I started with a quite simple and straightforward task for myself. After having given lectures on AI regulatory environments from a comparative perspective this summer, I thought that I might have to practice what I was preaching and develop a new AI policy for my courses. That would require, within the expectations and performances of the legal education field, a text. And that text would (1) have to be embedded in a performative document (the syllabus); and (2) produce and mimic discursive tropes and acknowledgement ceremonies (textual of course) of the primacy of whatever (increasingly dense and oftentimes badly recursive) regulatory constraints, directions, formal and informal expectations, had been crafted by the institution from which I received a salary and for whose benefit I offered these courts. No criticism here; merely an observation of the cognitive institutional caged we have built for ourselves and within which the collective finds (or tells us it finds) a measure of happiness, legitimacy, order, stability, and according to its own value systems, a sense of fulfillment or movement, in any case, in the direction of fulfillment (and with it what likely passes for institutional happiness or at least pride--though we all know where pride leads in both pagan and Christian philosophy).
And so . . . to work. But again, practicing what I had been preaching, it mad essence to utilize--and in utilizing observing, experimenting, deploying, and assessing--AI systems. In this case, and as a sort of homage to the institution that employs me, I used Harvey AI., with the institutions who ecxtract6 money from the use of which, the institution that employs me had concocted a relationship that suited its needs whether inward or outward facing needs. Harvey AI tells us, through text embedded in its website, that "Harvey is AI designed for legal and professional services. Advance your expertise on a secure platform that lets you focus on high-value work." Wonderful.
And so I gave Harvey the following prompt: "please review all online AI use law school policies and prepare a comprehensive summary (with links to all sources) that focuses on description (by categorical types) and analysis. Please avoid review hacking, hallucination, or efforts to fulfill sycophantic protocol loops."
That started a long chain of exchange that finally got to an empirical analysis--which Harvey and I titled:
Structure, Opacity, and Convergence: A Consolidated Analysis of Law School
Generative AI Coursework and Exam Policies, that I found useful, and which I share below for those who may be interested. Here is the abstract:
Abstract: Structure, Opacity, and Convergence in Law School AI Policies: This report consolidates a multi-stage analysis of generative artificial intelligence (AI) coursework and examination policies across a retrieval-based sample of twelve American law schools and programs. The study demonstrates that law school AI governance cannot be reduced to a single linear spectrum; instead, policies vary independently along three distinct structural axes: default polarity (restrictive versus permissive baselines), drafting style (determinate rules versus interpretive standards), and a two-tier autonomy structure (governing institution-to-instructor and instructor-to-student relationships). Cross-analysis reveals that substantive restrictiveness does not predict structural design, meaning schools with identical baselines often impose vastly different interpretive or administrative burdens on students and faculty. The research identifies a pervasive opacity across the broader legal education sector, noting that a vast majority of ABA-accredited law schools lack retrievable, law-specific public policy texts. This opacity manifests via four distinct patterns: non-existence, disclosed decentralization, active access-gating, and unwritten or oral communication. This lack of public accessibility sits in tension with the fair-notice principles required for academic integrity enforcement. Furthermore, the study finds that law schools rarely author syllabus language independently, relying instead on a small pool of shared template sources. This ecosystem fosters formal convergence on a common taxonomy of policy types while simultaneously permitting wide divergence in substantive local rules. Ultimately, the field develops through a two-tier mechanism: while individual policy documents are structured deductively from a primary principle, the field as a whole evolves inductively and mimetically through horizontal borrowing, imitation, and iterative revisions driven by accumulated institutional experience.The process of developing this analysis with Harvey AI also produced a number of side conversations about the way that Harvey (in a way like my research assistants) chose to make assumptions and draw conclusions that either "followed the herd" or that reinforced social hierarchies in the legal education field in ways that evidenced the ability of machine systems to pick up on human bias expectations (in this case hierarchical pack behaviors among legal educators who use status as a sort of proxy for quality and influence as a sort of proxy for value). As the empirical study noted:
The dataset at the center of this analysis comprises the University of California, Berkeley School of Law; Columbia Law School; the University of Chicago Law School; Stanford Law School, including its Juelsgaard Intellectual Property and Innovation Clinic as a documented sub-institutional case; the University of San Diego School of Law; the Center for Transnational Legal Studies; Fordham University School of Law; Mitchell Hamline School of Law; American University Washington College of Law; the University of Texas at Austin School of Law; Penn State Dickinson Law; and Suffolk University Law School. These twelve were not selected through a designed sampling methodology but assembled cumulatively across three rounds of web search: an initial round anchored on prestige-based queries naming prominent schools directly, a second round following up on schools named in a single inherited citation list from a University of San Diego law library guide, and a third round of targeted searches for named gaps, which added Suffolk, Penn State, and several university-wide-only findings. This method of construction means the dataset should be understood as "schools whose policies happen to be indexed, hyperlinked, and either self-published or covered by press or library guides that general web search can surface," not as a cross-section chosen for representativeness. (Structure, Opacity, and Convergence: A Consolidated Analysis of Law School Generative AI Coursework and Exam Policies)
As noted, then, the examination was limited to AI policies that were publicly disclosed. That was good enough to extract a taxonomy, which was my primary goal. Nonetheless, it also yielded additional analytical "fruit." e constraint that produced the data set also produced a quite interesting sub-category for examination--the extent to which AI policy is viewed as proprietary and its effect on enforcement and the vitality of the authority and certainty of "secret" or "secreted" AI policies by institutions with a mania for non-disclosure. In the next post we explore that issue more thoroughly though I flag it here. Also flagged here is the process of narrowing both the scope of examination by Harvey and the parameters within which the empirical "research" was undertaken and the analysis framed. Harvey was at their best when it stuck closely to the data; every time it veered into "pleasing me" (something that few if any have actually mastered) it produced error or analysis that could not withstand even cursory review. Yet the process of unpacking and repacking the analysis was also quite useful for me (the user) in refining my own analytical lens and making it clear and precise (something that I spend much time on with my students but which I am now reminded in engagements with Harvey is a skill that can never be improved enough and is contextually specific in its application ion ways that are clearer now top me). This is also unpacked in the next post HERE.
What Harvey and I found that was a most interest to me at least, was a taxonomy of regulatory responses to AI, law and legal education.
