The allure of the “non-human lawyer” is no longer a plot point for science fiction. In law firms and government agencies across the country, artificial intelligence is already performing the heavy lifting—sifting through thousands of pages of discovery, drafting routine motions, and organizing evidence into chronological timelines with a speed that no human associate could match.
But as the legal industry embraces these efficiencies, a quiet conflict is emerging in the classroom. At Valparaiso University, the tension isn’t about whether the technology works—it’s about what happens to the human brain when it stops doing the hard work of thinking. For the next generation of attorneys, the ability to delegate research to an algorithm may come at the cost of the very critical thinking skills required to win a case in a courtroom.
The shift is systemic. Law schools nationwide are currently rewriting their curricula to integrate AI, attempting to balance the necessity of technical proficiency with the traditional rigors of legal analysis. However, for some educators, the “streamlining” of law is a dangerous shortcut that threatens to produce a generation of lawyers who are proficient with software but paralyzed during a cross-examination.
The “Point One” Philosophy
Elizabeth Gingerich, a business law professor at Valpo, has seen the impact of AI firsthand—not in the brilliance of the output, but in the erosion of student effort. Noticing a decline in the depth of reading and writing in her classroom, Gingerich took the unconventional step of writing her own textbook for her business law courses to remove the friction and struggle that often leads students toward AI shortcuts.
Gingerich, who also serves as the editor-in-chief of the Journal of Value-Based Leadership, employs AI detectors on global submissions to maintain academic integrity. But her primary defense against the “AI crutch” isn’t software—it’s experiential learning.
In her course, students are required to visit the Porter County Courthouse to observe and document actual criminal and civil cases. The mandate is strict: no AI. Gingerich argues that the process of manual observation and writing is where the actual learning occurs.
“I tell my students, ‘you must start at point one,’” Gingerich said. “When you get your paper back from me, you’re going to have to read it all over. I give extra points for manually typing new things in. You learn that way.”
She notes that AI-generated reports often betray themselves through a lack of authentic observation. When a student’s paper describes the courtroom proceedings with a generic, clinical tone—noting that defendants rose when the judge entered, followed by a broad commentary on the American legal system—it is usually a sign that the student delegated the observation to a machine rather than engaging with the room.
Efficiency vs. Argumentation
The divide between academic preparation and professional reality became clear for Elinor Grochowski, a recent history graduate. While Grochowski avoided AI during her undergraduate studies, she encountered its professional application during a legal internship with a U.S. Government agency.
The agency was trialing Westlaw Precision, an AI-powered legal research database designed to accelerate the way lawyers find precedents and organize facts. Grochowski discovered that while the AI was an exceptional organizer, it lacked the ability to think strategically.
“It didn’t construct arguments, which is part of what I had to do,” Grochowski said. “It ended up taking a bunch of evidence documents, the affidavit I had written up, and organized it into a massive chronological file.”
For Grochowski, the experience was unsettling. The AI had performed a task that was a core part of her job description, and it had done so with high efficiency. This highlights a growing gap in the legal field: the difference between information management and legal strategy.
| Task | AI Capability (e.g., Westlaw Precision) | Human Attorney Requirement |
|---|---|---|
| Document Review | Rapidly categorizes and chronologizes evidence. | Identifies nuanced contradictions in testimony. |
| Legal Research | Locates relevant case law and citations. | Synthesizes cases to build a novel legal theory. |
| Drafting | Generates standard templates and summaries. | Tailors arguments to the specific temperament of a judge. |
| Courtroom Presence | Not applicable. | Real-time pivots during cross-examination. |
The Cognitive Cost of Automation
Beyond the efficiency gains, there are two primary risks currently weighing on legal educators: data privacy and cognitive atrophy.
The privacy concern is straightforward but severe. Legal work involves some of the most sensitive data imaginable—trade secrets, privileged client communications, and sealed court records. Grochowski noted that placing this volume of sensitive data into the hands of a few AI providers is inherently dangerous.
The second risk is more insidious. Gingerich argues that by bypassing the “struggle” of research and writing, students are failing to build the mental infrastructure necessary for trial work. In a courtroom, a lawyer is frequently interrupted by a judge or blindsided by a witness. These moments require “on-your-feet” thinking that cannot be outsourced to a prompt.
“You’re not building a resource in your brain,” Gingerich said. “If you’re doing any trial work relying upon AI, it’s going to show when it’s your time to cross-examine. If you’re asked questions about it, you’re going to say, ‘Oh, wait a minute – I’ve got to read that,’ because it’s not there.”
This lack of hands-on experience, she believes, is actively defeating the development of the brain, turning potential advocates into mere operators of software.
The Future of the “Brightest”
The integration of AI into law is inevitable, but Gingerich suggests that this will actually increase the value of traditional critical thinking. As basic research and drafting become commoditized, the market value of a lawyer will shift away from the ability to find information and toward the ability to analyze it.
“With AI, the only jobs out there down the road will be for the best and the brightest,” Gingerich said. “If you’re not using your brain on your own, it’s going to show.”
For students at Valpo and beyond, the challenge is to use AI as a tool for productivity without letting it become a replacement for the cognitive labor that defines a legal mind.
As law schools continue to refine their AI policies, the American Bar Association (ABA) is expected to provide further guidance on the ethical use of generative AI in legal practice and the minimum competencies required for new admittees to the bar. These updates will likely determine how “point one” learning is standardized across the profession.
Do you think AI will eventually replace the need for junior associates, or will it simply change what they are taught? Share your thoughts in the comments below.
