Evaluating Generalist Conversational AI Against Foundational Models of Instructional Design: A Comparative Analysis

The rapid integration of Generative AI into instructional engineering presents a critical challenge: verifying the capacity of these tools to strictly adhere to systemic theoretical models of learning, despite the risk of generating "pedagogically hallucinated" content that possesses surface plausibility but lacks structural validity. This study addresses this gap by systematically evaluating the performance of generalist conversational AIs against foundational principles of Instructional Design (ID). Adopting a qualitative comparative analysis of four state-of-the-art models available in October 2025—GPT-5 (OpenAI), Gemini 2.5 Pro (Google), Claude Sonnet 4.5 (Anthropic), and DeepSeek V3.2 (DeepSeek AI)—we assessed their outputs for complex design scenarios against a multi-dimensional framework grounded in authoritative theories, including Biggs’s Constructive Alignment, Merrill’s First Principles of Instruction, and Universal Design for Learning (UDL). Results reveal a "paradox of competence without comprehension," where models demonstrate high factual reliability and linguistic fluency but exhibit significant shortcomings in maintaining logical pedagogical consistency, particularly regarding assessment alignment and accessibility standards, with only Claude Sonnet 4.5 demonstrating a notable proactive partnership posture. Consequently, we conclude that current generalist LLMs cannot function as autonomous expert designers and argue for a shift in professional practice toward Critical AI Literacy, where the human designer leverages AI for ideation but remains the essential guarantor of the pedagogical architecture.