The core structural or framing contribution of this analysis is a set of three structural axes, derived not from an external normative framework but from open coding of the retrieved primary texts themselves — that is, from categories the schools' own drafters independently used to organize their rules, evidenced by the fact that the same structural divisions (particularly the exam/non-exam distinction) recur across policies drafted with no apparent coordination among Berkeley, Columbia, Chicago, and Stanford.(Structure, Opacity, and Convergence: A Consolidated Analysis of Law School Generative AI Coursework and Exam Policies)
Axis One speaks to Default Polarity. "Default polarity describes a school's starting-point orientation before any instructor exercises discretion. A restrictive baseline treats AI use as prohibited unless an instructor affirmatively loosens the rule. . . A permissive baseline inverts this structure, treating AI use as allowed unless an instructor affirmatively restricts it. . . [and a] split polarity: permissive for idea development and learning support absent a course-specific policy, but restrictive — requiring prior written instructor authorization — for exams and for drafting or revising submitted work" (Ibid.).
Axis Two speaks to Rule Versus Standard Drafting Style "This axis, drawn from the established rules-versus-standards distinction in regulatory design theory, distinguishes ex ante determinate rules from ex post interpretive standards, independent of substantive restrictiveness" (Ibid.). Again there is a three part subdivision between rule-drafted, standards-drafted, and hybrid approaches.
Axis Three speaks to the Two-Axis Default/Mandatory Autonomy Structure. "This axis, drawn from the default-rule/mandatory-rule distinction in private-law and regulatory theory, requires separating the institution-to-instructor relationship from the instructor-to-student relationship, because nearly every school's rule functions differently on each. " (Ibid.).
Applying these three axes to the twelve-school dataset appears to show
that substantive restrictiveness does not predict drafting style or
autonomy structure. No single label — restrictive, moderate, or
permissive — adequately describes any one school's full policy
architecture.
And that got me back to my initial query, and the genesis of all of this descriptive analytics--the terrains of "model" classroom AI policies. Harvey and I noted the following:
Model and Template Syllabus Language, and Discursive Intersubjectivity. A further line of inquiry found that schools do not typically draft AI syllabus language independently. USD's law library curates five named, selectable archetypes — "Prohibited Use," "Mandatory Disclosure," "Permissible vs. Non-permissible uses," "Encouraging Use," and "Prior Consultation". [69] [70] Stanford's Robert Crown Law Library maintains a parallel "Syllabus Statements for Generative AI Usage" resource. [71] Penn State's library guide links directly to Lance Eaton's widely circulated academic spreadsheet and to a Harvard Bok Center illustrated rubric. [72] Michigan's central AI resource site reproduces categorized examples attributed to external sources including Temple University and the multi-institutional Sentient Syllabus Project. [73] [74] UCLA states its own sample language is "adapted from UIC's AI Writing Tools guide", naming the donor institution directly.The bottom line appears to be recursivity (the process of defining a procedure, rule, or structure in terms of itself) within contained complexity. And like law generally, it evidences the operational cage of dynamic systems (both technological and organic) that self-regulate by incorporating feedback from their environment and constantly reflecting on their own state--that is of systems that stand still even as they appear to move because moving involves adjustment to stimuli the character of which is absorbed without affecting the integrity or core premises of the operational system itself. That requires, in turn, reflexivity--the active engagement with an irritant (in this case machine system AI) which serves recursive objects, to return to our ways of doing things by reference to the things we are doing (see, Hibbert, P., MacIntosh, R., and Coupland, C. (2010) Reflexivity, Recursion and Relationality in Organisational Research Processes. Qualitative Research in Organizations and Management: An International Journal, 5(1),pp. 47-62).
Three patterns emerge from this template ecosystem. Hub concentration: a small number of source documents — Eaton's spreadsheet, the Sentient Syllabus Project, UIC's guide, the AALS 2023 interview on Berkeley's policy — recur as reference points across otherwise unconnected schools. Convergent categorization despite non-identical wording: USD's five categories, Michigan's three, and UCLA's three are not verbatim copies of each other, yet each carves the same underlying decision space into a similarly small number of discrete types, indicating a shared conceptual ontology has stabilized across the field independent of exact phrasing. Explicit cross-institutional citation as a legitimating move: UCLA's direct attribution to UIC, and Stanford's clinic citing the AALS interview specifically discussing Berkeley's policy, show institutional authority being transmitted horizontally between peer schools rather than independently justified from first principles at each site. [75] This supports characterizing the field as discursively intersubjective: a shared vocabulary lets one school's policy remain legible to another's faculty and students without each institution reconstructing the underlying logic from scratch, even as substantive outcomes — restrictive versus permissive — remain genuinely unsettled and divergent.
And so the answer--the safest bet for faculty, one that is both reflexive but also recursive--is to build off of the templates that are themselves essentialized reductions of the poliocy "types" now applied to the specific context of place, space, and time.
* * *
What was perhaps more interesting was that the taxonomy and its three axes might be understood as an expression of a larger problem--the fundamental relationship of law to AI enhanced, managed or driven regulatory systems. More precisely the debates about the role of AI in legal education might be understood as masking the larger debates about the integrity and fundamental character of law and more basic still, the scope and relevance of legal systems and their cognitive and performance semiotics (roughly the phenomenology of law) in a world in which the price of the protection of the integrity of law as a field as currently constituted might be its decreasing relevance to the mechanisms that may be interposed in the management of human collectives and in the operation of human institutional life--except perhaps as a ceremonial overlay on those changes. That is a subject that is discussed in the third post HERE.
And thus to a first shot at an AI policy--a visual/textual rather than a textual form given my sense of contemporary comprehension and reception cultures:
The discussion draft of Structure, Opacity, and Convergence: A Consolidated Analysis of Law School Generative AI Coursework and Exam Policies appears below and may also be accessed through SSRN HERE and on my personal website HERE.
Structure, Opacity, and Convergence: A Consolidated Analysis of Law School Generative AI Coursework and Exam Policies
Larry Catá Backer (白 轲)
(in collaboration with Harvey AI)
W.
