# ConnectED: A Curriculum-Aligned AI System for Vietnamese Instructional Lesson Planning and Student Learning

arXiv: https://arxiv.org/abs/2607.28647 · HTML: https://mlsanity.com/paper/2607.28647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Process reward models for reasoning, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Process reward models for reasoning](https://mlsanity.com/alignment/process-reward-models-for-reasoning/)

## Builds on (most cited first, 22 of 22)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [SeaLLMs -- Large Language Models for Southeast Asia](https://mlsanity.com/paper/2312.00738/) (2023)
- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation](https://mlsanity.com/paper/2205.06457/) (2022)
- [Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation](https://mlsanity.com/paper/2408.04394/) (2024)
- [Navigating LLM Ethics: Advancements, Challenges, and Future Directions](https://mlsanity.com/paper/2406.18841/) (2024)
- [LearnLM: Improving Gemini for Learning](https://mlsanity.com/paper/2412.16429/) (2024)
- [Designing for human-AI complementarity in K-12 education](https://mlsanity.com/paper/2104.01266/) (2021)
- [Pedagogical Alignment of Large Language Models](https://mlsanity.com/paper/2402.05000/) (2024)
- [VNHSGE: VietNamese High School Graduation Examination Dataset for Large Language Models](https://mlsanity.com/paper/2305.12199/) (2023)
- [GPT-4 as a Homework Tutor can Improve Student Engagement and Learning Outcomes](https://mlsanity.com/paper/2409.15981/) (2024)
- [PhoGPT: Generative Pre-training for Vietnamese](https://mlsanity.com/paper/2311.02945/) (2023)
- [Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models](https://mlsanity.com/paper/2403.02715/) (2024)
- [Manimator: Transforming Research Papers into Visual Explanations](https://mlsanity.com/paper/2507.14306/) (2025)
- [Using Large Language Models for education managements in Vietnamese with low resources](https://mlsanity.com/paper/2501.15022/) (2025)
- [GreenMind: A Next-Generation Vietnamese Large Language Model for Structured and Logical Reasoning](https://mlsanity.com/paper/2504.16832/) (2025)

## Abstract (arXiv)

This paper presents ConnectED, a human-centered AI system that supports the full instructional lifecycle in Vietnamese education by linking curriculum-aligned lesson design, interactive student learning, and feedback-driven refinement. Built on VietEduQwen, a Vietnamese educational large language model trained via supervised fine-tuning and direct preference optimization, the system ensures academically accurate, pedagogically appropriate, and student-safe interactions. ConnectED operationalizes the ADDIE framework through structured prompt templates aligned with Official Dispatch No. 5512/BGDDT-GDTrH, where each phase serves as both a generation step and a teacher validation gate. The Evaluation phase further closes the loop by connecting student performance data with iterative lesson improvement. Beyond lesson generation, the system integrates a student-facing interactive environment, enabling continuous collection of learning signals to support teacher decision-making. Evaluation on 3,119 questions from the 2025 Vietnamese National High School Examination shows that VietEduQwen achieves 87.02% accuracy, outperforming Qwen3-8B by 6.10 percentage points. Surveys of teachers (n=18) and students (n=214) demonstrate strong satisfaction with curriculum alignment, lesson clarity, and usability. In practice, lesson preparation time is reduced from 3--4 hours to approximately 30--45 minutes with teacher-in-the-loop review. Ablation studies confirm that both DPO training and ADDIE-based orchestration contribute independently to system performance, highlighting the importance of structured teacher oversight for practical deployment.