Artificial intelligence (AI) is already transforming K-12 education in various ways—teachers use it for grading and data analysis, parents rely on chatbots for school-related queries, and students turn to AI for homework help. However, most current AI tools are task-specific, following predefined rules without much autonomy, according to the original article by Alexandria Ng, EdWeek Market Brief.
A more advanced form of AI, known as agentic AI, is emerging—one that can independently make decisions, set goals, and adapt strategies in real time. Unlike traditional AI, which might flag at-risk students or automate grading, agentic AI could dynamically adjust lesson plans based on student needs or even schedule interventions with counselors—all without direct human input.
A more advanced form of AI, known as agentic AI, is emerging—one that can independently make decisions, set goals, and adapt strategies in real time. Unlike traditional AI, which might flag at-risk students or automate grading, agentic AI could dynamically adjust lesson plans based on student needs or even schedule interventions with counselors—all without direct human input.
What Makes Agentic AI Different?
Charles Elliott, Head of Industry at Google Cloud, explains the distinction: "Let’s imagine two worlds. The first one is the more linear, narrow AI, like a chatbot... But in the world of an agentic AI system, you perhaps have agents that [say], ‘I’m going to block 15 minutes for you to read a little bit more about this thing before you get in the class. And by the way, I’m going to make sure you connect with this person ahead of time.’" This level of autonomy could revolutionize personalized learning. For example, if a student is nervous about an Advanced Placement (AP) course, agentic AI could provide tailored prep materials, suggest study strategies, and even prepare the teacher with relevant questions before class.
Potential Benefits and Risks
Agentic AI’s ability to "connect dots" using vast datasets could help identify learning gaps and recommend interventions. However, concerns about data privacy, bias, and accuracy remain. Elliott emphasizes that human oversight is still essential: "Agentic AI, like most successful AI systems today, absolutely requires humans in the loop. It’s [humans’] expertise, the empathy—all the things that AI is not really capable of. Agentic AI is meant to be assistive. It’s not there to sit in the class with a student. It’s just more about helping them engage."
Current and Future Applications
Some early use cases include:
Elliott advises developers to prioritize pedagogical principles over mere efficiency:
"You also want to make sure that you’re not just building confidence, but that you’re actually focusing on the pedagogical principles... When we define student success, it’s about skills acquisition, not just giving them answers." As agentic AI evolves, education companies must balance innovation with ethical considerations—ensuring that AI enhances learning without replacing the human touch that remains vital in education.
- Automated grading with feedback – AI can assess student work and suggest improvements while keeping teachers in the loop.
- Personalized learning adjustments – If a student struggles with a concept, AI could generate supplementary diagrams or podcasts.
- Proactive interventions – Identifying at-risk students and recommending support before issues escalate.
Elliott advises developers to prioritize pedagogical principles over mere efficiency:
"You also want to make sure that you’re not just building confidence, but that you’re actually focusing on the pedagogical principles... When we define student success, it’s about skills acquisition, not just giving them answers." As agentic AI evolves, education companies must balance innovation with ethical considerations—ensuring that AI enhances learning without replacing the human touch that remains vital in education.