How AI tools boost learning performance?

It's well known that we learn better through our own experiences. This approach is called experiential learning. This approach, developed in the 1980s by David Kolb, is grounded in the idea that learning occurs through a continuous cycle of experiencing, reflecting, conceptualizing, and applying.https://shorturl.at/cA0gn

In the context of modern education, particularly in online courses, integrating AI-powered feedback tools can significantly enhance this learning process. Research has shown that these tools can boost student performance by nearly 50%, with a notable example being a study involving 200,000 Australian students, which demonstrated substantial improvements when AI feedback was incorporated https://shorturl.at/8ev8z.

Let’s explore how AI can be leveraged to optimize each stage of Kolb’s learning cycle, creating a more effective and personalized learning experience.

Stage 1: Having the actual experience

When you do something or have a new specific experience, it becomes part of your learning process. For example, a child trying to stand up for the first time is gaining an actual experience. For adults, if you're learning how to do SEO, this might involve activities like collecting keywords or similar tasks. The key is that you need to actively engage in doing it.

In online courses, this stage often involves watching videos, completing tasks, or using simulators. Unfortunately, not all courses offer opportunities for real-world practice, which can limit how much learners gain from this stage. The responsibility for a practice is shared with learner.

Stage 2: Reflection on the experience

The learner reflects on the new experience in light of their existing knowledge. For example, you might realize that manually collecting keywords is inefficient and time-consuming, or that you don’t yet know how to select the right ones effectively.

Many modern platforms integrate quizzes, self-assessment forms, or peer reviews to encourage learners to reflect.

AI-powered feedback tools / AI coach can take this further by analyzing a learner's responses, offering tailored feedback, and pointing out areas for reflection.

Imagine a coding course where a student builds a simple application. After completing the task, the course could prompt the learner to reflect on the process with questions like:

• "What was the most challenging part of this task?"
• "What errors did you encounter, and how did you resolve them?"
• "What would you do differently next time?"

Stage 3: Learning from the experience

Reflection generates new ideas or leads to modifications of existing concepts, enabling the learner to gain deeper insights from their experience. At this stage, it’s essential to identify how to improve and refine one’s approach, often guided by expert advice or structured frameworks for learning.

A data analysis course could ask learners to revisit and correct a flawed analysis they previously submitted. The course would offer detailed, step-by-step feedback on what was done incorrectly and the rationale behind it. In online courses, this feedback can be delivered either through personalized human review or by leveraging AI-driven insights.

AI-powered feedback tools can offer immediate, actionable feedback (for example, smart suggests) to help learners improve. AI systems could further enhance this stage by dynamically tailoring expert advice to individual performance.

Stage 4: Trying out what you have learned

The final stage requires learners to apply their new knowledge and skills in practice. This could involve real-world tasks or simulations.

Many online courses encourage this but often leave the responsibility on the learner. For example, a course might suggest learners create a project or solve real-world problems but not follow up on whether it was completed.

AI coach can provide follow-up tasks based on past performance, track progress, and even offer reminders to ensure learners stay on track. Additionally, AI-powered coaching tools could provide encouragement, ensuring learners remain accountable and motivated as they practice.

The perfect combo: AI and human feedback

Most online courses greatly benefit from expert support and feedback. In my opinion, a combination of AI-driven tools and human input is the best approach for improving learning performance. AI can handle immediate, data-driven insights, while human experts can provide nuanced, empathetic guidance.

Human support could even be offered as an additional service for those seeking a deeper learning experience. This semi-automated approach would strike the perfect balance between scalability and personalization, drastically enhancing learning outcomes.

Has anyone tried using AI feedback tools or an AI coach in their learning process?

What are your thoughts or experiences with them?

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