Published on 8 August 2025
The widespread and accelerating adoption of AI across all industries means including AI in curricula is essential if students are to be profession-ready when they graduate. Different industries grapple with specific AI adoption and adaptation issues. This means that specific curricula need to be developed that include where the points of debate are, as well as skills in implementation so that students can engage with future employers from the day they graduate.
To properly prepare students with digital literacies and skills to creatively and critically engage with AI, rather than simply implement AI packages in the workplace, takes industry-specific planning and integration throughout a study program.
A backmapping process for AI use
This blog post outlines a backmapping process I developed for integrating AI across the curriculum in the Bachelor of Social Sciences, and expanded into a template that I have presented widely across the university.
My starting point was to understand how AI was changing how social science research and public policy was being done in industry, and working backwards from this to integrate AI into degree programs (Saragi Turnip & Khanna Pathak, 2025).
The framework for this process was adapted from the work of sociolinguist Basil Bernstein (2000), who described how knowledge was transformed into pedagogy through selecting, organising and transmitting knowledge in education. Maton (2014) extended this to focus on the underlying epistemic knowledge structures of a discipline, including how different forms of knowledge influence teaching and learning practices.
Maton (2014) describes three interrelated domains: production fields, where new knowledge is created; recontextualization fields, where knowledge from production fields is selected and reshaped for educational purposes; and reproduction fields, which are sites of teaching and learning where knowledge is transmitted and assessed in pedagogical contexts.
Figure 1 (below) shows the three steps involved in backmapping these domains to integrate AI into curriculum and program design.
1. Research the use of AI in industry
This step examines what type of new knowledge and skills are being produced in a profession, discipline or industry. This is often reported in industry and academic conferences and in industry and peer-reviewed journals or can be gathered by interviewing practitioners and professionals.
For the Bachelor of Social Science I reviewed evidence relating to AI use in social research and public policy, the twin core focuses in the degree, undertaking a rapid desk review of the peer-reviewed literature on social scientists’ use of AI and attending methods conferences. For AI use in government policy, colleague Diana Perche and I reviewed how AI use was described in government and administrative sources, including peer-reviewed literature, government inquiries and grey literature like government reports.
We determined that AI was already being used widely by social researchers, including in literature reviews and in machine learning for data analysis, but that currently its use in qualitative analysis was giving inconsistent results. The critical industry conversations in government included thinking about AI in relation to data bias, data sovereignty, technological standards, sustainability and its use as an extractive technology. We also noted how it was being regulated through requirements for disclosure statements and outright prohibitions.
2. Graduate capabilities
Some professions and industries now have specific accreditation standards and ethical guidance about AI use. In others, such guidelines may need to be inferred; for instance, by consulting widely used texts or recognised industry leaders. We found that a particularly helpful step was developing a statement of key capabilities a student must develop in relation to AI in public policy, which we presented at industry conferences for feedback. The statement was then broken down into specific knowledge, skills and attributes specific to public-policy use.
3. AI into curriculum and program design
This step builds AI integration into existing program-review processes, maps stages of AI knowledge and foundational and extended skill development across the program and considers where mitigations be needed so students don’t skip important learning through reaching for AI shortcuts. This creates a consistency across a program for how AI is discussed and taught to students. In the Bachelor of Social Sciences this has included:
- Integrating AI research across the program through considering how and where it can be integrated across the four core research-methods courses, and creating opportunities for AI use (or refusal) and sequencing this progressively; creating more space for foundation skills by moving some research skills into second year; and adopting industry declarations on AI use based on those in published research journals.
- Undertaking a risk assessment of what key skills research students might miss if they uncritically adopt AI-generated text, such as critical research-reading skills, research-validation skills, and coding and analytic skills. These can be taught side-by-side in the classroom and tested in assessments (including more process-oriented assessments) and made explicit in rubrics.
- Slowing the first-year methods course to help students develop critical research-reading skills. Students also undertake a scaffolded assessment exercise using AI for literature reviews where they compare different search-engine results from both conventional and AI-driven databases.
Ongoing monitoring
Even before the advent of AI, critical theorist Harmut Rosa (2013) proposed that modernisation could be understood as a process of social acceleration, in which changes in technology, institutions and individual life rhythms outpaced both societal and individual abilities to adapt.
Rosa sought ways to bridge the gap between the “controllability of things” and the “uncontrollability of experience” (Susen, 2024). This approach, integrating industry-relevant AI use into existing continuous-review processes offers a way of insuring that how we work with our students in this accelerating arena keeps pace with professional practices and industry needs.
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