Finally, although this study emphasizes multi-stakeholder governance and policy intervention, it does not yet provide a granular implementation roadmap at the institutional level. Pilot programs testing GCAL-CDI-aligned curricula, workplace protocols, and AI design principles in real-world settings could produce critical feedback for refining both measurement tools and intervention strategies.

Addressing these limitations will require interdisciplinary collaboration among cognitive scientists, technologists, educators, and policymakers. By iterating on both the measurement and application of GCAL-CDI, future research can advance from predictive diagnosis toward experimentally validated methods of reversing cognitive decline—transforming the framework from a conceptual blueprint into a robust global standard for cognitive sustainability in the age of AI.

# 7. Conclusion

This study formalizes the concept of national cognitive resilience as the capacity of a country to preserve and regenerate metacognitive friction, epistemic novelty density, and human interpretive effort despite increasing AI integration. In the AI era, the boundaries between thinking and automation, learning and consumption, and originality and replication are quietly dissolving. The silent consequence is GCA, the recursive weakening of human epistemic agency through sustained reliance on AI-mediated knowledge production. This erosion is not accidental; it arises from design logics that privilege frictionless efficiency and predictive optimization over ambiguity, novelty, and reflection.

To address this challenge, the study introduces a triadic framework consisting of the GCAL, which models how cognitive outsourcing normalizes atrophy across generations; the CDI, which operationalizes cognitive resilience through the metrics of MF, END, and AIR; and the GCAL-CDI-Recovery model, which offers a reversal blueprint for restoring cognitive vitality through education, interface design, governance, and ethical oversight. GCAL provides a temporal-explanatory structure to trace the drift of cognitive norms, while CDI offers a quantitative lens to measure epistemic conditions in real time. Together, they convert resilience from a vague aspiration into a measurable national capability comparable in strategic significance to economic competitiveness or ecological stability.

By integrating GCAL phase placement with CDI scoring, this study develops the National Cognitive Resilience Scorecard for Singapore, Canada, the United Kingdom, the United States, Germany, France, South Korea, Japan, India, and China. This scorecard enables comparative benchmarking to determine each nation's relative standing, temporal tracking to estimate the time before progression to higher-risk GCAL phases, and strategic targeting to identify which cognitive dimensions require urgent reinforcement. The findings reveal that high-resilience nations such as Singapore and Canada, which combine early-phase GCAL positions with high CDI scores, require guard strategies to preserve their strengths. Mid-resilience nations such as the United Kingdom, the United States, and Germany, positioned in the middle GCAL phases, must implement restore strategies within the next decade to halt drift. Low-resilience nations such as India and China, already in advanced phases with low CDI scores, require immediate rebuild strategies to prevent entry into Phase 5 within three to five years.
The results show clearly that AI readiness does not equate to cognitive resilience. Nations with advanced AI infrastructure, such as China and the United States, may record low CDI scores when novelty is eroded and automation reliance is high. Conversely, countries like Singapore, and Finland outside this dataset, sustain high CDI scores by embedding cognitive scaffolding into their educational and governance systems, even with similar AI maturity levels. Historical precedents such as the Gutenberg press, industrial schooling, and the screen revolution demonstrate that cognitive systems can recover from compression and conformity, but only through deliberate pedagogical reform and institutional innovation. The GCAL-CDI integration provides a way to operationalize such recovery in the AI age, transforming abstract warnings into actionable and measurable policy levers.

Strategic action must involve coordinated engagement across governance, technology design, ethics regulation, education, and civil society. Governments should integrate GCAL-CDI benchmarking into national AI and education policies, legislate novelty preservation quotas, and enforce human-in-the-loop governance in critical domains. Technology developers should reject frictionless design in favor of systems that encourage delay, doubt, and reflective complexity. International regulatory bodies must include epistemic integrity alongside fairness and safety as pillars of AI ethics. Educational institutions should realign pedagogy to reward ambiguity tolerance, reflective delay, and conceptual synthesis rather than only performance metrics. Individuals and communities must cultivate epistemic hygiene by resisting cognitive shortcuts, documenting reasoning paths, and practicing deliberate reflection.

The study acknowledges certain limitations—both methodological, as discussed in the Research Methodology section, and conceptual, as outlined in the Discussion section with associated future directions and recommendations. These include its reliance on theoretical modeling and secondary data, the absence of longitudinal validation for the GCAL framework, and its initial grounding in cognitive assumptions. Future research should pilot CDI testing in live environments, conduct decade-spanning cohort studies, adapt the framework to reflect diverse epistemic traditions, and empirically test the Recovery Model through agent-based simulations and dynamic system modeling.

The broader implication is that cognition must be treated as a civilizational asset, equal in strategic importance to ecological sustainability or democratic stability. Just as environmental degradation prompted coordinated global climate action, cognitive degradation demands epistemic governance that is transdisciplinary, generational, and anticipatory. Embedding GCAL-CDI metrics into AI policy, designing reflective human-AI interfaces, and prioritizing epistemic plurality over algorithmic conformity are essential to achieving this.