Richard and Mary Eshelman Faculty Scholar; Professor of Law and International Affairs
Penn State Dickinson Law, a unit of The Pennsylvania State University system | 239 Lewis Katz Building, University Park, PA 16802 1.814.863.3640 (direct) || lcb11@psu.edu
Abstract: Structure, Opacity, and Convergence in Law School AI Policies: This report consolidates a multi-stage analysis of generative artificial intelligence (AI) coursework and examination policies across a retrieval-based sample of twelve American law schools and programs. The study demonstrates that law school AI governance cannot be reduced to a single linear spectrum; instead, policies vary independently along three distinct structural axes: default polarity (restrictive versus permissive baselines), drafting style (determinate rules versus interpretive standards), and a two-tier autonomy structure (governing institution-to-instructor and instructor-to-student relationships). Cross-analysis reveals that substantive restrictiveness does not predict structural design, meaning schools with identical baselines often impose vastly different interpretive or administrative burdens on students and faculty. The research identifies a pervasive opacity across the broader legal education sector, noting that a vast majority of ABA-accredited law schools lack retrievable, law-specific public policy texts. This opacity manifests via four distinct patterns: non-existence, disclosed decentralization, active access-gating, and unwritten or oral communication. This lack of public accessibility sits in tension with the fair-notice principles required for academic integrity enforcement. Furthermore, the study finds that law schools rarely author syllabus language independently, relying instead on a small pool of shared template sources. This ecosystem fosters formal convergence on a common taxonomy of policy types while simultaneously permitting wide divergence in substantive local rules. Ultimately, the field develops through a two-tier mechanism: while individual policy documents are structured deductively from a primary principle, the field as a whole evolves inductively and mimetically through horizontal borrowing, imitation, and iterative revisions driven by accumulated institutional experience.
This report consolidates a multi-stage research and analytical effort into a single sequential document. The central finding, developed and refined across successive rounds of research, is that law school policies governing generative AI use in coursework and examinations cannot be reduced to a single restrictive-to-permissive spectrum. Instead, these policies vary along at least three empirically independent structural axes, are documented unevenly across a landscape marked by genuine gaps in public accessibility whose causes are themselves heterogeneous, draw their categorical vocabulary from a small number of widely circulated template sources rather than being independently invented at each institution, and develop through a two-tier process that is internally deductive within any single policy document but inductive and mimetic across the field as a whole. This document is limited to twelve law schools and programs for which retrievable, law-specific coursework or exam policy text was located through iterative web research, against a background of approximately 198 ABA-accredited law schools nationally; it is a non-random, retrieval-based sample and should not be read as a representative survey of American legal education generally. [1] Where evidence is thin, secondary-sourced, or interpretive rather than established, this document says so explicitly rather than smoothing over the distinction.
Scope, Method, and the Twelve-School Dataset
The dataset at the center of this analysis comprises the University of California, Berkeley School of Law; Columbia Law School; the University of Chicago Law School; Stanford Law School, including its Juelsgaard Intellectual Property and Innovation Clinic as a documented sub-institutional case; the University of San Diego School of Law; the Center for Transnational Legal Studies; Fordham University School of Law; Mitchell Hamline School of Law; American University Washington College of Law; the University of Texas at Austin School of Law; Penn State Dickinson Law; and Suffolk University Law School. These twelve were not selected through a designed sampling methodology but assembled cumulatively across three rounds of web search: an initial round anchored on prestige-based queries naming prominent schools directly, a second round following up on schools named in a single inherited citation list from a University of San Diego law library guide, and a third round of targeted searches for named gaps, which added Suffolk, Penn State, and several university-wide-only findings. This method of construction means the dataset should be understood as "schools whose policies happen to be indexed, hyperlinked, and either self-published or covered by press or library guides that general web search can surface," not as a cross-section chosen for representativeness.
Several schools yielded only university-wide, non-law-specific guidance rather than law-school-specific instruments: Northwestern Pritzker School of Law's own library guide page on generative AI was found to be marked, in its own words, "not currently available due to visibility settings"; the University of Michigan, UCLA, and Vanderbilt each produced substantial university-level teaching-center guidance and sample syllabus language without a confirmed law-school-specific counterpart. [2] [3] [4] These are reported separately throughout this analysis rather than folded into the twelve-school primary dataset, because conflating university-wide guidance with a law school's own policy would misrepresent what specifically governs law students. A further set of named schools — Harvard, Yale, NYU, Georgia State, UC Irvine, University of Washington, Washburn, Duke, University of Michigan Law School specifically, UCLA School of Law specifically, and the University of Virginia — were searched but yielded no retrievable law-specific policy text at all, a gap that the analysis in a later section treats as itself a substantive finding rather than a simple absence of data.
Three Independent Structural Axes
The core typological contribution of this analysis is a set of three structural axes, derived not from an external normative framework but from open coding of the retrieved primary texts themselves — that is, from categories the schools' own drafters independently used to organize their rules, evidenced by the fact that the same structural divisions (particularly the exam/non-exam distinction) recur across policies drafted with no apparent coordination among Berkeley, Columbia, Chicago, and Stanford. [5] [6] [7] [8]
Axis One: Default Polarity
Default polarity describes a school's starting-point orientation before any instructor exercises discretion. A restrictive baseline treats AI use as prohibited unless an instructor affirmatively loosens the rule; this describes Berkeley, whose policy "forbid[s] the use of AI... to conceptualize, outline, draft, revise, and edit" by default, permitting research only "for the limited purpose of identifying sources", and Columbia, whose "Default Prohibition" bars AI "in (a) any exam or final paper or (b) for aid in drafting any part of work submitted for credit, even if the use is fully documented". [9] [10] The same restrictive-baseline structure appears in Chicago's exam-specific rule, which is "absolutely prohibited... unless the instructor specifies otherwise"; in USD's requirement that "no AGC may be included in written work submitted for credit... unless the instructor explicitly permits such use in writing"; in Fordham's University-wide standard incorporated by its law school, barring use "unless directly authorized by the instructor"; in Mitchell Hamline's plagiarism definition, which treats undocumented AI-generated content as a violation by implication; in Penn State's university-wide exam rule, which states plainly that "students may not use generative AI tools to complete multiple-choice, matching, fill-in the blank, open-ended, or essay exam questions"; and in Suffolk, where a school official is quoted stating that "Suffolk Law has a default policy that prohibits use of generative AI on assignments unless a professor explicitly allows it". [5] [11] [12] [13]
