Seek and You Shall Find: Design&Evaluation of a Context-Aware Interactive Search Companion

2026-01-16

AI Summary

This study addresses the challenge that users in high-stakes domains such as health often struggle to effectively search for and evaluate information due to overconfidence in their digital literacy. The authors propose and evaluate a context-aware interactive search assistant that, for the first time, embeds contextual microlearning directly into real-world search engine results pages. Through lightweight, rule-based prompts, the system encourages users to clarify their information needs, refine queries, explore a broader set of results, and mitigate cognitive biases—all while minimizing cognitive load to foster reflective search behaviors. User studies demonstrate that the assistant increases query submissions by 75%, doubles the number of results examined, and significantly improves performance on complex search tasks. Future work aims to integrate large language models to enable adaptive interventions.

Technology Category

• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Search
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Semantics and Knowledge: Applications of semantic technologies for improving search, browsing, recommendation, personalization
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Many users struggle with effective online search and critical evaluation, especially in high-stakes domains like health, while often overestimating their digital literacy. Thus, in this demo, we present an interactive search companion that seamlessly integrates expert search strategies into existing search engine result pages. Providing context-aware tips on clarifying information needs, improving query formulation, encouraging result exploration, and mitigating biases, our companion aims to foster reflective search behaviour while minimising cognitive burden. A user study demonstrates the companion's successful encouragement of more active and exploratory search, leading users to submit 75 % more queries and view roughly twice as many results, as well as performance gains in difficult tasks. This demo illustrates how lightweight, contextual guidance can enhance search literacy and empower users through micro-learning opportunities. While the vision involves real-time LLM adaptivity, this study utilises a controlled implementation to test the underlying intervention strategies.

Problem

• online search
• search literacy
• critical evaluation
• digital literacy
• information seeking

Innovation

• context-aware search
• interactive search companion
• search literacy
• micro-learning
• query refinement