Journal Publications

• Lange, P. and Kurrle, P. and Pado, U. (2024). Prädiktionsgüte der Rückfederung in der Blechbearbeitung. WT Werkstattstechnik, 114(3).
• Pado, U. and Eryilmaz, Y. and Kirschner, L. (2023). Short-Answer Grading for German: Addressing the Challenges. International Journal of Artificial Intelligence in Education.
• Pado, U. and Pado, S. (2021). Determinants of Grader Agreement: An Analysis of Multiple Short-Answer Corpora. Language Resources and Evaluation.
• Pado, U. (2020). Freitextfragen digital. Bewertung und Bewerterunterstützung im Bereich der educational applications. Mitteilungen des Deutschen Germanistenvereins, 67(2), pp. 211–222. — Auf Einladung des Herausgebers. / By invitation from the editor.
• Kiefer, C. and Pado, U. (2015). Freitextaufgaben in Online-Tests: Bewertung und Bewertungsunterstützung. HMD Praxis der Wirtschaftsinformatik, 52(1), pp. 96–107.
• Erk, K. and Pado, S. and Pado, U. (2010). A Flexible, Corpus-driven Model of Regular and Inverse Selectional Preferences. Computational Linguistics, 36(4), pp. 723–763.
• Pado, U. and Crocker, M. and Keller, F. (2009). A Probabilistic Model of Semantic Plausibility in Sentence Processing. Cognitive Science, 33, pp. 794–838.

Book Chapters

• Pado, U. and Knebusch, A. and Mehmedovski, K. (2025). Computer-Based Methods for Adaptive Teaching and Learning. In: Advanced Technologies and the University of the Future, Springer, Cham.

Conference and Workshop Publications

• Pampel, B. and Martin, S. and Pado, U. (2026). From Capable to Trustworthy: Empowering Educators to Build Reliable AI Chatbots with Retrieval-Augmented Generation. In: Revised Selected Papers of the 17th International Conference on Computer Supported Education (CSEDU 2025), LNCS, Springer Nature. — to appear.
• Pado, U. (2026). HFT at BEA 2026 Shared Task 2: Blunt-Edge Models for Hybrid Grading. In: Proceedings of the 21st Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2026), San Diego, Association for Computational Linguistics. — to appear.
• Pado, U. (2026). Hybrid Evaluation of Tutor Dialogues. In: Proceedings of the 22nd Conference on Intelligent Tutoring Systems, LNCS, Springer Nature. — shortlisted for Best Paper, to appear.
• Pado, U. and Pampel, B. (2026). TutorBots in Context: A Multi-Lens View on Educational Practice. In: Proceedings of the 18th International Conference on Computer Supported Education - Volume 1.
• Pado, U. and Pampel, B. (2026). Linguistics to LLMs: Teaching with and about Chatbots. In: Proceedings of the 7th Workshop on Teaching Natural Language Processing (TeachNLP 2026), Rabat, Morocco. — Recipient of the Audience Award.
• Pado, U. and Mehmedovski, K. and Knebusch, A. (2026). Turning CAT into MOUSE: Adaptive Mechanisms in Student Exercise Selection. In: Proceedings of 2025 6th AIET Conference, Munich.
• Pampel, B. and Martin, S. and Pado, U. (2025). Regaining Control: Enabling Educators to Build Specialized AI Chat Bots with Retrieval Augmented Generation. In: Proceedings of the 17th International Conference on Computer Supported Education - Volume 2: CSEDU, Porto.
• Knebusch, A. and Pado, U. (2023). Working at Your Own Pace: Computer-based Learning for CL. In: Proceedings of KONVENS 2023.
• Willms, N. and Pado, U. (2022). A Transformer for SAG: What Does it Grade?. In: Proceedings of NLP4CALL, Leuven.
• Pado, U. (2022). Assessing the Practical Benefit of Automated Short-Answer Graders. In: AIED Proceedings Vol.2, pp. 555–559.
• Mieskes, M. and Pado, U. (2019). Summarization Evaluation meets Short-Answer Grading. In: Proceedings of NLP4CALL, Turku.
• Mieskes, M. and Pado, U. (2018). Work Smart - Reducing Effort in Short-Answer Grading. In: Proceedings of NLP4CALL at SLTC, Stockholm.
• Pado, U. (2017). Question Difficulty - How to Estimate Without Norming, How to Use for Automated Grading. In: Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA-12) at EMNLP, Copenhagen.
• Pado, U. (2016). Get Semantic With Me! The Usefulness of Different Feature Types for Short-Answer Grading. In: Proceedings of COLING, Osaka.
• Pado, U. and Kiefer, C. (2015). Short Answer Grading: When Sorting Helps and When it Doesn’t. In: Proceedings of the 4th NLP4CALL workshop at Nodalida, Vilnius.
• Ortiz Suarez, N. and Lemcke, J. and Pado, U. (2014). Labelling Business Entities in a Canonical Data Model. In: Proceedings of KONVENS, Hildesheim.
• Bernstein, J. and Suzuki, M. and Cheng, J. and Pado, U. (2009). Evaluating Diglossic Aspects of an Automated Test of Spoken Modern Standard Arabic. In: Proceedings of SLaTE 2009, Birmingham, UK.
• Cheng, J. and Bernstein, J. and Pado, U. and Suzuki, M. (2009). Automatic Assessment of Spoken Modern Standard Arabic. In: Proceedings of the 4th Workshop on Innovative Use of NLP for Building Educational Applications, NAACL-HLT, Boulder, CO.
• Pado, S. and Pado, U. and Erk, K. (2007). Flexible, Corpus-Based Modelling of Human Plausibility Judgements. In: Proceedings of EMNLP, Prague.
• Pado, U. and Crocker, M. and Keller, F. (2006). Modelling Semantic Role Plausibility in Human Sentence Processing. In: Proceedings of EACL, Trento.
• Pado, U. and Keller, F. and Crocker, M. (2006). Combining Syntax and Thematic Fit in a Probabilistic Model of Sentence Processing. In: Proceedings of CogSci, Vancouver.
• Baldewein, U. and Erk, K. and Pado, S. and Prescher, D. (2004). Semantic Role Labeling with Chunk Sequences. In: Proceedings of CoNLL-2004, Boston, MA.
• Baldewein, U. and Erk, K. and Pado, S. and Prescher, D. (2004). Semantic Role Labelling With Similarity-Based Generalisation Using EM-based Clustering. In: Proceedings of SENSEVAL-3, Barcelona.
• Baldewein, U. and Keller, F. (2004). Modelling Attachment Decisions with a Probabilistic Parser: The Case of Head-Final Structures. In: Proceedings of CogSci, Chicago, IL.

PhD Thesis

• Pado, U. (2007). The Integration of Syntax and Semantic Plausibility in a Wide-Coverage Model of Sentence Processing. PhD Thesis, Saarland University.

Other

• Pado, U. (2025). Adaptive Exercise Selection - Adaptivity in Project KNIGHT. — Workshop "Adaptive Learning in Higher Education", Universität Ulm.
• Pado, U. (2025). Computer-Adaptive Testing Approaches for Exercise Selection. — 3rd Workshop on Technology-Based Assessment, IWM Universität Tübingen.