Modeling the Impact of AI Tool Usage on Independent Thinking: Evidence from a Multivariate Regression Analysis

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, SI1 (2026), pp. 334–342
Proceedings of Selected Papers from the
National Conference on Emerging Trends in Commerce and Management
NCETCM-2K26
2026-03-30 to 2026-03-31
Vijayawada, Andhra Pradesh, India
Organized by Andhra Loyola College, Vijayawada, India
eISSN: 3107-9075

This work is licensed under a Creative Commons Attribution 4.0 International License .

Modeling the Impact of AI Tool Usage on Independent Thinking: Evidence from a Multivariate Regression Analysis

M.V.S. Ramalakshmi, P. Jayasaradadevi

Assistant Professor, Gayatri Vidya Parishad College for Degree and PG Courses (A), Visakhapatnam, India

Abstract

The rapid integration of Artificial Intelligence (AI) tools into academic and professional contexts has raised important concerns regarding their impact on cognitive processes, particularly independent thinking. This study aims to examine how different patterns of AI tool usage influence independent thinking using a multivariate regression framework. Data were collected from 181 students through a structured questionnaire, focusing on key variables such as time spent on AI usage, verification of AI responses, unverified AI reliance, and avoidance of thinking. The study adopts a quantitative research design and employs multiple linear regression analysis to examine the relationship between AI usage patterns and independent thinking. The analysis was conducted using the Google Colab platform, which provides a cloud-based environment for executing Python code. Data preprocessing and analysis were performed using libraries such as Pandas, NumPy, and Statsmodels. This approach enables efficient data handling, model estimation, and statistical validation, ensuring robustness and reproducibility of the results. By analyzing multiple dimensions of user interaction with AI tools, the study aims to provide a nuanced understanding of how technology-mediated behaviors influence cognitive outcomes. The findings contribute to the emerging literature on human AI interaction by highlighting the importance of user engagement patterns in determining cognitive implications. The study offers practical, policy, and technological insights, emphasizing the need for responsible and reflective use of AI tools to preserve independent thinking in increasingly technology-driven environments.

Keywords: Artificial Intelligence, Independent Thinking, Cognitive Offloading, Regression Analysis, AI Usage Behavior.

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DOI: 10.66710/ijersem.v2si1.43

Open Access • Peer Reviewed Article

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This article is part of Volume 2, Special Issue 1 (May 2026)

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2026-05-19