Artificial Intelligence in Physiotherapy Education: A Multi‐Country Middle East Survey of <scp>AI</scp> Acceptance and Barriers Among University Students

Abstract

ABSTRACT Artificial Intelligence (AI) is reshaping healthcare education, yet structured AI training within physiotherapy programmes remains uneven across the Middle East. We conducted a cross‐sectional, multi‐country online survey of 3195 undergraduate and internship‐level physiotherapy students from nine Middle Eastern countries (Egypt, Jordan, Lebanon, Libya, Palestine, Saudi Arabia, Sudan, Tunisia, and the United Arab Emirates). Using Technology Acceptance Model (TAM) constructs—perceived usefulness (PU) and perceived ease of use (PEOU)—we examined factors associated with AI acceptance and students' perceived barriers to AI integration in physiotherapy education. AI acceptance differed significantly by country (highest in the UAE and lowest in Tunisia), gender (male &gt; female), academic level, GPA, and income ( p &lt; 0.05). Prior AI workshop participation, use of specific AI tools (e.g., DeepSeek), perceived time‐management benefits, and trust in AI were associated with higher acceptance. In multivariable regression, these sociodemographic and AI‐exposure variables explained 24.4% of the variance in AI acceptance. These findings indicate substantial regional disparities in AI preparedness and access, supporting the need for equitable, competency‐based AI education and governance policies tailored to physiotherapy training across the Middle East.

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