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Large Language Scholarship: Generative AI In The Legal Academy

Alan Z. Rozenshtein (Minnesota; Google Scholar) & Kevin Frazier (Texas), Large Language Scholarship: Generative AI in the Legal Academy:

SSRNRapid advances in artificial intelligence (AI) have ushered in an era where machines meaningfully assist—and in some cases independently produce—legal reasoning, doctrinal synthesis, and scholarly writing. This Article introduces the concept of “Large Language Scholarship,” arguing that AI’s integration will significantly transform legal academia, altering both the quantity and quality of scholarship with profound implications for law professors, law schools, and the legal system. Unlike existing scholarship that focuses on preliminary demonstrations of AI’s capabilities and the associated ethical concerns, we offer a comprehensive analysis of the systemic institutional impacts of AI adoption.

In this Article we make three contributions. First, we predict the inevitability of AI-driven transformation in legal scholarship, driven by efficiency gains, rapid technological advancement, professional incentives, and practical undetectability. Second, we critically examine the implications for key stakeholders: the legal profession may gain broader analytical resources yet face increased misinformation risks; academics could experience enhanced productivity alongside intensified publication pressures and cognitive deskilling; students may gain richer research experiences while risking loss of traditional skills; and law schools must navigate shifting faculty recruitment dynamics. Finally, we provide practical guidance on responsibly integrating AI into scholarship, ensuring AI complements rather than replaces essential human judgment and creativity. We include a practical appendix detailing specific AI tools, strategies, and recommendations for effective research, drafting, and editing.

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