Dr Elaine Tan, Senior Lecturer, Newcastle University Business School
This article presents a practical approach to embedding artificial intelligence (AI) tools within entrepreneurship education using a lean innovation module as a structured learning environment. Designed for final-year undergraduates, the module introduces students to generative AI as a shared resource and tool for use in the entrepreneurial process, supporting core tasks such as ideation, market research, customer insight development, and business model design. Co-creation was adopting within this module to undertake the shared endeavour of exploring AI’s potential, an approach advocated by Selwyn (2022) to support navigation of the emerging phenomena.
Co-Design to navigate ‘messiness’ of experimentation
The decision to employ a co-creation approach stemmed from recognising students’ established use of AI and seeking to harness their existing knowledge and expertise to foster engagement and collaboration. Initially, a frank discussion with students regarding their AI practices shaped the module’s design. We agreed that, mirroring wider student behaviours (as highlighted in a recent Higher Education Policy Institute report by Freeman, 2025), students would likely utilise AI tools for assignments. Rather than restricting this, we embraced the opportunity to guide them in effectively leveraging these tools as an integrated part of their learning. This approach would encourage students to use AI to support various tasks in appropriate ways, while simultaneously promoting transparency and collaborative sharing of outputs with peers and the module leader for critical evaluation. We (the students and staff) also recognised that, despite the increasing prevalence of AI, it remains a relatively new tool with; potentially overstated claims about its capabilities (Selwyn, 2022), that there were limitations in terms of what data could be used with the platforms (e.g. no personal data should be entered), and that all outputs should be examined critically for bias and error. Therefore, exploring its practical application and realising its potentially would likely be experimental, iterative and messy (Luckin & Holmes, 2016). In the spirit of co-creation, the expertise of students themselves as existing users of the tools was also acknowledged (Cook-Sather, Healey, and Matthews, 2021), and their input and feedback on the running of the module as a whole-class co-creation (Bovill, 2020) was both valued and appreciated.
What did students do?
Throughout sessions, students engaged in a variety of hands-on activities, utilising AI as a creative and critical tool. For example, they employed AI chat agents to brainstorm innovative ideas, developed detailed customer personas through AI-driven interviews. The students also collaboratively built value proposition and comprehensive business model canvases using AI that they then critically inspected and discussed. Whilst students had freedom to explore how AI tools could be used in the innovation process, these activities were carefully scaffolded with facilitative classroom prompts to ensure that outputs evaluated, challenged and iteratively improved.
The module’s core approach centered on empowering students to actively explore and experiment with AI, transforming them from passive learners into partners in the innovation process. One interesting outcome of the approach and use of AI was that, because ‘wrong’ answers were often attributed to the AI, rather than a student’s perceived misjudgement, removed much of the anxiety associated with presenting ideas (Cooper, Downing, and Brownell, 2018). Students were more inclined to contribute to class discussions as ‘their’ ideas were not being critiqued but presented as a neutral starting point for shared exploration. They also were able to use AI to create a starting point avoiding ‘blank page syndrome’ and speed up the initial stages of exploration. Instead, conversations focused on what could be done to improve on the outputs, what details were missing, and the quality and was highly experimental. Students were then tasked to improve on the outputs based on their conversation and provide more detailed prompts to support the development. The process fostered a more confident and collaborative environment, where students were comfortable iterating and building upon each other’s insights. The goal was always to test and refine concepts rather than a formal critique of an individual’s thinking.
When exploring MVPs the co-creation approach of the module paid dividends as students sought to further contribute and shape the exercises generating tangible products for experimentation. The independence assigned to students to explore and contribute had allowed them to develop their own awareness of tools, and at this stage in the module, they had begun to explore a wider range. This exploration extended beyond initial tools like ChatGPT, driven by a desire for more reliable outputs and a broader range of capabilities. Students uncovered and shared valuable resources, fostering a dynamic, collaborative learning environment. In their explorations they identified from the wealth of available tools which they were able to use to quickly and easily build suitable MVPs for exploration. These took the form of mock landing pages, wireframe apps, explainer videos, all quickly and easily created with AI. Students in class shared amongst the group the resources as they were discovered, passing comment as to how they had found them and how they rated them, which created an extremely interactive and engaging session for both students and staff.
The eventual assessment of the module was a collation of the outputs of the exercises alongside a reflective commentary demonstrating the development of their entrepreneurial idea. The final section of the assessment was a reflection on the process itself, what they had learned about both innovation and about themselves as potential entrepreneurs.
Concluding comments
Delivered through an experiential and co-creative framework, the module positioned AI as a shared tool to help students iterate on their ideas, interrogate assumptions, and simulate early-stage engagement with customers. No assertion of ownership of the tools by either staff or students was made. This contributed to the open and exploratory nature of the module and encouraged students to interact and contribute.
Key outcomes from this approach include improved student engagement, deeper reflection, and accelerated student progress through early-stage innovation activities. Students reported greater confidence in articulating business ideas, supported by their ability to use AI to generate options, test scenarios, and reframe problems. They also exhibited greater caution in uncritically accepting the outputs generated, indicating an increased AI literacy. Students reported becoming increasingly aware of bias and the tendency for tools to simply agree with any idea, no matter how incorrect or implausible, rather than present challenge. From a module leader’s perspective, the facilitated integration of the tools in classrooms was highly effective in promoting engagement and interaction. It created a less hierarchical environment with students more inclined to contribute and actively participate.
A critical concern emerging about student use of AI is that the continued use of the tools is detracting from creativity (Runco, 2023) and student ability to think critically (Kim et al., 2024). Reflecting on the activities of this module, these are fully justified concerns. At the beginning, students were indeed inclined to take the first output of the tool without question. Students saw the platforms more as a “service” for generating answers, similar to Google or a search engine, rather than as a tool to support them in developing their own thinking. Careful facilitation of the use of the tools, with reference to the module content, and the creation of a culture of candour about their adoption to support the development of literacy (Tan, 2013), was an effective way of prompting students to continue to engage with learning independently while still reaping the benefits of the affordances of the tools.
What is becoming evident and uncomfortable is that the conversation regarding use of AI tools has now shifted the dial when exploring assessment. The use of AI tools by students can now be taken as a near-universal certainty and has created what Song (2024) describes as a crisis. In recent assessment sessions, staff have witnessed a step change in how students are completing their work, with the use of AI now commonplace and unprecedented numbers of academic misconduct cases reported (Goodier, 2025). Educators are now challenged with addressing these conditions, with many reverting to traditional exam conditions to avoid uncertainty of academic misconduct. This practice-led model offers some insights into one way of integrating AI without undermining academic integrity or reducing learning depth. The module context and content of Lean Innovation, a process of experimentation, learning by failure, and exploration, lent themselves well to this approach. However, the possibility of embedding AI within a pedagogic framework that values iteration, reflection, and co-creation is not limited to this topic. Educators can equip students with future-facing skills while maintaining focus on mindset development, creativity, and critical thinking.
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