Yifan Liu, Jaime Arguello, Orland Hoeber, Chang Liu et al. — cs.IR, cs.AI, cs.HC 📄 Full text (arXiv)
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. Three thematic clusters emerged: foundations and principles (guiding theories, design principles for human-centered GenAI-enhanced search), applications and opportunities, and search-as-learning — the idea that academic search systems should foster higher-order cognitive processes including synthesis, critical evaluation, and knowledge construction. The workshop emphasized transparency, credibility, research integrity, and long-term scholarly needs. The 'search-as-learning' theme is most directly relevant to ai-literacy and ai-education domains, as it reframes academic search not merely as information retrieval but as a learning activity. This connects to formative-assessment and self-regulated-learning research exploring how AI-mediated research tools shape student learning processes.
Related Pages
- knowledge-tracing-irt — Knowledge tracing models and IRT for student modeling
- intelligent-tutoring — AI tutoring systems and adaptive instruction
- learning-analytics — Data-driven analysis of learning processes
- ai-literacy — Understanding and evaluating AI tools
- student-modeling — Representing learner knowledge and behavior
- cs-education — Computing education research and pedagogy
- self-regulated-learning — Metacognitive strategies for independent learning
- formative-assessment — Ongoing assessment to inform instruction
- automated-grading — AI-assisted evaluation of student work
- scaffolding — Instructional support that fades with competence
Citations
APA: Yifan Liu, Jaime Arguello, Orland Hoeber, Chang Liu et al. (2026). Report on CHIIR 2026 Workshop on Generative AI and Academic Search (GAI&AS). arXiv:2606.08936. cs.IR, cs.AI, cs.HC.