Introduction: The rapid adoption of artificial intelligence (AI) in higher education demands systematic frameworks to develop AI literacy across diverse institutional stakeholders. Existing approaches often lack clarity, inclusivity, and progression pathways, limiting their effectiveness. This protocol outlines a methodology for creating the AI Literacy Framework (ALiF), a comprehensive model designed to address the needs of students, faculty, and staff in higher education. Methods: ALiF will be developed using a three-phase mixed-methods approach. Phase 1 involves a systematic literature review and expert consultation to design the initial framework. Phase 2 refines the framework through Delphi studies and stakeholder focus groups. Phase 3 validates the framework via expert evaluation and pilot implementation across three higher education institutions. Data collection includes 30 expert interviews, 24–30 focus groups, and quantitative and qualitative analysis of pilot outcomes. Expected Outcomes: The study will deliver (1) a validated AI literacy framework, (2) detailed progression pathways for diverse stakeholder groups, (3) implementation strategies tailored to higher education contexts, and (4) tools for assessing institutional readiness and literacy levels. Supporting outputs will include assessment instruments and toolkits for implementation. Significance: This protocol provides a foundation for institutions to systematically develop, implement, and assess AI literacy programs. Its multi-stakeholder focus and robust methodology contribute significantly to addressing gaps in existing research and practice. Potential limitations include resource-intensive implementation and the need for longitudinal validation.