Exploring and Practicing AI Empowerment in Education

2.1 Machine Perception Technology Empowering Teaching

Providing students with interactive learning opportunities through intelligent learning terminals is one of the typical methods of machine perception technology empowering education. It changes the original one-way knowledge transmission model primarily taught by teachers into a two-way information dissemination model of teaching and learning interaction. For example, utilizing various sensors to collect data on students’ learning processes is a specific manifestation of perception technology empowering teaching. This collected data can also be uploaded to a smart education platform for further deep mining, providing a basis for constructing personalized learning plans for students.

Using point-reading technology to achieve paper-based learning is a common learning mode for young children, and interactive point-reading can offer children exploratory learning opportunities, allowing younger children to engage in gamified learning while avoiding screens, thus providing a good technical solution for protecting children’s eyesight.

Perception technology empowering teaching can also utilize various sensors and structural accessories to support students in autonomously designing intelligent devices. By collecting data on these devices, monitoring and evaluating students’ practical results can be achieved.

2.2 Machine Cognition Technology Empowering Teaching

Teaching empowerment can be achieved through visual recognition or voice recognition technologies. For example, visual recognition technology can enable automatic grading of assignments, voice recognition technology can facilitate intelligent knowledge Q&A, and face and emotion recognition technologies can monitor students’ learning states. These technologies can be widely applied in various innovative teaching practices.

2.3 Machine Learning Technology Empowering Teaching

Machine learning technology empowers teaching by constructing machine learning algorithms to intelligently simulate some learning tasks, thus providing students with reference paths for accomplishing tasks, aiming to inspire students’ thinking. During exploratory learning processes, students often engage in many random explorations, making it difficult to respond to various situations in advance. Therefore, machine learning algorithms can be utilized to assist in guiding the exploratory learning process. For instance, in chess teaching, machine learning can be used to implement gaming algorithms, providing students with optional operations for reference.

2.4 Swarm Intelligence Technology Empowering Teaching

Constructing an IoT teaching system to empower teaching is one of the typical applications of swarm intelligence technology in education. A well-established IoT system can capture students’ learning data, and students’ self-constructed intelligent agents can be integrated into the IoT teaching system as terminal nodes for acquiring learning data, providing a practical teaching environment that supports students to “learn by doing,” “learn by using,” and “learn by creating.” For example, as shown in Figure 1, students can connect their designed IoT smart fan devices to the teaching system, uploading the data obtained from their creations to the smart education platform in real-time. This swarm intelligence technology supports various team-based practical teaching activities.

2.5 Human-Computer Interaction Technology Empowering Teaching

As shown in Figure 2, human-computer interaction technology empowering teaching can support students’ exploratory learning through the design of interactive teaching software. Utilizing various interaction methods such as visual, voice, and wearable devices presents a more engaging practical teaching format, which is beneficial for enhancing students’ learning interests and improving their autonomous learning abilities.

2.6 Knowledge Graph Technology Empowering Teaching

As shown in Figure 3, knowledge graph technology includes knowledge representation and modeling, knowledge acquisition, knowledge fusion, knowledge storage, knowledge computation, and knowledge operation and maintenance. Constructing knowledge graphs related to teaching allows computers to acquire knowledge and vividly present knowledge, clearly organizing and expressing the knowledge in textbooks and in the mind. Computers can generate test questions based on the stored knowledge and provide targeted assistance based on students’ responses. Knowledge graph technology enhances the systematization of teaching content and the interactivity of the teaching process.

In the practice of AI empowerment in education, the team has independently developed a teaching software design environment for Chinese programming—Han Language—and a remote practical teaching platform based on cloud-edge collaboration, providing strong support for deepening educational reform.

3.1 Development of Interactive Teaching Software

3.2 Remote Practical Teaching Platform: Development of Intelligent Teaching System Based on Cloud-Edge Collaboration

As shown in Figure 5, the teaching process adopts the form of “expert teacher + class tutor” for instruction, with expert tutors providing unified teaching online and class tutors maintaining order and answering simple questions offline. Students have their identity IDs, and the data generated during the learning process is uniformly uploaded to the cloud platform for storage and analysis.

3.3 Teaching Organization and Learning Evaluation Based on Learning Data

The teaching process includes group work and mutual evaluation. First, teachers design the framework of the teaching content, grouping students by different themes to complete different learning tasks, utilizing software and hardware learning resources to form group learning outcomes. Then, the intelligent teaching system collects and manages all outcomes uniformly, forming a learning management system that can be shared and interacted with online, open to all students. As shown in Figure 6, students can view, learn, test, and comment on other groups’ learning outcomes on this platform, while the platform automatically collects and organizes students’ scores, accuracy, and comments, achieving closed-loop management of the learning process.

The team actively utilizes its independently developed teaching software and systems for AI empowerment in education to conduct diverse practical explorations, achieving full coverage of education from kindergarten to university, as well as adult education AI empowerment (see Figure 7).

In practical explorations, the team actively advocates the concept of integrating science and art, aiming to cultivate students’ “three qualities” (scientific quality, technological quality, and artistic quality) and “three capabilities” (discovery capability, realization capability, and presentation capability). By constructing the “4T” (Tutors, Teachers, Trainees, and Teams) teaching model, the team implements a five-step teaching method of “Teach, Learn, Practice, Exhibit, Evaluate,” achieving interactive teaching led by teachers and centered on students.

