Reading, Lifelong Learning, and Ethical Reasoning in the Age of AI

Jun 17, 2026

What does it mean to be truly well educated?

The Association of American Colleges and Universities (AACU) spent years answering that question through its VALUE Project: Valid Assessment of Learning in Undergraduate Education. Sixteen rubrics define, calibrate, and offer the affordance of measurement for the learning outcomes that matter most to the professors who teach them in undergraduate education. Each rubric provides a common, if somewhat fragmented, national framework for evaluating student achievement across campuses and disciplines.

I want to look at three of those rubrics — Reading, Lifelong Learning, and Ethical Reasoning — in this essay. Recently, I published an essay exploring the disjuncture between an amorphous notion of critical thinking we reveal in the discourse of higher education (egregiously so in the research on AI in education where it’s as if the only voice on CT is Bloom) and written communication as these constructs are defined and elaborated in the rubrics. If you’ve read that piece, this one should flow fairly smoothly.

These three rubrics occupy genuinely distinct intellectual territory. Strong readers are not automatically curious learners. Skilled ethical reasoners need not be avid readers. Curious learners, avid readers, and ethical reasoners alike can, as the Bard taught us, “smile and smile and smile and be a villain.”

Each warrants its own rubric with due humility. Yet examined together, they reveal a coherent vision of a significant part of what undergraduate education is ultimately for: the gradual development of a mind capable of navigating complexity with comprehension, curiosity, and judgment.

Shared Architecture

Beneath their different substance, these three rubrics share a discernible structure. Each measures the movement over a four-year clock from externally driven, surface-level performance toward internally motivated, reflective judgment. Each treats self-awareness about one’s reading knowledge and skills, learning dispositions, and ethical beliefs as foundational rather than incidental.

Each reserves its highest marks for students who can operate across unfamiliar contexts without formal scaffolding. Implicitly in each, earning an undergraduate degree marks the moment a lifelong learner becomes aware of different ways to self-scaffold, to take up serendipitous opportunities that deepen experience and knowledge. Together, they describe not three separate skills but three expressions of the same underlying intellectual maturity.

The rubrics organize student performance across four levels: Benchmark (the floor of meeting expectations), two Milestones, and Capstone (the undergraduate ideal). Understanding what each level actually describes is essential to understanding what these rubrics are really asking — and what is now at stake in the age of AI.

The Benchmark Student

The benchmark reader approaches texts expecting to find right answers and facts to display for credit. She can identify aspects of a text when a question directs her to, but she is not yet reading so much as responding to prompts. The text is an obstacle between her and a grade. Interestingly enough, AI flips this script. She is now prompting and then reading.

The benchmark lifelong learner completes required work. That is the entirety of the description for Initiative: words that function almost as a rebuke. He explores topics only at a surface level, makes vague references to prior learning without applying it, and reviews past experiences without arriving at any clarified meaning or broader perspective. The benchmark here is a description of compliance. Translated into an AI context, this learner is likely vulnerable to distortions.

The benchmark ethical reasoner can recognize basic and obvious ethical issues but fails to grasp their complexity or interrelationships. She can name an ethical theory without doing much with it, states her core beliefs without examining their origins, and can only apply ethical frameworks with external support — in a class, in a group, or in a fixed-choice setting. Left on her own with a genuinely new ethical problem, she is not yet equipped. As near as I can tell, the jury is out on the question of AI as a hero or villain artificial ethical reasoner.

What unites these three portraits is dependence: on assignments for direction, on instructors for clarification, on familiar contexts for application. The benchmark student in each rubric is waiting to be told what to do and given a hand to hold. The rest of the rubric describes the long process of learning not to wait. Tragically, dependence as a learner might very well morph into dependence on an AI interlocutor.

Before accepting that portrait as a description of what students are, however, we should ask a harder question: whether it is a description of what they would become under any circumstances or whether we have made them.

American schooling initiates children into literacy through daily regimens of behaviorist phonics instruction: decoding as discipline, correctness as the goal. It teaches writing in prescribed structures: the paragraph, then the five-paragraph essay, then the argument scaffolded by a tidy acronym, something about claims and warrants.

By eighth grade in Texas in 2025, students were being asked to produce extended written responses to prompts like “Should homework be mandated?” — questions chosen not because they matter to anyone but because they are gradable.

The architecture of schooling, from its earliest moves through high school, is designed to produce students who wait for the prompt, answer the prompt, stop, and wait to be told how they did. We have built the benchmark student. We should not be surprised that she shows up.

The Capstone Student

The capstone reader has moved far beyond decoding. She recognizes implications of texts for contexts that extend beyond the assigned task and beyond what the author explicitly intended. She participates in disciplinary conversation, not just extracting and constructing meaning from texts but responding to texts as a member of a community of readers. Reading has become, for her, a form of ongoing intellectual engagement with the world.

The capstone lifelong learner pursues knowledge and experience that flourish entirely outside classroom requirements. He generates opportunities to expand his abilities rather than merely completing them. His reflection reveals significantly changed perspectives, evidence of genuine growth rather than performed introspection. His curiosity has become self-sustaining, no longer dependent on academic structures to keep it alive.

The capstone ethical reasoner can do something particularly demanding. She states a position, articulates the strongest objections to it, and defends against those objections adequately and effectively. She is not just ethically aware; she is ethically courageous, willing, nay, demanding that her position be genuinely tested.

What unites these three portraits is autonomy and agency of judgment, of curiosity, of conscience. The capstone student in each rubric has internalized the criteria so thoroughly that she no longer needs the rubric at all. That disappearing act is the miracle: responsibility for learning has been completely released by the teacher and grasped firmly by the learner.

