Towards truly intelligent and personalized ICALL systems using Fluid Construction Grammar

Intelligent Computer-Assisted Language Learning (ICALL) aims to design effective systems for the analysis of learners’ production in a target language ensuring both successful learning and motivated learners. Most of the existing systems, however, focus extensively on the form rather than on the meaning of language. To create effective systems facilitating personalized language learning both form and meaning should be considered. The reason behind this is that language is a continuous flow of information passing from one user or agent to another, both during comprehension and production. This becomes even more relevant in the case of second or foreign languages (L2), where certain linguistic choices may be dictated by inexact form-meaning links construed by the learner. In this research project, we focus on the analysis of the spoken production of adult learners of German, taking argument and information structure as a use case. We use Fluid Construction Grammar as a formalism, which captures relevant linguistic aspects at both the syntactic level (form) and the semantic level (meaning). Its particularity lies in the possibility of closely monitoring bidirectional form-meaning interactions starting from constructions of different nature modeled in an extensively customizable way. Our work is in progress, and we focus on ways to provide helpful feedback on meaning. German displays a rather articulated grammar and obtaining insights not only on its formal but also on its semantic correctness could offer important steps forward for Intelligent CALL. The design of computational systems for Intelligent CALL that can effectively support L2 learners in personalized learning requires a grammatical framework that is computationally effective and offers linguistic and acquisitional perspicuity (Schulze & Penner, 2008).