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Diane Litman

Faculty, University of Pittsburgh Intelligent Systems Program

Professor, University of Pittsburgh Department of Computer Science

Senior Scientist, Learning Research & Development Center

Research Interests

My research is in the area of artificial intelligence, and includes contributions in the areas of artificial intelligence and education, computational linguistics, knowledge representation and reasoning, natural language learning, spoken language, and user modeling.

Rahimi, Z., Litman, D., Correnti, R., Wang, E., & Matsumura, L. C. (2017). Assessing students’ use of evidence and organization in response-to-text Writing: Using natural language processing for rubric-based automated scoring. International Journal of Artificial Intelligence in Education, 1-35.

Chandrasekaran, M. K., Epp, C. D., Kan, M-Y., Litman, D. (2017). Using Discourse Signals for Robust Instructor Intervention Prediction. Proceedings of the 31st AAAI Conference on Artificial Intelligence.

Nguyen, H., Xiong, W., Litman, D. (2017). Iterative design and classroom evaluation of automated formative feedback for improving peer feedback localization. International Journal of Artificial Intelligence in Education, 1-41.

Fan, X., Luo, W., Menekse, M., Litman, D. & Wang, J. (2017). Scaling Reflection Prompts in Large Classrooms via Mobile Interfaces and Natural Language Processing. Proceedings of the 22nd ACM Conference on Intelligent User Interfaces (IUI '17), pp363-374. ACM, New York, NY, USA.

Rahimi, Z., Litman, D. (2016). Automatically Extracting Topical Components for a Response-to-Text Writing Assessment. Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, 277–282,

Litman, D., Young, S., Gales, M., Knill, K., Ottewell, K., Van Dalen, R., Vandyke, D. (2016). Towards Using Conversations with Spoken Dialogue Systems in the Automated Assessment of Non-Native Speakers of English. Proceedings of the SIGDIAL 2016 Conference, pp. 270–275.

Forbes-Riley, K., Zhang, F., Litman, D. (2016). Extracting PDTB Discourse Relations from Student Essays. Proceedings of the SIGDIAL 2016 Conference, pp. 117–127.

Litman, D. (2016). Natural language processing for enhancing teaching and learning. Proceedings of the 30th AAAI Conference on Artificial Intelligence.

Nguyen, H., Litman, D. (2016). Improving argument mining in student essays by learning and exploiting argument indicators versus essay topics. Proceedings of the 29th International Florida Artificial Intelligence.

Luo, W., Litman, D. (2016). Determining the quality of a student reflective response. Proceedings of the 29th International Florida Artificial Intelligence Research Society Conference.

Zhang, F., Litman, D., Riley, K. F. (2016). Inferring Discourse Relations from PDTB-style Discourse Labels for Argumentative Revision Classification. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2615–2624, Osaka, Japan.

Nguyen, H. V., Litman, D. J. (2016). Context-aware Argumentative Relation Mining. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 1127–1137.

Luo, W., Liu, F., Litman, D. (2016). An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses. Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka, Japan, 2016.

Zhang, F., Hwa, R., Litman, D., Hashemi, H. B. (2016). ArgRewrite: A web-based revision assistant for argumentative writings. Proceedings of NAAVL-HLT 2016 (Demonstrations), 37-41.

Zhang, F., Litman, D. (2016). Using context to predict the purpose of argumentative writing revisions. Proceedings of NAAVL-HLT 2016, 1424-1430.

Nguyen, H., Litman, D. J. (2015). Extracting argument and domain words for identifying argument components in texts. Proceedings of the 2nd Workshop on Argumentation Mining. 22-28.

Fan, X., Luo, W., Meneske, M., Litman, D., Wang, J. (2015). CourseMIRROR: Enhancing Large Classroom Instructor-Student Interactions via Mobile Interfaces and Natural Language Processing. Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, 1473-1478. http://dx.doi.org/10.1145/2702613.2732853

Luo, W., Fan, X., Meneske, M., Wang, J., & Litman, D. (2015). Enhancing Instructor-Student and Student-Student Interactions with Mobile Interfaces and Summarization. Proceedings NAACL HLT Companion Volume.

Zhang, F., Litman, D. (2015). Annotations and classification of argumentative writing revisions. Proceedings at the Tenth Workshop on Innovative Use of NLP for Building Educational Applications.

Rahimi, Z., Litman, D., Wang, E., Correnti, R. (2015). Incorporating coherence of topics as a criterion in automatic response-to-text assessment of the organization of writing. Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, 20-30.

Lipshultz, M., Litman, D., Katz, S., Albacete, P., & Jordan, P. (2014). Predicting semantic changes in abstraction in tutor responses to students. International Journal or Learning Technology, 9(3), 281-303.

Ong, N., Litman, D., & Brusilovsky, A. (2014). Ontology-based argument and automatic essay scoring. Proceedings of the First Workshop on Argumentation Mining.

