Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

A Comparative Study of AI‐generated and Human‐crafted Learning Objectives in Computing Education

Published in Journal of Computer Assisted Learning, 2025

The study explores if it is feasible for a state‐of‐the‐art LLM to support curricular design by proposing lists of high‐quality learning objectives.

Recommended citation: Doyle, Aidan, Pragnya Sridhar, Arav Agarwal, Jaromir Savelka, and Majd Sakr. "A comparative study of AI‐generated and human‐crafted learning objectives in computing education." Journal of Computer Assisted Learning 41, no. 1 (2025): e13092.
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It Cannot Be Right If It Was Written by AI: On Lawyers’ Preferences of Documents Perceived as Authored by an LLM vs a Human

Published in Artificial Intelligence and Law, 2024

This study examines whether the perception of legal documents’ by lawyers and law students varies based on their assumed origin (human-crafted vs AI-generated).

Recommended citation: Harasta, Jakub, Tereza Novotná, and Jaromir Savelka. "It cannot be right if it was written by AI: On lawyers’ preferences of documents perceived as authored by an LLM vs a human." Artificial Intelligence and Law (2024): 1-38.
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Computationally Assessing Suspicion

Published in University of Cincinnati Law Review, 2024

We propose a computational method of evaluating suspicion in the situations when police officers detain motorists and offer experimental results from early efforts demonstrating its feasibility.

Recommended citation: Oliver, Wesley M., Morgan A. Gray, Jaromir Savelka, and Kevin D. Ashley. "Computationally Assessing Suspicion." U. Cin. L. Rev. 92 (2023): 1108.
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Empirical Legal Analysis Simplified: Reducing Complexity Through Automatic Identification and Evaluation of Legally Relevant Factors

Published in Philosophical Transactions of the Royal Society A, 2024

This paper investigates the potential for reducing the complexity of AI and Law and empirical legal studies projects through a novel annotation methodology that relies on GPT Family Models to assist human annotators.

Recommended citation: Gray, Morgan A., Jaromir Savelka, Wesley M. Oliver, and Kevin D. Ashley. "Empirical legal analysis simplified: reducing complexity through automatic identification and evaluation of legally relevant factors." Philosophical Transactions of the Royal Society A 382, no. 2270 (2024): 20230155.
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The Unreasonable Effectiveness of Large Language Models in Zero-shot Semantic Annotation of Legal Texts

Published in Frontiers in Artificial Intelligence, 2023

We examine the performance of GPT-4 and GPT-3.5-turbo(-16k), comparing it to the previous generation of GPT models, on three legal text annotation tasks involving diverse documents such as adjudicatory opinions, contractual clauses, or statutory provisions. We also compare the models’ performance and cost to better understand the trade-offs.

Recommended citation: Savelka, Jaromir, and Kevin D. Ashley. "The unreasonable effectiveness of large language models in zero-shot semantic annotation of legal texts." Frontiers in Artificial Intelligence 6 (2023): 1279794.
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Legal Information Retrieval for Understanding Statutory Terms

Published in Artificial Intelligence and Law, 2021

We propose a novel task of discovering sentences for argumentation about the meaning of statutory terms.

Recommended citation: Savelka, Jaromir, and Kevin D. Ashley. "Legal information retrieval for understanding statutory terms." Artificial Intelligence and Law 30, no. 2 (2022): 245-289.
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A Law School Course in Applied Legal Analytics and AI

Published in Law in Context: A Socio-Legal Journal, 2020

In this paper we describe how we co-designed a semester-long course entitled ‘Applied Legal Data Analytics and AI’, and twice taught it to combined groups of law students and students from technical departments.

Recommended citation: Savelka, Jaromir, Matthias Grabmair, and Kevin D. Ashley. "A law school course in applied legal analytics and AI." Law in Context: A Socio-Legal J. 37 (2020): 134.
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Sentence Boundary Detection in Adjudicatory Decisions in the United States

Published in Traitement automatique des langues, 2017

We report results of an effort to enable computers to segment US adjudicatory decisions into sentences.

Recommended citation: Savelka, Jaromir, Vern R. Walker, Matthias Grabmair, and Kevin D. Ashley. "Sentence boundary detection in adjudicatory decisions in the united states." Traitement automatique des langues 58 (2017): 21.
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A Model Framework for Publishing Grey Literature in Open Access

Published in Journal of Intellectual Property, Information, Technology & Electronic Communication Law, 2013

In this paper we present a model framework for placing grey literature documents into an online, publicly accessible repository, providing an effective mechanism to avoid liability for a grey literature repository operator.

Recommended citation: Myska, Matej, and Jaromir Savelka. "A model framework for publishing grey literature in open access." J. Intell. Prop. Info. Tech. & Elec. Com. L. 4 (2013): 104.
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Dangers of Over-enthusiasm in Licensing under Creative Commons

Published in Masaryk University Journal of Law and Technology, 2013

In this paper we assess the Creative Commons licensing scheme that offers a simple, user-friendly tool to allow anyone to distribute or build upon others’ work without the necessity of drafting legal documents.

Recommended citation: Koščík, Michal, and Jaromir Savelka. "Dangers of over-enthusiasm in licensing under creative commons." Masaryk University Journal of Law and Technology 7, no. 2 (2013): 201-227.
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