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Astles, Paul; Shulga, Katia; Simper, Mary; Moore, Eleanor and Openshaw, James
(2024).
Abstract
This abstract presents a session on the impact of Generative Artificial Intelligence (GAI), particularly in online distance learning within higher education, as exemplified by the arrival of ChatGPT. The Open University's Learning Design (LD) team engaged with various departments within Learner and Discovery Services (LDS) to explore GAI's implications on pedagogy, assessment, course production, and student experiences. The initiative promoted networking, knowledge exchange, and transparency, utilizing a workshop format facilitated on Miro to gather qualitative data on colleagues' perspectives and concerns regarding GAI. Concurrently, a student survey provided authentic viewpoints on AI use. Through thematic analysis and cross-examination of staff and student feedback, the team produced a comprehensive report informing AI integration strategies within the institution. Impact of the work included improved AI literacy, proactive staff preparation for working with AI, enhanced communication strategies, and collaborative opportunities for future initiatives. This work not only influenced policy implementation but also strengthened the LD team's ability to facilitate informed discussions and decision-making regarding AI across diverse roles and responsibilities within the institution.
The session would be a presentation related to the content outlined within this description followed by time for questions. An example of how we want to frame the discussion would be to reflect on questions like ... What has your AI journey been?
Generative artificial intelligence (GAI) was given a rocket boost in to the mainstream by the huge impact of ChatGPT (Crawford et al., 2023). Within the Open University (OU) there was a need to get people talking and thinking about the impact of GAI in higher education, specifically in relation to their own roles. For the Learning Design (LD) team this meant exploring the ways in which GAI challenged pedagogy, specifically assessment and course production. Additionally, the team wanted to understand the impact it may have on students.
LD work within the Learner and Discover Services (LDS) which contains 17 different teams that work under ‘library services, design development and production, engagement and partnerships, and business’ in total there are several hundred staff. Rather than work in silos, the LD team saw this as an opportunity to network with colleagues to exchange knowledge and skills. It allowed different teams to find out more about what they were doing in response to GAI, and to have an opportunity to talk openly about concerns. This initiative snowballed and supported our connection to broader initiatives across the university.
We adopted a transparent pedagogy (Bearman and Ajjawi, 2023) this meant partly making our intentions clear to colleagues. Then partly about helping them to understand what to expect from us, and what is expected of them. We provided an opportunity for openness and freedom of expression about GAI and its use in their context, inviting groups from different areas of LDS to attend and contribute to a workshop session facilitated on Miro. This provided a rich set of qualitative data to reflect on and assess the concerns, needs and values that our colleagues had attached to GAI use.
The implementation of our intervention with colleagues was pitched at a deliberate, high-level overview of what GAI is within the context of how it might be considered in each different area of work. The presentation was quite generic, but the discussions were specific to colleagues’ contexts. At the time we demonstrated ChatGPT by playing a pre-recorded screencast because it was during the period where it kept going offline. Over a period of 3 months, we developed a bank of qualitative data from discussions had with colleagues across the institution. These were thematically analysed and grouped according to if they provided information about opportunities or challenges for colleagues. Within these broader categories there were clusters of responses grouped by theme.
Adjacent to this data we engaged students via a survey. It helped to identify students’ concerns and highlight authentic viewpoints about the use of AI. These outcomes were cross-examined to establish commonalities between staff and student voice. A report was created to share our findings. The helped to guide our work moving forwards and provide a clear evidence base for our approach to supporting use of AI within our institution.
Impact of our work:
Everyone at the same level of AI literacy by having access to our workshop.
Opportunity to prepare staff before a tool is brought in - and giving them the opportunity to contemplate possible benefits and challenges to their role, in theory, making it safer and more open (cog in the process of change management).
Evaluation of responses and discussions had within the workshop session to establish how staff feel about AI.
The evaluation shared with LDS leadership and shared more widely at an institutional level to help influence future implementation of policy.
Strengthened our understanding as learning designers who work with academics, about the strength of feeling around AI. It also informed how we talk to colleagues and frame our conversations, as we’ve spoken to people with a wide range of job roles and responsibilities. This awareness extended to both positive and negative impacts so that we can support informed decision making and discussion about AI.
The LD team built many links for future collaboration to reduce the risk of replicating work and increase the ability to signpost to each other.
References
Bearman, M. and Ajjawi, R., (2023) Learning to work with the black box: Pedagogy for a world with artificial intelligence. British Journal of Educational Technology, 54(5), pp.1160-1173.
Crawford, J., Vallis, C., Yang, J., Fitzgerald, R., O'dea, C. and Cowling, M., (2023) Artificial Intelligence is Awesome, but Good Teaching Should Always Come First. Journal of University Teaching & Learning Practice, 20(7), p.01.
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