From Thinking Like a Lawyer to Prompting Like One: AI, Legal Education, and the Evolving Role of the Junior Transactional Associate

by Jozy Allen, Associate Member, University of Cincinnati Law Review Vol. 95

I. Introduction

“Results (external answers or solutions) may be hurried; processes may not be forced. They take their own time to mature. Were all instructors to realize that the quality of mental process, not the production of correct answers, is the measure of educative growth something hardly less than a revolution in teaching would be worked.”¹

In the past, junior associates used to take days to painstakingly search for non-compete covenants and unindexed indemnities within a hundred-page deal document.² Nowadays, a well-calibrated AI is able to identify those same liabilities in just a few seconds.³ Instead of merely carrying out cautious trial runs, corporate legal departments and firms have recently moved to integrating generative platforms into their deal rooms.⁴ With this shift in efficiency, corporate clients no longer want to pay junior associates to learn on the job; most Chief Legal Officers now demand AI-powered due diligence and automatically generated contract redlining to reduce billable hours.⁵ This change has led to a reduction in legal fees for companies, yet it has quietly eliminated the fundamental document review that used to be the mainstay of a junior associate’s work.⁶ By giving such routine tasks over to AI, the legal industry is removing the career pathway that new lawyers once relied on.⁷

Replacing junior associates with emerging AI leads to an immediate developmental crisis: firms gain instant productivity today at the cost of partner-level legal judgment tomorrow.⁸ For decades, law firms relied on an implicit arrangement: clients paid for junior associate hours, and, in return, junior associates gained thousands of hours of hands-on experience reviewing disclosure schedules and redlining contracts under partners’ guidance.⁹ As AI assumes more of these manual tasks, the traditional engine that converted fresh law graduates into seasoned practitioners is stalling.¹⁰

The crisis cannot be resolved through small adjustments.¹¹ This Post argues that law schools must actually teach their students how to use AI appropriately.¹² Law firms have to reorganize their junior apprenticeships around supervised outputs.¹³ Lastly, regulatory bodies must set ethics rules that are suitable for the modern automated practice.¹⁴ The argument of this Post is developed in three parts. Part II sets out the background of transactional legal training: the traditional apprenticeship model, the way generative AI (“GAI”) is now taking over the entry level document review work, the current state of AI training in law firm, and the existing ethical and regulatory guidance. Part III introduces a three-pronged approach to institutional reform. First, modernizing the law school curriculum to replace blanket bans on AI with active oversight and output verification. Second, restructuring the firm apprenticeships model around simulated transactions and mandatory partner review, and third, updating ethical and supervisory rules to require duties of independent verification and robust AI governance. Finally, Part IV concludes by reiterating that in an age of automation the preservation of human legal judgement is absolutely fundamental to the value and authority of the legal profession.

II. Background

A. The Traditional Apprenticeship

In the past, law firms trained junior associates within the routine of constantly reviewing documents.¹⁵ It is a well-known saying that law school teaches students to think like lawyers, not to become one.¹⁶ While law school does teach them to analyze appellate decisions and to interpret statutes, it leaves the practical aspects of deal-making to be learned during training at the firm.¹⁷ Young lawyers used to learn these practical methods by repeatedly reviewing contract redlines and boilerplate clauses with a partner watching closely.¹⁸ The continuous feedback they received showed them how theoretical legal risks actually behave in real deals and the way experienced lawyers work around such risks.¹⁹ After spending thousands of hours doing this manual work, new attorneys developed the muscle memory and the intuitive ability to spot errors that are necessary for exercising independent legal judgment.²⁰

B. AI is Removing the Traditional Training Tasks

Today’s domain-specific GAI tools are able to carry out contract analysis in just a few minutes, thus getting rid of the amount of work at the entry level that had previously been used to develop law firm associate skills.²¹ Instead of a group of new associates spending two weeks going through commercial leases or checking cross-references between IP assignments, AI can digest thousands of pages in minutes.²² Partners naturally welcome these improvements in productivity, but young lawyers lose the slow, unglamorous practice that forms legal intuition.²³

C. Current Law Firm Approaches to AI Training

In response to these disruptions, law firms are taking on associate training in contrasting ways.²⁴ While most firms continue to rely on ad hoc learning or informal partner oversight, forward-thinking firms are turning their summer associate programs into highly structured, competency-focused training schemes.²⁵ The summer associates are placed in controlled AI sandboxes and simulation laboratories where they carry out standard transactional modules, parallel drafting exercises both without and with the assistance of AI, practice constructing structured prompts under the criticism of partners, and are assessed on the basis of rubrics when it comes to verifying their outputs.²⁶ For example, rather than having a summer associate spend two weeks manually going through physical data rooms, firms are now training them to direct an AI review tool, check the tool’s findings against the original deal records, and then give a formal oral briefing to the partners highlighting the provisions that the AI has misunderstood.²⁷

