Focus
This presentation showcases how a new subject used student voice, agency, and partnership integrated feedback to improve subject design, delivery and assessment. We enabled student voice (Cook-Sather 2019) to co-create (Bovill 2020) and form ‘student partnerships’ (Matthews & Dollinger 2023) to develop student agency.
Background
Arts Discovery was delivered for the first time in Semester 1, 2022 as a flagship subject for all commencing Bachelor of Arts students at an Australian University. This subject incorporates student voice to improve curriculum design, pedagogical practices, assessment types and teaching resources.
Description
To support critical reflection of subject’s successes, challenges, areas for improvement, we gathered different forms of evaluation and feedback, including pre-, mid-, end-semester surveys, reflective responses, focus groups, meetings with student representatives. Listening to student voice, increasing student agency, establishing student partnerships have been fundamental drivers for significant modifications in assessment, teaching resources, curriculum design introduced over the first three years of this subject’s delivery, alongside ensuring student engagement and motivation
Methods
Observations and evaluations from students and staff informed major and minor changes in subject design since its introduction. Focus groups (in-person and on-line), were conducted to collect data from each semester cohort to gauge student opinion and experience. Using an Action Research approach allowed us to include self-reflection in developing our process for revisions during teaching (Rauch et al. 2019).
Contribution
We found that there has been an increase in student awareness of the importance of student voice and agency. Student engagement improved through their agency to connect content and context of learning with personal experience and expectations. This led to a staff-student partnership process around existing curriculum and assessment practice.
Engagement
This presentation uses reflective questions and conversation prompts to connect with the audience and incorporate their perspectives on authentic student engagement in curriculum and assessment designs.

Focus: Exploration of causal attribution styles of students who are not making satisfactory progress with their studies.

Background: At the University of South Australia, a process called Academic Review (AR) identifies and supports students who are deemed to be making unsatisfactory progress in their studies (failing 50% or more of their courses or a practice-based assessment). Current AR processes involve support staff, however resourcing is challenging. As such, students are often referred to extracurricular supports, with unfortunately limited engagement (Sasso, 2017). We sought to objectively measure what we had anecdotally observed over several years working with AR students – that causal attribution styles (Weiner, 1986) appear to differ within this cohort and influence students’ motivation to engage.

Description: To guide development of an embedded, low-cost intervention targeting causal attribution, we explored differences in causal attributions between AR students and their peers from the general population (GP).

Method: Data was collected from AR (n=44) and GP (n=53) groups in the Justice and Society Academic Unit completing the CDS II Scale (McAuley, 1992). Independent t-tests were conducted on the 4 sub-scales: Locus of causality, Stability, Personal Controllability, External control.

Evidence: AR students had significantly higher levels of Stability (M=13.6, SD=6.01) compared to their GP peers (M=10.1, SD=5.14; outcomes viewed as fixed with little room for improvement). Significantly lower levels of Personal Controllability were also observed for AR students (M=16.8, SD=4.78) compared to GP peers (M=20.3, SD=4.91). These findings suggest interventions target these two factors, which are also associated with lower academic performance (Cortez-Suarez, 2008).

Contribution: To our knowledge, this is the first study of its kind to explore differences in causal attribution in students who are undergoing Academic Review.

Engagement: Discussion points - What have you heard students attribute academic underperformance to? What supports have you seen that help students rethink challenges?

Focus
The research aimed to understand how learning designers use generative AI (gen-AI) tools in their practice. This showcase examines the changing role of third space professionals who incorporate gen-AI technologies.

Background
In 2022, user-prompted gen-AIs were massified (e.g., MS Co-Pilot, DALL-E, ChatGPT) (Crawford, et al., 2023). This massification of free tools led to gen-AI’s incorporation across higher education, and research into academic integrity, privacy, ethics, and curriculum (e.g., Sok & Heng, 2023).

Description
Given the benefits of gen-AI partnerships (Halaweh, 2023), we want to understand the impacts on our practice in the third space. In our roles, we experienced the capability of gen-AI to innovate but also to unbundle (White et al., 2020) aspects of our roles and sought to investigate this phenomenon.

Method
Using a case study approach, the authors – learning designers at an Australian research university – detailed their use of gen-AI in designing courses. Data was gathered through six months of community of practice meetings using a constructivist research approach to prompt discussions about experiences in gen-AI's application. The themes form the data set for this presentation.

Evidence
This presentation will share the authors’ experiences, juxtaposed with contemporary gen-AI literature to explicate the lessons learned.

Contribution
The presentation contributes experience and knowledge to the scholarship of gen-AI's implementation in practice. It presents how a human-in-the-loop framework (Mosqueira-Rey et al., 2023) can be reimagined to understand how human and machine actors' interactions are identified and categorised in the third space.

Engagement
Attendees will be prompted to reflect and interrogate the role of gen-AI in changing practice within higher education, and the extent to which it is an innovator or unbundler in their context. They will be invited to share where they see value and risks in potential future applications, and to identify appropriate scope of use.