Artificial intelligence need not be inherently corrosive. If embedded with intentional friction, ambiguity, and human-centered design, it can serve as a scaffold for cognitive enhancement rather than a substitute for human thought. The future lies not in reverting to pre-AI modes of cognition but in encoding novelty, delay, and ethical reflexivity into AI's evolution. The question is no longer what AI is doing to us but what we are willing to design, preserve, and defend in our cognitive futures. The tools of recovery—GCAL's structural mapping, CDI's diagnostic precision, and their integration in the National Cognitive Resilience Scorecard—are now available. What remains is the collective will to act, deliberately, reflectively, and imaginatively, to ensure that the algorithmic age becomes not an era of epistemic atrophy but a renaissance of human thought.
# Appendix 1: National AI Strategy References

1. Government, U. S. (2024). *National Artificial Intelligence Initiative – United States*.
https://www.ai.gov

2. State Council of the People's Republic of China. (2017). *New generation artificial intelligence
development plan – China*.
http://english.www.gov.cn/archive/publications/2017/07/20/content_281475742458322.htm

3. Government of the United Kingdom. (2024). *Office for Artificial Intelligence – United Kingdom*.
https://www.gov.uk/government/organisations/office-for-artificial-intelligence

4. Government of Canada. (2024). *Pan-Canadian Artificial Intelligence Strategy – Canada*. https://ised-isde.canada.ca/site/ai-strategy/en

5. Federal Government of Germany. (2023). *Artificial intelligence strategy – Germany*. https://www.ki-strategie-deutschland.de/home.html

6. Government of France. (2023). *National strategy for artificial intelligence – France*.
https://www.enseignementsup-recherche.gouv.fr/en/artificial-intelligence-46695

7. Government of Singapore. (2024). *AI Singapore – Singapore*. https://www.aisingapore.org/

8. Ministry of Science and ICT, Republic of Korea. (2023). *National artificial intelligence strategy –
South Korea*. https://www.msit.go.kr/eng/bbs/list.do?sCode=ENG&mId=4&mPid=2

9. Cabinet Office of Japan. (2023). *Artificial intelligence technology strategy – Japan*.
https://www8.cao.go.jp/cstp/english/index.html

10. Government of India. (2024). *National artificial intelligence portal – India*. https://indiaai.gov.in/

11. Select Committee on Artificial Intelligence, United States. (2023). *National artificial intelligence
research and development strategic plan*. The White House. https://www.whitehouse.gov/wp-
content/uploads/2023/05/National-AI-RD-Strategic-Plan-2023.pdf

12. Department for Science, Innovation and Technology. (2023). *National AI strategy: AI action plan –
United Kingdom*. Government of the United Kingdom.
https://assets.publishing.service.gov.uk/media/64f89dd8e7e8e1000c1b0981/national_ai_strategy_ai_action_plan.pdf

13. Innovation, Science and Economic Development Canada. (2022). *Pan-Canadian artificial intelligence
strategy: Second phase*. Government of Canada. https://ised-isde.canada.ca/site/ai-strategy/en/pan-canadian-artificial-intelligence-strategy-second-phase

14. Government of France. (2022). *National strategy for artificial intelligence*. Ministry of Higher
Education and Research. https://www.enseignementsup-recherche.gouv.fr/sites/default/files/2022-12/strategie-nationale-ia-2022-en-21454.pdf

15. Government of Singapore. (2019). *National artificial intelligence strategy*. Smart Nation Singapore.
https://www.smartnation.gov.sg/docs/default-source/default-document-library/national-ai-strategy.pdf

16. Ministry of Science and ICT, Republic of Korea. (2019). *National artificial intelligence strategy*.
https://english.msit.go.kr/eng/bbs/view.do?sCode=eng&mId=4&mPid=2&bbsSeqNo=44&nttSeqNo=4
4

17. Strategic Council for AI Technology, Cabinet Office of Japan. (2017). *Artificial intelligence
technology strategy*. New Energy and Industrial Technology Development Organization.
https://www.nedo.go.jp/content/100865202.pdf

18. NITI Aayog, Government of India. (2018). *National strategy for artificial intelligence: #AIforAll*.
Government of India. https://www.niti.gov.in/sites/default/files/2019-01/NationalStrategy-for-AI-Discussion-Paper.pdf
**Statements and Declarations:**

**Data Availability**

No new empirical data were generated or analysed in this study. The article is based exclusively on secondary sources such as scholarly publications, case reports, media narratives, National AI strategy websites and policy documents, all cited within the manuscript.

**Author contributions**

Prashant Mahajan: Conceptualization, Methodology, Investigation, Formal analysis, Writing – Original Draft, Writing – Review & Editing, Visualization, Project administration.

**Conflict of Interest Statement**

The authors declare that there are no commercial or financial relationships that could be construed as a potential conflict of interest in conducting or publishing this research.