D. Model Rules and State Bar Guidance

The regulatory framework governing legal practice sets baseline ethical duties that directly impact AI adoption.²⁸ The American Bar Association’s (”ABA”) Model Rule 1.1 establishes a fundamental duty of competence, which Comment 8 expands to include staying educated on the benefits and risks with relevant technology.²⁹ In 2024, the ABA issued Formal Opinion 512, which explicitly addressed GAI tools and clarified that lawyers must understand the tools’ capabilities, maintain confidentiality, and independently verify AI outputs.³⁰ Further, Model Rule 1.6 imposes a duty to protect client confidentiality when using third-party software, while Model Rules 5.1 and 5.3 require partners and supervisory lawyers to make reasonable efforts to ensure that subordinate lawyers and automate technological tools comply with professional obligations.³¹

III. Discussion

A. Pedagogical Reform: Teaching a New Form of Literacy

Trying to keep AI out of the classroom does not solve the problem as it fails to prepare law students for contemporary practice.³² When first facing AI, law schools responded by introducing blanket bans under their Honor Code, viewing AI as a threat to academic integrity rather than as a tool used in the workplace.³³ In reality, such absolute bans ensure that graduates end up using AI secretly without ever learning how to properly check the output of such systems.³⁴ The solution lies in order: students must first build a solid understanding of manual legal reasoning before being taught how to steer and verify machine-generated outputs.³⁵

Above all, law schools can no longer get away with merely teaching an abstract ‘thought process’ and putting off the practical aspects until after graduation when students join law firms.³⁶ For more than 100 years, these schools have been able to rely on the familiar institutional argument that the purpose of law school is simply to teach students how to think like a lawyer, using the Socratic method and the case analysis of appellate opinions while leaving the practical side of being a lawyer to the law firms that employ them.³⁷ However, this long-standing division of labor has always depended on corporate clients indirectly paying for the onboarding of junior associates, a form of support which is now vanishing.³⁸

In a legal market enabled by AI, where the volume of routine tasks has been automated, a law graduate who knows appellate doctrine but is unable to mark up a commercial agreement, construct structured prompts, use legal domain tools, or carry out line-by-line verification of AI outputs will become an immediate operational liability.³⁹ It is necessary for law schools to recognize that merely teaching the cognitive “thought process” is no longer enough; they should integrate specific, practical, and immediately useful lawyering skills directly into their main curriculum.⁴⁰ In order to do this, the legal writing and clinical programs must be expanded to include hands-on work in contract design, navigating automated workflows, carrying out structured due diligence exercises, and performing output audits.⁴¹

Moreover, if conceptual theory is to be replaced by practical skills then the traditional system of assessment has to be changed.⁴² The standard exams used in law schools reward speed and memorization on the basis of the assumption that lawyers still have to write each word from scratch rather than depending on oversight.⁴³ The kind of skills required in modern legal practice are entirely different: they include redlining automated drafts, detecting gaps in the reasoning, spotting hallucinated legal authority, and so on.⁴⁴ Therefore, the courses offered at law school should include exercises in which students are assessed according to their ability to identify subtle legal inaccuracies and hallucinated citations in documents produced by AI.⁴⁵ If law students are taught to function as thorough auditors, legal education will shift from simply giving passive writing instruction to providing active oversight training.⁴⁶

B. Firm Redesgin: The Junior Associate Role Must be Reimagined

An associate who has never spent hours looking for clauses in a deal room will not know what a risky indemnity looks like, nor will an associate who has never spent weeks comparing commercial leases know when a subtle change-of-control provision threatens a deal.⁴⁷ As a result, law firms are creating a troubling paradox: they expect senior associates to handle partner-level calls even though they never gained the hands-on experience needed to develop that judgment.⁴⁸ Rather than ask clients to cover the cost of training junior associates to utilize AI, law firms should establish well-structured internal AI apprenticeships that make use of simulated transactions, sandbox drafting tools, and require mandatory review by partners.⁴⁹ With clients now refusing to pay for first-year associate due diligence, firms are seeing effectively the ending of the practice of having clients fund associates’ on-the-job training.⁵⁰ In this new arrangement, junior associates produce the initial drafts with the aid of AI tools and then have to justify their redlines to senior partners.⁵¹ This system of supervision not only maintains a high level of discipline but also enables associates to learn how to manage AI workflows in a safe and effective manner.⁵²

