Inferential Language is Limited and Unevenly Distributed in Popular Texts Used For Literacy Tutoring

Inferencing is essential for reading comprehension and higher-order thinking (Kendeou et al., 2019). Teachers are encouraged to teach students inferential language (Foorman et al., 2016), but the role of connected text itself in scaffolding inferencing remains unclear. In this study, we examined if texts used in a high-impact tutoring program for elementary students vary in inferential language. We identified four categories of inferential language: mental state terms, emotional state terms, metacognitive knowledge, and cognition and active processing. We used an AI-driven text analysis to tally these in the nine most popular text sources. Findings revealed minimal inferential language overall, with significant variation across sources, p < 0.001. As text difficulty increased, mental and emotional state terms (e.g., think, feel, happy) became more common, but academic and metacognitive terms (e.g., realize, reflect, analyze) remained scarce. Results highlight the need for further research on how text complexity influences student outcomes.