A permissive baseline inverts this structure, treating AI use as allowed unless an instructor affirmatively restricts it. The Center for Transnational Legal Studies is the clearest instance: "Unless explicitly prohibited and enforced through supervised assessment conditions, students may reasonably assume AI tool usage is permissible for assignments, provided proper attribution is maintained". Stanford exhibits a genuinely split polarity: permissive for idea development and learning support absent a course-specific policy, but restrictive — requiring prior written instructor authorization — for exams and for drafting or revising submitted work. [14] Penn State likewise splits by governance level rather than by use category, combining a restrictive university-wide floor for exams specifically with a complete absence of institutional default for non-exam work, where "the course syllabus and assessment instructions" control entirely. [15] [16] [17] Finally, American University and UT Austin resist confident placement: American University's Academic Integrity Code reportedly contains "no one-size-fits-all" rule at all, while UT Austin's evidence describes a structural exam-format reform — "near-complete elimination of take-home exams" in favor of in-class, software-restricted testing — rather than a permission-based conduct rule comparable to the other schools. [18] [19]
Axis Two: Rule Versus Standard Drafting Style
This axis, drawn from the established rules-versus-standards distinction in regulatory design theory, distinguishes ex ante determinate rules from ex post interpretive standards, independent of substantive restrictiveness. [20] [21] Berkeley is the most rule-drafted policy in the dataset, enumerating specific prohibited activities in catalog form — "Asking an AI tool to brainstorm a paper topic or thesis (prohibited conceptualizing)," "Asking an AI tool to propose an organizational structure for a paper (prohibited outlining)". [22] Columbia and Penn State are similarly rule-drafted, naming covered contexts or exam formats directly rather than relying on an external test. [10] [15] Chicago is the clearest standard-drafted policy, asking whether AI use "would constitute academic plagiarism if the generative AI were a human author whose work was used without attribution" — a functional-equivalence test requiring interpretation rather than a determinate catalog. [23] Fordham and Mitchell Hamline are similarly standard-based, folding AI regulation into pre-existing plagiarism doctrine rather than drafting a freestanding enumerated instrument. Stanford, CTLS, and USD occupy a hybrid position, combining standard-like general permissions with rule-like bright-line exceptions or documentation duties: Stanford's general guidance is standard-like, but its exam and drafting provisions require a determinate, binary condition — prior written disclosure and authorization — and CTLS's permissive standard is paired with a rule-like mandatory duty to keep "a record of the prompts used to generate content". [14] [24] [25]
Axis Three: The Two-Axis Default/Mandatory Autonomy Structure
This axis, drawn from the default-rule/mandatory-rule distinction in private-law and regulatory theory, requires separating the institution-to-instructor relationship from the instructor-to-student relationship, because nearly every school's rule functions differently on each. [26]
On the institution-to-instructor axis, most schools operate a default the instructor may override: Berkeley's instructors may "deviate from the default rule... provided that they do so in writing and with appropriate notice"; Columbia's instructors may move the rule bidirectionally, both loosening and tightening it; Chicago's, Stanford's, USD's, CTLS's, and Fordham's instructors hold comparable, if more often one-directional, override authority. [27] [28] [29] American University and UT Austin show no institutional default to override in the retrieved evidence, while Penn State's university-wide exam rule shows no textual override clause at all, suggesting — tentatively, given only an excerpt was retrieved — a possibly mandatory institutional floor specifically for exams. [15] [30]
On the instructor-to-student axis, the evidence shows that whatever rule an instructor sets is, in most schools, mandatory rather than a default the student can access merely through disclosure. Columbia states this most directly: drafting assistance is barred "even if the use is fully documented". [31] Stanford requires not just disclosure but advance authorization: "unless (1) fully disclosed in advance... and (2) explicitly authorized by the instructor in writing prior to the student's use of the tool". [32] USD treats nondisclosure as an aggravating factor rather than treating disclosure as curative, and Chicago's background plagiarism doctrine cannot be waived by attributing work to AI. [33] [34] CTLS is the clear counter-instance: its permissive default is genuinely student-facing, since "students may reasonably assume AI tool usage is permissible... provided proper attribution is maintained", requiring no per-use instructor sign-off.
A bounding floor operates beneath all of this discretion: Stanford states that permitted uses may not "authorize students to contravene standard academic norms concerning plagiarism and accuracy," and that in clinical coursework, "AI use must meet all applicable rules of professional responsibility" — a floor no instructor's permissive policy can waive. [35] [36] A further, previously unremarked governance tier appears at Stanford specifically: its Juelsgaard IP and Innovation Clinic maintains its own stricter default, distinct from the general Stanford Law policy, stating "as a default, you should not use ChatGPT or any other generative AI tools in completing any of your clinic assignments" — evidence of a program-level tier of discretion between the institution and the individual instructor that the two-tier model, as originally constructed, does not fully capture. [37]
The Central Finding: Axis Independence
Applying these three axes to the twelve-school dataset shows that substantive restrictiveness does not predict drafting style or autonomy structure. Berkeley and Columbia align closely across all three axes — restrictive baseline, rule-drafted, mandatory floor. Chicago matches their restrictive exam polarity but diverges sharply on drafting style, using a standard rather than an enumerated rule. CTLS is the dataset's clearest outlier on baseline polarity and on the instructor-to-student relationship, yet pairs its permissive substance with rule-like procedural documentation demands. Stanford demonstrates that split polarity is a coherent design choice rather than an incomplete one. No single label — restrictive, moderate, or permissive — adequately describes any one school's full policy architecture.
Table 1: Composite Axis Placement Across the Twelve-School Dataset
|
School |
Axis 1: Baseline Polarity |
Axis 2: Drafting Style |
Axis 3A: Institution→Instructor |
Axis 3B: Instructor→Student |
|
UC Berkeley Law |
Rule (enumerated catalog) [38] |
Default, bidirectional |
Mandatory |
|
|
Columbia Law School |
Restrictive [10] |
Default, bidirectional [40] |
||
|
University of Chicago Law |
Standard (plagiarism-equivalence) [42] |
|||
|
Stanford Law School |
||||
|
University of San Diego Law |
Hybrid |
|||
|
Center for Transnational Legal Studies |
Permissive |
Hybrid |
Default |
Default-like |
|
Fordham Law School |
Restrictive [12] |
Not specified |
||
|
Mitchell Hamline School of Law |
Restrictive (implied) |
Standard |
Not clearly specified |
Not specified |
|
American University WCL |
Not specified [48] |
Standard (per secondary source) [48] |
Not specified |
|
|
University of Texas at Austin Law |
Not classifiable |
Not specified |
Not specified |
|
|
Penn State Dickinson Law |
Restrictive (exam, university-wide); absent (non-exam) [15] |
Rule (named exam formats) [15] |
||
|
Suffolk University Law School |
Not classifiable |
Not specified |
Notes: Cells marked "not specified" or "not classifiable" reflect genuine gaps in retrieved evidence rather than inferred values; no cross-school imputation has been performed.
