What Is This Guide?

The BEL+T Generative AI guide was first published in June 2023. This guidance was last updated on 12 Dec 2024.

This BEL+T Guidance on Generative AI (genAI) offers ABP subject coordinators, and other educators in built environments disciplines, some key concepts and definitions in this fast-developing space. It reviews more general advice and guidance, and complements MCSHE and University guidance (April 2023 update) with a focus on some issues of specific relevance to built environment educators. This guidance is also influenced by the University of Melbourne AI Principles, which have been designed to guide actions around the adoption and use of AI tools and systems.

The BEL+T Guidance on genAI was initially developed in 2023, and has been updated in anticipation of Semester 1, 2024. This guidance will continue to be updated as institutional advice is released, and as the broader academic discussions of these issues develop.

Of course, the range of pedagogies and teaching practices relevant to built environment disciplines is particularly wide. Some subjects take teaching approaches that are closer to HASS disciplines, while others are more aligned to STEM pedagogies. This means that the types of genAI tools and issues relevant to teaching are similarly broad. In addition, studio pedagogies introduce practices as well as concerns relating to creativity and original authorship in a genAI context.

The table below provides a summary of each of the sections within this guidance. These sections can be accessed using the navigational sidebar on the top-left corner of the page.

There is much to be learned and many nuances to this complex and evolving space, and there are clearly significant ways in which these new tools are likely to impact built environment disciplines and education. There are many opportunities for innovation as well as valid concerns to consider, particularly relating to how students develop foundational knowledge and critical perspectives, the implications of biased datasets and the treatment of intellectual property. For all subjects, clear and careful assessment design offers a heightened focus on what our students are learning, and what they will need to learn, as they engage with these tools.

We are looking forward to your comments on this guide, and to sharing the excellent and creative approaches that ABP educators are taking in this dynamic and evolving landscape – keep watching this space!

What is genAI	This section introduces genAI, explaining its principles and highlighting examples of its applications. It explores how genAI generates outputs and highlights various models used for generating those outputs.
GenAI in the built environment	This section provides an overview of the impact of genAI in Built Environment disciplines. It presents insights from ABP academics on the evolving role of genAI in professional practice, highlighting the implications for future graduates' knowledge and skills.
GenAI in learning and teaching	This section introduces the complicated landscape of genAI through a learning and teaching lens. It outlines the challenges and opportunities of genAI with a focus on student perspectives, biases and data-related concerns and considerations pertaining to creativity and intellectual property.
GenAI and self-directed learning	This section outlines how students might use genAI to support and supplement their learning.
GenAI and assessment design	This section provides an overview of assessment design in built environments education in the new context of genAI. It explores opportunities and complexities, emphasises the importance of aligning learning outcomes with AI literacy skills, and provides recommendations for meaningful assessment tasks and collaborative approaches involving genAI.
Assessment design for collaboration with genAI	This section presents a framework for understanding and implementing genAI in assessment through the lens of group work dynamics. It introduces three distinct approaches to assessment design - individual, cooperative, and collaborative - exploring how each type defines different roles and relationships between students and genAI. The section provides detailed guidance on task design considerations, learning objectives, and evaluation criteria, supported by practical examples and level descriptors for each assessment type.
GenAI and student academic integrity	This section captures guidance for teaching staff on University policy governing students’ use of generative AI, in the context of academic integrity and academic misconduct.
GenAI for assessment of student submissions	This section outlines the University's current guidelines on using AI tools for assessing student work. It presents key institutional policies and procedures for 2024, clarifying staff responsibilities when using genAI for assessment and feedback.