Evaluating the Effect of AI Language Models on Learning and Reasoning Abilities

Kishore Kumar N

doi:https://doi.org/10.47001/JAIET/2025.207004

Authors

Kishore Kumar N

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

Abstract

This study systematically investigates the cognitive effects of AI-assisted reasoning by analyzing how the utilization of large language models (LLMs), such as ChatGPT, influences critical thinking, memory retention, and decision-making processes in young adults. With the growing integration of generative AI tools in academic and professional contexts, concerns have arisen regarding cognitive offloading, wherein individuals delegate complex reasoning, information retrieval, and analytical tasks to computational systems rather than engaging in in-depth cognitive processing themselves. While AI systems can enhance productivity and accessibility, excessive reliance may modify learning patterns, reduce reasoning depth, and impede long-term knowledge consolidation. The research employs a controlled experimental design with participants randomly assigned to one of three conditions: (1) no AI assistance, (2) AI assistance accompanied by structured metacognitive prompts aimed at fostering reflection and self-explanation, and (3) unrestricted AI use without guidance. Participants complete a series of tasks varying systematically in cognitive demand and domain, encompassing creative composition, analytical reasoning, and factual problem-solving exercises. Cognitive performance is assessed pre- and post-intervention to evaluate both immediate and short-term effects of AI exposure. Primary dependent measures include standardized critical thinking scores, immediate and delayed memory recall accuracy, decision-making confidence ratings, reasoning quality indices, and task completion times. Additional behavioral metrics, such as frequency of AI consultation and prompt complexity, are recorded to examine patterns of human-AI interaction. The theoretical framework integrates Cognitive Load Theory, to assess the impact of AI on intrinsic and extraneous cognitive load; Dual Process Theory, to examine potential shifts between intuitive and analytical reasoning; and metacognitive regulation models, to evaluate reflective monitoring and control processes during AI-assisted tasks.

Keywords

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

Citation of this Article

Kishore Kumar N. (2025). Evaluating the Effect of AI Language Models on Learning and Reasoning Abilities. Journal of Artificial Intelligence and Emerging Technologies (JAIET). 2(7), 17-21. Article DOI: https://doi.org/10.47001/JAIET/2025.207004

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[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.