AI's role in education: How AI is changing the way we learn

In the field of education, the adaptability of artificial intelligence (AI) has attracted deep attention. Learners, educators and policymakers alike expect technology to align with and build on research-based understandings of learning, particularly to support learning for students impacted by the pandemic.

A key insight of AI-based learning is its adaptability. Technology has the role of “meeting student needs, building on student strengths, and growing student knowledge and skills.” However, adaptations can sometimes be too specific and limited, and important parts of learning can be missed or underdeveloped. Therefore, an important goal is to work "toward comprehensive AI models that adapt to a vision of learning and avoid limiting learning to what the AI ​​is currently capable of modeling well."

Even advances in "large language models" have their limits. AI models are narrower, the learning environment may change; there is a lack of "common sense" human judgment; all of which may lead to AI responses that are "unnatural or incorrect." New AI designs must take this into account.

While Intelligent Tutoring Systems (ITS) have made significant advances in giving feedback to students, the importance of human teachers motivating students and helping them self-regulate remains important. Human teachers understand their students better than most education technology and are able to identify a "teachable moment" that AI cannot.

In order to expand the core model of the AI ​​system, we can start from the following points:

1. Student-centered adaptive design

In adaptive design, The AI ​​system can analyze students' learning data, identify their points of interest, and provide them with relevant learning materials and tasks. For example, if a student shows a strong interest in the Tang Dynasty when learning history, the AI ​​system can automatically adjust the learning path and provide more learning materials and tasks about the culture of the Tang Dynasty. This personalized learning path can increase student motivation and engagement. In a study involving 1,000 students, students who used this adaptive design improved their learning by an average of 15%.

2. Inclusion and diversity

AI systems can adapt to students with different language backgrounds by providing learning materials in multiple languages. For example, if a student is from Guangdong and usually speaks Cantonese, the AI ​​system can provide Cantonese versions of learning materials to help them better understand and master knowledge. In addition, the AI ​​system can also provide appropriate learning support and resources based on students' learning progress and abilities to meet their personalized learning needs. In a foreign study involving 500 students from multilingual backgrounds, students who used this inclusive and diverse design improved their learning effectiveness by an average of 20%.

3. Innovation and openness

The AI ​​system can encourage students to explore and innovate by providing open project tasks. For example, if a student shows a strong interest in environmental protection while studying science, the AI ​​system can provide an open-ended task about designing and implementing an environmentally friendly project. In the process of completing this task, students can not only learn and master relevant scientific knowledge, but also develop their innovative thinking and problem-solving abilities. In a study involving 2,000 students, students who used this innovative and open-ended design improved their creativity and problem-solving skills by an average of 25%.

4. Extended Assessment and Feedback

AI systems can help students understand their mistakes and how to correct them by providing detailed feedback. For example, if a student is having trouble solving a math problem, the AI ​​system can provide detailed feedback and suggestions to help the student understand their mistakes and guide them on how to correct them. This kind of detailed feedback can help students gain a deeper understanding of knowledge and improve their problem-solving skills. In a study involving 1,500 students, students who used this extended assessment and feedback design improved their problem-solving skills by an average of 30%.

On AI in education, "two broad views" have emerged: "AI supports student learning" and "Supports learning about AI and related technologies." On the latter front, it emphasizes the importance of students being educated about AI, not only about what it can do, but also about the risks it poses.

Research and development efforts attempt to address AI learning strategy recommendations. Our key recommendations are to reveal the strengths and limitations of AI models in upcoming edtech products and focus on AI models that closely align with the learning vision. AI is developing rapidly now, and we should distinguish between products with simple AI features and products with more complex AI models.

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