The ICAIL Framework: A Critical and Inclusive Approach to AI Literacy for Non-Technical Learners in Higher Education

Abstract

The emergence of generative artificial intelligence (AI) presents both opportunities and complex ethical challenges within higher education. Despite widespread adoption, structured opportunities for developing inclusive and critical AI literacy for non-technical learners remains limited. This paper introduces the Inclusive Critical AI Literacy (ICAIL) Framework, a conceptual model informed by critical pedagogy, Universal Design for Learning (UDL), and current research on AI literacy. Organized around five domains—Understanding AI, Analyzing and Critically Evaluating AI, Applying and Collaborating with AI, Digital Citizenship, and Social Responsibility—the ICAIL framework promotes analytical skills, ethical reflection, and practical competence in AI literacy for non-technical learners. ICAIL emphasizes flexible learner-centered approaches that adapt to diverse educational contexts and learner variability. By integrating critical perspectives on equity, bias, and social impact with inclusive educational practices, this framework addresses existing gaps in AI literacy education. Future research should empirically evaluate ICAIL’s effectiveness in diverse educational settings, particularly in its potential ability to reduce inequalities for marginalized learner groups. This paper contributes to educational research by providing actionable guidance for educators who seek to responsibly integrate generative AI into curricula, supporting critical thinking, ethical awareness, and inclusive practices in an increasingly AI-focused educational landscape.

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