Hardi K. Abdullah

Generative AI has reached the early, ideational phase of architectural design, the stage most tightly bound to conceptual creativity, yet its effect on student creativity remains unevenly investigated. This study examines the transfer of creativity from handmade concept to AI refinement. In a first-year studio, 45 students developed a bus-stop design and then reworked it using AI visualisation tools. Pre-AI and post-AI designs were rated by experts using the same nine-dimensional Consensual Assessment Technique (CAT) rubric; the prompt log was text-mined; and the two streams were linked for each student and analysed. Results suggest a modest rise in total creativity (3.38 to 3.71; Wilcoxon p = 0.022, dz = 0.38), yet concentrated in appropriateness; functional adequacy (+0.80), structural feasibility (+0.78), and environmental response (+0.56) increased significantly, while novelty dimensions remained unaltered. The cohort suggested a reordering: weaker starters gained most and several strong starters declined. Although baseline input creativity dominated the prediction of output quality and prompt behaviour added no significant variance once it was controlled, gains were largest when students let AI transform their original concepts. Results indicate that in the context of this study, generative AI acts as a leveller and redirector of creativity, strengthening the buildability of weaker designs without raising originality, with direct implications for how studios deploy and teach the tool. As results are based on a single first-year cohort, institution, design brief, and an observational within-student design, the findings are limited to exploratory evidence of conditional creativity transfer rather than causal evidence.

Artificial Intelligence (AI) is being integrated into the architectural profession via processes such as generating images based on design parameters; providing support to help create written descriptions of designs; assisting in the visualisation of buildings before they are built; allowing for greater use of parameters in construction; providing documentation to help complete projects in record time; and assisting in making design decisions. The success of integrating AI into practice depends not only on the use of tools but also on firms’ maturity in integrating AI into their workflows, employees’ capabilities, project teams’ operational efficiency, the impact of investment decisions, and the overall governance of the profession. This research evaluates the maturity of AI integration in architectural practice in the Kurdistan Region of Iraq. To do so, it develops and operationalises the AI Integration Maturity Index (AIMI), an eight-component formative composite index scored 0–37 and organised into five maturity bands (Non-adopter, Exploratory, Occasional, Integrated, and Advanced Strategic). The index comprises adoption, usage, diversity of tools, breadth of workflows, project penetration, staff involvement, training/capacity building, and governance/strategic focus. The AIMI is treated as a literature-derived formative diagnostic tool rather than a universal weighting standard, and was developed through a structured literature synthesis, expert pilot review, and internal-structure validation. Accordingly, its component logic and internal statistics are reported as a transparency and coherence check rather than as reflective reliability claims: Cronbach’s alpha (0.922) is presented descriptively to show component co-movement given the formative specification, while inter-coder reliability (kappa = 0.96) supports the qualitative benchmark coding. The study employs a mixed-methods descriptive comparative methodology consisting of a structured survey instrument administered to 100 architectural firms operating in the local market and structured asynchronous text-based interviews with 10 international architectural firms, comparing the resulting profiles with selected, generally accepted benchmarks and with a sample of leading firms engaged in architectural practice worldwide. On average, total AIMI scores in the local sample were 18.14 out of 37, indicating that local firms are broadly adopting and using AI (82% currently use AI on a regular or occasional basis). By contrast, the international sample yielded a mean AIMI score of 28.60, indicating that local firms exhibit a significantly lower level of maturity than the international benchmark, with the largest gaps in staff involvement, project penetration, and overall engagement with AI use. The paper concludes that the primary challenge facing architectural firms in the Kurdistan Region is no longer basic awareness or technological infrastructure, but rather the transition from broad and superficial AI adoption to a systematic, structured, project-based, and well-governed integration of AI within the architectural profession.

Community- and values-based approaches to the conservation of architectural heritage are increasingly emphasised. Yet empirical evidence on how local communities assess the potential of interventions for built heritage remains limited. Hence, when examining heritage interventions based on prevailing theories, visual scenario testing is considered a significant communication tool. Therefore, this study investigates local community preferences for different intervention levels in Koya City’s urban heritage using Artificial Intelligence (AI)-assisted visualisations that span a continuum of interventions. The visualisations serve as the basis for the survey, which was used to explore local preferences for the various heritage intervention scenarios. The correspondence between the AI-assisted visuals and the theoretical interventions was assessed by experts before the survey. The findings suggest that low- to moderate-intensity conservation strategies that preserve architectural character are consistently preferred by the survey community. In contrast, interventions that involved considerable physical change were markedly less favoured. Results from the expert validation test indicate that low- to moderate-intervention levels were reliably visualised using AI-assisted visualisations, while the higher-intervention levels were considered less representative. The supplementary calibration further highlights the importance of visual granularity in participatory heritage evaluation. However, the study remains exploratory and limited by the use of AI-assisted visualisations, a single case-study context, and the difficulty of translating nuanced conservation doctrines into visually discrete categories. This study makes a dual contribution by providing empirical evidence of local preferences across a continuum of conservation interventions, and by proposing an AI-assisted visual methodology to bridge expert conservation theory with public understanding.

