Pathways to Learning Artificial Intelligence: An Exploratory Study Using the Theory of Planned Behavior

Abstract

Background Artificial intelligence (AI) has a transformative potential in nursing. The adoption of AI in Indian nursing education is often hindered by limited resources and traditionally structured education systems. In this context, developing evidence‐based insights to enhance AI integration into nursing education in India is imperative. Objective This study examined the relationships and mediating effects of subjective norms, self‐efficacy, personal relevance, basic knowledge, and behavioral intention on AI learning among undergraduate nursing students in India, using a modified theory of planned behavior (TPB) framework. Methods An explanatory, cross‐sectional survey was conducted among 335 nursing students in Punjab, India, to examine factors influencing learning of AI, using a modified TPB framework. Data were collected by the investigator through a self‐administered questionnaire based on TPB constructs, including attitude, subjective norms, perceived behavioral control, and behavioral intention, with actual learning as the dependent variable. Data were collected by the investigator through Google Forms administered in classrooms. Hypothetical paths were analyzed using SPSS 28.0. Results Contrary to the TPB assumptions, behavioral intention did not predict learning ( β = 0.17, p = 0.08), whereas subjective norms emerged as the strongest predictor ( β = 0.42, p < 0.001). Basic knowledge showed no direct influence on learning ( β = 0.17, p = 0.08), whereas self‐efficacy showed marginal significance ( β = 0.28, p = 0.051). Personal relevance influenced mediators such as subjective norms ( β = 0.42) and basic knowledge ( β = 0.36) but lacked a direct effect on learning, highlighting systemic barriers. Discussion This study challenges individual‐centric models by prioritizing sociocultural factors and systemic barriers in AI education, revealing that collective support and applied training are more important than intention to learn AI. The findings suggest that social influence significantly impacts AI learning, whereas the intention to engage with AI does not necessarily lead to actual learning. Additionally, personal relevance influences key mediators but does not directly enhance learning outcomes. These insights highlight the need for policy reforms that focus on infrastructure development, peer and faculty support, and practical learning opportunities that can help translate students’ interest in AI into meaningful learning outcomes.

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