The Student Experience Survey (SES) 2024 underscores a consistent message across Australian higher education: student engagement, teaching quality, and a sense of belonging are central to meaningful learning. While the overall satisfaction remains relatively stable, typically in the low-to-mid 70% range, engagement and belonging continue to lag behind, more so in digitally mediated contexts.

Recent SES data also highlights a growing recognition that students value not just disciplinary knowledge, but skills that prepare them for complex, real-world work: critical thinking, collaboration, communication, and adaptability. These are essential for what is often described as the “future-ready graduate”— a learner capable of navigating uncertainty, making informed decisions, and contributing meaningfully to professional and civic contexts.

AI and the Disruption of Established Assumptions

Gen-AI is now embedded in how students learn, write, and produce work. Its ability to generate essays, summaries, and analyses challenges assumptions about authorship, assessment, and what counts as evidence of learning.

Beyond concerns of academic integrity, AI reshapes pedagogical practice itself. As Neil Selwyn observes, digital technologies alter the practices and power relations of education. When students can produce outputs with minimal effort, assessments that focus primarily on production risk losing their value as indicators of capability.

The question becomes not what students can produce, but how they engage with knowledge, evaluate outputs, and apply insights in authentic contexts. AI introduces epistemic challenges that require recalibrating the focus of higher education toward skills, judgment, and relational learning.

Reframing the Human Edge: The DAC Model

In response to these challenges, within the interdisciplinary problem-based learning units that I coordinate at the University of Sydney, I have tried to conceptualise the “human edge” through discernment, agency, and connection, something I refer to as the DAC Model.

• Discernment refers to the capacity to critically evaluate knowledge, including AI-generated outputs. This aligns with Barnett’s (2007) notion of “critical being,” emphasising the ability to navigate uncertainty and make informed judgments. Discernment supports future-ready graduates who can identify bias, evaluate evidence, and make decisions in complex work contexts. In the era of free internet and information, judgement that is reliable and sound is still very much in a human domain. And, we recognise that developing discernment as a skill requires explicit guidance, and often clear evaluative frameworks such as the CRAAP Test.
• Agency reflects students’ ability to take ownership of learning, act intentionally, and remain authors of their work. Education is about cultivating responsible subjects, not passive consumers of knowledge. Agency is closely tied to employability: graduates who can act decisively, reflect on their decisions, and adapt to new challenges are highly valued in professional settings.
• Connection captures relational engagement: the sense of belonging, recognition, trust and interaction that underpins motivation, persistence, and collaboration. Connection equips students with interpersonal skills, team capabilities, and professional networks that are central to future-ready graduates.

AI accentuates the significance of these capabilities. Where AI produces answers, discernment is critical. Where AI produces work, agency is essential. Where AI enables individualised learning, connection must be intentionally fostered.

From Concept to Practice

These strategies indicate a refinement of established good practice, rather than a fundamental shift away from it². Their urgency is amplified in AI-mediated learning environments where content generation is effortless, but critical and collaborative capabilities distinguish the graduate.

A Reflective Account of Practice

Foregrounding DAC has reshaped both learning design and student engagement in my teaching. Real-world, complex-problem solving cultivates discernment. Iterative, agile and developmental feedback fosters agency. Ongoing dialogue nurtures connection.

Several student reflections from my industry and community project units highlight the impact of this deliberate focus on the human elements in my teaching:

“As I am a student who is really engaged by challenging tasks, I enjoyed the real-world applicability… she challenged our way of thinking which enhanced our critical thinking skills.”

“[Name] has been amazing at taking time to give detailed feedback… Our project would never have been as fruitful without her time and support.”

“...I’ve achieved lots of interdisciplinary experiences after accomplishing my study in USYD, including study abroad in Germany about political science and stepped into various industries... to widen my knowledge. Your words are sincerely inspiring and keep influencing my career... Your dedication and passion for teaching and research have truly made a significant impact... I’ve personally benefited greatly from your insights and guidance... Your ability to make complex subjects engaging and your commitment to student success are truly admirable. Thank you for everything you do.”

These insights indicate that students respond not only to content and problem-solving tasks, but to how they are positioned in the learning process: encouraged to think critically, take ownership and engage collaboratively. These are precisely the capabilities that SES identifies as central to employability and the future-ready graduate.

Tensions and Limits

While the DAC Model offers a useful lens, it is not without tensions. It risks placing responsibility on students without fully accounting for structural constraints such as large class sizes, limited resources, and casualised teaching workforces, which can restrict opportunities for connection and personalised feedback³.

Fostering agency is also complicated by AI: its efficiency can encourage students to outsource thinking, making careful curriculum and assessment design essential to promote reflection, originality, and ethical engagement.

Discernment, meanwhile, is unevenly distributed. Students enter higher education with varying levels of epistemic preparedness and digital literacy; without scaffolding, an emphasis on critique may advantage some while disadvantaging others. Finally, connection is increasingly mediated through digital platforms, reshaping relational depth and dialogue. While AI can support interaction, it cannot replace the relational learning that underpins key employability skills such as teamwork, communication, and leadership.

DAC is therefore perhaps best understood as a heuristic: a tool for foregrounding capabilities that must be deliberately designed into learning, rather than a complete solution to the challenges AI introduces.

Conclusion: Designing for Employable, Future-Ready Graduates

In understanding my role as an educator, I see that AI does not diminish it—it refines and sharpens it. As Laurillard (2012) argues, teaching is a design practice, defined by the conditions we create for learning. In a context where content is abundant, the value of higher education lies in how students think, take ownership, and connect with others.

The DAC Model emphasises that the human edge—discernment, agency, and connection—is central to preparing students for an AI-mediated world. The challenge is not simply to articulate these qualities, but to design learning environments that sustain them, ensuring graduates are not only academically capable, but also future-ready.

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