Most importantly, this overhaul of the structural setup has to take place before the first-year associates record their very first billable hour, thereby completely transforming the conventional summer associate experience.⁵³ In previous years, the summer associate programs had essentially functioned as high-paid social courtship rituals, interrupted only by ad hoc and uncoordinated research tasks that partners seldom used in connection with actual client work.⁵⁴ In an environment enabled by AI, where initial research memos and contract drafts can now be generated in just a few seconds, law firms can no longer afford to have summer associates passively shadow partners or take part in unstructured social activities.⁵⁵ As shown by the forward-thinking firms who are implementing AI sandboxes, transitioning to structured, competency-based summer programs ensures that incoming associates develop applicable habits before entering active practice.⁵⁶ By establishing clear, structured benchmarks during the summer program, law firms make sure that the incoming associates have the practical auditing habits needed to take part safely in an AI-accelerated practice.⁵⁷

Moreover, law firms should put their competency frameworks into practice rather than merely relying on the personal preferences of partners when it comes to associate development.⁵⁸ Currently, senior partners just give associates an AI-produced draft without explaining the risk allocations involved or looking at the associates’ suggested amendments, as a result of which the learning opportunity is wasted.⁵⁹ The firms need to set up compulsory checkpoints for partner review and create two-way feedback mechanisms, considering associate supervision as a firm-wide investment rather than as unbillable administrative work.⁶⁰ Firms must adopt this structured approach because relying on informal oversight creates severe legal liability and erodes long-term institutional competence.⁶¹ By institutionalizing mandatory partner checkpoints, firms protect work product quality, reduce exposure to AI-generated errors, and build a resilient pipeline for future partners who possess true legal judgement.⁶² For the broader legal profession, this practice preserves the essential standard of care, ensuring that automated efficiency does not degrade the quality of legal representation or dilute the professional duty lawyers owe to the public.⁶³

C. Model Rules of Ethics and State Bar Associations: Keeping the Rules Relevant

Since technological competence is no longer optional, blindly relying on AI breaches a lawyer’s fundamental duty of care.⁶⁴ The ABA’s Model Rule 1.1 and Formal Opinion 512 make it clear that it constitutes malpractice to defer legal analysis to an AI without at the same time providing some level of supervision.⁶⁵ Under current firm practices, unvetted reliance on AI outputs directly breaches a lawyer’s fundamental duty of competence.⁶⁶ Additionally, when junior associates put encrypted client deal documents into public AI chat boxes or when partners fail to monitor AI usage, firms violate Model Rule 1.6 regarding confidentiality as well as Model Rules 5.1 and 5.3 regarding supervisory responsibilities.⁶⁷ Allowing automated tools to generate client deliverables without meaningful partner oversight creates direct exposure to ethical misconduct and partner liability.⁶⁸

Ethics bodies such as the ABA have already taken important first steps by issuing Formal Opinion 512, but the current professional responsibility rules are still too reactive and need to be thoroughly and explicitly modernized in order to regulate AI-assisted legal representation.⁶⁹ First, with regard to Model Rule 1.1 (Competence), the state bar associations must update Comment 8 to establish an explicit duty for independent verification.⁷⁰ This rule should codify that that filing or submitting legal documents or authorities which have been generated by AI without having first checked them against the original sources by means of documented cross-referencing amounts to ethical misconduct by definition.⁷¹ Second, state bars must modernize Model Rules 5.1 and 5.3 (Supervision) to define clear protocols for AI governance and multi-agent workflows.⁷² Regulators should require firms to implement enterprise-level confidentiality protections under Model Rule 1.6 and define specific supervisory boundaries for automated tools.⁷³ Defining at what point executing autonomous AI workflows without real-time human oversight constitutes a failure of supervision will provide clear, enforceable boundaries for modern practice.⁷⁴

IV. Conclusion

AI won’t take the place of experienced lawyers, but it has permanently disrupted the pipeline that lawyers traditionally used to learn their trade.⁷⁵ The automation of document review and contract drafting is not a temporary phenomenon; it marks a permanent change in the way legal work is carried out.⁷⁶ Seeing AI as a means of reducing payroll costs overlooks the long-term risks: if young lawyers never learn how deals are put together, the profession won’t have competent decision-makers down the road.⁷⁷ The continued development of associates will only be possible if law schools and law firms give up passive document review and instead adopt deliberate, supervised reps before the previous method is completely abolished.⁷⁸

