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A. Ibrahim

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Open access Aug 2026

AI literacy and academic engagement in higher education: the mediating role of foreign language anxiety

The rapid integration of artificial intelligence (AI) in higher education has raised questions about how technological competencies influence student outcomes, particularly in foreign language learning where anxiety significantly affects performance. This study investigated whether foreign language anxiety (FLA) mediates the relationship between AI literacy (AIL) and academic engagement (AE) among 1,052 English as a foreign language (EFL) learners enrolled in foreign language programs at Al-Azhar University, Egypt. Participants completed three validated instruments: the artificial intelligence literacy scale (AILS), the short-form foreign language classroom anxiety scale (S-FLCAS), and the academic engagement scale (AES). Mediation analysis was conducted using PROCESS Model 4 with 5,000 bootstrap iterations to generate bias-corrected confidence intervals (CI). Correlation analyses revealed that AIL positively correlated with AE (r=.31, p<.001) and negatively with FLA (r=-.35, p<.001), while anxiety demonstrated a significant negative correlation with engagement (r=-.21, p<.001). Mediation analysis confirmed that FLA partially mediated the AIL –engagement relationship, with the indirect effect accounting for 12.2% of the total effect and the direct effect comprising the remaining 87.8%. These findings indicate that AIL enhances AE both directly and indirectly through anxiety reduction, suggesting that institutions should develop comprehensive AIL programs that address both technical skills and the affective dimensions of technology integration in language learning contexts.

A. Ibrahim, Mohamed Megahed Nasreldeen, Hesham Hussein Yakout et al. · 0 citations
Open access Jul 2026

Development and psychometric validation of the AI chatbots acceptance and perception scale for higher education students

The validated scale demonstrates strong theoretical alignment with established technology acceptance frameworks while extending traditional models to accommodate AI-specific considerations, and provides researchers and educational institutions with a reliable, theoretically grounded instrument for assessing and optimizing AI chatbot implementation in higher education contexts.

A. Ibrahim, Mohamed Ali Nemt-allah · 0 citations

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