Part one of seven in the Artificial Intelligence in Education series. Source cutoff: Oct 30, 2026.
Generative artificial intelligence makes competent-looking intellectual work cheap. A student can produce an explanation, program, summary, or essay in seconds. The result may be insightful, mediocre, subtly wrong, or fabricated, and may also look better than the student’s understanding.
That change exposes an old weakness: a large gap between performance and learning. Much of education was built to move large numbers of students through lectures, assignments, examinations, grades, and credentials at manageable cost. Assessing performance in a deep learning environment is harder, more labor-intensive, and more expensive, requiring attention, practice, feedback, revision, direct experience, and judgment.
Artificial intelligence makes the gap impossible to ignore and provides an opportunity to address it.
The short answer
A polished answer is no longer reliable evidence that a student can produce, explain, or defend it.
The early research does not establish that artificial intelligence is generally good or bad for learning. It shows that the tool, the task, the timing of assistance, and the definition of learning can change the result.
Colleges should adapt quickly because the world graduates are entering has already changed. Their strongest response is not a universal policy but a clearer account of what students must learn and better evidence that they learned it.
Education was built around scalable proxies
A university education promises more than information. It promises knowledge, judgment, technique, sustained attention, and the ability to act when the answer is not obvious. Yet the current system rewards something narrower: produce the paper, pass the exam, earn the grade, collect the degree.
Each of those steps compresses a lot of information. A grade turns many judgments into one symbol. A transcript turns years of work into a pattern of symbols. A degree gives employers and other institutions a convenient signal when direct evaluation is costly.
The problem is not that proxies like grades, transcripts, and degrees exist; large institutions need ways to make decisions. The problem is that these proxies have become the objective. Students are incentivized to protect the grade, satisfy the rubric, choose safer work, and reproduce the expected form.
In short, be careful what you measure; you will get that.
The much less expensive system favors what can be delivered and scored at scale. The more demanding system asks students to discuss, build, investigate, revise, defend, and apply. Good teachers have often used laboratories, studios, fieldwork, oral examinations, mentoring, and repeated practice because those methods reveal how students think and what they can do independently.
Even when its use is permitted and disclosed, artificial intelligence makes the finished product even less informative, revealing little about what the student noticed, misunderstood, corrected, retained, or can now do alone.
Two experiments show why design matters
In a randomized high-school mathematics experiment, nearly 1,000 students at one school used ordinary classroom resources, a general GPT-4 interface, or a tutor designed with teacher-supplied solutions, common errors, and instructions to offer hints rather than answers.[1]
Both artificial-intelligence groups performed better during practice. After the tool was removed, students who had used the general interface performed below the control group. The designed-tutor group performed about the same as the control group. The general interface improved assisted work but weakened independent performance after the assistance disappeared.
A randomized crossover experiment in a Harvard physics course produced a different result. Students using a carefully engineered AI tutor earned higher immediate post-test scores than students in an active-learning class and reported greater engagement.[2] The tutor used expert-written prompts, solutions, videos, sequencing, and self-pacing.
I’m convinced that artificial intelligence can and will improve learning. Results will depend on how the tool is designed, when it intervenes, what expertise shapes it, and what students must still do themselves. The work will probably need to be adapted at several levels, from the institution and discipline to the course, assignment, and individual student. That is harder than distributing a generic chatbot, but it can produce much better learning at scale and at reasonable cost.
Define learning before choosing the tool
Any serious evaluation of learning should examine at least five outcomes, both with and without artificial-intelligence assistance:
Performance while assistance is available.
Independent performance after assistance is removed.
Retention after time has passed.
Transfer, meaning the ability to use what was learned in a materially different problem or setting.
Judgment about when the tool is wrong, incomplete, or inappropriate.
A tool may improve one outcome and leave the others unchanged. It may save time, provide feedback, or help a student attempt harder work. It may also complete the intellectual step the student needed to practice.
This is why asking whether artificial intelligence is good for learning is too vague. An answer-giving chatbot, a tutor that withholds the answer, a simulation, and a critic that challenges an argument are different interventions. An immediate quiz, a delayed examination, an oral defense, and a new real-world problem are different measures.