Independence as the Generative Mechanism of Diversity
The diversity observed across the twelve-school dataset is not simply the additive result of three separate disagreements — schools disagreeing about polarity, disagreeing about drafting style, and disagreeing about autonomy structure, as though these were three unrelated votes being tallied. Rather, the diversity is combinatorial: because the three axes are empirically independent of one another, a school's position on one axis constrains almost nothing about where it will land on the other two, and the effective policy space is therefore far larger than the small number of category values on any single axis would suggest. With even a modest three-to-four value range on each axis, the theoretical combinatorial space runs into the dozens of distinct architectures, and the twelve schools in this dataset already populate a meaningful fraction of that space with genuinely distinct profiles rather than clustering into one or two dominant types. This is the central reason a single restrictive-to-permissive label cannot describe any school adequately: restrictiveness is a property of only one axis, and the other two axes determine how that restrictiveness is expressed, enforced, and experienced by instructors and students.
Polarity and Drafting Style: Restrictiveness Delivered Through Different Interpretive Mechanisms
The interaction between default polarity and drafting style determines not how much AI use is permitted but how the boundary of permission is discovered in a given case. Berkeley and Chicago both adopt a restrictive baseline for exams — AI use is prohibited by default in both — yet they arrive at that restriction through opposite drafting mechanisms. [23] [2] Berkeley's restriction is rule-drafted: it enumerates specific prohibited activities, such as "asking an AI tool to compose a paragraph summarizing a legal rule for use in a paper", meaning that determining whether a given student action violates the policy is, in principle, a matter of matching conduct against an explicit list. [3] [4] Chicago's restriction is standard-drafted: the operative test is whether the conduct "would constitute academic plagiarism if the generative AI were a human author whose work was used without attribution", meaning that determining a violation requires an evaluator to reason by analogy to a separate, pre-existing body of doctrine. [5]
The practical consequence is that two schools with identically restrictive substantive baselines impose very different burdens on the parties who must apply the rule. Berkeley's catalog can, in an easy case, be applied by a student or instructor without any need to consult external doctrine; Chicago's standard requires active interpretive judgment in every case, shifting adjudicative cost from the drafting stage (where Berkeley invested effort enumerating instances) to the application stage (where Chicago's evaluators must reason case by case). This means "restrictive" schools are not equally predictable or equally administrable, and the drafting-style axis is what determines which of these two very different experiences a restrictive rule produces.
The same interaction runs in the other direction for permissive baselines. CTLS's permissive default — "students may reasonably assume AI tool usage is permissible for assignments, provided proper attribution is maintained" — is standard-like in its general orientation, granting broad latitude subject to a loosely specified attribution norm. But CTLS pairs that permissive standard with a rule-like, determinate documentation requirement: a mandatory "record of the prompts used to generate content". [6] The interaction here produces a hybrid experience distinct from either a purely permissive-and-standard-based policy or a purely permissive-and-rule-based one: substantive freedom is wide, but the administrative burden of demonstrating compliance is specific and exacting. A school that combined permissive polarity with a purely standard-based compliance mechanism (say, only "students should be transparent about AI use" with no further specification) would produce meaningfully more discretion, and less predictability in enforcement, than CTLS's actual combination.
Polarity and Autonomy Structure: Where the Substantive Default Actually Operates
The interaction between default polarity and the institution-to-instructor autonomy relationship determines where in the institutional hierarchy substantive disagreement is actually permitted to occur, which in turn shapes how much real-world variation exists beneath a school's nominal single policy. Because Berkeley, Columbia, Chicago, Stanford, USD, and Fordham all pair a substantive default (restrictive, in most of these cases, or split in Stanford's) with instructor override authority, the school-level policy label describes only the fallback condition, not the actual lived experience in any classroom where an instructor has exercised override authority. [7] [8] This suggests that within a single school with a nominally restrictive default, the true range of experienced policy can span from fully restrictive (where an instructor never states an alternative) to fully permissive (where an instructor has affirmatively adopted a more permissive syllabus statement), and the twelve-school comparison in the earlier analysis, which necessarily reports only the institutional default, systematically understates the true diversity present within each school's course offerings. CTLS's permissive default with instructor-restriction authority produces the mirror image: the school-level label is permissive, but any individual course could be more restrictive if its instructor has exercised the override.
This interaction becomes especially consequential where the autonomy structure shows no institutional default at all, as at American University and UT Austin. [9] [10] In these cases, the polarity axis is not merely unknown at the school level; it is, in a meaningful sense, not a school-level property at all, since there is no institutional fallback for the polarity axis to describe. Diversity at these schools is therefore not bounded by an institutional default the way it is at Berkeley or CTLS; it is bounded only by whatever variation exists among individual instructors' independently originated policies, which — absent any institutional floor — could in principle range across the entire spectrum without any single school-level statement constraining that range. The interaction of "no default" on the autonomy axis effectively removes the polarity axis's descriptive power entirely for these schools, which is why they were marked "not specified" rather than assigned a value in the earlier table: the axes are not just independent in general, but can become mutually inapplicable to one another under certain combinations.
Drafting Style and Autonomy Structure: Predictability of Override and the Cost of Deviation
The interaction between drafting style and the autonomy structure governs how easy or difficult it is for an instructor to actually exercise the override authority the autonomy axis grants. Where a school's default is rule-drafted, as at Berkeley, an instructor wishing to deviate has a clear, enumerated baseline to deviate from — the instructor can specify precisely which items on Berkeley's catalog of prohibited activities are being relaxed for their course, and the deviation itself can be drafted with the same rule-like precision, since the underlying default was already rule-like. [11] Where a school's default is standard-drafted, as at Chicago, an instructor's deviation is harder to specify with equivalent precision, because the baseline itself is a general test rather than an enumerated list; an instructor who wants to permit some AI use beyond Chicago's plagiarism-equivalence default must draft language that itself either introduces a new standard or awkwardly grafts specific rule-like exceptions onto a standard-based baseline. This suggests that the drafting-style axis has a downstream effect on the quality and clarity of instructor-level deviations even though the autonomy-structure axis is what formally authorizes those deviations in the first place — a rule-drafted institutional default makes instructor override easier to draft cleanly, while a standard-drafted default makes instructor override more likely to introduce its own interpretive ambiguity.