**Funding**

The author received **no specific** grant or sponsorship or funding from any funding agency in the public, commercial, or not-for-profit sectors for the preparation of this article.

**Ethical Statement**

This study did not involve human participants, animal subjects, or any intervention that would require institutional ethical approval. All data used in this research were obtained from publicly available sources or derived from theoretical modeling. No personal, identifiable, or sensitive information was collected, stored, or analyzed.
## References

1. Gerlich M. AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. *Societies*. 2025;15(1):1-28. doi:10.3390/soc15010006

2. Zhai C, Wibowo S, Li LD. The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. *Smart Learn Environ*. 2024;11(1). doi:10.1186/s40561-024-00316-7

3. Xu C, Sun Y, Zhou H. Artificial Aesthetics and Ethical Ambiguity: Exploring Business Ethics in the Context of AI-driven Creativity. *J Bus Ethics*. 2025;199(4):671-692. doi:10.1007/s10551-024-05837-2

4. Al-Zahrani AM. Balancing act: Exploring the interplay between human judgment and artificial intelligence in problem-solving, creativity, and decision-making. *Igmin Res*. 2024;2(3):145-158.

5. Lee HP (Hank), Sarkar A, Tankelevitch L, et al. The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. *Conf Hum Factors Comput Syst - Proc*. Published online 2025. doi:10.1145/3706598.3713778

6. Fabio RA, Plebe A, Suriano R. AI-based chatbot interactions and critical thinking skills: an exploratory study. *Curr Psychol*. 2025;44(9):8082-8095. doi:10.1007/s12144-024-06795-8

7. Devaki V. Academic integrity and human cognitive development of learners: Is artificial intelligence replacing the human brain? In: *AI Applications and Strategies in Teacher Education*. IGI Global; 2025:195-222.

8. Shah SS, Asad MM. Impact of Critical Thinking Approach on Learners' Dependence on Innovative Transformation Through Artificial Intelligence. *Lytras MD, Alkhaldi A, Malik S, Serban AC, Aldosemani T*, eds. *Evol Artif Intell High Educ Challenges, Risks, Ethical Considerations*. Published online November 25, 2024:0. doi:10.1108/978-1-83549-486-820241010

9. Bodislav DA, Georgescu RI. Neuroeconomics in the Age of AI: how machine learning alters human decision-making at the neural level. *Theor Appl Econ*. 2025;32(2 (643), Summer):301-312.

10. Peeters F, Berkhof J, Delespaul P, Rottenberg J, Nicolson NA. Diurnal mood variation in major depressive disorder. *Emotion*. 2006;6(3):383.

11. Blair C. How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability. *Behav Brain Sci*. 2006;29(2):109-125.

12. Han H. Neural correlates of moral sensitivity and moral judgment associated with brain circuitries of selfhood: A meta-analysis. *J Moral Educ*. 2017;46(2):97-113.

13. Reynolds Jr DJ, Stiles WB, Grohol JM. An investigation of session impact and alliance in internet based psychotherapy: Preliminary results. *Couns Psychother Res*. 2006;6(3):164-168.

14. Floridi L. The Philosophy of Information. Published online January 27, 2011. doi:10.1093/acprof:oso/9780199232383.001.0001

15. Anders G. *Über Die Seele Im Zeitalter Der Zweiten Industriellen Revolution*. Beck; 1985.

16. Kosmyna N, Hauptmann E, Yuan YT, et al. Your brain on chatgpt: Accumulation of cognitive debt when using an ai assistant for essay writing task. *arXiv Prepr arXiv250608872*. Published online 2025.

17. Stanford IHCA. AI Overreliance Is a Problem. Are Explanations a Solution? Published online 2023. https://hai.stanford.edu/news/ai-overreliance-problem-are-explanations-solution

18. Microsoft Research. *The New Future of Work Report 2024*. Microsoft; 2024. https://www.microsoft.com/en-us/research/wp-content/uploads/2024/12/NFWReport2024_1.27.2025.pdf

19. OECD. OECD Learning Compass 2030. *OECD Futur Educ Ski*. Published online 2023. https://www.oecd.org/en/data/tools/oecd-learning-compass-2030.html

20. Lahlou S. Mitigating Societal Cognitive Overload in the Age of AI: Challenges and Directions. In: *ICLR 2025 Workshop on Bidirectional Human-AI Alignment*. ; 2025.

21. Patil S V, Srinivas SB, Tussing D V, Rhee J. Addressing the flexible use of cognitive flexibility constructs: toward a multifaceted approach. *Acad Manag Ann*. 2025;19(1):74-131.

22. Aberšek B, Flogie A, Pesek I. *AI and Cognitive Modelling for Education*. Springer; 2023.
23. Gibson D, Kovanovic V, Ifenthaler D, Dexter S, Feng S. Learning theories for artificial intelligence promoting learning processes. *Br J Educ Technol*. 2023;54(5):1125-1146.