Zhang, F., & Litman D. (2014). Sentence-level rewriting detection. The 9th Workshop on Innovative Use of NLP for Building Educational Applications.

Nguyen, H. V., & Litman, D. J. (2014). Improving peer feedback prediction: The sentence level is right. The 9th Workshop on Innovative Use of NLP for Building Educational Applications.

Rahimi, Z., Litman, D. J., Correnti, R., Matsumura, L. C., Wang, E., & Kisa, Z. (2014). Automated scoring of an analytical response-to-text assessment. In Intelligent Tutoring Systems (pp. 601-610). Springer International Publishing.

Diane Litman, Richard Correnti, and Lindsay Clare Matsumura, LRDC Research Scientists have been awarded an IES grant for their project, "Response-to-Text Tasks to Assess Students' Use of Evidence and Organization in Writing: Using Natural Language Processing for Scoring Writing and Providing Feedback At-Scale."

July 1, 2016

Two of the three nominees for Best Student Paper at the 29th International Florida Artificial Intelligence Research Society Conference were first-authored by LRDC graduate students of Diane Litman. Wencan Luo, “Determining the Quality of a Student Reflective Response,” and Huy Nguyen, “Improving Argument Mining in Student Essays by Learning and Exploiting Argument Indicators versus Essay Topics.”

April 11, 2016

Diane Litman, LRDC Senior Scientist, has been elected to a three-year term as Councilor of the Association for the Advancement of Artificial Intelligence (AAAI) -- a nonprofit scientific society devoted to advancing the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines.

2015

Diane Litman (Co-Principal Investigator) and Rebecca Hwa (Principal Investigator, Department of Computer Science) have been awarded a grant by the National Science Foundation to investigate whether natural language processing (NLP) methods can help students learn to make a more concrete connection between the abstract principles of rewriting (e.g., "A paper should have a clear thesis") and the particular contexts in which the revision is carried out.

August 5, 2015

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LRDC Senior Scientist and Professor of Computer Science Diane Litman, and co-author Kate Forbes-Riley, received the Best Paper Published in Speech Communication (2011-2013) award by the International Speech Communication Association (ISCA). The award was given for "Benefits and Challenges of Real-time Uncertainty Detection and Adaptation in a Spoken Dialogue Computer Tutor," (2011), volume 53, issue 9, at the Interspeech 2014 conference in Singapore.

September 2014

The first Intelligent Systems Program Yearly Newsletter.

September 2014

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A list of recent LRDC award recipients is on page 18 of the August 28 issue of the University Times. Einat Heyd-Metzuyanim, Melissa Libertus, Diane Litman, Charles Perfetti, Christian Schunn, Natasha Tokowicz, Tessa Warren, and Jingtao Wang were all mentioned.

August 2014

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Diane Litman along with Steve Young, University of Cambridge, received a Distinguished Visiting Fellowship Award for collaborating "Dialogue systems for teaching & assessing conversational skills in second language learning."

August 2014

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Congratulations to Christian Schunn, Diane Litman, and Amanda Godley. "An Intelligent Ecosystem for Science Writing Instruction," was awarded a The National Science Foundation (NSF) grant.

July 2014

Diane Litman is a recent recipient of Google's Faculty Research Awards. She is researching how to enable computers to derive meaning from human language so the computers can analyze student peer assessments in "massively open-access online courses," or MOOCs. Article appeared in May 19th issue of the Pitt Chronicle.

June 2014

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Diane Litman has been awarded a 2014 Google Faculty Research Award for Natural Language Processing.

March 20, 2014

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Diane Litman has been awarded a 2014 Google Faculty Research Awards for Natural Language Processing.

February 2014

Institute of Education Sciences awarded Diane Litman a three-year grant for "Intelligent scaffolding for Peer Reviews of Writing."

August 20, 2012

University Times

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Diane Litman Awarded Senior Member Status by the AAAI

September 29, 2011

University Times

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Intelligent Systems Expert, Diane Litman, Works to Make Computers More Compatible With and Useful to People

Summer 2011

Pitt Magazine

University Researchers Find Business Applications for Watson Converge Magazine: Technology in Education

April 4, 2011

Convergemag.com

IBM's Watson Comes to Pitt, Carnegie Mellon University

March 28, 2011

Pitt Chronicle

James Chen Award for the Best Student Paper for "Inducing Effective Pedagogical Strategies Using Learning Context Features" by Chi, VanLehn, Litman and Jordan

2010

18th International Conference on User Modelling, Adaptation and Personalization (UMAP), Kona, Hawaii

Outstanding Reviewer Award for ITS2010

Best Paper Award for "Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning To Induce Pedagogical Tutorial Tactics" by Chi, VanLehn and Litman

2010

10th International Conference on Intelligent Tutoring Systems (ITS), Pittsburgh, PA