From Reactive to Proactive Defense: Forcing Litigation onto the Merits in a Civil Case — with AI-Augmented Litigation Strategy
Published 2026-04-08
Keywords
- Generative AI; Pro se litigation; Summary judgment; Evidentiary burden; Access to justice; Cognitive augmentation
How to Cite
Copyright (c) 2026 Viktor Wang

This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
This article examines the strategic shift from reactive to proactive defense in civil litigation, particularly in cases characterized by weak evidentiary foundations and narrative-driven claims. It argues that modern litigation increasingly involves “AI-amplified narratives,” where generative tools enhance the sophistication and volume of filings without necessarily strengthening underlying proof. Using a multiple case study and phenomenological approach, the study analyzes how defense strategy can pivot to re-center litigation on the evidentiary record. Key mechanisms include targeted third-party subpoenas, corrective motion practice, and trial-readiness signaling. The article further explores how artificial intelligence, when used appropriately, functions as a diagnostic tool to identify inconsistencies, organize complex records, and test opposing arguments. However, it emphasizes that AI cannot replace legal reasoning or evidentiary judgment. The findings demonstrate that the decisive advantage in litigation lies not in narrative construction, but in forcing claims to withstand evidentiary scrutiny.
References
- Ashley, K. D. (2023). Artificial intelligence and legal analytics: New tools for law practice in the digital age. Cambridge University Press.
- Bommarito, M. J., & Katz, D. M. (2023). Generative artificial intelligence and the future of legal practice. Harvard Journal of Law & Technology, 36(2), 421–445. https://jolt.law.harvard.edu
- Casey, A. J., Niblett, A., & Talley, E. L. (2024). Generative artificial intelligence and legal analysis: The coming transformation of litigation. Stanford Law Review Online, 76, 1–18. https://lawreview.stanford.edu
- Eisenberg, T., & Lanvers, C. (2023). What is the settlement rate and why should we care? Journal of Empirical Legal Studies, 20(1), 1–25. https://doi.org/10.1111/jels.12334
- Finlay, L. (2023). Phenomenology for therapists: Researching the lived world. Wiley.
- Grossman, M. R., & Cormack, G. V. (2023). Technology-assisted review in civil litigation: Current trends and future directions. Richmond Journal of Law & Technology, 29(3), 1–35. https://jolt.richmond.edu
- Marcus, R. L. (2024). Reviving proportionality in discovery. University of Pennsylvania Law Review, 172(4), 987–1032. https://scholarship.law.upenn.edu
- Moustakas, C. (2023). Phenomenological research methods (Rev. ed.). Sage.
- Patton, M. Q. (2023). Qualitative research & evaluation methods (5th ed.). Sage.
- Remus, D., & Levy, F. (2024). Can robots be lawyers? Computers, lawyers, and the practice of law. Georgetown Journal of Legal Ethics, 37(1), 45–78. https://www.law.georgetown.edu/legal-ethics-journal
- Susskind, R. (2023). Online courts and the future of justice. Oxford University Press.
- Surden, H. (2024). Artificial intelligence and law: An overview. Georgia State University Law Review, 40(2), 1305–1345. https://readingroom.law.gsu.edu/gsulr
- van Manen, M. (2023). Researching lived experience: Human science for an action sensitive pedagogy (2nd ed.). Routledge.
- Yin, R. K. (2024). Case study research and applications: Design and methods (7th ed.). Sage.
- Zhang, Y., Chen, H., & Liu, S. (2023). Machine learning in legal document review: Efficiency and accuracy trade-offs. Artificial Intelligence and Law, 31(2), 245–268. https://doi.org/10.1007/s10506-022-09325-4
