When generative AI first entered the mainstream, universities worldwide scrambled to respond. Many adopted a prohibitive stance. Thankfully, the University of Sydney has taken a more measured approach, guiding responsible AI use through evolving, pragmatic policies that align assessment to the age of generative AI. This has opened the door to teaching innovations that meet the moment without undermining academic rigour.
In Business Information Systems (BIS), where we study how emerging technologies shape business and society, this openness is essential. Research suggests that AI can augment certain aspects of creativity, particularly idea generation. Our discipline is well-positioned to help students move beyond task execution and develop creative, reflective, and autonomous thinking skills that will define success in the post-AI workplace. Rather than treat AI as a shortcut or a threat, we position it as a partner in problem framing, creative ideation, and stakeholder communication.
This ethos underpins the redesign of INFS3600, the BIS capstone unit, which Dr Raffaele Ciriello leads. In this unit, final-year students consolidate their undergraduate learning by tackling the big problems confronting business and society today: from youth mental health to curriculum reform, from workplace safety to digital inclusion in remote communities. Students choose and scope their own project within these parameters. They investigate the problem domain, engage with stakeholder perspectives, and develop a working prototype that addresses the identified needs.
The core aim is to avoid rushing towards a technocratic solution. Instead, students reframe the problem, grasp its complexity, and outline ways to serve stakeholders equitably. This requires both analytical rigour and creative expression. That’s where GenAI becomes part of the craft.
A Multimodal, Multi-Role Project
Students work in diverse roles (business analyst, solution architect, team leader, implementation lead, and design evaluator), mirroring typical BIS professional roles. Their deliverables include a structured written report, a working prototype, and a visual or narrative storyboard that makes the proposed solution intuitive to end users. Alongside this, students may submit a creative “scrapbook” documenting their thinking process, including sketches, scenarios, failed iterations, pivots, and GenAI outputs.
Many students now use GenAI tools (e.g., ChatGPT, DALL·E, MidJourney, Canva, Consensus) for brainstorming, scenario-building, and language refinement. Some go further by creating cartoons to illustrate stakeholder pain points, simulating interview responses with inaccessible groups (such as minors or survivors of workplace harassment), or testing prototype interfaces with synthetic evaluators. In one project, students working on AI in NSW schools used GenAI to simulate parent, teacher, and student perspectives, iteratively refining their prompts to move beyond sycophantic flattery and surface helpful critique.
These activities are guided by rigorous academic rubrics, and student work is evaluated not only for feasibility and clarity, but for creativity, stakeholder empathy, and critical reflection on the social, economic, and environmental impacts of digital innovation.
Table 1. Excerpt of rubric for creative work
Making AI Use Accountable, Not Punishable
One of the key challenges was that students, despite being allowed to use AI, were often reluctant to acknowledge it. Many feared penalties or suspicion. To shift this mindset, Dr Ciriello introduced a small but strategic 5% assignment: the GenAI Strategy.
Submitted in Week 3, it requires students to articulate how they plan to use AI in their learning, what they will not use it for, and how they will evaluate and document its contributions. Evaluating the quality of generative AI outputs is a key digital skill for students, both at university and in their future careers. This includes identifying risks (such as hallucinated content or ethical shortcuts) and proposing safeguards. The task not only safeguards academic integrity; it also gives students early, meaningful feedback on their approach to technology-enhanced learning. It has become a valuable mechanism to normalise thoughtful, critical AI engagement.
Viva Voce: Defending Ideas, Not Deliverables
To further support originality and accountability, all students participate in a viva voce: an oral discussion where they explain their individual contributions, design decisions, and use of genAI tools. Rather than a high-pressure examination, the viva is framed as a professional conversation: an opportunity to reflect, justify, and demonstrate the kind of critical thinking and situated adaptation that cannot be delegated to machines.
These discussions often reveal layers of insight not visible in written reports, including shifts in student thinking, unexpected discoveries, and moments of friction where genAI helped, but also misled. In this way, the viva closes the loop between process and product, making learning visible.
Embodied Learning: Present in the Room, Not Just Online
After various iterations during and post Covid, Dr Ciriello has deliberately structured the capstone unit around in-person design workshops, weekly consultations, and viva voce discussions. Hybrid teaching is not offered because the creative, dialogical nature of the work thrives in the classroom. Students share works-in-progress, seek real-time feedback, and adapt dynamically. The pedagogy remains analogue in spirit even when digital in medium. AI tools can support, but creativity still emerges from context, conversation, and critique.
A Living Prototype for Post-AI Education
We are still in early days in the genAI transformation of education. But if we want to prepare students to lead in an AI-infused future, we should embed it meaningfully in learning and assessment. This means a shift from prohibiting AI to preparing students to engage with it thoughtfully. It also means moving past gimmickry to designing experiences where students use genAI to reframe problems, not just reword essays.
The approach developed in INFS3600 is experimental, and it is still evolving. But it shows what becomes possible when we consider genAI as a tool for pedagogical innovation.
What’s Next
If you are interested in partnering to redesign your unit, please reach out to the Educational Innovation team for a general consult. You may also want to visit the following pages for more information:
- Learn more about Educational Innovation at the University of Sydney: https://educational-innovation.sydney.edu.au/teaching@sydney/about-educational-innovation/our-services/
- For business education expertise, contact Business Co-Design (https://intranet-business.sydney.edu.au/teaching/education)
