## 4 Discussion and Conclusions
### Results Interpretation and Implications  
The review on large-scale 3 DP architectural structures produced using AI was performed on a representative literature sample, which included 21 relevant papers examined using bibliometric and content analyses. The bibliometric analysis shows that the reviewed papers have been published over the past five years, with a growing trend in the number of publications in the past two years. Additionally, the bibliometric analysis revealed that the journals covering engineering, fields, architecture, and construction are most frequently published. Moreover, co-occurrence network analysis discovered keywords that were strongly interrelated within the literature sample. This analysis enabled the identification of three principal research thematic frames that coincide with AI tools' application domains in the production of large-scale 3 DP architectural structures: AI-driven design, optimization, and diagnostics. Keywords included co-occurrence analysis and isolated AI techniques (including ML, ANN, and computer vision) commonly applied in the production of architectural structures by 3 DP. Further, full-text content analysis of the literature sample deeply reviewed specific AI techniques and their applications. Lastly, the systematic review on using AI technologies in creating 3D-printed architectural structures enabled knowledge synthesis, technology assessment, and knowledge gap identification. The synthesis and summary of existing published research papers related to the application of AI technologies in the production of 3D-printed architectural structures were presented in the study. Consequently, the study helps consolidate the current state of knowledge in the field and provides a comprehensive overview of the subject matter. Moreover, light was shed on the way that AI techniques, such as ML, ANN, and computer vision, have enhanced the design, optimization, and diagnostics of 3D-printed architectural structures, providing information on technological development and prospective use. By synthesizing the literature, the study offers insights into techniques and integration strategies relevant to architectural production. The capabilities, limitations, and potential of AI technologies that support the production of 3D-printed architectural structures were discovered. Reviewing literature facilitated the assessment of the state of technology, identifying its strengths and weaknesses, and providing insights into its feasibility and applicability in the architectural field. Through our examination, we identified literature, methodologies, algorithms, or frameworks utilizing AI to design or optimize designs for structural integrity, aesthetics, energy efficiency, or other criteria. The study also inquired how AI can enhance structural performance, material efficiency, and environmental sustainability. This helped perceive the benefits and limitations of AI-based approaches in achieving desired outcomes. A gap analysis and identification of areas for further research within the subject matter domain were conducted. Analysis of existing literature enabled the identification of topics or aspects that have yet to be extensively explored, leaving the way for future research and development. In addition, the literature review highlights innovative approaches, novel applications, and emerging trends, generating new ideas and possibilities. The findings of this research provide researchers and practitioners with information on existing research and applications of 3D-printed architectural structures created using AI. By investigating and developing AI-driven strategies for 3D printing of building structures, researchers can drive innovation, improve efficiency, and expand the capabilities of architectural practice. The study can also be valuable for architects and engineers looking to understand the technical aspects, benefits, and limitations of 3 DP and AI technologies used in architecture.