Generative AI has been widely debated within educational settings, particularly around academic integrity, leading to educator apprehension about its use in teaching and learning. However, support for the use of generative AI is growing among different stakeholders, including educators, employers, students and policymakers from organisations like Innovate UK and the Alan Turing Institute (The Alan Turing Institute, 2023; Zhou et al., 2024). This support is driven by the recognition that AI skills are becoming essential in today’s business world. As a result, educators are exploring ways to integrate AI into learning, preparing students for the ever-changing world. To this accord, opinion papers have been written on how to effectively integrate it in learning activities (Lodge et al., 2023; Zhou & Schofield, 2024). In a recent published opinion paper, we suggested how generative AI could be used as an additional learning stakeholder to enhance students’ educational experiences (Zhou & Schofield, 2024). While initially our ideas were theoretical, we have put some into practice in a postgraduate capstone project module, serving as a pilot for our suggestions. This module, which serves as an innovative alternative to the traditional dissertation, challenges students to address real-world sustainability issues posed by our partner organisations, which are integral learning stakeholders. By integrating generative AI into this module, we position it as another crucial stakeholder in learning that helps develop key employability skills and provides insights into managing sustainability challenges. This blog focuses on two specific examples where we successfully applied generative AI in the capstone project module learning activities.

In our first application, we demonstrated to our students how to use generative AI as research assistant. We introduced students to tools like Connected Papers and Consensus, which helped them locate relevant papers within their project questions. Additionally, we introduced Elicit, an AI tool that allows students to search, summarise, extract data from, and interact with, academic papers. This approach helped students move beyond traditional search methods like Google Scholar, offering a more integrated way to conduct a comprehensive literature search. This is particularly beneficial in the capstone project module, given that research questions and challenges are provided by client organisations, providing a more structured and specific area of focus for the literature search. Further, we also demonstrated the use of AI tools such as ChatGPT to design questionnaires and conduct data analysis, equipping students with modern tools to handle various research tasks.   

The second example we share is showing students how to use generative AI, such as Gamma and Canva, to build their professional profiles. Instead of relying solely on traditional tools they are already familiar with, we introduced them to the capabilities of generative AI for designing professional bios and profiles. The primary goal was not to instruct on profile design but to familiarise students with innovative tools that are beneficial for both professional and personal use. These tools allowed students to generate a professional team profile in just two minutes with clear prompts, proving more efficient than traditional presentation tools. Students also gained the advantage of having their grammar corrected and their bios paraphrased to achieve a more professional and appealing profile, thereby making a stronger impression on their clients.

‘Feedback from both clients and students indicates that incorporating AI into learning activities has led to more engaging, productive and professional educational experiences. Our survey results were overwhelmingly positive, with all students reporting that generative AI enhanced their learning. Specifically, they found it beneficial in supporting critical thinking, streamlining the research process, and refining their presentations.’

Reflections and guidance for educators

Feedback from both clients and students indicates that incorporating AI into learning activities has led to more engaging, productive and professional educational experiences. Our survey results were overwhelmingly positive, with all students reporting that generative AI enhanced their learning. Specifically, they found it beneficial in supporting critical thinking, streamlining the research process, and refining their presentations.

Using the capstone project module as a pilot project to trial our suggestions from our opinion piece allowed us to identify significant challenges in applying our suggestions. Based on our experience, we suggest the following:

• Educators need to consider using free versions of generative AI instead of the subscription-based versions to ensure accessibility and inclusion when introducing students to generative AI.
• Educators should design activities that make use of generative AI as an addition to the learning process. We suggest activities be designed from the basic level before moving to a higher order of learning. For example, showing students how to use generative AI to build their professional profiles, generate ideas or chat and critique literature.
• Educators should be prepared to adapt their teaching strategies effectively to ensure that generative AI is integrated in a way that does not take away from the learning process, but rather exposes students to its ethical and responsible use in whatever activities they use it for. The capstone project demonstrated integrating generative AI tools in a way that enhances, rather than bypasses, the learning process.

Our implementation of the use of generative AI through the capstone project module has highlighted practical ways of integrating AI tools into learning activities, ensuring they are used responsibly, ethically and inclusively. We will continue to refine our approach and adapt our teaching strategies to fully make use of the potential of generative AI to enhance student learning experiences.

References

Lodge, J. M., de Barba, P., & Broadbent, J. (2023). Learning with generative artificial intelligence within a network of co-regulation. Journal of University Teaching & Learning Practice, 20(7). https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3480&context=jutlp

The Alan Turing Institute (2023). AI skills for business competency framework. Draft framework for public consultation. https://www.turing.ac.uk/sites/default/files/2023-11/final_bridgeai_framework.pdf

Zhou, X., & Schofield, L. (2024). Using social learning theories to explore the role of generative artificial intelligence (AI) in collaborative learning. Journal of Learning Development in Higher Education, 30. https://doi.org/10.47408/jldhe.vi30.1031

Zhou, X., Zhang, J. J., & Chan, C. (2024). Unveiling students’ experiences and perceptions of artificial intelligence usage in higher education. Journal of University Teaching and Learning Practice, 21(6). https://doi.org/10.53761/xzjprb23