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Interactive Knowledge Assistance Track (iKAT)

Last updated: March 16, 2026

Overview

While the conversational interaction paradigm becomes more and more prevalent for search, the challenge of adapting system responses to individual users (i.e., personalization) still remains a challenge for conversational retrieval systems. Since answer relevance is highly subjective, personalization is necessary to build effective user-centric retrieval systems. From the beginning, personalization of conversational search is the central goal of the Interactive Knowledge Assistance Track (iKAT). The iKAT shared task can look back on four years under former name, Conversational Assistance Track (CAsT), and three years under the current name, iKAT, hosted at TREC. In 2026, iKAT will be organized in collaboration with the SCAI workshop that will take place at SIGIR 2026 in Melbourne, Australia.

iKAT @ SCAI 2026

In 2026, iKAT will be hosted in the context of the 10th anniversary of the Search-Oriented Conversational AI (SCAI) workshop at SIGIR. The focus of iKAT 2026 will be to explore the robustness of conversational retrieval systems towards diverse interaction strategies of individual users. Participant systems interact with various simulated users to provide tailored responses to individual information needs, benchmarking how robust and resilient the systems are when facing various behavioral traits and search strategies.

More information on the submission procedure and guidelines will follow soon!

Important Dates

Preliminary dates for iKAT 2026 are listed below. These dates might be still subject of change.

  • Release of submission guidelines: ASAP
  • System submission opens: May 27, 2026.
  • System submission deadline: June 10, 2026.
  • Report submission deadline: July 17, 2026.
  • SCAI Workshop: July 24, 2026.

Submit Your Runs

We will soon share details of how to submit the runs. Stay tuned!

Track Coordinators

Mohammad Aliannejadi, University of Amsterdam, The Netherlands. Dr. Aliannejadi is an Assistant Professor at the IRLab (formerly known as ILPS), the University of Amsterdam in The Netherlands. His research is in modeling user information needs with a focus on recommender systems, unified (meta) search, and conversational systems.

Simon Lupart, University of Amsterdam, The Netherlands. Simon is a Ph.D. student at the IRLab supervised by Dr. Aliannejadi and Prof. Kanoulas. He worked in IR for the past two years at Naver Labs Europe, and joined UvA to focus on conversational search.

Marcel Gohsen, Bauhaus-Universität Weimar, Germany. Marcel is a PhD student at the chair of Intelligent Information Systems supervised by Prof. Dr. Benno Stein. His research concentrates on the intersection between information retrieval and natural language processing with a particular focus on conversational search, user simulation, and generative IR.

Zahra Abbasiantaeb, University of Amsterdam, The Netherlands. Zahra is a Ph.D. student at the IRLab supervised by Dr. Aliannejadi. She is working on conversational search and recommendation. Earlier, she has also worked on patent reference mining. Zahra obtained her masters in AI from the Amirkabir University of Technology with a focus on question answering systems.

Nailia Mirzakhmedova, Bauhaus-Universität Weimar, Germany. Nailia is a PhD student at the chair of Intelligent Information Systems supervised by Prof. Dr. Benno Stein. Her research focuses on computational argumentation, framing, and user simulation for the evaluation of conversational search systems.

Johannes Kiesel, GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany. Dr. Kiesel is the leader of the Big Data Analytics team at GESIS - Leibniz Institute for the Social Sciences in Cologne, Germany. His research interests include conversational search, argumentation systems, and user simulation.

Jeff Dalton, University of Edinburgh, Scotland. Dr. Dalton is a Associate Professor (Reader) and Chancellor's Fellow at the School of Informatics, the University of Edinburgh. He is also a Turing AI Fellow and PI for the GRILL Lab. His research focuses on new methods for machine understanding of language and text data using deep neural networks and entity knowledge graphs for improving information seeking applications.

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