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Investigating lifelong learners' adoption of generative artificial intelligence using PLS-SEM and fsQCA within the
1Faculty of Education, Open University of China, Beijing, China.
Scientific Reports
|July 16, 2026
Summary
Lifelong learners adopt generative artificial intelligence (GenAI) when performance, effort, social factors, motivation, price, and habit are considered. Configurational pathways reveal combined factors driving GenAI adoption in education.
Area of Science:
- Educational Technology
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Generative Artificial Intelligence (GenAI) integration into lifelong education systems requires understanding learner adoption dynamics.
- The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) provides a framework for analyzing technology adoption.
Purpose of the Study:
- To investigate the factors influencing lifelong learners' behavioral intention and usage of Generative Artificial Intelligence (GenAI).
- To identify configurational pathways associated with high behavioral intention and usage of GenAI among lifelong learners.
Main Methods:
- Employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze quantitative data from 436 lifelong learners.
- Utilized fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify causal configurations of factors leading to GenAI adoption.
Main Results:
- PLS-SEM confirmed that performance expectancy, effort expectancy, social influence, hedonic motivation, price value, and habit significantly predict behavioral intention and usage.
- fsQCA identified distinct configurational pathways leading to high behavioral intention and usage, highlighting the importance of combined factors.
Conclusions:
- Findings provide empirical evidence supporting the UTAUT2 model in the context of GenAI adoption by lifelong learners.
- Offers actionable insights for educators and policymakers to facilitate effective GenAI integration in lifelong learning environments.
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