Assessment of Five Generative Artificial Intelligence Tools in Theory of Machines and Mechanisms Through Bloom's Taxonomy

ABSTRACT Background: The rapid development and adoption of generative artificial intelligence (GAI) tools, particularly following ChatGPT's release, have created new opportunities and challenges for engineering education. While several studies have explored AI capabilities in mechanical engineering, systematic analysis across different cognitive skill levels remains limited. Purpose: This study evaluates and compares the performance of five conversational AI tools (ChatGPT, Gemini, Claude, DeepSeek, LeChat) in Theory of Machines and Mechanisms (TMM), analyzing their capabilities across the six cognitive levels of revised Bloom's taxonomy: L1‐Remember, L2‐Understand, L3‐Apply, L4‐Analyze, L5‐Evaluate, and L6‐Create. Methods: A set of 48 TMM questions was developed, with eight questions targeting each of Bloom's taxonomy levels. Four mechanical engineering professors interacted with the five AI tools using standardized prompts. Responses were anonymized and independently evaluated by two graders using a 5‐point Likert scale across three aspects: technical quality, accuracy and completeness, and quality of writing. Statistical analysis was performed through ANOVA, Kruskal–Wallis test, and linear mixed‐effects models, to examine differences among AI tools and cognitive levels. Results: AI tools demonstrated generally good performance, with mean scores ranging from 3.76 to 4.22 for technical quality and 3.79 to 4.27 for accuracy and completeness. Writing quality scores consistently exceeded 4.65. LeChat performed significantly worse than the other tools. Performance was notably weaker at the L6‐Create level compared to all other cognitive levels. Conclusions: GAI tools show promise as educational resources for TMM, particularly at lower cognitive levels, though significant limitations persist in creative tasks requiring higher‐order reasoning and synthesis.