AI and the Architecture It Inherited

The arrival of capable AI language models has triggered a predictable institutional panic, and the panic has generated a predictable misdiagnosis. The problem, in this telling, is that AI allows students to fake sophisticated thinking or, said differently, to produce capstone-level outputs without capstone-level development. The solution at first was detection, prohibition, and surveillance.

This solution got the situation almost exactly backwards.

AI did not create the gap between the appearance of educated thinking and its reality. That gap was already the central achievement of the system we built. Automated text classifiers were already scoring student writing at ETS long before ChatGPT. Algorithms were already selecting prompts for their gradability rather than their intellectual substance.

Students were already learning to produce responses that satisfy assignments rather than build funds of knowledge. AI did not conquer the architecture of schooling. It simply revealed how thoroughly that architecture had already been weakened by compliance, by measurement, by the reduction of learning to legible output.

Consider what AI actually demonstrates when it performs at academic levels. It has passed the USMLE — the United States Medical Licensing Examination — not at the top of the distribution, but at the standard required for licensure. It has passed the bar exam. It has passed other licensing examinations.

These are not trivial instruments. They represent the accumulated judgment of professional communities about what minimum competence requires. When AI meets that standard, the appropriate response is not to conclude that AI is faking medicine or law. It is to ask what those examinations are actually measuring and whether the answer is the same thing we thought it was.

Would a medical school admissions committee admit a student on the basis of a test score alone?

The VALUE rubrics were designed to measure orientation toward learning, particular habits of mind, deliberate transference of knowledge from participatory contexts inside and outside the curriculum. One’s orientation toward learning is part of a lived identity as evidenced through actual human cultural participation. That distinction matters more now than it ever has.

Because AI cuts both ways. The same capacity that allows a disengaged student to produce fluent text without thinking also allows an engaged student to think at levels previously inaccessible without years of preparation.

A first-year student who wants to stress-test an ethical position now has access to the most rigorous, tenacious interlocutor imaginable, one that will articulate the strongest counterarguments without social judgment and without fatigue. A curious learner whose questions have always outrun the available runway can now pursue those questions indefinitely. I can testify to that.

A reader who has always been too uncertain to argue with a difficult text now has a machine that can model what it looks like to read vigorously just to see. For students already oriented toward learning, already somewhere on the developmental arc the rubrics describe, AI is not a threat to that development. It is an accelerant.

The students who use AI to avoid thinking and the students who use AI to think harder are not distinguished by AI. They are distinguished by everything that happens to them as human beings going about their daily lives, especially in school.

Whether they were taught that the purpose of a text is to yield a correct answer or to reward deep engagement; whether the prompts they were given were worth thinking about; whether they were ever asked to write their own prompts; whether anyone in their education modeled what it looks like to be genuinely curious. The benchmark student who waits for direction was trained to wait. AI waits with them and makes polished output on demand.

This is the real 2026 benchmark problem. Not that AI has changed what educated thinking looks like — the rubrics still describe it appropriately and somewhat clearly at least for our time — but that the system designed to produce educated thinkers was already, at its foundations, designed to produce ever more sophisticated compliance.

AI has made that contradiction impossible to ignore.

The Political Conclusion

This is a policy problem, and policy problems require political will. But the policy has to address the right problem, and our politicians have consistently misdiagnosed the case literally for centuries. Accountability generates compliance, and accountability is core to law and order. But classrooms are not courtrooms.

The right problem is not AI.

The right problem is that American schooling, from its earliest literacy instruction through its standardized exit examinations, is organized around the production of legible compliance rather than the development of genuine intellectual capacity.

We built the benchmark student intentionally by design through behaviorist reading instruction, formulaic writing curricula, and assessment instruments that reward students for producing what classifiers can score. We then sent that student to college and called her underprepared.

We proposed surveilling her AI use while leaving every other element of the architecture intact.

The fix requires something more fundamental than policy tweaks administered through the existing machinery. What it requires is a dedicated national center, represented at the White House level, organized entirely around learning science — not accountability metrics, not workforce pipelines, not the next cycle’s testing contract.

This center’s mandate is to translate what we actually know about how human beings develop as readers, learners, ethical reasoners, and the other outcomes we value into the daily instructional life of American schools. The research base for that center already exists.

The learning science is not in dispute among the people who do it. What is in dispute is whether anyone in a position of political authority will take it seriously enough to fund it, staff it, and protect it from the interests that have always preferred compliance to capacity. My nominee to lead it is Linda Darling-Hammond.

Darling-Hammond has spent a career building precisely the case for action — on teacher preparation, on assessment reform, on the relationship between equitable resource distribution and genuine learning outcomes. She was on President Obama’s transition team and was available then for Secretary of Education.

She has the intellectual credibility the work requires and the political experience to navigate the environment in which it would have to operate. The knowledge exists. The person exists. What does not yet exist is the will to act on what we know rather than on what is easy to measure. The opportunity to rebuild may not show up for another two years, but the groundwork can be laid.

The VALUE rubrics describe a student who no longer needs to be given a rubric. That disappearing act — so complete that external assessment and scaffolding become unnecessary — is, in miniature, the promise of liberal education and the promise of democratic citizenship. It is the person who can read a text against the grain, pursue knowledge independently, and hold an ethical position under pressure.

We know how to build that person and, despite the adversity amidst the diversity, despite the contradictions inherent in our current model of schooling, we build them in every state year after year. Somehow we manage to produce really smart people. We have simply, for a very long time, for some unholy reason, chosen not to extend high-quality educational resources to every child.

AI has made that choice visible. What we do with the visibility is, still, up to us.

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