24. Mota-Valtierra G, Rodríguez-Reséndiz J, Herrera-Ruiz G. Constructivism-based methodology for teaching artificial intelligence topics focused on sustainable development. *Sustainability*. 2019;11(17):4642.

25. Cope B, Kalantzis M. The cybernetics of learning. *Educ Philos Theory*. 2022;54(14):2352-2388.

26. Adadi A, Berrada M. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). *IEEE access*. 2018;6:52138-52160.

27. Groupmos PP. The Cybernetic Artificial Intelligence (CAI): A new scientific field for modelling and controlling Complex Dynamical Systems. *IFAC-PapersOnLine*. 2024;58(3):145-152.

28. Farkaš I. Transforming Cognition and Human Society in the Digital Age. *Biol Theory*. Published online 2024:1-13.

29. Deary IJ, Cox SR, Hill WD. Genetic variation, brain, and intelligence differences. *Mol Psychiatry*. 2022;27(1):335-353.

30. Reddy KJ. Neuroplasticity and Technology. In: *Innovations in Neurocognitive Rehabilitation: Harnessing Technology for Effective Therapy*. Springer; 2025:137-169.

31. León-Domínguez U. Potential cognitive risks of generative transformer-based AI chatbots on higher order executive functions. *Neuropsychology*. 2024;38(4):293.

32. Wingström R, Hautala J, Lundman R. Redefining Creativity in the Era of AI? Perspectives of Computer Scientists and New Media Artists. *Creat Res J*. 2024;36(2):177-193.
doi:10.1080/10400419.2022.2107850

33. Mellamphy NB. Humans "in the loop"?: Human-centrism, posthumanism, and AI. *Nat Cult*. 2021;16(1):11-27.

34. Braidotti R. Posthuman critical theory. *Crit posthumanism Planet Futur*. Published online 2016:13-32.

35. Chaaban Y. Exploring research ethics through the lens of critical posthumanism in the age of Artificial Intelligence. *Teach High Educ*. Published online 2025:1-16.

36. Nath R, Manna R. From posthumanism to ethics of artificial intelligence. *AI Soc*. 2023;38(1):185-196.
doi:10.1007/s00146-021-01274-1

37. Fricker M. *Epistemic Injustice: Power and the Ethics of Knowing*. Oxford university press; 2007.

38. Vygotsky LS. *Mind in Society: The Development of Higher Psychological Processes*. Vol 86. Harvard university press; 1978.

39. Lakatos L, Janka Z. Evolution of human brain and intelligence. *Ideggyogy Sz*. 2008;61(7-8):220-229.

40. Hebb DO. The first stage of perception: growth of the assembly. *Organ Behav*. 1949;4(60):60-78.

41. Deary IJ, Johnson W, Houlihan LM. Genetic foundations of human intelligence. *Hum Genet*. 2009;126:215-232.

42. Deary IJ, Penke L, Johnson W. The neuroscience of human intelligence differences. *Nat Rev Neurosci*. 2010;11(3):201-211.

43. Shaw P. Intelligence and the developing human brain. *Bioessays*. 2007;29(10):962-973.

44. Elsayary A, Ragab JK. Digital Neuroplasticity: How Prolonged Technology Use Reshapes Neural Pathways Over Time. *Interaction*. 2025;5:6.

45. Gkintoni E, Antonopoulou H, Sortwell A, Halkiopoulos C. Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy. *Brain Sci*. 2025;15(2). doi:10.3390/brainsci15020203

46. Flavell JH. Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. *Am Psychol*. 1979;34(10):906.

47. Piaget J, Cook M. *The Origins of Intelligence in Children*. Vol 8. International universities press New York; 1952.

48. Shanmugasundaram M, Tamilarasu A. The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review. *Front Cogn*. 2023;2:1203077.
49. Pan D, Long T, Zhu X, Li J. Exploring Engineering Undergraduate's Critical Thinking Patterns in AI-Enhanced Learning: A TCSA Method. In: *International Conference on Blended Learning*. Springer; 2025:291-303.

50. Sweller J. Cognitive load during problem solving: Effects on learning. *Cogn Sci*. 1988;12(2):257-285.

51. Bloom BS. Taxonomy of Educational Objectives: The Classification of Educational Goals. *Educ Object*. 1956;250.

52. Floridi L, Cowls J. A unified framework of five principles for AI in society. *Mach Learn City Appl Archit Urban Des*. Published online 2022;535-545. doi:10.1002/9781119815075.ch45

53. Floridi L, Cowls J, Beltrametti M, et al. AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. *Minds Mach*. 2018;28(4):689-707. doi:10.1007/s11023-018-9482-5

54. Alvarado R. AI as an epistemic technology. *Sci Eng Ethics*. 2023;29(5):32.

55. Arriagada-Bruneau G, López C, Davidoff A. A Bias Network Approach (BNA) to Encourage Ethical Reflection Among AI Developers. *Sci Eng Ethics*. 2024;31(1):1.

