Abstract This study addresses the need for context-specific Artificial Intelligence (AI) literacy research in West Africa, confronting challenges such as interrelation of AI literacy dimensions, ethical concerns, and a scarcity of localized studies. It investigates AI literacy among university students in Ghana and Nigeria through a quantitative cross-sectional survey of 427 participants (n = 206 Ghana, n = 221 Nigeria). The investigation focuses on four interconnected dimensions from the ABCE framework: Affective (motivation, self-efficacy), representing emotional engagement with AI; Behavioral (collaboration, intentional use), reflecting active participation in AI-related tasks; Cognitive (knowledge, critical thinking), encompassing understanding and application of AI concepts; and Ethical, pertaining to awareness and commitment to AI's societal implications. Using partial least squares structural equation modeling (PLS-SEM), findings confirm that affective factors positively influence cognitive outcomes, mediated by behavioral engagement and ethical understanding. Notably, country differences do not significantly affect these relationships, thereby justifying the analysis of the combined dataset and highlighting shared patterns in AI literacy development across the two contexts. This consistency validates a common underlying mechanism for AI literacy development in these West African contexts. The study shows the importance of integrating technical AI skills with ethical principles, collaborative learning, and culturally appropriate strategies. Specifically, it offers actionable strategies for strengthening affective learning, designing collaborative behavioral interventions, embedding ethical reasoning into curricula, and contextualizing pedagogies for regional realities, thereby informing stakeholders on effective AI education in West Africa.

Abstract The current study investigates how faculty members in Chinese universities are navigating the integration of generative artificial intelligence tools into second language curricula, focusing on specific pedagogical and institutional challenges. Using an exploratory sequential mixed-method approach (N = 776), we examined how faculty perceive and address key areas of concern: curriculum adaptation, assessment transformation, student engagement, ethical concerns, professional development needs, and pedagogical shifts. Unlike prior studies, which often generalize faculty experiences, this study uniquely captures nuanced faculty strategies and gaps in readiness across a large-scale context in Chinese higher education. Results indicated that faculty members exhibited significant usage of generative artificial intelligence tools both for personal and professional purposes. They also used generative artificial intelligence tools for formative assessment practices, with limited application in summative assessments. Results indicated faculty faced Generative AI tool usage challenges like curriculum adaptation, assessment transformation, student engagement with generative artificial intelligence tools, ethical concerns, professional development, and pedagogical shifts. The findings also demonstrated that faculty members were struggling to address these challenges. Furthermore, outcomes indicated that professional development and ethical concerns directly, indirectly, positively and significantly contribute to helping faculty to enhance student engagement and pedagogical shifts in their teaching practices. Additionally, curriculum adaptation and assessment transformation have also mediated these relationships. These findings contribute novel insights into how professional development can support effective GenAI integration. Implications are offered for faculty development programs, curriculum design policies, and institutional governance in the era of AI-enhanced education.