With the rapid advancement of Large Language Models (LLMs) and Artificial Intelligence (AI) agents, agentic workflows are showing transformative potential in education. This study introduces the Agentic Workflow for Education (AWE), a four-component model comprising self-reflection, tool invocation, task planning, and multi-agent collaboration. We distinguish AWE from traditional LLM-based linear interactions and propose a theoretical framework grounded in the von Neumann Multi-Agent System (MAS) architecture. Through a paradigm shift from static prompt-response systems to dynamic, nonlinear workflows, AWE enables scalable, personalized, and collaborative task execution. We further identify four core application domains: integrated learning environments, personalized AI-assisted learning, simulation-based experimentation, and data-driven decision-making. A case study on automated math test generation shows that AWE-generated items are statistically comparable to real exam questions, validating the model's effectiveness. AWE offers a promising path toward reducing teacher workload, enhancing instructional quality, and enabling broader educational innovation.

In the evolving landscape of education, robotics has emerged as a powerful tool for fostering creativity, critical thinking, and problem-solving skills among students of all ages. This innovative approach to learning seamlessly integrates STEM (Science, Technology, Engineering, and Mathematics) concepts, creating an engaging and immersive learning experience. Educational robotics transcends traditional classroom settings, transforming learning into a hands-on, experiential endeavor. Students are actively involved in the design, construction, and programming of robots, allowing them to apply theoretical concepts to practical applications. This hands-on approach fosters deeper understanding and retention of knowledge, making learning more meaningful and enjoyable. In this paper, the potential of simulation robotics is evaluated as a hands on interactive learning experience that goes beyond traditional robotic classroom methods.

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning. A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy.

DeepLearning.Ai is founded by the AI pioneer -- Andrew Ng, who is also the Co-Founder of Coursera, DeepLearning AI and Adjunct Professor at Stanford University. Through the guided series of lectures and exercises, you will build the cognitive skills you need to deeply understand the concepts and applications of AI.