56. Mohamed S, Png MT, Isaac W. Decolonial AI: Decolonial theory as sociotechnical foresight in artificial intelligence. *Philos Technol*. 2020;33(4):659-684.

57. Nihei M. Epistemic injustice as a philosophical conception for considering fairness and diversity in human-centered AI principles. *Interdiscip Inf Sci*. 2022;28(1):35-43.

58. Ofosu-Asare Y. Cognitive imperialism in artificial intelligence: counteracting bias with indigenous epistemologies. *AI Soc*. 2025;40(4):3045-3061.

59. Laakasuo M, Herzon V, Perander S, et al. Socio-cognitive biases in folk AI ethics and risk discourse. *AI Ethics*. 2021;1(4):593-610. doi:10.1007/s43681-021-00060-5

60. Raza SA. A paradigm shift to ethical decision-making—Incorporating systemic epistemology into complex socio-technical decision support systems research. *J Decis Syst*. 2022;32(1):177-200.

61. Weber J, Prietl B. AI in the age of technoscience: On the rise of data-driven AI and its epistem-
ontological foundations. In: *The Routledge Social Science Handbook of AI*. Routledge; 2021:58-73.

62. Chaka C. Fourth industrial revolution—a review of applications, prospects, and challenges for artificial intelligence, robotics and blockchain in higher education. *Res Pract Technol Enhanc Learn*. 2023;18. doi:10.58459/rptel.2023.18002

63. Trauth-Goik A. Repudiating the Fourth Industrial Revolution Discourse: A New Episteme of Technological Progress. *World Futures*. 2021;77(1):55-78. doi:10.1080/02604027.2020.1788357

64. Shutikova MI, Beshenkov SA, Mindzaeva E V. Information and cognitive technologies in the context of the 4th technological revolution: Educational aspects. *Sib Fed Univ*. Published online 2019. https://elib.sfu-kras.ru/handle/2311/125584

65. Gregor S, Benbasat I. Explanations from intelligent systems: Theoretical foundations and implications for practice. *MIS Q*. 1999;23(4):497-530. https://www.jstor.org/stable/249487

66. Holford WD. The future of human creative knowledge work within the digital economy. *Futures*. 2019;105:67-77. doi:10.1016/j.futures.2018.09.004

67. Oosthuizen J. *The Determinants of Fourth Industrial Revolution Leadership Dexterity: A Proposed Framework for 4IR Intelligence and Subsequent 4IR Leadership Development*. Unpublished White Paper; 2017. https://www.researchgate.net/publication/315114030

68. Spöttl G, Windelband L. The 4th industrial revolution – its impact on vocational skills. *J Vocat Educ Train*. 2021;73(4):509-530. doi:10.1080/13639080.2020.1858230

69. Tabatabaei-Jafari H, Shaw ME, Cherbuin N. Cerebral Atrophy in Mild Cognitive Impairment: A Systematic Review with Meta-Analysis. *Alzheimer's Dement Diagnosis, Assess Dis Monit*. 2015;1(4):487-504. doi:10.1016/j.dadm.2015.10.002

70. Saykin AJ, Wishart HA, Rabin LA, et al. Older Adults with Cognitive Complaints Show Brain Atrophy Similar to That of Amnestic MCI. *Neurology*. 2006;67(5):834-842. doi:10.1212/01.wnl.0000234032.77541.a2

71. Sluimer JD, van der Flier WM, Karas GB, et al. Whole-brain Atrophy Rate and Cognitive Decline: Longitudinal MR Study of Memory Clinic Patients. *Radiology*. 2008;248(2):590-598.
72. Petersen RC, Caracciolo B, Brayne C, Gauthier S, Jelic V, Fratiglioni L. Mild Cognitive Impairment: A Concept in Evolution. *J Intern Med*. 2014;275(3):214-228. doi:10.1111/joim.12190

73. Kelaiditi E, Cesari M, Canevelli M, et al. Cognitive Frailty: Rational and Definition from an (IANA/IAGG) International Consensus Group. *J Nutr Heal Aging*. 2013;17(9):726-734. doi:10.1007/s12603-013-0367-3

74. George AS, Baskar T, Srikaanth PB. The Erosion of Cognitive Skills in the Technological Age: How Reliance on Technology Impacts Critical Thinking, Problem-Solving, and Creativity. *PU Int Res Publ*. Published online 2024. http://www.puirp.com/index.php/research/article/view/63

75. Aghaziarati A, Rahimi H. The Future of Digital Assistants: Human Dependence and Behavioral Change. *J Foresight Psychol Heal*. Published online 2025. https://www.journalfph.com/index.php/jfph/article/view/6

76. Jadhav A. *Distributed Atrophy: How AI Shapes and Shrinks Our Cognitive Habits*. OSF Preprints; 2025. https://osf.io/hzuw7/download

77. Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, et al. Whitepaper: Defining and Investigating Cognitive Reserve, Brain Reserve, and Brain Maintenance. *Alzheimer's Dement*. 2020;16(9):1305-1311. doi:10.1002/alz.12105

78. Dergaa I, Ben Saad H, Glenn JM, Amamou B. From Tools to Threats: A Reflection on the Impact of Artificial-Intelligence Chatbots on Cognitive Health. *Front Psychol*. 2024;15. doi:10.3389/fpsyg.2024.1259845

79. Shalaby A. Classification for the Digital and Cognitive AI Hazards: Urgent Call to Establish Automated Safe Standard for Protecting Young Human Minds. *Discov Educ*. 2024;2:Article 42. doi:10.1007/s44217-024-00042-0

80. Girma AH. The Role of Artificial Intelligence in Shaping Human Interaction and Cognitive Function. *KUE J Educ*. 2025;3(1):69. https://kje.kue.edu.et/index.php/kje/article/view/166

81. MIT Media Lab. *Your Brain on ChatGPT: A Cognitive-Ecological Study of AI-Assisted Composition*. Massachusetts Institute of Technology; 2025.

82. Microsoft Research. *The Impact of Generative AI on Critical Thinking*. Microsoft Corporation; 2025.

83. Lee H, Sarkar A, Tankelevitch L, Drosos I. The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. *ACM Conf Proc*. Published online 2025. doi:10.1145/3706598.3713778

84. Chen Y, Wang Y, Wüstenberg T, Kizilcec RF, Fan Y. Effects of generative artificial intelligence on cognitive effort and task performance: Study protocol for a randomized controlled experiment among college students. *Trials*. Published online 2025. https://link.springer.com/content/pdf/10.1186/s13063-025-08950-3.pdf

85. Singh A, Gupta S, Chatterjee D. *Reflective Prompts and User Engagement in Generative Search Environments*; 2025.

86. Ogunleye T, Adeyemi K, Onasanya S. *The Erosion of Conceptual Depth in AI-Written Academic Assessments*. University of Cape Town; 2024.

87. Ruiz-Rojas LI, Salvador-Ullauri L, Acosta-Vargas P. Collaborative working and critical thinking: Adoption of generative artificial intelligence tools in higher education. *Sustainability*. 2024;16(13):5367. https://www.mdpi.com/2071-1050/16/13/5367

88. OECD. *Education 2030: Future of Learning and AI*. Organisation for Economic Co-operation and Development; 2023. https://www.oecd.org/education/

89. Wei X, Wang L, Lee LK, Liu R. The effects of generative AI on collaborative problem-solving and team creativity performance in digital story creation: an experimental study. *Int J Educ Technol High Educ*. Published online 2025. https://link.springer.com/article/10.1186/s41239-025-00526-0

90. Masson A. Cognitive impacts of AI use in higher education assessment in the UK. *Br J Educ Technol*. Published online 2021.

91. Kim J, Lee SS, Detrick R, Wang J, Li N. Students-Generative AI interaction patterns and its impact on academic writing. *J Comput High Educ*. Published online 2025.
https://link.springer.com/article/10.1007/s12528-025-09444-6

92. Levin I, Marom M, Kojukhov A. Rethinking AI in Education: Highlighting the Metacognitive Challenge. *BRAIN Broad Res Artif Intell Neurosci.* 2025;16(S1). doi:10.70594/brain/16.S1/21

93. Zafar MB, Ali H, Yasin T. *Reimagining Human Creativity in the Age of Generative AI: A Multi-Method Meta-Thematic Synthesis*.; 2025. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5229201

94. Lakatos E, Janka A. Affective pedagogy in minority education. *Hungarian Educ Rev*. Published online 2008.

95. Goriounova NA, Mansvelder HD. Genes, cells and brain areas of intelligence. *Front Hum Neurosci*. 2019;13:44.

96. Aizenstein HJ. *Chatbot Fatigue and Emotional Disengagement in Brazilian Public Schools*. Educational Policy Reports; 2024.

97. Zapata GC. Generative AI Technologies, Multiliteracies, and Language Education. In: Springer; 2025. https://books.google.com/books?id=nkNiEQAAQBAJ

98. Petzold K, Müller J, Schramm F. Algorithmic decision-making and non-linear career penalties in AI-assisted hiring. *Int J Soc Res Methodol*. 2021;24(5):601. doi:10.1080/13645579.2021.1892373

99. Mischos C, Thanos I, Kyriakou H. Reinforcing conformity: Predictive logics and innovation thresholds in AI-enabled recruitment. *Technol Soc*. 2023;75. doi:10.1016/j.techsoc.2023.102361

100. Yadav P. *Cognitive Sustainability in the Age of AI: A Philosophical Framework for Understanding Competency Erosion and Cognitive Stratification in Human-AI Systems*.; 2025. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5317987

101. Schwarz E. Günther Anders in Silicon Valley: Artificial intelligence and moral atrophy. *Thesis Elev*. 2019;153(1):94-112.