We need to start by naming what students should know and be able to do. Then decide where assistance helps, where independent performance matters, and what evidence can distinguish the two.
Practical risks
Authorship rules built around the final artifact are becoming harder to enforce. Colleges need to provide assignments that make formulation, reasoning, revision, explanation, and responsibility visible.
Student interactions with artificial-intelligence tools may include education records, personal information, unpublished work, or material belonging to someone else. Federal guidance requires institutions to examine control, authorized use, redisclosure, and security when online services handle education records.[3] UNESCO frames these concerns within a broader call for human-centered educational design.[4]
Generative systems can also produce confident errors. The National Institute of Standards and Technology organizes artificial-intelligence risk around governing, mapping, measuring, and managing the system across its life cycle.[5] In education, that means someone must be able to check the output and must own the decision to use it.
A student who never develops the underlying knowledge cannot be the reliable human reviewer the system assumes. Engineering disciplines, including software development, often use separate quality-assurance reviews for the same reason: important work should be checked by people with the knowledge and responsibility to find errors.
Adapt because the outside world has already changed
Students are using these rapidly changing tools, and employers are reorganizing work around them.
Graduates will be expected to frame problems, organize context, judge outputs, detect failure, work across disciplines, and accept responsibility for decisions made with artificial-intelligence assistance. Colleges that fail to expose students to real-world environments will leave students unprepared.
Colleges can act now and improve their approaches over time. A college can change a course, name what students should learn, compare assisted and independent work, evaluate retention and transfer, protect student data, and revise the design when it fails.
A better diagnostic for colleges
The first question is not, “Do we allow AI?” It is, “What are students supposed to learn here?”
Then ask:
What must the student understand and do without assistance?
Where can artificial intelligence provide explanation, feedback, simulation, or productive challenge?
How will the course evaluate retention, transfer, and judgment?
What information enters the system, and who is responsible when something goes wrong?
What result would cause the course to revise or stop the design?
Making these changes will require hard work and change management. The answers will differ across a literature seminar, nursing clinical, engineering laboratory, and accounting course. That is a feature, not a failure. The common requirement is a clear learning objective and honest evaluation within each course.
The work ahead
A finished answer is becoming cheaper. Deep learning is not. It still requires time, practice, feedback, experience, and human attention.
Performance is what a student can produce under stated conditions. Learning is the durable change in what the student knows, can use in a new setting, and can judge. Colleges and employers need evidence of both.
We do not yet know which designs will work best across subjects, institutions, and students. But colleges are preparing students for a world in which artificial intelligence is already part of serious work. They need to adapt now, evaluate the results, and keep improving the design.
Part two, What the University Is Still For, turns to the institution itself. If information and competent assistance are increasingly abundant, what is the university still for?
Questions, corrections, or disagreements are welcome. You can reach me directly at dave@aworkingmodel.com.
Sources
1. Bastani et al., “Generative AI without guardrails can harm learning: Evidence from high school mathematics.” Proceedings of the National Academy of Sciences, Jun 25, 2025; corrected Aug 20, 2025. Randomized field study at one Turkish high school over four short sessions.
2. Kestin et al., “AI tutoring outperforms in-class active learning.” Scientific Reports, Jun 3, 2025. Randomized crossover study of 194 students, two lessons, and immediate post-tests in one Harvard physics course.
3. U.S. Department of Education, Student Privacy Policy Office, FERPA FAQ on using online tools in a course. Current guidance accessed Sep 28, 2026. Operational privacy guidance, not a finding about learning.
4. UNESCO, “Guidance for Generative AI in Education and Research.” Published Sep 7, 2023; updated Jan 16, 2026. International guidance on privacy, human oversight, inclusion, and pedagogical design.
5. National Institute of Standards and Technology, “AI Risk Management Framework.” AI RMF 1.0 released Jan 26, 2023; Generative AI Profile released Jul 26, 2024. Voluntary risk-management guidance, not an educational-effectiveness study.