Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS)

The CHIIR 2026 Workshop on Generative AI and Academic Search convened researchers to examine how GenAI is transforming academic research systems beyond traditional document retrieval. Discussions centered on three themes—foundations, applications, and search-as-learning—emphasizing human-centered design principles that prioritize research integrity, transparency, and higher-order cognitive support.

The CHIIR 2026 workshop represents a significant convergence between two mature research communities: human-information interaction specialists and information retrieval experts, now united around GenAI-enhanced academic search. This event signals growing recognition that generative AI requires fundamentally different evaluation frameworks and design philosophies than traditional search engines. Rather than optimizing for relevance ranking of discrete documents, the workshop participants grappled with how to support synthesis, summarization, and conversational research workflows while maintaining scholarly credibility.

The academic search domain has long faced challenges balancing usability with research integrity. Traditional search engines prioritize speed and scale, often at the expense of nuanced scholarly needs. GenAI introduces both opportunities—enabling natural language interaction and knowledge synthesis—and risks, particularly around hallucinations and citation accuracy. The workshop's emphasis on transparency, credibility, and research integrity reflects these dual concerns.

For developers and technology platforms, this workshop reveals market demand for purpose-built research infrastructure that GenAI can enhance. The focus on search-as-learning suggests opportunities for educational technology and knowledge management tools that go beyond current LLM chatbot implementations. The structured discussion around design principles and methodological approaches indicates the field is moving toward evidence-based development rather than ad-hoc integration.

Looking ahead, the community's emphasis on human-centered design and long-term scholarly needs will likely shape how academic institutions adopt GenAI technologies. Success will depend on moving beyond generic LLMs toward specialized systems trained on vetted academic content with transparency mechanisms built in from the start.

• →GenAI is expanding academic search beyond document retrieval toward synthesis, summarization, and conversational research workflows.
• →Research integrity, transparency, and credibility emerge as critical design considerations for GenAI-enhanced search systems.
• →The workshop unified two research communities around human-centered principles for evaluating next-generation academic search tools.
• →Search-as-learning frameworks represent an emerging research direction that leverages GenAI to support higher-order cognitive processes.
• →Partnerships and community-building efforts are essential for advancing GenAI integration into academic research infrastructure.