The case for reinterpreting our graduate qualities
Discussion of artificial intelligence in higher education has largely focused on assessment, academic integrity and new technologies. The more fundamental question is:
What should distinguish a university graduate in a world where AI can perform tasks once considered evidence of expertise?
The implications for universities extend beyond curriculum, assessment and technology adoption. This does not require inventing a new set of graduate qualities or adding a standalone one for “AI fluency”. Instead, we must reconsider what distinguishes a graduate and how graduate qualities should be interpreted. The evidence of the impact of AI outlined in this article suggests renewed attention should be given to the fundamentally human capabilities and qualities that support judgement and responsible action.
A range of influential frameworks converge on the key graduate qualities we must develop. The OECD’s Learning Compass positions student agency at the centre of learning. The OECD work on human flourishing emphasises enabling individuals to live meaningful lives and contribute to the wellbeing of others. Recent UNESCO and OECD works similarly argue that educational systems should strengthen human judgement, moral reasoning, critical reflection and participation in society.
Workforce policy is reaching similar conclusions. The recent report by Jobs and Skills Australia proposes a human-centred definition of skill, or “a valued and purpose-driven human ability that is acquired or refined through learning and practice.” Through this lens, skills cannot be reduced to discrete tasks or technical procedures. They involve judgement, adaptability, ethical reasoning and the ability to act effectively in complex situations. A wide range of other workforce analyses, such as those from the World Economic Forum and McKinsey, indicate a growing agreement that generative AI is more likely to transform work than eliminate it entirely. As AI becomes embedded across occupations, demand is increasing for workers who can exercise judgement, solve unfamiliar problems, adapt to change and combine human expertise with technological tools.
Assessment evidence points in the same direction. The OECD’s PISA 2025 report documents declining performance in many secondary education systems, particularly in areas requiring deep engagement with information, critical evaluation and sustained reasoning. The report also argues that future success depends on capacities such as metacognition, creativity, ethical judgement, systems thinking and collaboration.
A similar perspective emerged from the Castlereagh Statement, developed through a national summit hosted by the University of Sydney. The Statement highlights enduring human capabilities such as curiosity, discernment, creativity, adaptability, collaboration and learning how to learn and argues that these have become more important as AI tools develop.
Taken together, these findings point to a clear consensus: the capabilities that matter most are not primarily technical. Across workforce, educational and human development perspectives, attention is converging on the same capabilities, particularly in complex and uncertain environments. These include judgement, adaptability, creativity, lifelong learning and the ability to work effectively with both AI and other people. As AI becomes more technically sophisticated, the distinctive contribution of graduates lies in how they apply knowledge responsibly when facing ambiguous problems, incomplete data, and conflicting priorities.
Graduate qualities, capabilities and competencies
As universities consider the implications of AI for graduates, it is helpful to distinguish between graduate qualities, capabilities and competencies. These terms are often used interchangeably, but they describe different aspects of graduate development and assurance of learning:
- Competencies provide observable evidence of the ability to perform specific tasks or activities to an expected standard within defined disciplinary, professional or social contexts (Frank et al., 2010).
- Capabilities are broader and more adaptable. They describe the capacity to integrate knowledge, experience and personal attributes in order to act effectively in unfamiliar, complex or changing situations (Jobs and Skills Australia; Stephenson, 1998). Ethical reasoning, creativity, collaboration, learning agility and judgement are examples of capabilities because they enable individuals to respond where there is no predetermined solution or established procedure. TEQSA’s recent report also describes adaptive capabilities, which allow graduates to work in complex, novel and AI-rich settings, and to adapt and transfer their demonstrable skills effectively and ethically (Lodge et al., 2026).
- Graduate qualities express the enduring characteristics, dispositions and aspirations that a university seeks to cultivate in its graduates. They provide a shared framework across disciplines while recognising that they will be developed and demonstrated differently (Barrie, 2007; University of Sydney).
Jobs and Skills Australia’s recent work offers a practical frame to connect these ideas through a human centred definition of skill aligned with competencies:
A skill is a valued and purpose-driven human ability that is acquired or refined through learning and practice
Graduate qualities then shape how these capabilities are exercised and competencies represent their demonstration and assurance through assessable tasks in disciplinary and professional practice.
