AI Adoption for Collaboration: Factors Influencing Inclusive Learning Adoption in Higher Education - Ibrahim Youssef Alyoussef; Amr Mohammed Drwish; Fatimah Adel Albakheet; Rafdan Hassan Alhajhoj; Amal Ahmed Al-Mousa (IEEE Access)
The integration of artificial intelligence (AI) technologies into education has gained considerable attention for its potential to revolutionize teaching and learning practices. Despite this, the adoption of AI in higher education, particularly to support inclusive learning environments, remains insufficiently studied. This research examines the factors affecting the adoption of AI for collaboration among university students, with a focus on promoting inclusivity. Data was gathered from 443 students at King Faisal University using a survey designed to measure constructs related to AI adoption in collaborative learning. The questionnaire, based on the Technology Acceptance Model (TAM), was tailored to the educational AI context. “Structural equation modeling (SEM)” and hypothesis testing were applied to evaluate the relationships between constructs. The results indicated that “perceived ease of use”, “perceived usefulness”, and “behavioral intention to use AI” are significant predictors of AI adoption among students. Additionally, trust and familiarity with AI were identified as key drivers of behavioral intention. However, the hypothesis that the perceived quality of AI output influences “perceived usefulness” was not supported, suggesting students may not strongly link the quality of AI outputs to its usefulness in academic contexts. This study adds to the body of literature by providing empirical insights into the factors influencing AI adoption for collaboration, offering practical recommendations for creating more inclusive and effective educational environments. The study highlights the importance of building student trust and familiarity with AI to enhance its adoption for inclusive and collaborative learning in higher education.