In this study, we built a Large Language Model (LLM)-powered interactive narrative game for French-speaking primary school learners. The system consists mainly of three modules: an initialization module for story structuring, a generation module for iterative generation of story texts and illustrations based on player choices, and a language scaffolding module for providing contextualized explanations of player-selected textual elements. Text generation is prompted using five curriculum-informed proficiency bands spanning primary to early secondary reading levels. A classroom feasibility study was conducted with 35 students aged 9-10 and two teachers at two primary schools in French-speaking Switzerland. Results showed positive student attitudes towards the stories and illustrations and high perceived ease of use. However, students perceived the game’s usefulness for French literacy activities as moderate, with many noting that the story language was below their level. Occasional issues in character consistency and repetitive illustrations across scenes were also mentioned. Teachers highlighted potential pedagogical benefits when the system is combined with additional classroom activities.

Second language writers rarely receive the detailed feedback they need to improve, as comprehensive feedback takes teachers days per class. To solve this, we present WrAFT, a Writing Assessment and Feedback Tool, that delivers reliable scores along with effective comprehensive feedback for argumentative essays. WrAFT adopts a modular design by dividing the automated writing evaluation (AWE) into essay scoring, surface-level feedback and deep-level feedback. In building the system, various Large Language Models (LLMs) have been evaluated, including LLaMA-3.3-70B-Instruct, GPT-4o, and Claude 3.7, through both supervised fine-tuning and direct prompting. Evaluation shows that WrAFT achieves state-of-the-art performance in scoring with a Quadratic Weighted Kappa (QWK) of 0.84 on a proprietary dataset of 480 TOEFL Independent Writing essays. More importantly, human evaluation of system-generated feedback also reveals high approval ratings (96.14% for surface-level, 93.03% for deep-level macro feedback, and 94.69% for deep-level micro feedback). Publicly available interactive user interface has also been developed for the system.