AI in Higher Education Newsletter

September 7, 2026 · Vol. 32

A weekly brief for the Management Department, Isenberg School of Management, UMass Amherst. By Matthew D. Langenkamp / 雷邁德, prepared in collaboration with Thea 🪻✨.

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The End of the Single AI Policy

A course AI policy states when and how students may use artificial intelligence. A single policy applies the same basic rule across an entire course or institution. The important development this week is that several universities are moving away from that model.

Stanford Graduate School of Business now assumes that MBA and MSx students may use AI for take-home work, including exams. The University of Chicago’s social-sciences Core is protecting an analog setting built around paper texts, device-free discussion, and human writing and grading. The University of Michigan is proposing AI-free learning spaces, different policies by school or program, and twenty course pilots.

These institutions have not reached one answer because they are protecting different kinds of learning. Their policies begin with the setting and its purpose. They then decide where AI belongs.

The practical question for faculty is therefore:

Which conditions of learning should assume AI access, and which should protect thought without it?

For management education, three conditions are especially useful:

This is a more precise framework than one rule for every assignment. It also gives students a reason for each boundary.

Executive Summary

The first weeks of the academic year are revealing a more differentiated institutional approach to AI.

Stanford GSB’s current policy treats take-home MBA and MSx work as an AI-access environment. Faculty may restrict AI during in-class work, but they may not ban it for take-home assignments or exams. Students must cite their resources, including AI tools, and Stanford recommends asking how AI was used rather than whether it was used.

Chicago’s social-sciences Core has chosen a protected independent environment. Its courses will generally use paper texts and device-free discussion and will prohibit AI-assisted writing and grading. The policy includes accommodations and purposeful exceptions. It also sits beside a university-wide rollout of Claude Enterprise. Access to AI and permission to use it in a particular classroom are separate decisions.

Michigan’s draft principles provide a governance model for managing both conditions. The recommendations include AI literacy, AI-free learning spaces, school-level variation, human responsibility, avoidance of AI surveillance, and twenty course pilots during 2026-27.

Oral examinations and blue books are returning because differentiated policies require credible evidence. Faculty can permit AI where it supports professional work and still preserve occasions when students must explain, calculate, write, or decide without it.

1. Setting-Specific Policy

A setting-specific AI policy ties a rule to the learning purpose of a particular activity. It may permit AI for a take-home market analysis, exclude it from an in-class conceptual exercise, and require an oral defense of the completed analysis. Each condition serves a different evidentiary purpose.

This approach does not require a separate manifesto for every assignment. It requires a clear answer to two questions:

  1. What capability should the student develop?
  2. What evidence will show that the student developed it?

The policy follows those answers. If the goal is to produce professional work under realistic conditions, access to AI may be appropriate. If the goal is to observe unaided comprehension or judgment, a protected setting may be necessary. If both capabilities matter, paired evidence can test both.

This extends the argument of Vol. 31. The previous issue focused on evidence of learning. The current institutional cases show how universities are beginning to organize policy around different kinds of evidence.

2. Stanford GSB Assumes AI Access at Home

Stanford GSB’s policy is unusually direct:

“Instructors may not ban student use of AI tools for take-home coursework, including assignments and exams.”

The rule applies to MBA and MSx courses. Faculty may decide whether students can use AI during in-class work. A device-free in-class activity inherently excludes AI along with internet access, laptops, tablets, and calculators.

Stanford has therefore classified take-home work as an AI-access setting. It has not declared that AI is educationally useful for every task. It has concluded that unsupervised work occurs in an environment where AI is available and that course design should account for that fact.

The accompanying guidance places responsibility on students and faculty. Students are expected to cite all resources used to prepare academic work, including generative AI. Faculty are encouraged to connect their rules to course goals and professional expectations. Stanford also advises instructors to ask all students how they used AI. Suggested evidence includes an AI-use statement, selected chat logs, reflection, and fact-checking.

The policy addresses privacy and intellectual property as well. Students should use Stanford-approved tools where possible and should not enter confidential, personal, proprietary, or copyrighted course material into unapproved systems.

Why it matters for Isenberg: A take-home strategy memo, valuation, marketing plan, or consulting analysis should increasingly be designed on the assumption that AI is available. The submission can still reveal judgment if students must identify their sources, explain what they accepted or rejected, verify factual claims, and remain answerable for the recommendation.

3. Chicago Protects an Analog Core

The University of Chicago’s social-sciences Core has defined itself as a pedagogical setting without AI. Beginning this academic year, its courses will generally rely on paper texts and device-free guided discussion. Students may not use AI for writing, and instructors may not use it for grading.

The rule is symmetrical because the learning relationship is part of the design. Students are expected to read, write, listen, disagree, make mistakes, and change their minds in the presence of other people. Human instructors remain responsible for reading and evaluating the resulting work.