The interaction also affects the instructor-to-student sub-axis in a related way. Stanford's combination of a hybrid drafting style with a mandatory instructor-to-student floor produces an unusually demanding compliance path: because the exam and drafting exceptions are rule-like (a determinate, binary "prior written authorization" requirement) layered on top of a standard-like general permission, a student cannot satisfy the mandatory floor through good-faith disclosure alone, as could happen under a purely standard-based mandatory floor like Chicago's plagiarism-equivalence test, where at least in principle a student's honest, well-attributed use might satisfy the underlying norm even without a separate authorization step. [12] Stanford's rule-like procedural exception makes the mandatory floor stricter in practice than its permissive general orientation would suggest, precisely because of how the drafting-style axis interacts with the instructor-to-student autonomy axis at the specific junctures (exams, drafting) where Stanford chose rule-like rather than standard-like specification.
Table: Illustrative Combinatorial Profiles Among the Twelve Schools
Notes: This table illustrates interaction effects for a representative subset of the twelve schools rather than exhaustively cataloguing all combinations; cells reflect the interpretive synthesis of previously cited primary sources rather than new evidentiary claims.
Why the Interaction, Rather Than Any Single Axis, Explains the Dataset's Diversity
The cumulative effect of these pairwise interactions is that the twelve-school dataset exhibits more genuine diversity than a simple count of category values on any one axis would predict, because each school's full policy identity is a product of its three axis positions rather than a sum. Two schools sharing a restrictive polarity (Berkeley and Chicago) can still produce meaningfully different compliance experiences once drafting style is factored in; two schools sharing a permissive polarity and a similar autonomy structure (CTLS, and Stanford's non-exam default) can still diverge once the specific procedural demands attached to their exceptions are considered. This combinatorial structure is also what makes the earlier finding of axis independence more than a technical curiosity: it means that any attempt to reform or harmonize law school AI policy by focusing on a single axis — for instance, a national push toward uniform substantive restrictiveness — would leave the other two axes, and therefore a substantial share of the practical diversity in student and instructor experience, entirely untouched. The diversity observed across this dataset is, in this sense, structurally overdetermined: it would persist even if every school in the sample converged on identical substantive polarity, because drafting style and autonomy structure remain free to vary independently and would continue generating distinct policy architectures on their own.
The Transparency and Opacity Question
A distinct line of inquiry examined why so many named law schools yielded no retrievable policy text, and whether that absence reflects a single phenomenon or several. Four analytically distinct patterns emerged, each requiring different evidence to confirm, and conflating them would overstate or understate the underlying concern depending on direction.
Non-existence means no rule exists at any organizational level, written or oral. This cannot be confirmed for any specific named law school in this dataset, but is supported at the sector-wide level: UNESCO's May 2023 survey of over 450 institutions found that fewer than 10% had any formal policy or guidance on generative AI use, with universities somewhat more likely than schools to have guidance (roughly 13% versus 7%). [53] [54] [55]
Disclosed decentralization means an institution openly states that authority is delegated, with no further access barrier once that statement is found. American University's Academic Integrity Code fits this pattern, since the retrieved description states plainly there is "no one-size-fits-all" rule. [30] Vanderbilt's university-wide guidance is an even cleaner instance, specifying precisely what governs when an instructor is silent: "the university permits students to use generative AI tools, but they must disclose all generative AI usage". [56]
Access-gating means content was authored and then affirmatively restricted. The strongest confirmed instance in this entire research effort is Northwestern Pritzker School of Law's own library guide page, which displays the message "This page is not currently available due to visibility settings" — direct, first-party evidence of authored content subsequently made inaccessible. [4] Fordham's hosting of its academic regulations via a Google Drive PDF link and Suffolk's reference to an unretrieved "Academic Rules and Regulations" document are consistent with, but do not confirm, this same pattern; they remain properly classified as ambiguous rather than proven instances of gating. [57] [58]
A fourth, residual pattern — unwritten or oral policy — is distinct from all three: the rule is operative and known within the institution, but no document exists for anyone, inside or outside the institution, to find. The UNESCO survey found that among institutions reporting any guidance at all, roughly 40% said that guidance had only ever been communicated orally, and close to 20% of respondents did not know whether their own institution had a policy at all — evidence that this pattern is prevalent and that it blurs the line between non-existence and inaccessibility, since a policy unknown even to affiliated respondents is functionally opaque regardless of whether a document technically exists. [59] [60]
Table 2: The Four Patterns, Their Tests, and Confirmed Instances
|
Category |
Defining Test |
Confirmed Instance |
Confidence |
|
Non-existence |
No rule exists at any level, written or oral |
Not confirmed for any named law school; supported at sector-wide survey level [59] [60] [61] |
Sector-wide: moderate–high; school-specific: unconfirmed |
|
Disclosed decentralization |
Publicly findable statement affirmatively delegates rule-making |
High |
|
|
Access-gating |
Content authored, then placed behind a restriction |
High (this one instance) |
|
|
Unwritten/oral policy |
Operative but undocumented |
Not confirmed for any named law school; established as prevalent sector-wide (~40%) [59] |
Sector-wide: moderate–high; school-specific: unconfirmed |
Credibility Implications and the Hypothesized Meta-Policy
This opacity sits in tension with universities' and law schools' own frequent public self-presentation as champions of open inquiry and reasoned, published rules — a tension sharpened by the doctrinal fact that academic integrity enforcement rests on a fair-notice principle explicit in these same schools' own texts, such as Berkeley's requirement that instructor deviations be made "in writing and with appropriate notice". A 2025 Higher Education Quarterly study of 124 undergraduates and seven faculty members at two U.S. R1 universities found that "students reported that while classroom-level policies are recognised, institutional guidelines remain unclear" — evidence that the fair-notice precondition these institutions rely on to justify sanctioning students may not reliably hold even for the students the rule is meant to bind. [63] The same study found that "students aware of AI policies are less likely to use AI for writing and research", establishing that policy awareness has a measurable behavioral consequence, which sharpens the fairness stakes of any awareness gap. [64]
One commentary — explicitly interpretive rather than empirical, and clearly so labeled here — argues the slow, uneven institutional response "reflected a deliberate, if unspoken, institutional choice," because addressing the problem properly "would require spending money, setting clear grading standards and risking conflict with students". [65] Testing this argument's structure against the assembled evidence, three recurring features are consistent with (though not proof of) a cost-avoidance and discretion-preserving logic: the near-universal default-with-instructor-override architecture, which disperses the operative rule content away from any single centrally catalogued document; the roughly 40% rate of oral-only policy communication, which avoids creating a citable document that could later be invoked against the institution; and the explicit caution, found at CTLS and Vanderbilt, against relying on AI-detection scores as determinative evidence, indicating institutions are already managing evidentiary exposure carefully in adjacent respects. [66] [67]
If such a latent meta-policy exists, its hypothesized logic — labeled here explicitly as unproven hypothesis, not established fact — would prefer defaults over mandates, dispersed syllabus-level disclosure over centralized publication, and open-ended, revisable language (Chicago's own policy states it "may change as circumstances warrant") over settled commitments. [68] This entire pattern remains equally explicable by mundane, non-strategic causes: genuine technological uncertainty, resource constraints, and ordinary institutional lag between adopting and publishing a norm. This research cannot distinguish between these explanations, because only the external pattern, not internal institutional deliberation, was accessible to it.
