Integrated Model of AI Adoption in Higher Education (IMAI4HE)

Abstract

AI integration in higher education needs comprehensive frameworks to enhance the new forms of learning based on the student. This paper proposes solutions through the Integrated Model of AI Adoption in Higher Education (IMAI4HE) to improve the education process. The key factors explored in IAMI4HE adoption are based on awareness of the importance of one's career, innovation, and sustainability.

Data was collected from 749 Mexican university students in 2024. Three experts integrated Delphi panels and focus groups and designed the IAM4HE factors for the questionnaire. After this, the PLS-SEM analysis was applied to determine the underlying relationships.

The IMAI4HE framework was validated, showing mainly relationships between knowledge, motivation, trust, preparedness, and use intention. Practical recommendations include reinforcing knowledge to improve its relationship with preparedness and taking advantage of both relationships: motivation to trust and preparedness to use intention.

The IMAI4HE framework is based on multidisciplinary experts and users, focused on innovation and sustainability in the student's career development and AI adoption in higher education.

The relationships between motivation to trust and preparedness to use intentions are the strongest predictors for easy AI adoption in the IMAI4HE framework. However, theoretical knowledge alone is still not enough. Limitations include a Mexico-specific sample; hence, this study is still exploratory, not confirmatory.