://doi.org/10.48550/arxiv.2510.17753
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676. https://doi.org/10.1016/j.tics.2016.07.002
Robinson, T. R., & Burns, C. M. (2009). Computer algebra systems and their effect on cognitive load. Electronic Workshops in Computing. https://doi.org/10.14236/ewic/ndm2009.62
Rolin, K. (2017). Scientific community: A moral dimension. Social Epistemology, 31(5), 468. https://doi.org/10.1080/02691728.2017.1346722
Roth, C., & Cointet, J. (2009). Social and semantic coevolution in knowledge networks. Social Networks, 32(1), 16. https://doi.org/10.1016/j.socnet.2009.04.005
Ruja, H., & Popper, K. R. (1973). Objective knowledge: An evolutionary approach. Philosophy and Phenomenological Research, 34(2), 278. https://doi.org/10.2307/2106696
Russell, S., & Norvig, P. (1995). Artificial intelligence: A modern approach. Prentice Hall.
Russon, J. (2016). Aristotle’s epistemology. Academia. https://www.academia.edu/38260454/Aristotles_Epistemology
Sargeant, H., Jorgensen, M., Shah, A. H., Weller, A., & Bhatt, U. (2025). Unequal uncertainty: Rethinking algorithmic interventions for mitigating discrimination from AI. arXiv. https://doi.org/10.48550/arxiv.2508.07872
Sarkar, A., Xu, X., Toronto, N., Drosos, I., & Poelitz, C. (2024). When copilot becomes autopilot: Generative AI’s critical risk to knowledge work and a critical solution. arXiv. https://doi.org/10.48550/arxiv.2412.15030
Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417. https://doi.org/10.1017/s0140525x00005756
Sims, M. (2021). A continuum of intentionality: Linking the biogenic and anthropogenic approaches to cognition. Biology & Philosophy, 36(6). https://doi.org/10.1007/s10539-021-09827-w
Skitka, L. J., Mosier, K. L., & Burdick, M. D. (1999). Does automation bias decision-making? International Journal of Human-Computer Studies, 51(5), 991. https://doi.org/10.1006/ijhc.1999.0252
Sperber, D., Clément, F., Heintz, C., Mascaro, O., Mercier, H., Origgi, G., & Wilson, D. (2010). Epistemic vigilance. Mind & Language, 25(4), 359. https://doi.org/10.1111/j.1468-0017.2010.01394.x
Stanovich, K. E. (2017). What intelligence tests miss. Yale University Press. https://doi.org/10.12987/9780300142532
Stiegler, B. (1998). Technics and time, 1: The fault of Epimetheus. Stanford University Press. https://doi.org/10.1515/9780804799362
Stroud, B. (2019). Understanding human knowledge in general. In Routledge eBooks (p. 31). Informa. https://doi.org/10.4324/9780429033261-2
Taylor, H., Fernandes, B., & Wraight, S. (2021). The evolution of complementary cognition: Humans cooperatively adapt and evolve through a system of collective cognitive search. Cambridge Archaeological Journal, 32(1), 61. https://doi.org/10.1017/s0959774321000329
Teng, Q., Liu, Z., Song, Y., Han, K., & Lu, Y. (2022). A survey on the interpretability of deep learning in medical diagnosis. Multimedia Systems, 28(6), 2335. https://doi.org/10.1007/s00530-022-00960-4
Toffoli, S. D. (2024). Proofs for a price: Tomorrow’s ultra-rigorous mathematical culture. Bulletin of the American Mathematical Society, 61(3), 395. https://doi.org/10.1090/bull/1823
Torraco, R. J. (2016). Writing integrative literature reviews. Human Resource Development Review, 15(4), 404. https://doi.org/10.1177/1534484316671606
Veldman, W., & Swagerman, D. M. (2018). Correcting the incorrect: An exploratory study into the role of the controller in counteracting financial fake news. Archives of Business Research, 6(10). https://doi.org/10.14738/abr.610.5326
Waefler, T., & Schmid, U. (2021). Explainability is not enough: Requirements for human-AI-partnership in complex socio-technical systems. https://doi.org/10.20378/irb-49775
Webb, M., Fluck, A., Magenheim, J., Malyn-Smith, J., Waters, J., Deschênes, M., & Zagami, J. (2020). Machine learning for human learners: Opportunities, issues, tensions and threats. Educational Technology Research and Development, 69(4), 2109. https://doi.org/10.1007/s11423-020-09858-2
Wheeler, M. (2018). The reappearing tool: Transparency, smart technology, and the extended mind. AI & Society, 34(4), 857. https://doi.org/10.1007/s00146-018-0824-x
Wilburn, H. (2020). An introduction to Western epistemology. Oklahoma State University. https://open.library.okstate.edu/introphilosophy/chapter/an-introduction-to-western-epistemology/
Williams, G. Y., & Lim, S. (2024). Psychology of AI: How AI impacts the way people feel, think, and behave. Current Opinion in Psychology, 58, 101835. https://doi.org/10.1016/j.copsyc.2024.101835
Wilson, P. J., Lorenz, K., & Taylor, R. (1977). Behind the mirror: A search for a natural history of human knowledge. Man, 12, 535. https://doi.org/10.2307/2800563
Yamamoto, K. (2017). The transcendental aesthetic and absolute totality of conditions: The problem of metaphysics in the Critique of Pure Reason and a solution. International Journal of Humanities, Social Sciences and Education, 4(2). https://doi.org/10.20431/2349-0381.0402003
Yazdani, S., Shirvani, A., & Heidarpoor, P. (2021). A model for the taxonomy of research studies: A practical guide to knowledge production and knowledge management. Archives of Pediatric Infectious Diseases, 9(4). https://doi.org/10.5812/pedinfect.112456
Younas, A., & Zeng, Y. (2024). A philosophical inquiry into AI-inclusive epistemology. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4822881
Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2019). Algorithmic decision-making and the control problem. Minds and Machines, 29(4), 555. https://doi.org/10.1007/s11023-019-09513-7
Zhu, M., Hussin, S., & Hashim, H. (2025). EFL pre-service teachers’ professional identity in the age of AI: An integrative review (2015–2025). Environment and Social Psychology, 10(12). https://doi.org/10.59429/esp.v10i12.4361
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Dr. Ramon George Atento, Dr. Leah F. Quinto (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors retain copyright of their articles but grant the International Journal of Health & Business Analytics (IJHBA) the right of first publication.
