Higher Education

Preparing people for a future that cannot be predicted.

Universities have never been just places where experts transfer knowledge to students.
They've also been institutions of inquiry, scholarship, professional formation and intellectual development.

Yet many of the practices through which we organise higher education still place considerable emphasis on producing and demonstrating knowledge.

Students attend lectures, complete assessments and receive grades.
Institutions evaluate performance through measures that are often easier to record than the development of judgement.

Artificial intelligence makes the limitations of this arrangement increasingly difficult to ignore.

A student can now produce a polished essay, plausible analysis or technically competent response with assistance that may be difficult to distinguish from an independent understanding.

That doesn't mean the student has learned nothing.
Nor does it mean using AI is inherently incompatible with learning.
It means the quality of an output can no longer be treated as straightforward evidence of the capability behind it.

And that exposes a question universities can't avoid.

If information and plausible answers are becoming abundant, what should higher education actually develop?

The answer isn't more knowledge, but the people capable of understanding, questioning and responsibly applying what they know.

The Purpose of Higher Education

Higher education serves several purposes.

It develops disciplinary knowledge, prepares people for professional practice, advances research and contributes to the intellectual and cultural life of society.

Those purposes can't be reduced to a single capability.
But they share an important responsibility in helping people develop the understanding and judgement required to act beyond the immediate circumstances of their education.

A graduate should know something substantial about their field.
They should also be able to recognise the limits of that knowledge, evaluate competing explanations and respond or adapt when established methods no longer provide an adequate answer.

This difference becomes more consequential as artificial intelligence changes how knowledge work is performed.

AI can help students retrieve information, compare arguments, generate explanations and produce increasingly sophisticated outputs.

Those capabilities may support learning.
They can also conceal the difference between completing a task and developing the understanding required to complete it responsibly.

Knowledge Is Necessary. Judgement Determines Its Use.

Knowledge remains essential.

A medical clinician cannot exercise sound clinical judgement without understanding medicine.
An engineer cannot responsibly evaluate a design without relevant technical knowledge.

But knowledge alone can't determine what deserves attention, which trade-offs are acceptable or even how to respond when evidence is incomplete.
That requires judgement.

The educational challenge is therefore not to replace knowledge with generic critical thinking or transferable skills.
Rather, it's to develop judgement through serious engagement with disciplinary knowledge, evidence, practice and responsibility.

This is increasingly the value of higher education, where students can be trained to use what they know especially when the answer isn't obvious.

Beyond AI Detection

Much of the discussion about generative AI in higher education has focused on whether students are using it to cheat on assessments.

That concern is understandable. Universities do need confidence that submitted work provides meaningful evidence of student achievement.

But detecting AI use doesn't resolve the deeper educational problem.

A student can complete an assessment without AI and still fail to demonstrate understanding.
Another may use AI extensively while exercising considerable judgement in evaluating its outputs, correcting its errors and integrating the results into a defensible argument.

The presence or absence of AI is therefore not, by itself, a reliable measure of learning.

The more useful question is:

What capability is this assessment intended to demonstrate, and what evidence would allow us to recognise it?

• If the purpose is to assess a student's ability to interpret evidence, the assessment needs to reveal that interpretation.
• If the purpose is to evaluate professional judgement, students need opportunities to explain and defend their decisions.
• If the purpose is to develop disciplinary expertise, students must engage sufficiently with the underlying knowledge to recognise when an apparently convincing answer is wrong.

That doesn't mean abandoning academic integrity or permitting unrestricted assistance.
What it means is designing assessment conditions that align with the capabilities being evaluated.

Sometimes that will require independent work without AI.
Sometimes responsible AI use will be part of the capability being assessed.

The distinction should follow the learning purpose, not a blanket assumption about the technology.

The objective is to assess what students can understand, justify and responsibly do, rather than merely what they can submit.

Assessment Performance Is Not Learning

A student submits an excellent assignment.

The argument is coherent.
The references are appropriate.
The presentation is polished.
The work satisfies the rubric.

What have we learned about the student?

We know something about the quality of the submitted work.
We know considerably less about the capability that produced it.

This problem existed long before generative AI.
Students have always received assistance, relied on templates, rehearsed expected answers and learned how to satisfy assessment requirements.

AI makes this more difficult to ignore because it can contribute substantially to the production of competent outputs.

Performance tells us what was produced. Capability concerns what the learner can understand, justify and do when circumstances change.

A polished assignment may show genuine learning.
But the quality of the artefact alone cannot always establish how much understanding the student developed or exercised.

The reverse is also important.

A student who struggles through an unfamiliar problem, recognises a mistaken assumption and revises their approach may show meaningful learning despite producing a less impressive final result.

Assessment needs to recognise both the quality of the outcome and the evidence of capability appropriate to the learning objectives.

Making Judgement Visible

One response is to design opportunities for students to explain how they reached their conclusions.

