Office: 519 LRDC
Phone: (412) 624-7496
Professor, University of Pittsburgh Graduate Program in Intelligent Systems
Professor, University of Pittsburgh School of Law
Senior Scientist, Learning Research & Development Center
My research interests in Learning, Law and Computer Science are to:
- Develop computational models of case-based reasoning (CBR) in domains like law and practical ethics as an intellectual methodology for better understanding comparative evaluation with cases and principles and as a basis for intelligent computer systems to educate students and assist practitioners.
- Develop case-based and analogical reasoning as alternative techniques for representing and acquiring knowledge in Artificial Intelligence (AI) programs.
- Identify and analyze special legal problems posed by computer technology in such areas as intellectual property, commercial law, product liability, technology licensing, computer crime and privacy.
Savelka, J., & Ashley, K. D. (2018). Segmenting U.S. court decisions into functional and issue specific parts. Proceedings of JURIX 2018. Groningen, The Netherlands.
Ashley, K. (2017). Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge: Cambridge University Press.
Savelka, J., Walker, V. R., Grabmair, M., & Ashley, K. D. (2017). Sentence Boundary Detection in Adjudicatory Decisions in the United States. Traitement automatique des langues, 58(2), 21-45.
Barstow, B., Fazio, L., Schunn, C., Ashley, K., et al. (2017). Experimental evidence for diagramming benefits in science writing. Instructional Science, 45(3), 1-20.
Jabbari, F., Falakmasir, M., & Ashley, K. (2016). Identifying Thesis Statements in Student Essays: The Class Imbalance Challenge and Resolution. In Proceedings of the 29th International FLAIRS Conference, Special Track on Applied Natural Language Processing, Key Largo, Florida, USA.
Barstow, B., Fazio, L., Lippman, J., Falakmasir, M., Schunn, C. D., & Ashley, K. D. (2016). The impacts of domain-general vs. domain-specific diagramming tools on writing. International Journal of Artificial Intelligence in Education, 1-23.
Ashley, K. (2016). Putting argument mining to work: An experiment in legal argument retrieval using the LUIMA type system and pipeline. Dagstuhl Seminar 16161– Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments. April.
Grabmair, M., Ashley, K. D., Chen, R., Sureshkumar, P., Wang, C., Nyberg, E., & Walker, V. R. (2015). Introducing LUIMA: An Experiment in Legal Conceptual Retrieval of Vaccine Injury Decisions using a UIMA Type System and Tools. Proceedings, Fifteenth International Conference on Artificial Intelligence and Law, ICAIL 2015, 69-78, ACM, 2015.
Correnti, R., Stein, M.K., Smith, M., Scherrer, J., McKeown, M., Greeno, J., & Ashley, K. (2015). Improving Teaching at Scale: Design for the Scientific Measurement and Development of Discourse Practice. In L. Resnick & C. Asterhan (Eds), Socializing Intelligence through Academic Talk and Dialogue.
Baikadi, A., Schunn, C., & Ashley, K. (2015). Understanding revision planning in peer-reviewed writing. Proceedings of the 8th International Conference on Educational Data Mining, 1-4.
Šavelka, J. & Ashley, K. (2015). Transfer of predictive models for classification of statutory texts in multi-jurisdictional settings. In K. Atkinson. Fifteenth International Conference on Artificial Intelligence and Law (ICAIL 2015). New York: ACM, 2015, pp. 216-220.
Ashley, K., & Falakmasir, M. (2015). A global perspective on argument diagramming to support writing skills. 7th International Conference on Education and New Learning Technologies, EDULEARN15 Proceedings, pp. 7354-7363. Barcelona, July.
Lynch, C., Ashley, K. D., & Chi, M. (2014). Can Diagrams Predict Essay Grades?. Springer, To appear in Proceedings, 12th International Conference on Intelligent Tutoring Systems, Lecture Notes in Computer Science, Lecture Notes in Computer Science, 8474.
Ashley, K., & Savelka, J,. (2014). Book Review of Conflict Resolution and its Context From the Analysis of Behavioural Patterns to Efficient Decision-Making by Davide Carneiro, Paulo Novais, Jose Neves, Springer,. [to appear in International Journal of Online Dispute Resolution]
Sweeney, P. M., Bjerke, E. F., Potter, M. A., Guclu, H., Keane, C. R., Ashley, K. D., Grabmair, M., & Hwa, R. (2014) Network Analysis of Manually-Encoded State Laws and Prospects for Automation. In Winkels, R; Lettieri, N; Faro, S (Eds.) Network Analysis in Law, Collana: Diritto Scienza Tecnologia/Law Science Technology – Temi, 3, pp. 53-78. Napoli: Edizione Scientifiche Italiane.
