English as a second language (ESL) learners can benefit from language support features, such as the use of machine translation, to present different aspects of academic articles in their primary language or preferred language. Adapting academic search systems to accommodate the language proficiency of ESL learners can benefit learners to effectively find, understand, and use information, which could improve their learning outcomes. However, modelling user language competency and tailoring search system features towards language competency is challenging. In particular, determining the right moment to switch languages automatically in a conversational search could require analysing both the user's past behaviour and their current context.

We are looking for PhD students to investigate the design of adaptive multilingual search systems that dynamically change based on the user’s language profile, using features such as language background, language assistance history and language preference. The project will explore how these adaptations affect user’s search behaviour, search experience and learning during searching. Another component of the project will develop objective evaluation methods for adaptive search systems, including metrics and new techniques that can effectively evaluate adaptation.