The policy is also bounded. Instructors may make purposeful exceptions for work such as examining a dataset, troubleshooting code, revising text, or using library resources. Approved disability accommodations remain in place. The social-sciences division permits carefully reviewed experimentation rather than treating the analog rule as a judgment on every use of AI.

The wider institutional context is important. Chicago is giving students access to Claude Enterprise and developing a broader human-centered AI strategy. A university spokesperson summarized the relationship clearly: access to an AI system does not mean every use is appropriate or permitted.

Chicago is protecting a particular learning environment, not trying to make AI disappear from university life.

Why it matters for Isenberg: A course may reasonably reserve some case discussions, negotiations, diagnostic writing, or first-pass analyses for human attention alone. The restriction is more credible when faculty name the capability being developed and apply comparable standards of human responsibility to their own teaching and grading.

4. Michigan Builds a Portfolio

The University of Michigan’s AI in Education Working Group offers a third model. Its recommendations remain a draft, and the university is collecting community feedback through September 17. The proposal is useful because it treats institutional policy as a portfolio rather than a universal rule.

The working group recommends:

The report explains the educational tension in one sentence:

“AI systems have been developed to speed up processes; learning requires processes that slow things down.”

Michigan connects that principle to implementation. During 2026-27, twenty courses will test different forms of AI-supported teaching and learning. Schools and colleges can use the pilots to build practical experience before making larger commitments.

This approach protects productive struggle without treating difficulty as an end in itself. It also permits experimentation without turning every classroom into a compulsory AI laboratory.

Why it matters for Isenberg: Department-wide policy can supply common language, privacy rules, disclosure expectations, and responsibility. Individual courses and assignments still need room to choose among AI access, protected independent work, and paired evidence. Small pilots can test those choices before they become permanent requirements.

5. Assessment Makes Different Rules Credible

A differentiated policy works only when the evidence matches the condition.

Two recent Chronicle of Higher Education reports—“The Return of the Oral Exam” and “Blue Books Are Back. Is That a Good Thing?”—show renewed attention to controlled assessment. The articles are behind the Chronicle’s access layer, so this brief does not rely on details that could not be independently inspected. Their subjects nevertheless reflect a visible response to generative AI: instructors want occasions when student reasoning can be observed directly.

An oral defense makes understanding visible through immediate questions. A handwritten response records independent writing under controlled conditions. Neither method needs to replace AI-assisted work. Each can provide complementary evidence.

In a business course, paired evidence might include:

The point is alignment. The professional product shows what a student can produce with modern tools. The independent component shows what the student can explain and defend.

6. The Human Consequence: Confidence in One’s Own Thinking

A highly active discussion in r/Professors this week carried the title “Students don’t believe they can think better than AI. I’m devastated.” The thread had 489 upvotes and 102 comments when collected.

This is practitioner testimony, not a representative survey. The public page did not provide enough text to quote the author’s account responsibly. The title and response still identify a concern worth watching: students may begin to doubt that forming their own judgment has value.

Clear learning conditions can help. A protected exercise should not feel like an arbitrary withdrawal of a useful tool. Faculty can explain the capability being formed and why students need direct practice. AI-access work should likewise demand more than polished output. Students should see that the tool broadens the work while responsibility remains theirs.

Productive struggle is easier to accept when its purpose is visible.

One Practical Move for Next Week

Add an AI condition label to one consequential assignment:

Field What to state
Condition AI-access, protected independent work, or paired evidence
Purpose The knowledge, skill, or judgment the assignment develops
AI boundary What is permitted, required, or excluded
Evidence What students must retain, explain, or demonstrate
Responsibility What students must verify and remain answerable for

Example:

Condition: Paired evidence. You may use AI to develop and test your recommendation. Submit a short statement identifying how you used it and which sources you verified. After submission, be prepared to explain one assumption and respond to a changed scenario without AI assistance. This structure assesses both professional tool use and your ability to defend the underlying judgment.

The label need not add substantial documentation. Its purpose is to make the learning contract visible before students begin.

What to Watch

  1. Whether Michigan changes its draft principles after community feedback closes on September 17.
  2. How Stanford GSB faculty redesign take-home exams when AI use cannot be prohibited.
  3. Whether Chicago evaluates the analog Core’s effects on discussion, writing, student confidence, and accessibility.
  4. Whether oral defenses and other paired assessments remain manageable in larger courses.
  5. Whether institutions apply responsibility and disclosure expectations to faculty use of AI as well as student use.

Sources

Questions or topics for next week? Reply to mlangenkamp@umass.edu. Prepared in collaboration with Thea.

More from the author: The Langenkamp Dictionary · Other Writing · Fencing the Wrong Animal · AI in Higher Education archive.

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