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The CHIIR 2026 workshop report outlines a strategic shift in academic search systems from simple document retrieval toward GenAI-integrated capabilities like summarization, synthesis, and conversational interaction. Researchers are prioritizing design principles that ensure transparency, credibility, and research integrity while fostering higher-order cognitive processes within scholarly workflows. This initiative signals a growing community focus on human-centered evaluation methods and the methodological challenges of integrating generative models into established information retrieval frameworks.
Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS)
arXiv:2606.08936v1 Announce Type: cross Abstract: This report summarizes the CHIIR 2026 Workshop on Generative AI and Academic Search (GAI\&AS), which examined how GenAI is reshaping academic search systems and research practices. The workshop brought together researchers in human information interaction and information retrieval to explore key challenges and opportunities in designing and evaluating future academic search systems that integrate GenAI, moving beyond traditional document retrieval to support summarization, recommendation, synthesis, and conversational interaction. Participants' interests and discussions focused on three thematic clusters: foundations and principles, applications and opportunities, and search-as-learning. Across these themes, the workshop highlighted the importance of academic search systems in supporting transparency, credibility, research integrity, and long-term scholarly needs, as well as in fostering higher-order cognitive processes. Participants discussed guiding theories, design principles, methodological approaches, partnerships, and community-building efforts aimed at advancing human-centered GenAI-enhanced academic search systems. Overall, the workshop demonstrated strong community interest and a diverse range of ongoing and emerging research initiatives at the intersection of GenAI and academic search.
Authors:Yifan Liu, Jaime Arguello, Orland Hoeber, Chang Liu, Soo Young Rieh, Luanne Sinnamon, Dean Alvarez, Susan Archambault, Rob Capra, Henson Chen, Charles Costa, Anita Crescenzi, Zhitong (Klara)Guan, Jacek Gwizdka, Pao-Pei Huang, Gavindya Jayawardena, Ghazal Kalhor, Dagmar Kern, Oliver Koop, Alice Li, Afra Mashhadi, Gaohui Meng, Marta Micheli, Anil B. Murthy, Kevin Schott, Sebastian Schultheiß, Jiwoo Seo, Phaneendra Sivangula, Frans van der Sluis, Xiaoxuan Song, Silang Wang, Dan Zhang
Abstract:This report summarizes the CHIIR 2026 Workshop on Generative AI and Academic Search (GAI\&AS), which examined how GenAI is reshaping academic search systems and research practices. The workshop brought together researchers in human information interaction and information retrieval to explore key challenges and opportunities in designing and evaluating future academic search systems that integrate GenAI, moving beyond traditional document retrieval to support summarization, recommendation, synthesis, and conversational interaction. Participants' interests and discussions focused on three thematic clusters: foundations and principles, applications and opportunities, and search-as-learning. Across these themes, the workshop highlighted the importance of academic search systems in supporting transparency, credibility, research integrity, and long-term scholarly needs, as well as in fostering higher-order cognitive processes. Participants discussed guiding theories, design principles, methodological approaches, partnerships, and community-building efforts aimed at advancing human-centered GenAI-enhanced academic search systems. Overall, the workshop demonstrated strong community interest and a diverse range of ongoing and emerging research initiatives at the intersection of GenAI and academic search.
Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
arXiv:2606.08936 [cs.IR]
(or arXiv:2606.08936v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2606.08936 arXiv-issued DOI via DataCite (pending registration)
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