AI in Legal Scholarship: Navigating the Future of Authorship and Attribution in 2026

As artificial intelligence reshapes every field, legal scholarship faces a critical question: How should scholars present research that is generated in part by AI? This is not a hypothetical concern—it’s an April 2026 question, because the capabilities of AI are evolving rapidly, and so too must our standards for transparency and accountability in academic work.

Why This Question Matters Now

I’m raising this issue not just as a thought experiment, but as a practical challenge I’ve encountered in my own work. I’m sharing my experience in two parts. In this post, I’ll explain why I turned to AI for help with a complex scholarly problem. In my next post, I’ll detail what AI was able to do—and then pose the central question: What should I do with the AI-generated output?

I’m eager to hear your thoughts. Your feedback will help shape how we approach AI in legal scholarship moving forward.

The Historical Context Behind the AI Dilemma

A few years ago, I published a law review article titled Decryption Originalism: The Lessons of Burr, 134 Harv. L. Rev. 905 (2021). The article examined the original public meaning of the Fifth Amendment’s privilege against self-incrimination and its potential application to unlocking cell phones. The foundation of my research was a remarkable historical coincidence: the 1807 treason trial of Aaron Burr.

During the trial, Chief Justice John Marshall presided over extensive oral arguments and issued a subsequent opinion on whether the privilege applied to compelling testimony from Burr’s private secretary regarding a letter written in cipher that Burr was believed to have sent. My 2021 article relied heavily on a transcript of the proceedings, meticulously recorded in shorthand by a lawyer named Mr. Robertson.

Robertson’s transcript was extraordinary. He claimed to have captured every argument, every legal source, and even all the pincites—a verbatim reconstruction of the trial. Given the prominence and experience of the lawyers involved, I argued that these details likely reflected the Founding-era understanding of the privilege. My article presented a detailed reconstruction of the lawyers’ arguments, sources, and reasoning, all based on Robertson’s transcript.

The Discovery That Changed Everything

For years, Robertson’s transcript was the definitive source for the Burr trial. It was cited in histories of the case and referenced in 19th-century caselaw as the official report of the trial. I built my 2021 article on this foundation—until last year, when I learned of a second, independent transcript.

Another lawyer, Mr. Carpenter, had also claimed to have recorded the entire trial in shorthand, including legal sources and pincites. Like Robertson, Carpenter published his transcript as a book shortly after the trial. Yet his version had remained largely unknown to legal historians and scholars—until now.

This discovery created a dilemma. The premise of my 2021 article was based on Robertson’s transcript, which I had studied in exhaustive detail. But Carpenter’s independent transcript raised new questions: Which version is more accurate? How do these discrepancies affect the conclusions of my original scholarship?

Enter AI: A Tool for Reconciling Historical Records

Faced with two conflicting primary sources, I turned to AI for help. Using advanced natural language processing tools, I analyzed both transcripts to identify:

• Differences in phrasing and legal citations between Robertson’s and Carpenter’s versions;
• Inconsistencies in the reconstruction of arguments made by the lawyers involved;
• Potential errors or omissions in either transcript that could impact historical interpretation.

The AI’s analysis revealed subtle but significant variations—differences that could alter the historical narrative of the Burr trial and, by extension, the originalist interpretation of the Fifth Amendment privilege. This raised a critical question: How should I integrate these AI-generated insights into my scholarship?

The Core Question: Presenting AI-Assisted Legal Research

Now that AI has helped me uncover and analyze these discrepancies, I’m left with a pressing question: How do I ethically and transparently present this AI-assisted work?

In my next post, I’ll share the AI’s specific findings and outline the challenges of attributing authorship, ensuring accuracy, and maintaining scholarly integrity. But for now, I want to open the discussion: What standards should legal scholars adopt when using AI in their research?

Should we disclose AI’s role in every instance? How do we balance the benefits of AI-driven analysis with the need for human oversight and accountability? And most importantly—how do we ensure that AI serves as a tool for discovery rather than a replacement for rigorous scholarship?

I’d love to hear your perspectives. The future of legal scholarship may depend on the answers we find today.