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The aim of this study is to present design tactics (DTs) for supporting the adaptability of existing primary and middle school buildings into the emerging needs of coronavirus disease 2019 (COVID-19). The study introduces a novel algorithmic model for postoccupancy evaluation of the existing school buildings and provides solutions to enhance the adaptability of these buildings.

This study employs the DTs defined by the authors, integration of DTs to the algorithmic model and tests the usability of the proposed model in the selected sample set. The sample set consists of four primary and middle school buildings with different architectural qualities. The degrees of flexibility of the existing sample set are evaluated depending on the outcomes of the implementation.

The degrees of flexibility are achieved as a result of execution of the algorithmic model for each selected school building. Initial results of the case studies show that the flexibility of a school building is highly related to affordances and design decisions of the plan layout which were considered in the initial phases of the design process. Architectural qualities such as open plan and having sufficient voids in the interior and exterior space become prominent factors for ensuring flexibility.

Developing a systematic approach to the adaptation problem of primary and middle school buildings to postpandemic reuse is a novel research topic. Apart from this contextual originality, the proposed taxonomy for postpandemic reuse in terms of three levels of adaptation is a new conceptual framework. Moreover, the proposed algorithmic model itself can be considered as an original contribution, as well as a merge of qualitative values such as adaptation and flexibility with an algorithmic model.

This study aims to present a twofold machine learning (ML) model, namely, EDU-AI, and its implementation in educational buildings. The specific focus is on classroom layout design, which is investigated regarding implementation of ML in the early phases of design.

This study introduces the framework of the EDU-AI, which adopts generative adversarial networks (GAN) architecture and Pix2Pix method. The processes of data collection, data set preparation, training, validation and evaluation for the proposed model are presented. The ML model is trained over two coupled data sets of classroom layouts extracted from a typical school project database of the Ministry of National Education of the Republic of Turkey and validated with foreign classroom boundaries. The generated classroom layouts are objectively evaluated through the structural similarity method (SSIM).

The implementation of EDU-AI generates classroom layouts despite the use of a small data set. Objective evaluations show that EDU-AI can provide satisfactory outputs for given classroom boundaries regardless of shape complexity (reserved for validation and newly synthesized).

EDU-AI specifically contributes to the automation of classroom layout generation using ML-based algorithms. EDU-AI’s two-step framework enables the generation of zoning for any given classroom boundary and furnishing for the previously generated zone. EDU-AI can also be used in the early design phase of school projects in other countries. It can be adapted to the architectural typologies involving footprint, zoning and furnishing relations.