Integrating artificial intelligence into architectural education: a study exploring the perceptions of architecture instructors and students

Purpose This study explores the integration and use of AI tools in architectural education, focusing on the perceptions of a worldwide sample of architecture instructors and students. It investigates AI usage prevalence, patterns, motivations, ethical awareness, and offers suggestions for future integration. Design/methodology/approach A mixed-methods research design was employed, combining quantitative and qualitative research methods. Quantitative data were collected via two questionnaires from 97 instructors with different academic ranks and 324 students at various study levels, distributed across 32 countries and 90 universities, and were analyzed statistically. Qualitative data were gathered from the open-ended responses of the questionnaires and extended by two focus groups with students and instructors from Egypt, and were then analyzed using thematic analysis. Findings In architectural education, AI tools are widely used by students and accepted by instructors for design support and research, yet their design outputs remain of questionable quality and their impact on complex creative processes is limited. The motivations for using AI tend to be personal rather than institutionally encouraged. In addition to concerns about reliability and overreliance, transparency and ethical engagement are essential to uphold academic integrity. The findings highlighted the need for structured training and clear institutional policies to ensure AI complements, rather than replaces, human critical and creative thinking. The study also revealed differences between instructors' and students' perceptions of AI's capabilities. Research limitations/implications Limitations include a lower-than-targeted survey response rate, uneven geographic distribution, and small, homogeneous focus groups, however, the sample was sufficient to derive general indicators, as these remained stable with increasing sample size and closely approximated the target, Overall, the study highlights AI's transformative potential in architectural education, while advocating for ethical, informed, and context-sensitive integration across institutions. Originality/value This study contributes to the discourse on AI in architectural education by revealing the current state of AI use in architectural education and highlighting opportunities to enhance learning and design. Its international scope allows cross-cultural comparisons and provides guidance for curriculum development and institutional policies.

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Publication Details

Journal
International Journal of Architectural Research Archnet-IJAR
Published
2026-05-16
DOI
https://doi.org/10.1108/arch-11-2025-0532
Primary Topic
Architecture and Computational Design
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrating artificial intelligence into architectural education: a study exploring the perceptions of architecture instructors and students

Mohammed Ghonim, AbdelRahman Badawy, Nehad Eweda
International Journal of Architectural Research Archnet-IJAR
Architecture and Computational Design
article

Integrating artificial intelligence into architectural education: a study exploring the perceptions of architecture instructors and students

Mohammed Ghonim, AbdelRahman Badawy, Nehad Eweda
article en

Abstract

Purpose This study explores the integration and use of AI tools in architectural education, focusing on the perceptions of a worldwide sample of architecture instructors and students. It investigates AI usage prevalence, patterns, motivations, ethical awareness, and offers suggestions for future integration. Design/methodology/approach A mixed-methods research design was employed, combining quantitative and qualitative research methods. Quantitative data were collected via two questionnaires from 97 instructors with different academic ranks and 324 students at various study levels, distributed across 32 countries and 90 universities, and were analyzed statistically. Qualitative data were gathered from the open-ended responses of the questionnaires and extended by two focus groups with students and instructors from Egypt, and were then analyzed using thematic analysis. Findings In architectural education, AI tools are widely used by students and accepted by instructors for design support and research, yet their design outputs remain of questionable quality and their impact on complex creative processes is limited. The motivations for using AI tend to be personal rather than institutionally encouraged. In addition to concerns about reliability and overreliance, transparency and ethical engagement are essential to uphold academic integrity. The findings highlighted the need for structured training and clear institutional policies to ensure AI complements, rather than replaces, human critical and creative thinking. The study also revealed differences between instructors' and students' perceptions of AI's capabilities. Research limitations/implications Limitations include a lower-than-targeted survey response rate, uneven geographic distribution, and small, homogeneous focus groups, however, the sample was sufficient to derive general indicators, as these remained stable with increasing sample size and closely approximated the target, Overall, the study highlights AI's transformative potential in architectural education, while advocating for ethical, informed, and context-sensitive integration across institutions. Originality/value This study contributes to the discourse on AI in architectural education by revealing the current state of AI use in architectural education and highlighting opportunities to enhance learning and design. Its international scope allows cross-cultural comparisons and provides guidance for curriculum development and institutional policies.

International Journal of Architectural Research Archnet-IJAR
Cairo University (EG)
Openalex Percentile: Top 2%
Architecture and Computational Design
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