Faculty as Learning Agent: AI-Enhanced Instructional Design in Higher Education

The rapid integration of Artificial Intelligence (AI) into higher education has created an urgent need for faculty development programs that move beyond basic tool literacy toward pedagogical transformation. This conceptual paper introduces the Faculty as Learning Agent (FLA) Framework, a tripartite model designed to empower university faculty—particularly those in teacher education programs—to systematically integrate AI into their instructional design practice. The framework comprises three interconnected pillars: (1) Structural Analysis, which involves extracting competency maps and knowledge graphs from course syllabi to identify pedagogical "blind spots" before instruction; (2) Smart Design, which focuses on creating contextualized, performance-based tasks and lesson plans with embedded preventive (remedial) layers that are resilient to AI-assisted shortcuts; and (3) Data-Driven Feedback, which leverages AI for analyzing student outputs and generating personalized, growth-oriented feedback. The FLA Framework addresses a critical gap in current faculty development programs, which often focus on AI tool demonstration rather than pedagogical integration. By positioning faculty as proactive "learning agents" rather than passive content transmitters, this framework offers a scalable, evidence-informed approach to AI-enhanced teaching in higher education. The paper concludes with implications for faculty development, instructional design, and educational research.