Examining the Relationship With Kolb’s Experiential Learning Styles and Attitudes Toward Artificial Intelligence and ChatGPT

Abstract

Purpose. The purpose of this quantitative study was to examine the relationship between Kolb’s experiential learning styles and attitudes toward artificial intelligence (AI) and ChatGPT. The study aimed to determine whether certain learning styles were associated with more favorable or unfavorable perceptions of these technologies.

Theoretical Framework. David Kolb’s Experiential Learning Theory served as the theoretical framework for this study. Aligned with this foundation, the Kolb Experiential Learning Profile (KELP) was utilized to categorize and interpret participants’ individual learning styles.

Methodology. This quantitative, non-experimental, explanatory correlational study examined the relationship between experiential learning styles and attitudes toward AI and ChatGPT. Data were collected using a modified version of the General Attitudes Toward Artificial Intelligence Scale. A cross-sectional design was employed to capture participant responses at a single point in time. Descriptive statistics, one-way analyses of variance (ANOVAs), and Pearson correlation analyses were conducted to examine group differences and relationships between variables. Research quality was upheld through the use of validated instruments, rigorous data screening procedures, and strict adherence to ethical standards, including informed consent and Institutional Review Board approval.

Findings and Conclusion. Findings revealed that while most participants expressed generally positive attitudes toward artificial intelligence and ChatGPT, KELP learning style significantly influenced negative attitudes toward AI. Specifically, Balancing learners reported significantly higher levels of concern compared to Acting learners. No other subscale differences reached significance. Additionally, a strong positive correlation was found between positive attitudes toward AI and positive attitudes toward ChatGPT. These findings suggest that experiential learning styles play a meaningful role in shaping individuals’ perceptions of AI technologies.

Recommendations. Future research should investigate the relationship between learning styles and AI acceptance across more diverse populations to enhance generalizability. The Delphi method is recommended for gathering expert consensus on best practices for integrating AI tools, such as ChatGPT, into organizational settings. Educators, leaders and developers should consider tailoring training and support strategies to align with KELP learning styles, thereby improving AI adoption and user engagement.