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Relationship Between Generative Artificial Intelligence Tools and Conceptual Understanding among Undergraduate Students

Introduction

Relationship between generative artificial intelligence tools and conceptual understanding among undergraduate students. Study examines generative AI tools' relationship with undergraduate conceptual understanding. Findings show a positive correlation, highlighting potential and risks, urging structured AI integration.

Abstract

This study aimed to examine the relationship between the use of generative Artificial Intelligence (AI) tools and the conceptual understanding of undergraduate students, and to explore the frequency and purposes of AI tool usage among them. A quantitative correlational design was used, collecting survey data from 298 undergraduate students from two public universities; The Islamia University Bahawalpur and The Govt Sadiq College Women University Bahawalpur, Pakistan. The data covered students’ AI usage patterns and their self-perceived conceptual understanding. Findings showed moderate to high levels of AI tool usage, primarily for academic support, assignment writing, and clarification of complex topics. A significant positive correlation was observed between AI tool usage frequencies and perceived conceptual understanding. While AI tools such as ChatGPT, Claude, and Gemini have potential to enhance learning, the results highlight risks of superficial engagement and overreliance. Structured AI integration policies in higher education are recommended to ensure meaningful and sustainable learning outcomes and guide teachers how to use Artificial Intelligence tools in the classroom to get students more interested and help them remember what they learn.

Review

This study addresses a highly pertinent and timely topic: the relationship between generative AI tool usage and conceptual understanding among undergraduate students. Employing a quantitative correlational design with a substantial sample of 298 students from two Pakistani universities, the research successfully identifies prevalent patterns of AI tool usage, primarily for academic support, assignment writing, and clarification of complex topics. A key finding is the reported significant positive correlation between AI tool usage frequency and students' self-perceived conceptual understanding, suggesting a perceived benefit of these tools in learning processes, particularly within the context of the sampled institutions. While the study provides valuable initial insights into an emerging educational landscape, several methodological aspects warrant critical consideration. The primary limitation lies in the reliance on "self-perceived conceptual understanding." Without objective measures of conceptual mastery, it is challenging to ascertain the true impact of AI tools on actual learning outcomes. Students' self-assessments, while indicative of their confidence or perceived benefit, may not accurately reflect genuine comprehension or prevent superficial engagement, which the abstract itself acknowledges as a risk. Furthermore, as a correlational study, it can only establish associations, not causality; thus, the inference that AI tools "have potential to enhance learning" should be interpreted cautiously, as the mechanism of enhancement is not deeply explored or empirically proven by this design. Despite these limitations, the study offers important, albeit broad, recommendations for structured AI integration policies and teacher guidance within higher education. Future research would significantly benefit from employing mixed-methods approaches to explore the mechanisms through which AI tools influence conceptual understanding, perhaps combining self-report data with performance-based assessments or qualitative inquiries into the nature of student-AI interaction. Longitudinal studies could also provide insights into the sustained impact of AI usage on learning. This initial exploration lays a crucial foundation for understanding AI's role in student learning, but more rigorous methodologies are needed to fully unravel the complex interplay between AI tools and genuine conceptual development.

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