Contemporary housing design practices in Erbil’s hot–dry climate remain understudied with respect to indoor thermal comfort performance. This study evaluates indoor thermal comfort in traditional and contemporary dwellings in Erbil, Iraq, a hot–dry climate characterized by extreme summer conditions. An integrated methodology combining field measurements, locally calibrated climatic data, and validated computational simulation was applied to representative case studies. Indoor thermal comfort parameters were monitored, and a custom EnergyPlus Weather (EPW) file was developed to capture local climatic conditions. Year-round computational simulations were conducted using the Ladybug Tools workflow. Indoor thermal comfort was evaluated using the adaptive comfort model for the naturally ventilated traditional courtyard house and the PMV–PPD model for contemporary air-conditioned dwellings, in accordance with ASHRAE 55 Standard. Validation of the computational simulations against field measurements confirmed close agreement between the measured and simulated results. The findings indicate that the traditional house consistently achieved acceptable thermal comfort within adaptive comfort limits, while contemporary houses experienced persistent overheating and elevated discomfort indices. These results demonstrate that thermal underperformance in contemporary housing is primarily driven by architectural and envelope design rather than modeling uncertainty. The study highlights the effectiveness of vernacular, climate-responsive strategies and suggests their integration into sustainable house design in hot–dry regions.

In the design studios, daylighting and artificial lighting have a primary influence on providing visual comfort and students’ satisfaction with the environment. The design and drawing activities performed in these spaces require an acceptable lighting performance. The aim of this study is evaluating the lighting quality in the design studios and providing lighting design recommendations to improve the students’ creativity and productivity. Observations were performed to obtain physical studio data and existing lighting system. A simulation software, namely DiaLux Evo 10, is used to simulate the lighting performance in the existing design studio at the Department of Architecture, College of Engineering, Salahaddin University-Erbil (SUE). The simulation results of daylight factor (DF), illumination levels (Lux), and glare value are analyzed in accordance to the British/ European Standard (BS EN 12464-1:2021). The results reveal that the lighting performance at the studied design studio is not sufficient, thus the study suggests possible modifications for the type and quantity of existing artificial lighting.

In the quest to improve urban quality of life, creating functional and visually appealing urban green spaces plays a pivotal role. This study investigates the influence of five key physical characteristics—availability, maintenance, facilities, environment, and design—on the usability of urban green spaces in three case studies in Erbil: Italian City 2, Hewa City, and Ashti City 2 neighborhoods. The research aims to understand how urban green spaces impact investment housing projects, analyze the relationship between physical factors and urban design usability and explore individuals' perceptions regarding the influence of these factors on urban green space design. Through systematic observation and visual analysis of residential areas, the study reveals that these five physical qualities of green spaces consistently scored above 90%, indicating a strong alignment with usability elements. This suggests that these physical attributes significantly enhance the usability of urban green spaces, which, in turn, supports broader urban design goals. This research highlights the importance of integrating these physical characteristics when planning and designing urban green spaces, particularly within the context of investment housing projects in Erbil's case study neighborhoods. Such enhancements can positively impact urban development and residents' quality of life. Future research should delve into the long-term socio-economic effects of these green space improvments.

This study proposes a performance-based window design model for optimised natural ventilation potential by reducing the level of indoor carbon dioxide (CO₂) concentration and improving thermal comfort, consequently minimising supplementary heating/cooling loads. The model consists of several stages: (1) Knowledge acquisition, (2) establishing a relationship between window design and natural ventilation, (3) identifying performance criteria and the design of experiments (DOE), (4) conducting performance-based dynamic simulations, (5) evaluation of findings, and (6) making informed design decisions. The study also proposed an evaluation method by which assessments of indoor CO₂ concentration and adaptive thermal comfort are performed using the threshold suggested by the World Health Organisation (WHO, Geneva, Switzerland) and the acceptability categories of the British/European standard BS EN 15251:2007. The proposed model was applied to a single office inspired by the staff offices at the Department of Architecture, Eastern Mediterranean University, Famagusta, North Cyprus. The findings show that the developed model of performance-based window design enables the handling of various window design variables along with different performance criteria to determine the near-optimal window design alternatives for effective natural ventilation (NV) and mixed-mode (MM) offices. This model can guide architects in making informed decisions in the early stages of office window design.

The hybrid philosophy behind the mixed-mode (MM) strategy aims at achieving energy-efficient buildings and sustainable development. A performance-based open-plan office design facilitates the handling of multiple design parameters to identify optimal design solutions for effective MM offices. This research presents a method of open-plan office design for an improved natural ventilation potential and reduced supplementary heating/cooling loads in the early design stage within a Mediterranean climate. Different design variables including office size, layout aspect ratio, window orientation and fraction of window opening, with several factor levels, were studied. The design of experiment developed by the Taguchi method was applied to define the most informative simulation scenarios. Analysis of variance was utilised to indicate the effectiveness of each design parameter, while the signal-to-noise ratio approach identified the near-optimal level combinations that support informed decision-making. Suggested by the EN 15251:2007 standard, the hourly dynamic simulations were conducted using TAS Engineering. The measurement criteria included airflow rate, carbon dioxide (CO₂) levels, adaptive thermal comfort and air-conditioning (AC) loads. The calculated indicator was the number of hours in which a specific performance criterion is met during the occupancy period and the AC loads.

Natural ventilation through window openings is an inexpensive and effective solution to bring fresh air into internal spaces and improve indoor environmental conditions. This study attempts to address the “indoor air quality–thermal comfort” dilemma of naturally ventilated office buildings in the Mediterranean climate through the effective use of early window design. An experimental method of computational modelling and simulation was applied. The assessments of indoor carbon dioxide (CO₂) concentration and adaptive thermal comfort were performed using the British/European standard BS EN 15251:2007. The results indicate that when windows were opened, the first-floor zones were subjected to the highest CO₂ levels, especially the north-facing window in the winter and the south-facing window in the summer. For a fully glazed wall, a 10% window opening could provide all the office hours inside category I of CO₂ concentration. Such an achievement requires full and quarter window openings in the cases of 10% and 25% window-to-floor ratios (WFR), respectively. The findings of the European adaptive comfort showed that less than 50% of office hours appeared in category III with cross-ventilation. The concluding remarks and recommendations are presented.