K-12 educators are increasingly using Large Language Models (LLMs) to create instructional materials. These systems excel at producing fluent, coherent content, but often lack support for high-quality teaching. The reason is twofold: first, commercial LLMs, such as ChatGPT and Gemini which are among the most widely accessible to teachers, do not come preloaded with the depth of pedagogical theory needed to design truly effective activities; second, although sophisticated prompt engineering can bridge this gap, most teachers lack the time or expertise and find it difficult to encode such pedagogical nuance into their requests. This study shifts pedagogical expertise from the user's prompt to the LLM's internal architecture. We embed the well-established Knowledge-Learning-Instruction (KLI) framework into a Multi-Agent System (MAS) to act as a sophisticated instructional designer. We tested three systems for generating secondary Math and Science learning activities: a Single-Agent baseline simulating typical teacher prompts; a role-based MAS where agents work sequentially; and a collaborative MAS-CMD where agents co-construct activities through conquer and merge discussion. The generated materials were evaluated by 20 practicing teachers and a complementary LLM-as-a-judge system using the Quality Matters (QM) K-12 standards. While the rubric scores showed only small, often statistically insignificant differences between the systems, the qualitative feedback from educators painted a clear and compelling picture. Teachers strongly preferred the activities from the collaborative MAS-CMD, describing them as significantly more creative, contextually relevant, and classroom-ready. Our findings show that embedding pedagogical principles into LLM systems offers a scalable path for creating high-quality educational content.

Large Language Models (LLMs) have significantly advanced smart education in the Artificial General Intelligence (AGI) era. A promising application lies in the automatic generalization of instructional design for curriculum and learning activities, focusing on two key aspects: (1) Customized Generation: generating niche-targeted teaching content based on students' varying learning abilities and states, and (2) Intelligent Optimization: iteratively optimizing content based on feedback from learning effectiveness or test scores. Currently, a single large LLM cannot effectively manage the entire process, posing a challenge for designing intelligent teaching plans. To address these issues, we developed EduPlanner, an LLM-based multi-agent system comprising an evaluator agent, an optimizer agent, and a question analyst, working in adversarial collaboration to generate customized and intelligent instructional design for curriculum and learning activities. Taking mathematics lessons as our example, EduPlanner employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups, personalizing instructional design for curriculum and learning activities according to students' knowledge levels and learning abilities. Additionally, we introduce the CIDDP, an LLM-based five-dimensional evaluation module encompassing clarity, Integrity, Depth, Practicality, and Pertinence, to comprehensively assess mathematics lesson plan quality and bootstrap intelligent optimization. Experiments conducted on the GSM8K and Algebra datasets demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework. Our code is publicly available at https://github.com/Zc0812/Edu_Planner

For some time now, I have been wanting to write about some of the problems I observed during my time in the Learning Analytics world (which also crosses over into Artificial Intelligence, Personalization, Sentiment Analysis, and many other areas as well). I'm hesitant to do so because I know the pitchforks will come out, so I guess I should point out that all fields have problems. Even my main field of instructional design is far from perfect. Examining issues with in a field (should be) a healthy part of the growth of a field. So this will probably be a series of blog posts as I look at publications, conferences, videos, and other aspects of the LA/PA/ML/AI etc world that are in need of a critical examination.

We are already experiencing how AI is affecting some facets of our lives. The movement sensing lights that go on when you enter an unoccupied conference room or a parking area, intelligent sensors in cameras now that detect a smile and help take good pictures, cars with automatic parking features, Alexa switching on the living room lights, and Google adding items to your shopping list! We have personal assistants in our smartphones that we have rather got used to. The buzz around AI in eLearning has also acquired lot of weight in the last few years. Needless to say, AI is the primary topic of interest at most learning and technology conferences these days.

Organizations expend constant effort to deliver information employees need to know for their jobs. You depend on training to help your employees make more sales, provide better customer service, avoid regulatory issues, and make fewer mistakes. But training has no value if we can't retrieve the information we're taught. Training reinforcement is essential to ensure that knowledge and skills learned in training are applied on the job. If you are new to training reinforcement or a bit unfamiliar, here are seven key things to know.