Association between generative artificial intelligence-related factors and self-directed learning ability among...

Area of Science:

• Nursing Education
• Artificial Intelligence in Healthcare
• Educational Technology

Background:

• Generative artificial intelligence (AI) is increasingly used in healthcare and education.
• Limited research exists on generative AI's impact on nursing students' self-directed learning.
• This study addresses this gap by examining key factors influencing this relationship.

Purpose of the Study:

• To investigate the association between generative AI factors and self-directed learning ability in nursing students.
• To apply the Technology Acceptance Model (TAM) to understand user perceptions.
• To identify key predictors of self-directed learning in the context of generative AI use.

Main Methods:

• Cross-sectional online survey conducted in South Korea.
• 219 nursing students participated.
• Multiple regression analysis used to assess Technology Acceptance Model variables (perceived usefulness, ease of use, readiness, intention to continue use) and their association with self-directed learning ability.

Main Results:

• Readiness to use generative AI (β=0.345) showed the strongest association with self-directed learning ability.
• Perceived ease of use (β=0.201) and intention to continue use (β=0.184) were also significant predictors.
• Major satisfaction (β=0.216) was a significant covariate, with these variables explaining 62.6% of the variance.

Conclusions:

• Generative AI factors are significantly related to nursing students' self-directed learning ability.
• Educational strategies should focus on enhancing readiness, perceived ease of use, and intention to use generative AI.
• Incorporating major satisfaction into strategies can further foster self-directed learning in nursing students.