They might be asked to:

• Defend a decision when presented with competing evidence.
• Explain why they selected one method over another.
• Identify limitations in their own work.
• Respond to an unfamiliar variation of a problem.
• Evaluate an AI-generated recommendation and justify whether they would accept it.
• Reflect on how feedback changed their reasoning.

These activities are not automatically better assessments.
Their value depends on what the course is intended to develop.
But they can reveal dimensions of learning that a polished final artefact may not reveal.

The question for educators is less whether students can produce competent work.
It's more whether our assessment practices provide credible evidence that students are developing the capabilities their qualifications are supposed to represent.

From Knowledge to Judgement

Learning involves more than acquiring information or mastering a set of procedures.
It also involves developing the capacity to interpret situations, evaluate competing demands and recognise when the assumptions guiding our thinking need to be reconsidered.

This is where Robert Kegan's constructive-developmental theory offers a useful perspective, where he looks at how people develop increasingly complex ways of making meaning (Kegan, 1982).

A central distinction is between what we are subject to and what we can take as object.

We are subject to assumptions, expectations and ways of understanding the world that shape our thinking without necessarily being available for conscious examination.
When we can take those assumptions as object, we become better able to recognise, question and potentially revise them.

This has significant implications for higher education.

Students need to learn disciplinary frameworks.
They also need opportunities to exercise judgement within those frameworks and, where appropriate, examine the assumptions on which they rest.

1. Learn the Framework

Students first need a foundation in disciplinary knowledge, methods and standards.
They learn established concepts, encounter examples of competent practice and develop an understanding of what their field considers credible evidence.

Guidance and feedback are particularly important here.

The objective isn't unquestioning compliance. Far from it. It's acquiring enough understanding to participate meaningfully in the discipline.

Developmental question: What am I being asked to understand and demonstrate?

2. Exercise Judgement

As understanding develops, students can begin making increasingly defensible choices.
They compare evidence, consider alternative explanations, recognise uncertainty and explain why one approach is preferable to another.

The emphasis shifts from reproducing an expected answer towards taking responsibility for the reasoning behind it.

A student might use an established disciplinary method while recognising its limitations, or defend a conclusion while acknowledging credible objections.

Developmental question: What reasons justify my position?

3. Examine the Framework

A further educational ambition is helping learners recognise that the frameworks through which they interpret problems are themselves open to examination.
Professional standards, disciplinary assumptions and institutional expectations provide necessary structure.

But they are not beyond criticism.

A capable graduate should increasingly be able to recognise when an established approach no longer fits the problem, when competing perspectives expose its limitations and when revision is justified.

This is not about rejecting expertise or treating every established practice as arbitrary.
It's about developing the capacity to distinguish principles worth preserving from assumptions that deserve reconsideration.

Developmental question: What am I taking for granted, and when should I revise it?

A Developmental Perspective, Not a Fixed Sequence

These three movements are an educational interpretation inspired by Kegan's theory, not a reproduction of his formal developmental orders.

They shouldn't be treated as fixed stages through which every student progresses throughout a degree.
A learner may demonstrate sophisticated judgement in one domain while remaining heavily dependent on external guidance in another.
Nor does successfully questioning a disciplinary assumption establish that someone has undergone a structural developmental transformation in Kegan's sense.

The value of the model is in helping educators examine what their teaching and assessment practices encourage students to do.

• Are students learning established knowledge?
• Are they developing the judgement to use it?
• And are they gaining opportunities to examine the assumptions that shape their understanding?

Independent Judgement Doesn't Require Independent Execution

Professionals rarely work without assistance.
They consult colleagues, use specialised tools, seek expert advice and rely on institutional systems.

AI is becoming another part of that environment.

The educational objective is therefore not to produce graduates who can perform every task without support. That's a reactive error we're often seeing from educational institutions - to ban the technology.
Instead, we want to develop students who can use appropriate support while retaining the understanding required to evaluate recommendations, recognise limitations and take responsibility for consequential choices.

Education should develop more than the ability to produce answers. It should develop the capacity to examine the assumptions that make those answers seem right.

What This Looks Like in Practice

Consider a university student completing an assessment that requires them to evaluate a digital information system.

The student submits a technically competent analysis. They identify the relevant standards, apply the expected terminology and recommend improvements consistent with the assessment requirements.

The work earns a strong mark.

But imagine asking the student a further question:

What would make your recommendation wrong?

The student may be able to explain how the recommendation satisfies the rubric without being able to identify the assumptions on which it all depends.
Perhaps the recommendation assumes that users behave in a particular way, that the organisation has sufficient resources or that an established standard is appropriate for every situation.

The distinction becomes visible through our three educational movements.

1. Learning the framework means understanding the standards, concepts and methods used to evaluate the system.
2. Exercising judgement means selecting and applying those methods while explaining why they are appropriate.
3. Examining the framework means recognising the assumptions and limitations of the methods themselves, including when circumstances might justify a different approach.