Falakmasir, M. H., Ashley, K. D., Schunn, C. D., & Litman D. J. (2014). Identifying thesis and conclusion statements in student essays to scaffold peer review. Proceedings 12th International Conference on Intelligent Tutoring Systems (ITS). Honolulu, HI.
Ashley, K. (2013). Evaluating the uses of Values in A model of Legal Reasoning with Cases Incorporating Theories and Value. In H. Prakken, K. Atkinson, A. Wyner A (Eds.), Festschrift in Honor of Trevor Bench- Capon.
Ashley, K. (2013). Teaching Law and Digital Age Legal Practice with an AI and Law Seminar. Chicago Law Review, 88(3), 783-784.
Falakmassir, M. H., Ashley, K. D., & Schunn, C. D. (2013). Using Argument Diagramming to Improve Peer Grading of Writing Assignments.Proceedings of the 1st Workshop on Massive Open Online Courses (moocshop) at the 16th Annual Conference on Artificial Intelligence in Education (AIED 2013). Memphis, TN.
Grabmair, M., & Ashley, K. D. (2013). Using Event Progression to Enhance Purposive Argumentation in the Value Judgment Formalism. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law. ICAIL 2013, Jun 10-14; Rome, Italy, 73-82.
Ashley, K. D., & Walker, V. (2013). Toward Constructing Evidence-Based Legal Arguments Using Legal Decision Documents and Machine Learning. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law. ICAIL 2013, June 10-14; Rome, Italy, 176-180.
Sweeney, P., Bjerke, E., Potter, M., Guclu, H., Keane, C., Ashley, K., Grabmair, M., & Hwa, R. (2013) Network Analysis on Manually-Encoded State Laws and Prospects for Automation. Workshop on Network Analysis in Law at XIV International Conference on AI and Law (ICAIL 2013) Rome, June 14, 2013.
Ashley, K. D., & Walker, V. (2013) From Information Retrieval (IR) to Argument Retrieval (AR) for Legal Cases: Report on a Baseline Study. Proceedings of the 26th Annual Conference on Legal Knowledge and Information Systems (Jurix 2013), 29-38.
Ashley, K. D. (2013). Legal Knowledge and Information Systems. Jurix 2013: The Twenty-Sixth Annual Conference. IOS Press: Amsterdam.
Goldin, I., & Ashley, K. (2012). Eliciting Formative Assessment in Peer Review. Journal of Writing Research, Special issue on Redesigning Peer Review Interactions Using Computer Tools, 4(2), 203-237.
Kevin Ashley, Professor, Law and Intelligent Systems in the School of Law, and LRDC Senior Scientist received a grant from the Institute for Cyber Law, Policy, and Security 2018 Pitt Cyber Accelerator Grants Program. Winners receive funding for research projects that examine the swiftly changing technological landscape and the rules, practices and safeguards designed to keep it secure. Jaromir Savelka, PhD candidate, School of Computing and Information, and LRDC graduate student also received a grant.
November 19, 2018
Kevin Ashley was recognized as a distinguished alumnus at his alma mater (UMass Amherst CS PhD ’88) with an Outstanding Achievement and Advocacy (OAA) Award on Friday, May 1, 2015. The OAA recognizes UMass computer science innovators in settings around the world.
May 4, 2015
The first Intelligent Systems Program Yearly Newsletter.
Visiting Fellow, Law Department
European University Institute, Firenze
Senior Visiting Fellow, Institute for Advanced Studies
University of Bologna
Buchanan, Ingersoll & Rooney Faculty Scholar
IBM's Watson Comes to Pitt, Carnegie Mellon University
March 28, 2011
University of Pittsburgh Advisory Council on Instructional Excellence Innovation in Education Award
For project entitled, "A Peer-Review-Based Student Model for Ill-Defined Problem-Solving" $25,000.
Selected as a Fellow of the American Association of Artificial Intelligence.
"For significant contributions in computationally modeling case-based and analogical reasoning in law and practical ethics."
Chancellor's Distinguished Research Award
"You won special praise for developing a revolutionary artificial intelligence (AI) model of case-based reasoning in law. This award honors your development of a cognitive science model of practical ethical reasoning and an AI model of ethics case comparison as well as your pioneering work in applying AI to research in legal and practical ethical reasoning."
Outstanding Research Paper Award
Third International Conference On Case-Based Reasoning. Seeon, Germany
Distinguished Paper Award
First International Conference on Case-Based Reasoning. Sesimbra, Portugal
National Science Foundation Presidential Young Investigator Award (PYI)
Award combines direct and matching funds up to $100,000 per year for a period of five years to study case-based and analogical reasoning in law and legal education.
Philips Laboratories Award
for Best Student Paper at Fourth Conference on Artificial Intelligence Applications of the Institute of Electrical and Electronics Engineers, Inc. (IEEE). San Diego,
IBM Graduate Research Fellowship