102. Aizenstein H, Moore RC, Vahia I, Ciarleglio A. Deep learning and geriatric mental health. *Am J Geriat Psychiatry*. 2024;32(3):270-279.

103. Qureshi M. *The Impact of Artificial Intelligence: From Cognitive Costs to Global Inequality*.; 2025. https://www.researchgate.net/publication/392521505

104. Froom V. *Accountability, Autonomy, and Ethics in the Age of Intelligent Systems: Understanding Responsibility in Human–AI Collaboration*. Routledge; 2025. https://books.google.com/books?id=IdNmEQAAQBAJ

105. Watson S. *Emergent Discourses on Generative AI in Education and Society*.; 2025. https://www.researchgate.net/profile/Daba-Debo-2/publication/389211048_Emergent_discourses_on_generative_AI_in_education_and_society/links/67b8e18c96e7fb48b9c9c844/Emergent-discourses-on-generative-AI-in-education-and-society.pdf

106. Mahajan P. *The Soul of the AI: Governance, Ethics, and the Future of Human–AI Integration*. Zenodo; 2025. https://zenodo.org/records/10764332

107. Callaghan CW. *The Post Science Paradigm of Scientific Discovery in the Era of Artificial Intelligence: Modelling the Collapse of Ideation Costs, Epistemic Inversion, and the End Of ...*; 2025. doi:10.48550/arXiv.2507.07019

108. Camilleri P. Planning at the Edge of Tomorrow: A Structural Interpretation of Maltese AI-Related Policies and the Necessity for a Disruption in Education. In: *Planning at the Edge of Tomorrow*. Taylor & Francis; 2022. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003184157-3/planning-edge-tomorrow-patrick-camilleri

109. Harris A, Holt S. *Entagogy: Developing a Disruptive Framework for AI Education*.; 2025. https://osf.io/wk7m8/download

110. Karmi G. *Opening Pandora's Black Box in the Nexus of Media, Consciousness and AI*. 2025. https://search.proquest.com/openview/0620386f98e74381dea027cb67bf35ff

111. Kim D. *Exploring Generative AI-User Interactions Through Structural Coupling*.; 2025. https://eprints.gla.ac.uk/352564/1/352564.pdf

112. Deary IJ, Caryl PG. *Intelligence, EEG and Cognition: A Biological Perspective*. Cambridge University Press; 1997. https://www.cambridge.org/core/books/intelligence-eeg-and-cognition/8870A4DB5F1FA04B1B48C9293761B850
113. Alam A. Ethical Challenges and Bias in AI-Driven Marketing: Educational Imperatives and Policy Perspectives. In: IGI Global; 2025. https://www.igi-global.com/chapter/ethical-challenges-and-bias-in-ai-driven-marketing/383238

114. Naqvi WM, Ganjoo R, Rowe M, Pashine AA. Critical thinking in the age of generative AI: implications for health sciences education. *Front Artif Intell*. Published online 2025. https://www.frontiersin.org/articles/10.3389/frai.2025.1571527/full

115. Iqbal J, Hashmi ZF, Asghar MZ, Abid MN. Generative AI tool use enhances academic achievement in sustainable education through shared metacognition and cognitive offloading among preservice teachers. *Sci Rep*. Published online 2025. doi:10.1038/s41598-025-01676-x

116. Slimi Z, Villarejo-Carballido B. Unveiling the Potential: Experts' Perspectives on Artificial Intelligence Integration in Higher Education. *Eur J Educ Res*. 2024;13(4).

117. Arbib MA. Artificial intelligence meets brain theory (again). *Biol Cybern*. Published online 2025. https://link.springer.com/article/10.1007/s00422-025-01013-5

118. Weh L. An integrated embodiment concept combines neuroethics and AI ethics-Relational perspectives on artificial intelligence, emerging neurotechnologies and the future. *Ethics Sci Environ Polit*. Published online 2024. https://link.springer.com/article/10.1007/s11569-024-00457-6

119. Nguyen A. Human-AI shared regulation for hybrid intelligence in learning and teaching: Conceptual domain, ontological foundations, propositions, and implications for research. *HICSS Proc*. Published online 2025. doi:10.24251/HICSS.2025.006

120. Gkintoni E, Antonopoulou H, Sortwell A. Challenging cognitive load theory: The role of educational neuroscience and artificial intelligence in redefining learning efficacy. *Brain Sci*. Published online 2025. https://www.mdpi.com/2076-3425/15/2/203

121. Wiziack JC, Santos V Dos. Evaluating an integrated cognitive competencies model to enhance teachers' application of technology in large-scale educational contexts. *Heliyon*. Published online 2021. https://www.cell.com/heliyon/fulltext/S2405-8440(21)00033-5

122. Smolka M. Generative critique in interdisciplinary collaborations: from critique in and of the neurosciences to socio-technical integration research as a practice of critique in r (r) i. *Ethics Sci Environ Polit*. Published online 2020. https://link.springer.com/article/10.1007/s11569-019-00362-3

123. Zhu S, Yu T, Xu T, et al. Intelligent computing: the latest advances, challenges, and future. *Intell Comput*. 2023;2:6.

