Text-to-Image Artificial Intelligence in Interior Architecture Design: A Multi-Criteria Decision-Making Approach

This study comprehensively evaluates the contributions of text-to-image artificial intelligence (AI) systems to interior architectural design using multi-criteria decision-making (MCDM) methods. Six platforms, DreamStudio, MidJourney, Leonardo AI, Artbreeder, Craiyon and DALL-E, were examined by an expert panel of interior designers and architects and analyzed using MCDM techniques including TOPSIS, AHP, VIKOR, ELECTRE, and PROMETHEE. The analyses revealed DreamStudio’s strengths in spatial organization and material harmony, while MidJourney stood out for its ability to generate dynamic, balanced, and visually diverse compositions. Correlation analysis among the MCDM methods enhanced the reliability of the findings, particularly highlighting a strong alignment between TOPSIS and AHP (r=0.997). The study demonstrates the strong aesthetic potential of current AI systems but underscores their limitations in fundamental design elements like spatial logic and cultural relevance. Aesthetic biases and ethical considerations are also addressed. Future research should integrate user experience and designer perspectives to explore a more meaningful and holistic integration of AI into interior design processes.

Keywords: Generative AI, Text-to-Image AI Systems, Deep Learning, Spatial Organization, Architectural Design, Multi-Criteria Decision Making, Limitations of AI in Design