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A Multi-Perspective Learning to Rank Approach to Support Children's Information Seeking in the Classroom
We introduce a novel re-ranking model that aims to augment the functionality of standard search engines to support classroom search activities for children (ages 6 to 11).
Power-up! What Can Generative Models Do for Human Computation Workflows?
We identify junctures in typical crowdsourcing workflows at which the introduction of LLMs can play a beneficial role and propose means to augment existing design patterns for crowd work.
Using Conversational Artificial Intelligence to Support Children's Search in the Classroom
Further, we discuss preliminary ideas on strategies for evaluation.
To Infinity and Beyond! Accessibility is the Future for Kids' Search Engines
Seminal works have studied how children use mainstream search engines, as well as how to design and evaluate custom search engines explicitly for children.
CASTing a Net: Supporting Teachers with Search Technology
In this paper, we argue for the need to broaden the research focus to include teachers and how search technology can aid them.
