Linguistic Alignment Predicts Learning in Small Group Tutoring Sessions (2025)

Abstract: 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.

## introduction cognitive science has developed theories of mind, learning, and communication @xcite @xcite , which aim to explain how individuals construct and share knowledge. these theories, however, are difficult to investigate at scale in real-world contexts which require delving into complex or messy data. our work illustrates how natural language processing (nlp) techniques can bridge the gap between cognitive science theory, real-world outcomes, and applications in the context of education. the educational domain lends itself especially well to this exploration, as teachers and students have distinct roles, measurable outcomes, and there is an increasing need for computational tools to support effective learning processes. while many factors contribute to effective learning @xcite @xcite , the success of human tutoring programs underscores the central role of interpersonal communication and individual attention to learning. we argue that, at a basic level, the language used during tutoring can provide meaningful insights into the dynamics and quality of these interactions. this is grounded in the language-as-thought hypothesis: language reflects underlying psychological states and processes, including thoughts, feelings, and motivations within and between individuals @xcite @xcite . as language arises naturally in many types of interactions and serves a primary role in communication, it provides unique insights into social processes including shared knowledge construction, perspective taking, and emotional regulation @xcite @xcite . in this work we investigate linguistic alignment @xcite as a measurable component of interpersonal coordination in small-group tutoring. prior work suggests that linguistic alignment -convergence on word choice (lexical), grammar (syntactic), and meaning (semantic) -fosters mutual understanding, reduces cognitive load, and enhances conversational outcomes @xcite @xcite . while extensively studied in one-on-one dialogues @xcite @xcite and, to a lesser extent, in classroom and human-computer interactions @xcite , alignment remains underexplored in human small-group tutoring, particularly in relation to learning outcomes. by investigating the way tutors and students adapt their language to each other, we hope to gain further insight into their communicative roles and strategies, and the implications on learning. contribution using a longitudinal dataset of small-group tutoring sessions and student learning metrics collected over an academic year, we explore the following questions: (1) to what extent does linguistic alignment occur in small group tutoring? (2) how do students and tutors differ in their patterns of alignment? (3) how does linguistic alignment predict student learning outcomes? we find evidence of lexical, syntactic, and semantic alignment from both tutors and students, with tutors exhibiting higher degrees of alignment overall. importantly, we show that lexical alignment in particular predicts student learning gains. linguistic alignment offers a lightweight, interpretable, and computationally tractable measure of tutoring interactions. as such, it has a multitude of use cases in tutor training, real-time feedback for tutors, and in the design of intelligent tutoring systems -highlighting the way nlp techniques can be used for both scientific research and in research-based applications. ## discussion conversation is a joint task that involves participants presenting information, then seeking or indicating understanding; a process referred to as grounding @xcite , which involves some degree of both internal and synergistic cognitive processes @xcite alignment reliably occurs in small-group tutoring we first ask whether alignment occurs in small group tutoring sessions. in these sessions, the content is highly topically constrained, and the roles and amount of speech are imbalanced between students as tutors. to determine if the degree of alignment extends beyond chance overlap of phrasing, we compare actual alignment to a within-session shuffled baseline and find a significant difference between the two. semantic alignment is most similar to the baseline, suggesting longer-term temporal dependencies of the semantic content. lexical alignment, however, is substantially above baseline, indicating that while the content of the discourse remains stable, the specific word choices within turns fluctuates considerably. syntactic alignment is between the two, suggesting a moderate degree of repetition of syntactic structures across the discourse. students demonstrate lower alignment compared to tutors students consistently align less than tutors across all alignment measures. as tutors are responsible for guiding the discussion and scaffolding problem solving, they might be more likely to match student phrasing and terminology and follow their lead to facilitate student comprehension and reduce the cognitive load of processing novel speech. it might also signal tutors' uptake (i.e., building on) student ideas, which is a key pedagogical strategy @xcite . ## conclusion this study investigates linguistic alignment in small group tutoring and its association with student learning. our findings indicate that linguistic alignment occurs naturally in tutoring interactions, with students exhibiting lower levels of alignment than tutors. we show that lexical alignment varies more in the short term, while semantic content is more consistent. we demonstrate that lexical alignment predicts learning outcomes and the relationship varies based on the source of the alignment (student vs. tutor), suggesting that alignment may serve as a meaningful indicator of tutoring processes and outcomes. the practical implications of these findings extend to tutor training, automatic feedback tools, and itss.