This interactive workshop explores how AI can enhance practice-based learning by simulating real-world complexity in the classroom. The session will cover practicalities, challenges, and the broader potential of AI for creating high-fidelity learning experiences—offering fresh ideas for bringing the real world into teaching at LSBU.

 During the workshop we’ll: 
•    Showcase an AI-powered simulation that we have been using for teaching, where students interact with virtual stakeholders—each with their own agendas, personalities, and communication styles. The challenge? Students must engage effectively, ask the right questions, and gather the information necessary to move a project forward. 
•    Explore the system in action- walk through how it’s designed and used in teaching, reflect on how this approach builds students' confidence and competence in high-stakes communication, and brainstorm together to discuss some edge-cutting topics in the sector.  

The tool’s accessibility and dynamic adaptability promote inclusivity by accommodating diverse learning needs and encouraging active participation from all students. It also aligns with decolonisation efforts by centring a Global context, highlighting ethical considerations, power dynamics, and local stakeholder voices often absent from traditional curricula (Smith, 2012).

Inclusion should not only be considered in the access stage in high education, but also in every stage of education, including module design (Fornauf and Erickson, 2020). With online AI-generated design, the game not only provides students the same experience wherever they are but also increase engagement, which is important. Empirical studies show casual relationships between the student engagement and university score, and students’ achievement after graduation (Bachman & Bachman, 2011; Finn & Zimmer, 2012; James et al., 2009). In particular, subjects in Built Environment and Architecture found the increase of engagement of students in this disciplinary would promote students’ academic responsibility, accountability which lead to higher performance (Bachman and Bachman, 2011).