Adopting Artificial Intelligence in higher education offers opportunities to improve the instructional quality, enhance alignment with learning outcomes and reduce faculty workload. This conceptual study introduces the AI-Enhanced Instructional Quality (AI-EIQ) framework which is an instructor centered five-step model design to guide faculty members in adopting AI tools during the course design, outcome mapping, assessment development, moderation support, and reflective improvement. Drawing on current literature and established models such as Technology-Organization-Environment (TOE) and Technology Acceptance Model (TAM), the framework is supported by existing practices and accessible AI applications such as ChatGPT for content creation and rubric design as well as other tools that help with learning analytics and moderation summaries, while AI can support instructors in summarizing the grade distributions and suggesting narrative structures, final moderation remains the responsibility of the faculty member. A hypothetical application scenario illustrates how the proposed framework can be applied in higher education context to support instructional design and quality assurance. The study will highlight institutional readiness, ethical considerations and strategic recommendations for faculty training and responsible implementation. This study contributes a practical and adaptable model for instructors seeking to enhance teaching and learning outcomes and effectiveness through AI—Supported workflows, with potential for future pilot testing and empirical validation.