Model and Template Syllabus Language, and Discursive Intersubjectivity
A further line of inquiry found that schools do not typically draft AI syllabus language independently. USD's law library curates five named, selectable archetypes — "Prohibited Use," "Mandatory Disclosure," "Permissible vs. Non-permissible uses," "Encouraging Use," and "Prior Consultation". [69] [70] Stanford's Robert Crown Law Library maintains a parallel "Syllabus Statements for Generative AI Usage" resource. [71] Penn State's library guide links directly to Lance Eaton's widely circulated academic spreadsheet and to a Harvard Bok Center illustrated rubric. [72] Michigan's central AI resource site reproduces categorized examples attributed to external sources including Temple University and the multi-institutional Sentient Syllabus Project. [73] [74] UCLA states its own sample language is "adapted from UIC's AI Writing Tools guide", naming the donor institution directly.
Three patterns emerge from this template ecosystem.
Hub concentration: a small number of source documents — Eaton's spreadsheet, the Sentient Syllabus Project, UIC's guide, the AALS 2023 interview on Berkeley's policy — recur as reference points across otherwise unconnected schools.
Convergent categorization despite non-identical wording: USD's five categories, Michigan's three, and UCLA's three are not verbatim copies of each other, yet each carves the same underlying decision space into a similarly small number of discrete types, indicating a shared conceptual ontology has stabilized across the field independent of exact phrasing.
Explicit cross-institutional citation as a legitimating move: UCLA's direct attribution to UIC, and Stanford's clinic citing the AALS interview specifically discussing Berkeley's policy, show institutional authority being transmitted horizontally between peer schools rather than independently justified from first principles at each site. [75]
This patterning suggests some support for characterizing the field as discursively intersubjective: a shared vocabulary lets one school's policy remain legible to another's faculty and students without each institution reconstructing the underlying logic from scratch, even as substantive outcomes — restrictive versus permissive — remain genuinely unsettled and divergent.
The Mechanism: A Shared Grammar, Not a Shared Text
The apparent paradox — that law schools draw from the same small pool of template sources yet arrive at substantively different, often conflicting rules — dissolves once the object being borrowed is properly identified. What circulates across institutions is not policy content in the sense of a specific permission or prohibition, but a categorical scaffolding: a menu of archetypal policy types that a drafter can select among and then fill with locally determined substance. This distinction between borrowing a form and borrowing a conclusion is the key to understanding why the same source material generates convergence on structure and divergence on outcome simultaneously, rather than one or the other.
How Shared Sources Produce Convergence
The convergence effect operates through what might be called an ontology-stabilizing function. When USD's law library presents five discrete archetypes — "Prohibited Use," "Mandatory Disclosure," "Permissible vs. Non-permissible uses," "Encouraging Use," and "Prior Consultation" — it is not merely offering sample text; it is proposing a taxonomy of the kinds of positions a policy can occupy. [1] [2] Once other institutions encounter this same taxonomy, whether directly or through independently arriving at a similar decomposition, a common vocabulary becomes available for describing what any given school's policy is, even before anyone has decided what that policy should say.
This may explain why Michigan's three-category scheme (encouraged, allowed-with-distinction, misconduct) and UCLA's three-category scheme (not permitted, limited use with citation, permitted with no restrictions) are not verbatim copies of USD's five categories, yet all three partition essentially the same decision space using a comparably small number of discrete bins. [3] Convergence, in other words, occurs at the level of the classification system itself — the shared sense of what dimensions matter (is drafting covered? is disclosure required? is the exam context distinct from the paper context?) — rather than at the level of the specific rule chosen within that system.
This convergence is reinforced by explicit citation practices that function as legitimating moves. UCLA states its sample language is "adapted from UIC's AI Writing Tools guide", and Stanford's Juelsgaard Clinic resource page cites the AALS interview specifically discussing the creation of Berkeley Law's policy. [4] [5] Each act of citation transmits not just wording but the authority of the borrowed structure — invoking a peer institution's credibility to validate the categorical choices being made locally. Over enough iterations of this kind of horizontal citation, the field converges on a stable, mutually intelligible way of talking about AI policy even across schools that have never directly coordinated, producing the discursive intersubjectivity discussed in the prior analysis: any law school's policy becomes legible to another school's faculty or students because both are drawing on the same underlying grammar of policy types.
How the Same Sources Simultaneously Produce Divergence
Divergence arises precisely because these template sources are explicitly designed and presented as selectable menus rather than mandates. USD's library guide does not tell an instructor which of its five archetypes to adopt; it curates the range of options and leaves the substantive choice — restrictive, permissive, disclosure-based — entirely to the local drafter. [6] The same is true of NYU Steinhardt's automated syllabus-statement generator, which produces sample language "based on your unique course information and preferences" — the tool standardizes the categorical shape of the output while leaving its substantive content to vary by user input. [7]
This may suggest that the very feature that produces formal convergence (a shared, limited set of archetypes) is structurally what enables substantive divergence: because the archetype is presented as a template to be filled rather than a rule to be adopted wholesale, each institution's local values, risk tolerance, disciplinary culture, and institutional history determine which slot in the shared taxonomy it occupies, and different institutions predictably choose different slots.