Reimagining the graduate qualities for an AI-enabled world
AI does not diminish the importance of disciplinary knowledge, as effective use depends on expertise. Graduates need strong disciplinary foundations to formulate meaningful questions, evaluate evidence, recognise limitations, navigate uncertainty and exercise judgement. Knowledge remains essential but must be combined with the capacity to apply it effectively and responsibly in situations where AI is a routine part of study, work and decision-making.
These developments also highlight the continuing importance of focusing on our purpose. Universities prepare graduates for employment and enable contributions to communities, professions and society. As AI mediates communication, information exchange and decision-making, qualities such as empathy, cultural capability, collaboration, civic responsibility and ethical judgement become more significant. They support workplace effectiveness but also strengthen democratic participation, social cohesion and the responsible stewardship of emerging technologies.
The University of Sydney’s Education in the Age of AI Green Paper argues that AI should not be treated as separate from disciplinary knowledge, capabilities and their application. Instead of artificially creating a new set of graduate qualities or introducing a separate one for “AI fluency”, it claims that we should reconsider how our existing graduate qualities are understood, developed and demonstrated. This approach aligns with TEQSA’s first proposition: that institutions establish adaptive capabilities as core graduate attributes (Lodge et al., 2026). The Green Paper proposes the following interpretation of the University of Sydney graduate qualities.
| Graduate quality | Reinterpretation |
|---|---|
| Depth of disciplinary expertise | Apply deep disciplinary knowledge with judgement in technology-enabled professional and academic contexts. |
| Critical thinking and problem solving | Critically evaluate information, identify bias and limitations, and exercise independent judgement. |
| Oral and written communication | Communicate clearly, explain reasoning, and defend decisions in contexts where technology supports idea generation and communication. |
| Information and digital literacy | Engage confidently, critically and responsibly with AI and emerging technologies while maintaining human agency. |
| Inventiveness | Use creativity, innovation and technology to generate, test and refine ideas while maintaining originality and judgement. |
| Cultural competence | Understand the societal, ethical and environmental implications of technology and contribute to equitable outcomes. |
| Interdisciplinary effectiveness | Work across disciplines and integrate diverse perspectives in increasingly complex and technology-enabled environments. |
| Integrated professional, ethical and personal identity | Act with integrity, responsibility and courage, including in the use of AI and emerging technologies, and consider their ethical, social and environmental implications. |
| Influence | Lead and contribute to the responsible and beneficial use of emerging technologies in professions and society. |
In short, the capabilities that matter most remain deeply human: the ability to exercise judgement, act ethically, continuously learn, work with others, and apply knowledge responsibly in unfamiliar situations. AI has not altered what universities or employers value but it has raised the standard required to demonstrate them.
Further reading
- Barrie, S. C. (2004). A research‐based approach to generic graduate attributes policy. Higher Education Research & Development, 23(3), 261–275. https://doi.org/10.1080/0729436042000235391
- Castlereagh Summit (2026), The Castlereagh Statement
- Frank, J. R., Snell, L. S., Cate, O. T., Holmboe, E. S., Carraccio, C., Swing, S. R., … Harris, K. A. (2010). Competency-based medical education: theory to practice. Medical Teacher, 32(8), 638–645. https://doi.org/10.3109/0142159X.2010.501190
- Jobs and Skills Australia (2025), Generative Artificial Intelligence Capacity Study
- Jobs and Skills Australia (2026), A skills-first system can’t work without a human-centred definition of skill
- Lodge, J. M., et al. (2026), Assuring quality learning in a gen AI-integrated future: The role of adaptive capabilities, TEQSA
- McKinsey Global Institute (2026), Human skills will matter more than ever in the age of AI
- OECD (2025), Education for human flourishing
- OECD (2026), PISA 2025 Results (Volume I) – Future‑ready students
- OECD (2026), Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education
- OECD (2026), Future of Education and Skills 2030/2040
- Stephenson, J. (1998). The concept of capability and its importance in higher education. In J. Stephenson & M. Yorke (Eds.), Capability and quality in higher education (pp. 1–13). Kogan Page.
- TEQSA (2026), Assuring Quality Learning in a Gen AI-Integrated Future: The Role of Adaptive Capabilities
- UNESCO (2023), Guidance for generative AI in education and research
- UNESCO (2026), Sustaining education as a common good in the age of AI: the case for deliberative governance; discussion paper
- United Nations Development Programme (2025). Navigating AI with a Human Development Compass – Shaping Tomorrow’s Capabilities
- University of Sydney. Graduate Qualities.
- World Economic Forum (2025), Future of Jobs Report
