Modeling the interaction between gaze dynamics and linguistic structure remains a central challenge in scanpath prediction. Existing approaches either incorporate linguistic information in ways that do not fully respect the temporal progression of reading, or ignore linguistic structure altogether. Their effectiveness is further limited by the scarcity and heterogeneity of available eye-tracking data. To address these issues, we introduce EYELIGNR, a framework for spatiotemporal scanpath prediction that integrates linguistic information at the representation level. EYELIGNR conditions language representations on fixation order and aligns gaze and text within a shared embedding space through contrastive learning. Experiments on five reading corpora show that EYELIGNR generalizes effectively across reading conditions, improving over prior state of the art on multiple scanpath metrics (e.g., up to NLD ~57%, NLL ~27%, and FLA ~59%).

Cognitive science offers rich theories of learning and communication, yet these are often difficult to operationalize at scale. We demonstrate how natural language processing can bridge this gap by applying psycholinguistic theories of discourse to real-world educational data. We investigate linguistic alignment – the convergence of conversational partners’ word choice, grammar, and meaning – in a longitudinal dataset of real-world tutoring interactions and associated student test scores. We examine (1) the extent of alignment, (2) role-based patterns among tutors and students, and (3) the relationship between alignment and learning outcomes. We find that both tutors and students exhibit lexical, syntactic, and semantic alignment, with tutors aligning more strongly to students. Crucially, tutor lexical alignment predicts student learning gains, while student lexical alignment negatively predicts them. As a lightweight, interpretable metric, linguistic alignment offers practical applications in intelligent tutoring systems, educator dashboards, and tutor training.