The final movement doesn't make the earlier ones unnecessary.
The previous stages compound to the latter stages.

A student can't meaningfully question a technical standard they haven't understood.

Nor does questioning a standard automatically demonstrate better judgement.
The criticism must itself be supported by evidence and reasoning.

The educational opportunity is to design experiences in which students practise these different forms of thinking.

An assessment might require students to justify their methods, respond to changed circumstances or explain how new evidence would affect their recommendations.

Feedback could then address not only whether the student reached an acceptable conclusion, but how well they understood the conditions under which that conclusion would remain defensible.

The objective isn't to make every assessment more complicated.
It's to make the capabilities we claim to develop more visible in the work and assessments the students actually undertake.

Institutions Must Learn Too

Universities are asking students to develop capabilities for a changing world.
However, the institutions themselves face the same challenge.

Artificial intelligence is changing teaching, assessment, research and professional services.
Universities are responding through policies, technology investments, staff development and changes to educational practice.

But adopting a new technology isn't the same as institutional learning.

An institution can introduce AI tools, deliver training and publish guidance while leaving the assumptions behind its educational practices largely untouched.
It may become more technologically capable without becoming substantially better at evaluating whether those technologies improve learning.

The distinction resembles the developmental challenge facing students.

Institutions also operate within established frameworks.
They have assumptions about assessment, academic quality, professional roles, risk and what counts as successful performance.
Some of those assumptions remain useful.
Others may need reconsideration as the environment changes.

Institutional Learning Requires More Than Adoption

A university that learns effectively needs mechanisms for examining the consequences of its decisions.

That includes asking:

• What educational problem are we actually trying to solve?
• What assumptions underpin our current approach?
• What evidence would demonstrate that a change has improved learning?
• Where are our policies or incentives encouraging behaviour contrary to our stated objectives?
• What have we learned that should cause us to revise our approach?

These questions require more than technical expertise to effectively answer.
They require leadership willing to examine established practices, systems capable of detecting unintended consequences and institutional cultures in which evidence can challenge existing assumptions.

There is a very practical tension.

Universities need consistent standards, defensible assessment and appropriate safeguards. They cannot revise every process whenever a new technology appears.
That said, stability becomes a liability when it prevents institutions from recognising that the conditions supporting their existing methods have changed.

The task is therefore to distinguish what deserves continuity from what requires adaptation.
And, a university that teaches students to question their assumptions should be capable of examining its own.

Institutional learning begins when evidence can change not only what an organisation does, but how it understands the problem it is trying to solve.

What Should Higher Education Measure?

Universities need to assess what students know.

Disciplinary knowledge, technical competence and the ability to perform established tasks remain essential.

But those measures don't tell the whole story.

A more demanding question is:

What can graduates understand, justify and responsibly do when the answer isn't obvious?

That question has practical implications for curriculum and assessment.

It encourages educators to examine whether students are learning to interpret evidence, make defensible choices and recognise the limits of their understanding.

It also challenges institutions to distinguish successful assessment performance from the longer-term development of capability.

Not every capability can be measured through a single task. Not every learning outcome needs to involve ambiguity or independent professional judgement.

But universities should be able to explain how their programmes progressively develop the capabilities they claim graduates will possess.

Higher Education Within Stoic Futurism

Higher education brings several Stoic Futurist concerns together.

Human Judgement concerns the ability to interpret information, weigh competing priorities and make responsible decisions.

Information Literacy helps learners determine what deserves belief and how strongly the available evidence supports a conclusion.

Capability and Resilience concern whether people can continue learning and acting effectively when familiar methods become inadequate.

Leadership and Systems Thinking help explain how educational environments, institutional incentives and organisational decisions shape what students actually learn.

Adult development adds another dimension.
It asks whether education is helping learners examine the assumptions through which they understand themselves, their disciplines and the wider world.

Together, these ideas suggest that higher education's contribution extends beyond producing knowledgeable graduates.
It involves developing people capable of using knowledge responsibly while remaining open to the possibility that their existing understanding may need to change.

Preparing People for an Uncertain Future

Universities can't know precisely what professional environments their graduates will encounter.
Some established skills will remain important.
Others will change.
New technologies may alter how work is performed and what responsibilities people are expected to exercise.

Preparing students for that uncertainty requires more than just teaching them to use the tools currently available.

Students require opportunities to acquire serious knowledge, practise judgement, receive meaningful feedback and confront problems that resist straightforward answers.

Institutions will also need to examine whether their own educational practices are developing those capabilities.
This will require a profound shift in how education has been conducted in the last few decades.

A graduate's strongest preparation isn't a collection of answers that will remain correct indefinitely.
It's the capacity to recognise when an answer no longer fits, understand why, and exercise the judgement required to respond.