Construction grammar (CG) has been proposed as an adequate grammatical formalism for building intelligent language tutoring systems because it is highly compatible with the learning strategies observed in second language learning. Unfortunately, the lack of computational CG implementations has made it impossible in the past to corroborate these proposals with actual language tutoring prototypes. However, recent advances in Fluid Construction Grammar (FCG) now offer exciting new ways of operationalizing robust and open-ended language processing within a CG approach. This paper demonstrates its adequacy for ICALL applications through a case study on error diagnosis in the domain of Spanish tense, aspect and modal morphology. The performance of the FCG tutor is tested on the Spanish Learner Language Oral Corpus (SPLOCC 2). This first FCG Spanish error diagnostic prototype achieves an accuracy of 70% on a total of 500 conjugation errors in four oral tasks carried out by 20 low intermediate and 20 advanced English learners of Spanish. Follow-up experiments will test this prototype on larger learner corpora of differing proficiency levels.