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There is a growing mismatch between the skill demands of the industry and the offerings of academia. One way of reducing this mismatch is by improving collaborations between practitioners and instructors using web-networking platforms. However, it is important to understand practitioners’ considerations while collaborating with instructors. Therefore, this study identified these considerations in order to infer inputs for the design of the graphical user interface (GUI) of a web-based platform for connecting instructors and practitioners. A mixed method was adopted through a survey and focus group. A survey was used to capture practitioners’ considerations while collaborating with instructors for student development, and a focus group helped uncover an in-depth understanding of the study phenomena. The data were analyzed using descriptive and inferential statistics and thematic analysis. FindingsThe results show the willingness of practitioners to collaborate with instructors for student development, the ways by which practitioners are willing to meet instructors' course-support needs and their considerations in deciding to do so. Slight differences were observed between the results of the survey and the focus group regarding the ranking of the practitioners’ considerations. The study highlighted demographic differences in practitioners’ considerations when deciding on meeting instructors' course-support needs. The results provide a basis to deduce the GUI inputs of web-networking platforms for connecting instructors and practitioners. This study revealed practitioners’ design needs and GUI inputs to facilitate the design of web-networking platforms for connecting instructors and practitioners. This study also contributes to user interface design principles, theories on individual differences and practitioners’ involvement in student professional development. 

The digital and integrated representation of the physical and functional characteristics of buildings enabled by building information modeling (BIM) provides a computational environment for automated compliance checking (ACC) of building designs. The integration of natural language processing (NLP) and artificial intelligence (AI) with BIM brings further opportunities for ACC – it can empower BIM with text analytics and AI capabilities, thereby injecting intelligence and automation in the compliance checking processes. This chapter highlights emerging approaches that aim to facilitate and harness the marriage of BIM, NLP, and AI to enable the next generation of automated compliance checking systems (ACC) systems. This chapter (1) reviews different types of BIM-based ACC systems that leverage NLP and AI techniques, (2) discusses how NLP and AI techniques are applied in regulatory text analytics tasks and BIM information analytics tasks in the context of ACC, and (3) discusses the future trends of BIM-based ACC systems.