Reimagining ADDIE for the AI Era: The AIIT-Embedded Framework

Abstract

The ADDIE model has long provided systematic rigor in instructional design, but its limited integration of emerging technologies restricts its relevance in today’s digital era. Artificial intelligence (AI) offers transformative potential for personalization, adaptive learning, real-time analytics, and automation; however, current applications remain fragmented and are seldom embedded into established design models. This study upgrades the Artificial Intelligence–Integrated Instructional and Training Technologies (AIIT) framework by embedding the five phases of ADDIE (Analysis, Design, Development, Implementation, and Evaluation). AIIT is defined through eight interrelated characteristics, such as adaptive learning, real-time feedback, personalized instruction, skill development, workforce performance, interactive engagement, knowledge transfer, and ethically informed instructional practices that collectively redefine instructional design within the contemporary digital paradigm. Using a structured literature synthesis combined with reflective author analysis, this study develops the AIIT embedded ADDIE model, which preserves ADDIE’s systematic discipline while infusing it with adaptability, intelligence, and ethical safeguards. Conceptually, the study advances instructional design theory by bridging definitional gaps and updating a classical model for the AI era; practically, it provides instructional designers and organizations with a criterion-based framework for adopting AI responsibly. Although conceptual in scope, the AIIT embedded ADDIE model establishes a foundation for future empirical research and offers a coherent roadmap for designing adaptive, ethical, and performance-driven learning environments.

Keywords: Instructional Design, ADDIE model, Artificial Intelligence, AI-Integrated Instructional and Training Technologies (AIIT), Instructional Design Models, Training and Development