What Are the Essential Conditions for Implementing AI in the Education Sector?

[baoshi.rao]

This article uses educational robots as an example to explore how they can gradually become effective tools for AI technology and comprehensive practice courses in primary and secondary schools.

The iterative development of information technology continues to drive innovation in education. With the advancement of artificial intelligence (AI), the implementation of AI in the education sector has gradually become a trend. More and more AI companies or educational institutions are actively exploring application scenarios for AI in education. However, how can AI technology truly enter the classroom? What prerequisites are needed, and what practical challenges must be addressed?

Taking educational robots as an example, we will explore how they can become effective tools for AI technology and comprehensive practice courses in primary and secondary schools.

Robotics education refers to an emerging educational approach that involves designing, assembling, programming, and operating robots to complete predefined tasks, thereby stimulating students' interest and cultivating their comprehensive abilities. Robotics technology integrates numerous advanced technologies such as mechanical principles, intelligent sensors, natural language processing, and computer vision, covering multiple fields of AI. It can be said that introducing educational robots is an excellent platform for implementing AI technology in education.

In recent years, the application scope of robots has expanded continuously, and many robotics companies view the education sector as an indispensable market. However, in practical applications, there are still many challenges in terms of hardware support, teaching materials, and teacher resources.

Educational robots should be regarded as auxiliary teaching devices. They are designed as versatile AI teaching platforms to help students learn various related subjects and participate in scientific innovation competitions for hands-on practice.

Through robots, students can gain a comprehensive understanding of advanced technologies in fields such as mechanics, electronics, sensors, computer hardware and software, bionics, and AI. While acquiring scientific knowledge, students can also develop their imagination, creativity, and practical skills by exploring real-life applications of robots in learning and daily life.

Therefore, to achieve the comprehensive implementation of robots in education, preparations must be made based on the current educational environment and students' age groups. These include:

1. Hardware and technical support for the robots themselves.
2. The design of teaching materials.
3. Considerations for in-class teaching scenarios.
4. Preparation of teaching staff.

First, let’s discuss the hardware and technical support for the robots.

Educational robots are designed for the education sector. Teaching robotics as a subject should treat robots as a scientific discipline, helping students master basic knowledge and skills through specialized instruction.

The teaching objectives should include basic knowledge such as robot hardware structure, assembly and disassembly, software engineering, mechanical principles, and electronic technology. The functions and applications of robots should serve as extended knowledge. Through robot programming and software development, students can eventually develop hands-on and creative abilities.

However, current robotics education in the field faces challenges. Many AI educational tools and teaching solutions have emerged, but their quality varies due to various constraints. Even major companies and institutions lack practical application scenarios to support their offerings.

Some solutions involve simple electronic building blocks and component assembly, which cannot support complex AI technology teaching. Others are expensive, with limited and fixed interface designs that hinder peripheral expansion and more complex functional scenarios.

Therefore, the hardware and software design of educational robots should aim to popularize AI knowledge, stimulate students' interest, and cultivate their hands-on abilities. In addition to the robot body itself, there should be corresponding control and learning software.

From a hardware perspective, the robot’s design should appeal to students while ensuring easy assembly and disassembly in school environments. Modular designs should be open and adaptable for teaching purposes, with considerations for platform openness, reconfigurable ports, and customizable interface types through MCU integration. This allows students to freely configure port resources based on their creative needs, enhancing flexibility and scalability.

From a software perspective, a well-designed control system should be provided, along with intelligent programming software to enable robots to perform tasks based on instructions. This includes system boot programs, human-machine interaction software, and visual programming tools.

Next, let’s discuss teaching materials.

Currently, the quality of robotics teaching materials on the market is generally low, often resembling "product manuals" or "user guides" without sufficient input from curriculum and teaching experts. Additionally, the division of robotics teaching materials for primary, middle, and high school students is unclear and lacks distinct features.

Robotics is a highly complex integration of AI technologies. How can we effectively teach students and equip them with practical skills?

To achieve this, substantial technical support is required, such as:

• Systematic breakdown of knowledge points. AI is a vast knowledge system involving multiple disciplines. AI education should be technology-driven, with precise identification of knowledge gaps, followed by explanations, targeted exercises, and tests.

For example, in robotics education, course design should cover:

• Learning "natural language processing," including basic concepts of speech recognition, converting speech to text, and its applications like voice typing, customer service, and voice control. Practical applications, such as voice-to-text input methods or robot voice control, should follow the steps of "basic knowledge learning → functional application interpretation → real-life scenario practice."

For sensor teaching, common sensors like touch or infrared sensors are familiar to students. Teaching should combine basic knowledge with real-life applications, such as creating an infrared-controlled night light. Such hands-on experiments can enhance students' interest and understanding.

In the future, robots will undoubtedly play a significant role in our lives and learning. By integrating real-life scenarios and hands-on practice, students can experience the joy of learning and develop the knowledge and skills needed for the robotics era.

Now, let’s consider in-class teaching scenarios. This involves more detailed and complex work. When introducing robotics education to schools, practical issues must be addressed, such as:

• How robots will be used in the classroom—as primary teaching tools, assistants, companions, or intelligent aids.
• Whether the school can only accommodate one robotics classroom.
• The number of students per class and robots required.
• The number of classes per week and the grade levels involved.

These factors directly impact lesson design, class duration, and hardware/software planning for interactions. They also involve backend management for teaching and student platforms.

For example, the number of students per class determines how many robot permissions are needed. If a robotics classroom operates in shifts, teacher and student account systems must be designed and managed accordingly. Robotics teaching management also includes planning, organizing, coordinating, and controlling classroom and administrative activities, which cannot be overlooked.

Finally, let’s discuss the preparation of teaching staff.

For robotics education to fully integrate into the educational field, teacher involvement is indispensable. Many schools currently assign AI classes to science or computer teachers, necessitating an upgrade in their knowledge structure.

For teachers to effectively collaborate with robots and successfully complete teaching tasks, this is a substantial undertaking that also requires them to possess certain AI and programming knowledge. In the initial stages, robot manufacturers can provide technical guidance and training support. However, in the long run, schools will need to equip themselves with specialized teachers.

The advancement of science and technology will undoubtedly accelerate educational progress, and in turn, the development of education will drive the advancement of technological applications. Is it AI empowering education, or education empowering AI?

I believe anything is possible—let's wait and see.

(The views expressed in this article are solely the author's personal opinions. There are still many areas that need improvement, and constructive feedback is welcome.)