Beyond ChatGPT: A Comparative Mapping of AI Research in STEM and Engineering Education and the Contemporary AI Tool Ecosystem

Artificial intelligence (AI) is increasingly shaping higher education. However, the diversity of the contemporary AI tool ecosystem is only partially reflected in educational research. This study examines the research visibility of a curated set of 85 AI tools and systems in STEM, engineering, and laboratory education using a comparative mapping approach. The analysis revealed a marked concentration of research visibility: only 36 of the 85 tools were research-visible, while Conversational AI/LLMs accounted for 73.7% of the 927 positive Paper × Tool connections identified across 592 publications. Laboratory-based learning environments were particularly sparsely represented, with only 23 publications having an established laboratory context. Taken together, these findings reveal a mismatch between the technological and functional diversity represented in the curated contemporary AI tool ecosystem and the more concentrated patterns of research visibility identified for these tools. This mismatch can be understood as a broader complexity gap. The findings highlight the need for more function-oriented and context-sensitive AI research and contribute to current discussions on the responsible integration of AI into STEM and engineering education, particularly in laboratory-based learning environments.