Vol. 2026 No. 1 (2026): 2026 Continuous Issue
Articles

Augmented Self-Representation: How Artificial Intelligence Structured Evidence, Framed Summary Judgment, and Informed Settlement Strategy in a Multi-Unit Property Dispute.

PDF
  • Viktor Wang
Viktor Wang
Bio

Published 2026-02-13

Keywords

  • AI-integrated assessment,
  • Generative AI; Pro se litigation; Summary judgment; Evidentiary burden; Access to justice; Cognitive augmentation

How to Cite

Wang, V. (2026). Augmented Self-Representation: How Artificial Intelligence Structured Evidence, Framed Summary Judgment, and Informed Settlement Strategy in a Multi-Unit Property Dispute. International Journal of AI in Pedagogy, Innovation, and Learning Futures, 2026(1). Retrieved from https://journals.calstate.edu/ijaipil/article/view/6940

Copyright (c) 2026 Viktor Wang

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This article examines how artificial intelligence functioned as a structural cognition tool in a live multi-unit property dispute through the summary judgment stage. Acting pro se, the defendant used AI not to generate arguments in the abstract, but to organize evidentiary materials into legally dispositive categories: standing, unit-specific causation, admissible damages, and loss of use. By distinguishing narrative allegations from proof requirements under California summary judgment standards, AI-assisted drafting clarified burden allocation and exposed evidentiary gaps, including the absence of assignment documentation, expert source determination, and repair-payment records. The system also supported procedural compliance by structuring a rule-conforming separate statement of undisputed material facts and aligning exhibits with doctrinal elements. Beyond document preparation, AI contributed to strategic positioning for potential settlement or trial by reframing emotional accusations into evidentiary analysis. The case illustrates AI as cognitive augmentation in self-representation—enhancing analytical rigor while preserving human judgment and responsibility.

PDF

References

  1. American Bar Association Standing Committee on Ethics and Professional Responsibility. (2024, July 29). Formal Opinion 512: Generative Artificial Intelligence Tools. American Bar Association.
  2. Huang, X., Zhang, Y., Li, J., & Chen, R. (2025). Large language models in legal systems: A survey. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-025-05924-3 .
  3. Judicial Council of California. (2026). California Rules of Court, rule 3.1113: Memorandum. Judicial Branch of California.
  4. Judicial Council of California. (2026). California Rules of Court, rule 3.1350: Motion for summary judgment or summary adjudication. Judicial Branch of California.
  5. Lai, J., Gan, W., Wu, J., Qi, Z., & Yu, P. S. (2024). Large language models in law: A survey. AI Open, 5, 181–196. https://doi.org/10.1016/j.aiopen.2024.09.002 .
  6. National Center for State Courts. (2024). Artificial intelligence: Guidance for use of AI and generative AI in courts. National Center for State Courts.
  7. Thomson Reuters Institute. (2025). 2025 Generative AI in Professional Services Report. Thomson Reuters.
  8. Thomson Reuters Institute, & National Center for State Courts AI Policy Consortium for Law and Courts. (2025, February). Principles and practices for using AI responsibly and effectively in courts: A guide for court administrators, judges, and legal professionals. Thomson Reuters Institute & National Center for State Courts.
  9. van Manen, M. (2023). Phenomenology of practice: Meaning-giving methods in phenomenological research and writing (2nd ed.). Routledge.
  10. Osler, L., & Zahavi, D. (2024). Qualitative critical phenomenology. Phenomenology and the Cognitive Sciences, 23, Article 10034. https://doi.org/10.1007/s11097-024-10034-7 .