124. Tomasello M. The cultural origins of human cognition. *Harvard Univ Press*. Published online 1999.

125. Floridi L. The ethics of information. *Oxford Univ Press*. Published online 2013.

126. Akhtar F, Patel PK, Heyat MBB, et al. Smartphone addiction among students and its harmful effects on mental health, oxidative stress, and neurodegeneration towards future modulation of anti-addiction therapies: a comprehensive survey based on slr, Research questions, and network visualization. *CNS Neurol Disord Targets-CNS Neurol Disord*. 2023;22(7):1070-1089.

127. Masson R, Brusa C, Scoto M, Baranello G. Brain, cognition, and language development in spinal muscular atrophy type 1: a scoping review. *Dev Med Child Neurol*. 2021;63(5):527-536.

128. Gibson A, Clark B. Constructivist epistemologies in AI-mediated learning. *Educ Technol Res Dev*. 2023;71(4):1059-1080.

129. Dutton E, Van der Linden D, Lynn R. The Negative Flynn Effect: A Systematic Literature Review. *Intelligence*. 2016;59:163-169.

130. Hayles NK. Unthought: The power of the cognitive nonconscious. *Univ Chicago Press*. Published online 2017.

131. Kuhn D. A developmental model of critical thinking. *Educ Res*. 1999;28(2):16-25.

132. Shaw P. Neuroplasticity and cognitive development. *Annu Rev Psychol*. 2007;58:253-273.

133. Boden MA. *The Creative Mind: Myths and Mechanisms*. Routledge; 2004.

134. Weidinger L, al. et. Ethical and social risks of harm from Language Models. *arXiv Prepr*. 2021;arXiv:2112.

135. Blair C. How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive functioning. *Behav Brain Sci.*
2006;29(2):109. doi:10.1017/S0140525X06009034

136. Slimi R, Villarejo-Carballido B. Academic Ethics in the Age of AI: A case study at STKIP DDI Pinrang. *Apl J Penelit Ilm*. 2025;4(3). doi:10.59110/aplikatif.v4i3.667

137. Ozmen Garibay O, Winslow B, Andolina S, et al. Six human-centered artificial intelligence grand challenges. *Int J Human-Computer Interact*. 2023;39(3):391-437.

138. Hedstrom M, King JL. On the Lam: Library, Archive, and Museum Collections in the Creation and Maintenance of Knowledge Communities. Working paper, 2003. University of Michigan School of Information; 2003.

139. McNeely IF, Wolverton L. *Reinventing Knowledge: From Alexandria to the Internet*. WW Norton & Company; 2008.

140. Heller-Roazen D. Tradition's destruction: On the library of Alexandria. *October*. 2002;100:133-153.

141. Higgins C. The destruction of the Library of Alexandria as myth and metaphor. *a Libr*. Published online 2016:69.

142. Hodges C, Moore S, Lockee B, Trust T, Bond A. The difference between emergency remote teaching and online learning. *Educ Rev*. 2020;27(1):1-9.

143. Zalat MM, Hamed MS, Bolbol SA. The experiences, challenges, and acceptance of e-learning as a tool for teaching during the COVID-19 pandemic among university medical staff. *PLoS One*. 2021;16(3):e0248758.

144. Schleicher A. The Impact of COVID-19 on Education: Insights from" Education at a Glance 2020". *OECD Publ*. Published online 2020.

145. Sahlberg P. Does the pandemic help us make education more equitable? *Educ Res Policy Pract*. 2021;20(1):11-18.

146. Eisenstein EL. *The Printing Press as an Agent of Change*. Vol 1. Cambridge University Press; 1980.

147. Febvre L, Martin HJ. *The Coming of the Book: The Impact of Printing 1450-1800*. Vol 10. Verso; 1997.

148. Johns A. *The Nature of the Book: Print and Knowledge in the Making*. University of Chicago Press; 2000.

149. Cowan R, David PA, Foray D. The explicit economics of knowledge codification and tacitness. *Ind Corp Chang*. 2000;9(2):211-253.

150. Floridi L. *The Fourth Revolution: How the Infosphere Is Reshaping Human Reality*. Oxford University Press; 2014.

151. Pariser E. *The Filter Bubble: What the Internet Is Hiding from You*. penguin UK; 2011.

152. Arthur Jr W, Bennett Jr W, Stanush PL, McNelly TL. Factors that influence skill decay and retention: A quantitative review and analysis. *Hum Perform*. 1998;11(1):57-101.

153. Storm BC, Levy BJ. A progress report on the inhibitory account of retrieval-induced forgetting. *Mem Cognit*. 2012;40(6):827-843.