The lawyers who are successful in a market that is driven by AI won’t be the ones who shun technology or those who put total trust in it; rather, they will be the keen critics who are able to guide and check the work carried out by automation.⁷⁹ In such a situation, when software is taking over the routine tasks, a lawyer’s true value lies in judgment, negotiation strategy, advising clients, and creative problem-solving.⁸⁰ If we alter the way we teach law students, train associates, and enforce ethical standards, then the profession can make use of AI’s efficiency without losing the human intellect which gives the law its authority.⁸¹

Cover Photo by Conny Schneider on Unsplash

Author

Jozy Allen

Jozy Allen is a 2L JD Candidate from Cincinnati, Ohio. She completed her Undergraduate and Graduate degrees at Central Michigan University, where she played Division I lacrosse. In addition to Law Review, Jozy is the President of the Business Law Society and most recently worked at Squire Patton Boggs as a Summer Associate. Her legal interests are primarily corporate transactional law, and in her free time she coaches lacrosse!

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Sullivan, supra note 16; Segal, supra note 2. ↩︎
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Solace, supra note 2. ↩︎
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Id.; The Death, supra note 10. ↩︎
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Solace, supra note 2; Butalia, supra note 6; McGee, supra note 2. ↩︎
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Solace, supra note 2; Runyon, supra note 6. ↩︎
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Patrick, supra note 13. ↩︎
28
See generally ABA Standing Comm., supra note 14; Mathew J. Bester & Victoria Lageyre, Ethics and Generative Artificial Intelligence in Your Practice, ABA (June 1, 2026), https://www.americanbar.org/groups/antitrust_law/resources/newsletters/ethics-gen-ai-practice/ [https://perma.cc/Z45X-7VGT]. ↩︎
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Model Rules of Pro. Conduct r. 1.1 (A.B.A. 2026); Id. at cmt. 8; ABA Standing Comm., supra note 14, at 2, 4, 14; Bester, supra note 30.https://www.google.com/search?client=safari&rls=en&q=model+rule+1.1&ie=UTF-8&oe=UTF-8 https://perma.cc/Z4K2-M9CM; Rule 1.1 https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/rule_1_1_competence/comment_on_rule_1_1/https://perma.cc/UT33-B9W6. ↩︎
30
See generally ABA Standing Comm., supra note 14. ↩︎
31
Model Rules of Pro. Conduct r. 1.6 (A.B.A. 2026); Id. at r. 5.1; Id. at r. 5.3; ABA Standing Comm., supra note 14, at 6, 8, 9-11; Bester, supra note 30.https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/rule_1_6_confidentiality_of_information/https://perma.cc/GGR8-FMLW https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/rule_5_1_responsibilities_of_a_partner_or_supervisory_lawyer/https://perma.cc/BNJ5-4BH8 https://www.americanbar.org/groups/professional_responsibility/publications/model_rules_of_professional_conduct/rule_5_3_responsibilities_regarding_nonlawyer_assistant/https://perma.cc/YM2R-MGA8. ↩︎
32
Roberts, supra note 8; Patti Waldmeir, Adam Chilton and William H.J. Hubbard Discuss the Law School’s AI Strategy: Law Schools Tell Students to Put AI Away, U. Chi.: L. Sch. (Sep. 8, 2026), https://www.law.uchicago.edu/news/adam-chilton-and-william-h-j-hubbard-discuss-law-schools-ai-strategy [https://perma.cc/7GYV-FDAF]; Rethinking Legal Education in the AI Era, U. Chi.: L. Sch. (Jul. 9, 2026), https://www.law.uchicago.edu/news/ai-strategy-statement [https://perma.cc/JF5Z-7T75]. ↩︎
33
John Lande, Turning Risks of Cheating with AI into Opportunities for Better Teaching, U.. Mo. Sch. L. (Jan. 8, 2026), https://scholarship.law.missouri.edu/cgi/viewcontent.cgi?article=2366&context=facpubs [https://perma.cc/M585-TKYQ]; Waldmeir, supra note 34. ↩︎
34
Lande, supra note 35; Roberts, supra note 8; LawPay, supra note 4. ↩︎
35
Lande, supra note 35; Rethinking, supra note 34; The University of San Francisco School of Law Embeds GenAI Into Core Curriculum, U.. S. F. Sch. L. (Apr. 16, 2025), https://www.usfca.edu/news/university-san-francisco-school-law-embeds-genai-core-curriculum [https://perma.cc/EN5T-DBXY]. ↩︎
