Designing LMS and Instructional Strategies for Integrating Generative-Conversational AI
This addresses the problem of integrating AI into education for higher education institutions, but it is incremental as it builds on existing pedagogical theories and design methods.
This study tackled the challenge of delivering personalized and scalable learning in higher education by introducing a structured framework for designing an AI-powered Learning Management System (AI-LMS) that integrates generative and conversational AI, resulting in a practical model with modular components like configurable prompts and adaptive feedback loops.
Higher education faces growing challenges in delivering personalized, scalable, and pedagogically coherent learning experiences. This study introduces a structured framework for designing an AI-powered Learning Management System (AI-LMS) that integrates generative and conversational AI to support adaptive, interactive, and learner-centered instruction. Using a design-based research (DBR) methodology, the framework unfolds through five phases: literature review, SWOT analysis, development of ethical-pedagogical principles, system design, and instructional strategy formulation. The resulting AI-LMS features modular components -- including configurable prompts, adaptive feedback loops, and multi-agent conversation flows -- aligned with pedagogical paradigms such as behaviorist, constructivist, and connectivist learning theories. By combining AI capabilities with human-centered design and ethical safeguards, this study advances a practical model for AI integration in education. Future research will validate and refine the system through real-world implementation.