A recent comparative analysis of four widely used AI-enabled research discovery tools — Elicit, Typeset.io (SciSpace), Consensus, and Scite.ai — lookedat how they perform across different types of research queries and discovery contexts. The results make one thing clear: traditional keyword search remains superior for exactness. Image-based discovery remains largely unsupported. Perhaps most frustrating for experienced researchers is the loss of control. In traditional systems, a poorly performing query can usually be debugged: terms can be adjusted, fields constrained, logic refined. In AI-driven systems, failures are harder to diagnose. AI systems are particularly effective at summarizing bodies of literature, identifying themes, and synthesizing evidence across multiple papers. AI tools excel when the task involves interpretation, synthesis, or sense-making areas where traditional keyword search has always been weakest. The results suggest not a clean replacement of keywords, but the emergence of a hybrid future in which precision search and AI-driven synthesis coexist, sometimes uneasily. The most effective discovery environments will be hybrid systems that combine: Natural language interfaces for exploration and synthesis, keyword and metadata controls for precision and verification, and transparent signals that help users assess confidence and coverage. (excerpted from article below)
Hong Zhou and Hiba Bishtawi. (2026). Keywords re not dead but discovery is no long just research. Scholarly Kitchen. (2026). https://scholarlykitchen.sspnet.org/2026/01/06/keywords-are-not-dead-but-discovery-is-no-longer-just-search/
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