Authors

A. Manish Sundar

Department of Psychology, Rathinam College of Arts and Science, Coimbatore 642021, Tamilnadu, India

Abstract

This study systematically examines the cognitive implications of AI-assisted thinking by analyzing how the use of large language models (LLMs), such as ChatGPT, influences critical thinking ability, memory retention, and decision-making processes among young adults. As generative AI tools become increasingly embedded in academic environments and professional workflows, concerns have emerged regarding the phenomenon of cognitive offloading—where individuals delegate complex reasoning, information retrieval, and analytical tasks to computational systems rather than engaging in deep cognitive processing themselves. While AI systems can enhance productivity and accessibility, excessive reliance may alter learning patterns, reasoning depth, and long-term knowledge consolidation. The research adopts a controlled experimental design in which participants are randomly assigned to one of three conditions: (1) no AI assistance, (2) AI assistance with structured metacognitive prompts designed to encourage reflection and self-explanation, and (3) unrestricted AI usage without guidance. Participants complete a series of tasks that vary systematically in cognitive demand and domain, including creative composition, analytical reasoning, and factual problem-solving exercises. Performance is measured both before and after AI exposure to evaluate short-term and immediate post-intervention cognitive effects. Primary dependent variables include standardized critical thinking scores, delayed and immediate memory recall accuracy, decision-making confidence ratings, reasoning quality indices, and task completion time. Additional behavioral metrics, such as frequency of AI consultation and prompt complexity, are recorded to assess patterns of interaction. The theoretical foundation of the study integrates Cognitive Load Theory (examining how AI may reduce intrinsic and extraneous cognitive load), Dual Process Theory (analyzing shifts between intuitive and analytical reasoning), and metacognitive regulation frameworks (evaluating reflective monitoring and control processes during AI collaboration).

Keywords

IoT Security Edge Computing Anomaly Detection Machine Learning Real-Time Monitoring Data Encryption

Citation of this Article

A. Manish Sundar. (2025). The Role of LLM-Based Tools in Shaping Cognitive Skills Among Young Adults. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 2(1), 11-15. Article DOI: https://doi.org/10.47001/JAIET/2025.201003

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. Lu Fang, Ge Tang, Lu Zhang(2025), Comparative Study of Perceived and Actual Effectiveness of LLM Driven Tutors in Game-Based CFL Learning, Education Sciences 15 (11), 1502.
  2. Syed Azhar Hussain, Fahad Ayub, Nafeesa Ahmed(2025), Cognitive Load Management Through Adaptive AI learning System Implications for Student Focus and Retention, The Critical Review of Social Sciences Studies 3 (3), 701-719.
  3. Zana Buçinca, Maja Barbara Malaya, Krzysztof Z Gajos(2021), To trust or to think: cognitive forcing functions can reduce overreliance on AI in AI-assisted decision-making, Proceedings of the ACM on Human-computer Interaction 5 (CSCW1), 1-21.
  4. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
  5. Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
  6. Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive consequences of having information at our fingertips. Science, 333(6043), 776–778.
  7. Flavell, J. H. (1979). Metacognition and cognitive monitoring. American Psychologist, 34(10), 906–911.
  8. Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and multitasker. Teaching and Teacher Education, 67, 135–142.
  9. Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19.
  10. Mayer, R. E. (2009). Multimedia Learning. Cambridge University Press.
  11. Bubeck, S., et al. (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712.
  12. Bjork, R. A., & Bjork, E. L. (2011). Making things hard on yourself, but in a good way. Psychology and the Real World, 56–64.
  13. Dunlosky, J., et al. (2013). Improving students’ learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4–58.