The field of educational technology is witnessing a significant shift towards the integration of artificial intelligence (AI) and machine learning (ML) to enhance learning outcomes. Recent developments suggest a strong focus on creating adaptive, interactive, and personalized learning environments that can cater to diverse student needs. The use of large language models (LLMs) and neuro-symbolic systems is becoming increasingly prominent in the design of intelligent tutoring systems, automated assessment tools, and collaborative learning platforms. These advancements have the potential to revolutionize the way educational content is created, delivered, and evaluated. Noteworthy papers in this area include: Enabling Multi-Agent Systems as Learning Designers, which presents a novel approach to embedding pedagogical expertise into LLM systems for generating high-quality educational content. Toward Generalized Autonomous Agents, which introduces a neuro-symbolic AI framework for integrating social and technical support in education, demonstrating adaptability across domains.