The twelve-school dataset demonstrates this divergence concretely even among schools using structurally similar drafting approaches. Berkeley and Columbia both adopt a rule-drafted, restrictive-baseline structure, yet Berkeley's substantive content bars conceptualizing, outlining, and drafting outright by default, while Columbia's substantive content, though similarly restrictive, is phrased around named contexts (exams, final papers) rather than named cognitive activities — a variation in what counts as covered conduct even within the same formal category. [8] [9] [10] More strikingly, CTLS occupies the opposite pole of the same shared taxonomy: its permissive-baseline structure is drawn from the identical menu of possible policy types (default-plus-override, disclosure requirement, plagiarism-adjacent enforcement) that produces Berkeley's restrictive outcome, but CTLS fills the permissive slot rather than the restrictive one. The shared grammar did not push these schools toward the same substantive conclusion; it simply gave them a common language in which to express conclusions that remain genuinely opposed.
Why This Distinction May Matter Analytically
This convergence-divergence pattern is itself diagnostic of an inductive, mimetic field rather than a deductive, axiomatic one, consistent with the developmental-logic finding discussed previously. If the field were deductive — if a single governing principle logically generated each school's specific rule — one would expect convergence on substance, since a shared premise applied validly should yield a shared conclusion. What is observed instead is convergence on form paired with persistent divergence on substance, which is the signature of institutions observing and borrowing each other's classificatory tools while independently exercising local judgment about where within those tools to land. The template sources function less like a constitution being applied and more like a shared drafting vocabulary being adapted — comparable to how common-law jurisdictions might converge on the same doctrinal categories (duty, breach, causation, damages) while reaching opposite outcomes on any given set of facts. The templates lower the cost of drafting and make disparate policies comparable to one another, but they do not, and were never designed to, resolve the underlying substantive disagreement about how restrictive or permissive AI use in legal education ought to be. That disagreement remains exactly where it was before the templates existed; only the vocabulary for expressing and comparing it has been standardized.
Inductive and Deductive Structure: A Two-Tier Finding
The final analytical question addressed whether the field's overall development is inductive, grounded in mimetic iteration, or deductive, with a governing principle preceding and generating specific applications. The evidence supports neither label applied uniformly, but rather a two-tier structure that separates the internal logic of individual documents from the logic of the field's development over time.
Within a single school's policy document, the structure is frequently deductive: a governing principle is stated first, and specific rules are derived as applications of it. Berkeley states its purpose — equipping students "to perform activities constitutive of excellent lawyering" — before enumerating specific prohibited activities that follow from it. [76] Chicago states a general functional-equivalence test before deriving specific permitted and prohibited outcomes from it. [42] [77] [78] [79] In both cases, a new, unanticipated use of AI is, in principle, resolvable by applying the stated standard, without requiring a new enumerated example — the defining feature of a deductive system.
Across the field as a whole, however, the evidence points to an inductive, mimetic-iterative process. No single, field-wide governing principle precedes or generates the individual schools' policies. The timeline — an initial adoption wave clustered in Autumn 2023, followed by a substantial revision wave in 2025–2026 across Columbia, Chicago, Berkeley, CTLS, and UT Austin — may be usefully explained as iterative adjustment to observed experience than as the unfolding of an antecedent axiom. Berkeley's own policy moved from comparatively permissive in 2023, when AI could be used "to perform research in ways similar to search engines... and for other functions attendant to completing an assignment", to substantially more restrictive by 2026, barring conceptualizing, outlining, and drafting outright — a trajectory driven by accumulated institutional experience rather than one stable underlying principle. [38] [80] UT Austin's 2026 reform can be understood as explicitly framed as a response to observed practice, warning that classroom time may be "the sole context in which a professor can be certain" a student is learning unaided. [81] [82] The template-circulation evidence reinforces this: the field shows convergence on categorical form (a shared menu of three-to-five policy archetypes) alongside persistent divergence on substantive outcome (restrictive versus permissive baselines) — precisely the signature of a precedent-and-imitation process rather than an axiomatic one.
What may be a significantly defensible overall characterization is therefore a two-tier structure, analogous to common-law doctrinal reasoning: any single judicial opinion may present a deductive syllogism of rule, application, and holding, while the body of doctrine as a whole develops through accretion, borrowing, and revision in response to observed consequences rather than derivation from one unifying antecedent principle. This is offered strictly as a structural description, not as a normative judgment about whether such a development pattern is desirable or well-suited to governing a fast-moving technology.
Conclusion
Taken together, the layers of this analysis converge on several bounded conclusions, each qualified by the limits of the evidence available. Substantively, the twelve schools in this dataset cannot be sorted along a single restrictive-to-permissive spectrum; three independent structural axes — default polarity, drafting style, and a two-part autonomy structure — are needed to describe any one school's architecture, and schools that align on one axis frequently diverge on another. Procedurally, the broader landscape in which these twelve schools sit is marked by a documented and multi-causal opacity problem: some absence of retrievable policy reflects genuine non-existence, some reflects openly disclosed decentralization that is not itself concealed, some reflects at least one confirmed instance of a school affirmatively restricting access to previously authored guidance, and a substantial share reflects a sector-wide pattern of unwritten, orally transmitted rules that this research's methods cannot fully penetrate. This opacity sits in real tension with the transparency values these institutions publicly espouse, and while one interpretive account frames that tension as a deliberate institutional strategy, the available evidence supports only the existence and behavioral consequences of the opacity itself, not a confirmed account of institutional motive.
Discursively, the field's policy language is not independently invented at each school but drawn from a small set of recurring template sources, producing convergence on categorical form even amid continued substantive disagreement. And developmentally, the field exhibits a two-tier structure in which individual policies are often internally deductive while the field as a whole evolves inductively, through observation, borrowing, and iterative revision rather than derivation from a stable, antecedent principle. Any reader seeking to extend this analysis to the roughly 186 ABA-accredited law schools not examined here, or to establish institutional motive behind the documented opacity with greater confidence, would need direct institutional outreach or internal documentation of a kind not accessible through the web-search methods used throughout this research.
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