Yucheng Wang, Shen Yang, Jifan Yu, Haoxuan Li, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu, Lei Hou, Juanzi Li, Bin Xu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Daniel Zhang-Li, Joy Jia Yin Lim, Binglin Liu, Shangqing Tu, Zijun Yao, Hao Peng, Jifan Yu, Haoxuan Li, Zhanxin Hao, Ye He, Zekun Li, Jiangyi Wang, Lei Hou, Bin Xu, Xin Cong, Zhiyuan Liu, Huiqin Liu, Yu Zhang, Juanzi Li. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Personalized Learning Path Planning (PLPP) aims to design adaptive learning paths that align with individual goals. While large language models (LLMs) show potential in personalizing learning experiences, existing approaches often lack mechanisms for goal-aligned planning. We introduce Pxplore, a novel framework for PLPP that integrates a reinforcement-based training paradigm and an LLM-driven educational architecture. We design a structured learner state model and an automated reward function that transforms abstract objectives into computable signals. We train the policy combining supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO), and deploy it within a real-world learning platform. Extensive experiments validate Pxplore's effectiveness in producing coherent, personalized, and goal-driven learning paths. We release our code and dataset at https://github.com/Pxplore/pxplore-algo.

Designing effective English vocabulary question generation tools demands a shift from labor-intensive content creation to large language model (LLM) automation that can adapt to varied educational contexts. Current approaches tend to offer a limited variety of question types, which restricts their practical application in real classroom settings. To better meet the demands of English teaching institutions, we present VocQuiz, a vocabulary question generation system that 1) combines generalization capabilities of LLMs with reliable language resources, including dictionaries, NLP datasets and authentic corpora, to enhance both contextual relevance and linguistic accuracy; 2) supports multiple question types, such as similar word selection and word collocation, to accommodate various instructional requirements; and 3) employs an iterative workflow to iteratively generate and refine questions, ensuring high-quality outputs and consistent assessment standards. VocQuiz offers a practical, deployable solution that helps educators create quiz-based instructional materials, reducing preparation effort while effectively assessing students' mastery of vocabulary.

Educators face substantial workload pressures, with significant time invested in preparing teaching materials. Generating high-quality lecture scripts from multimodal presentations is a particularly demanding aspect of this preparation. This paper introduces EduCraft, a novel system designed to automate Lecture Script Generation (LSG), addressing key difficulties such as comprehensive multimodal understanding, long-context coherence, and instructional design efficacy. EduCraft features a modular architecture comprising: (1) a Multimodal Input Processing pipeline for robust data extraction and association from slides; (2) a core Lecture Script Generation Engine with instruction-guided VLM and Caption+LLM workflows for pedagogical synthesis; (3) an optional Knowledge Augmentation Module using Retrieval-Augmented Generation (RAG) for enhanced factual grounding; and (4) a Model Integration and Deployment Interface supporting diverse AI models and providing a deployable API. Extensive evaluations, including human assessments and a new automated evaluation framework, demonstrate that EduCraft significantly outperforms strong baselines and teacher-refined scripts in producing coherent, readable, and pedagogically sound lecture scripts. By effectively tackling core LSG challenges, EduCraft offers a practical, configurable solution to reduce educator workload and enhance educational content creation. We open-source EduCraft at https://github.com/wyuc/EduCraft.

The vast pre-existing slides serve as rich and important materials to carry lecture knowledge. However, effectively leveraging lecture slides to serve students is difficult due to the multi-modal nature of slide content and the heterogeneous teaching actions. We study the problem of discovering effective designs that convert a slide into an interactive lecture. We develop Slide2Lecture, a tuning-free and knowledge-regulated intelligent tutoring system that can (1) effectively convert an input lecture slide into a structured teaching agenda consisting of a set of heterogeneous teaching actions; (2) create and manage an interactive lecture that generates responsive interactions catering to student learning demands while regulating the interactions to follow teaching actions. Slide2Lecture contains a complete pipeline for learners to obtain an interactive classroom experience to learn the slide. For teachers and developers, Slide2Lecture enables customization to cater to personalized demands. Slide2Lecture's online deployment has made more than 200K interactions with students in the 3K lecture sessions. We release our implementation at https://github.com/NewEduAI/Release.

Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle to generate coherent outputs beyond 1,000 words. We find that the primary limitation is the absence of long output examples during supervised fine-tuning (SFT). To tackle this issue, we introduce LongWriter-V-22k, a SFT dataset comprising 22,158 examples, each with multiple input images, an instruction, and corresponding outputs ranging from 0 to 10,000 words. Moreover, to achieve long outputs that maintain high-fidelity to the input images, we employ Direct Preference Optimization (DPO) to the SFT model. Given the high cost of collecting human feedback for lengthy outputs (e.g., 3,000 words), we propose IterDPO, which breaks long outputs into segments and uses iterative corrections to form preference pairs with the original outputs. Additionally, we develop MMLongBench-Write, a benchmark featuring six tasks to evaluate the long-generation capabilities of VLMs. Our 7B parameter model, trained with LongWriter-V-22k and IterDPO, achieves impressive performance on this benchmark, outperforming larger proprietary models like GPT-4o. Our models, data and code are available at: https://github.com/THU-KEG/LongWriter-V.

Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.