Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS)
2026-06-08 • Information Retrieval
Information RetrievalArtificial IntelligenceHuman-Computer Interaction
AI summaryⓘ
The authors report on a 2026 workshop where experts discussed how generative AI (GenAI) is changing academic search tools and research habits. They explored new features like summarizing papers, recommending relevant work, and having conversational interactions instead of just finding documents. The discussions centered on core ideas, practical uses, and how search can support learning. The authors highlight the importance of designing AI systems that promote trust, credibility, and deep thinking in research. They also emphasize partnerships and community efforts to create better AI-powered academic search tools.
Generative AIAcademic searchInformation retrievalHuman information interactionSummarizationRecommendation systemsConversational AIResearch integritySearch-as-learningHuman-centered design
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, 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.