Designing for change

Iteratively shaping AI literacy PD to support adaptable educators

Abstract

Since OpenAI’s release of ChatGPT (November 2022), generative AI models have proliferated and evolved rapidly. Beyond chat-based interactions and text generation, newer multi-modal and reasoning models incorporate image, video and audio generation and interpretation, as well as citation tools. These tools are increasingly embedded into mainstream productivity systems (e.g., Copilot in the Microsoft suite), lowering barriers to everyday use. The widespread availability of AI tools has had a profound impact on education. In Australia, the AI disruption has prompted the higher education sector to rethink existing assessment practices to mitigate misuse while adopting approaches that support the ethical, critical, and responsible use of AI (Lodge et al., 2023). As Huijser et al. (2024) explain, the challenge for academics and professionals supporting teaching and learning in higher education is not just to build technical proficiency with these tools, but also to build staff capabilities to critically engage with AI’s complexities and make informed decisions on its appropriate use in teaching contexts (Markauskaite et al., 2022).

The project aims to design and iteratively refine a layered professional development (PD) suite to support staff AI literacy, defined as the capabilities enabling individuals to critically evaluate, communicate, and collaborate with AI (Long & Magerko, 2020). In this design case, I ask: How can responsive PD scaffold staff adaptability, critical judgment, and ethical engagement with AI? This approach responds to critiques that Technology Enhanced Learning (TEL) PD initiatives primarily focus on knowledge and skill acquisition and to calls for PD that goes beyond tool proficiency by intentionally designing for “learning about AI…, learning with AI…, and learning for human-AI collaboration” (Carvalho et al., 2022, p.2).

The PD suite iterates an earlier pilot approach to support staff AI literacy at the same institution (Tibbs et al., 2024). This presentation focuses primarily on the workshops, which were delivered in six cycles (2024-2025). The 2024 workshops provided opportunities for first encounters with AI, tool comparisons, ethical discussions, and assessment design considerations. By 2025, informed by facilitator observations and reflections, transcripts, participant artefacts, evaluation survey responses, evolving AI technologies, and institutional infrastructure, the workshops were iteratively redesigned across cycles, alongside an updated self-paced module. The 2025 workshops were underpinned by socio-constructivist principles: more space was created for dialogue, critical evaluation of AI outputs was emphasised, and collaborative peer design challenges and the crafting of contextually relevant agents for practical use were included. They also incorporated a critical pedagogical orientation and elements of transformative learning, where staff interrogated assumptions about AI through engagement with ethical and Aboriginal and Torres Strait Islander perspectives, and explored their professional obligations to support students’ AI literacy development. The next planned layer in the suite, an AI in Learning and Teaching micro-credential, is currently in development.

The evidence-driven, situative approach is grounded in educational design research (McKenney & Reeves, 2018) and design-based professional learning (Friesen & Brown, 2022); engagement and evaluation data from each workshop cycle informed the next cycle’s design. The PD suite is positioned to support staff’s critical and adaptive expertise, not just technical proficiency, to capably respond to the opportunities and uncertainties brought by AI (Markauskaite et al., 2022). Its layered architecture, from low-stakes self-paced exploration to applied collaborative workshops, and soon to reflective micro-credential, supports different starting points and scaffolded progression, enabling adaptation for institutions with different resource constraints. For example, a train-the-trainer model could be adopted so that tailored, discipline-specific workshops drawing on structured dialogue and critical reflection can be facilitated by school-based champions.

This case offers insights into how this PD model can create conditions for adaptive and critical AI literacy development for university educators, beyond one-off technical upskilling (Friesen & Brown, 2022). It underscores the value of design interventions attuned to shifting teaching and learning ecologies, building staff capabilities to both create with and critique AI. The presentation will illustrate these design cycles, share facilitator reflections and participant artefacts, and outline future directions.