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Henderson, supra note 11; Segal, supra note 2. ↩︎
37
Segal, supra note 2; see generally Maxeiner, supra note 9 ; Sullivan, supra note 16, at 4-6. ↩︎
38
Henderson, supra note 11, at 462; Segal, supra note 2; Medice, supra note 5. ↩︎
39
Roberts, supra note 8; Bhatti, supra note 14, at 2; Henderson, supra note 11, at 462. ↩︎
40
Sullivan, supra note 16, at 8; The University of San Francisco School, supra note 37. ↩︎
41
The University of San Francisco School, supra note 37;; Boswell, supra note 28. ↩︎
42
Henderson, supra note 11,at 500; Sullivan, supra note 16, at 7; Lande, supra note 23, at 3. ↩︎
43
Sullivan, supra note 16, at 6-7. ↩︎
44
Bester, supra note 30; Roberts, supra note 8. ↩︎
45
The University of San Francisco School, supra note 37; Boswell, supra note 28. ↩︎
46
Roberts, supra note 8; Evans, supra note 7. ↩︎
47
Solace, supra note 2. ↩︎
48
Id.; Evans, supra note 7. ↩︎
49
Henderson, supra note 11, at 462, 505; Patrick, supra note 13; Runyon, supra note 6. ↩︎
50
Henderson, supra note 2, at 462: Segal, supra note 2; Medice, supra note 5. ↩︎
51
olace, supra note 2; Patrick, supra note 13. ↩︎
52
Patrick, supra note 13; ABA Standing Comm., supra note 30, at 10-11. ↩︎
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Henderson, supra note 11, at 505-06; The Death, supra note 10; Runyon, supra note 6. ↩︎
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Segal, supra note 2; Henderson, supra note 11. ↩︎
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McGee, supra note 3; Patrick, supra note 13. ↩︎
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Henderson, supra note 11, at 465, 495-97; The Death, supra note 10. ↩︎
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Henderson, supra note 11; Patrick, supra note 13. ↩︎
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Henderson, supra note 11, at 495-96; The Death, supra note 10; Warren, supra note 13. ↩︎
59
Solace, supra note 2; Patrick, supra note 13. ↩︎
60
Patrick, supra note 13; ABA Standing Comm., supra note 14, at 10-11. ↩︎
61
Bester, supra note 30; see generally Bhatti, supra note 14; Patrick, supra note 13. ↩︎
62
Patrick, supra note 13; Evans, supra note 7 ↩︎
63
See generally Bhatti, supra note 14; Patrick, supra note 13; ABA Standing Comm., supra note 14. ↩︎
64
Model Rules of Pro. Conduct r. 1.1, supra note 31; ABA Standing Comm., supra note 14, at 2-3. ↩︎
65
Model Rules of Pro. Conduct r. 1.1, supra note 31; ABA Standing Comm., supra note 14, at 2-4; Bester, supra note 30. ↩︎
66
ABA Standing Comm., supra note 14, at 2-3; Bester, supra note 30. ↩︎
67
Model Rules of Pro. Conduct r. 1.6, supra note 33; Model Rules of Pro. Conduct r. 5.1, supra note 33; Model Rules of Pro. Conduct r. 5.3,supra note 33; ABA Standing Comm., supra note 14, at 6, 8-11; Bester, supra note 30. ↩︎
68
ABA Standing Comm., supra note 14, at 6, 10-11; Bester, supra note 30. ↩︎
69
Bhatti, supra note 14, at 1-3; see generally ABA Standing Comm., supra note 14. ↩︎
70
Bhatti, supra note 14, at 1-3; see generally ABA Standing Comm., supra note 14; Model Rules of Pro. Conduct r. 1.6, cmt. 8, supra note 31. ↩︎
71
Model Rules of Pro. Conduct r. 1.6, cmt. 8, supra note 31; ABA Standing Comm., supra note 14, at 3-4; Bhatti, supra note 14, at 1-3; Bester, supra note 30 (referencing sanctions in Mata v. Avianca, Inc., 678 F. Supp. 3d 443, 448 (S.D.N.Y. 2023)). ↩︎
72
Bhatti, supra note 14. ↩︎
73
Id.; ABA Standing Comm., supra note 14. ↩︎
74
Bhatti, supra note 14, at 6-7; Ass’n of Corp. Counsel, Association of Corporate Counsel Survey, supra note 4. ↩︎
75
Roberts, supra note 8; Evans, supra note 7. ↩︎
76
McGee, supra note 3: Ass’n of Corp. Counsel, New ACC Report, supra note 4. ↩︎
77
Evans, supra note 7; Solace, supra note 2. ↩︎
78
Patrick, supra note 13; Roberts, supra note 8; see generally Henderson, supra note 11. ↩︎
79
Evans, supra note 7; Roberts, supra note 8. ↩︎
80
Evans, supra note 7; McGee, supra note 3. ↩︎
81
Evans, supra note 7; Bhatti, supra note 14; see generally Henderson, supra note 11. ↩︎