Month: November 2025

Language learners make the fastest progress when reading texts that match their proficiency level. But most real-world texts are too hard—and manually adapting them is time-consuming. So the big question is: Can AI automatically simplify texts to a specific CEFR level without losing meaning? We explored exactly this in the TSAR 2025 Shared Task, where systems had to rewrite advanced English texts (B2+) to easier levels like A2 or B1. Our team submitted two different approaches: EZ-SCALAR and SAGA. EZ-SCALAR works like an expert panel of AI models. Two large language models (GPT-5 and Claude) each produce their own simplification, critique each other, refine their versions, and then a final “judge” model picks the best result. An extended version, EZ-SCALAR Lex, adds something extra: a vocabulary check using EFLLex, a…

Helping students apply energy ideas to everyday situations is a core goal in physics education. But not all students get there—and it’s not just about who knows the most content. In a 10-week classroom study with 165 students, we tracked both their energy understanding and their affective and metacognitive factors (like emotions, cognitive load, and self-regulation). Using k-means clustering on their learning trajectories, we identified three distinct student groups that differed in the coherence of their energy knowledge development. The key insight: Students who learned the most also felt more positive, experienced lower cognitive load, and used stronger metacognitive strategies. Those who struggled often felt overwhelmed or disengaged. The takeaway is clear: supporting emotions and self-regulation is just as important as teaching physics content. Instruction that addresses these factors can…