4.1 AI Empowerment in Various Levels and Types of Education

4.1.1 Early Education Stage: Cultivating Comprehensive Abilities in Young Children

The educational practice resources for the early education stage include the “Listening Xiao Fang Robot” and its accompanying comprehensive ability cultivation courses suitable for young children, utilizing teaching assistant robots to tell stories to children and conducting teaching through teaching game software, while using intelligent systems to statistically analyze, upload, and analyze students’ learning situations.

The “Listening Xiao Fang Robot” is a self-developed humanoid robot with a cubic structure that supports assembly and transformation, suitable for exercising children’s hands-on abilities and imagination. The course adopts a point-reading format for interaction, with interactive activities conducted on paper, effectively avoiding the harm of electronic screens to children’s eyes, thus achieving protection for children’s eyesight.

4.1.2 Primary Education Stage: Fun Learning Series Courses

The educational practice activities at the primary education stage combine life-like scenarios, using a project-driven approach to cultivate students’ information awareness and computational thinking, enhancing their hands-on abilities and problem-solving skills. Educational themes include fun learning robots, fun learning programming, and fun learning IoT. The courses start with robot assembly, building, and cognitive skill training, combining software and hardware for programming thinking and ability training, utilizing self-developed modular and graphical programming software as programming tools. During the learning process, students continuously face challenges and solve practical problems, increasing their sense of achievement, building self-confidence, enjoying the joy of learning, and improving their learning interests.

4.1.3 Secondary Education Stage: Creative Robotics Series Courses

The educational practice activities at the secondary education stage adopt an exploratory learning model that combines teaching and practice, guiding students to participate in exploratory learning, enhancing their comprehensive qualities and practical abilities, enabling them to integrate learned content with practical applications. Educational themes include smart cars, robotic arms, and transforming robots, encompassing rich principles and application technologies of sensors, controllers, and actuators. Students master AI technology applications and creative development capabilities through processes of design, programming, assembly, and debugging, cultivating habits of using computational thinking to solve practical problems in learning and life, and fostering awareness of intelligent system design and management, equipping them with basic practical abilities in AI.

4.1.4 University Stage: Teaching Practices for Cultivating Creativity

The university stage focuses on teaching reforms aimed at cultivating creativity, utilizing general education in courses like AI and robotics, and promoting students’ creativity and comprehensive quality through PBL and OBE teaching models. Teaching activities are highly open, supporting students to engage in autonomous learning during practical explorations. By designing stages for data collection, problem design, knowledge sharing, and team testing, learning is promoted through assessment; through task-driven and collaborative learning, students have opportunities to complete various projects in teaching, such as principles and implementations of intelligent cloud platforms, serial robotic arm sorting systems, IoT fan systems, musical magic boxes, smart cars, and their control methods. In the processes of creativity, design, production, and sharing, students experience the rewards of learning, enhancing their interest in academics and igniting their research desires.

4.1.5 Adult Education: Taking Smart Transportation as an Example

AI empowerment in adult education is set based on practical needs, with various themes. Taking the life-based smart transportation project as an example, it cultivates social learners’ scientific quality, technological quality, and artistic quality through the format of “Question – Inquiry – Planning – Implementation – Presentation.”

The project adopts a gradual approach to training learners, from programming to light a lamp to achieving wireless control of sensors, from designing and producing single intelligent agents to comprehensive debugging of system integration, with learners analyzing the data information needed for centralized control of devices, designing wireless centralized control solutions, debugging centralized management programs, ultimately achieving a comprehensive presentation of the entire smart transportation system.

4.2 Diversified Learning Organization Empowered by AI

4.2.1 Gamified Learning: Quality Education for Young Children

The early childhood project provides learning experiences for young children through gamified methods. This project uses the story background of “Fang Fang’s Adventure” to immerse children in this wonderful and fantastic story, making them the protagonists of the story. Each lesson is an adventure where children experience “challenges” with Fang Fang and think of “solutions,” ultimately using their intelligence to overcome the challenges.

The project employs three stages of teaching: “Learn, Play, Compete,” which involve self-exploration, self-study and testing, and collective game competitions. Students utilize robots to point-read and explore, obtaining story details and knowledge; then the robot asks students questions, and students need to find the correct answers to verify what they have learned; finally, the teacher organizes all students for collective game interactions, collecting students’ learning data to grasp their learning situations.

4.2.2 Competitive Learning: Innovations in Ideological and Political Education

To promote grassroots party building work and deepen the understanding of party history knowledge among party members, an interactive learning competition system for party knowledge has been developed based on intelligent technology, celebrating the 100th anniversary of the founding of the Communist Party of China. The system consists of point-reading robots, paper coding systems, and interactive learning software, forming a whole through network connections. It supports team learning, interactive learning, and collective competitions, verifying learning outcomes through “learning while testing”; through group learning and competition, it greatly enhances learners’ enthusiasm for learning and improves the effectiveness of grassroots party building activities.

4.2.3 Organizational Learning: Innovations in Professional Course Teaching

Addressing the reality of disconnection between theory and practice in university courses, a comprehensive course reform idea has been proposed. During the construction of the “Fundamentals of Robotics” course, the development and application of interactive teaching software have validated the realization of remote PBL model teaching based on the internet. The course breaks conventional norms and boldly reforms, achieving innovation in practical courses.

Through AI empowerment in education, the previous model of “teacher speaking, students listening” has been supplemented with “students speaking, teachers evaluating”. Under teachers’ guidance, students can complete the creativity, design, and implementation of intelligent systems in a short time, achieving innovation in teaching organization. This reflects the teaching philosophy of teachers leading and students being the main body, fully mobilizing students’ initiative in learning and stimulating their creativity; on the other hand, it reduces the repetitive labor of teachers, providing more time for teachers to enhance their comprehensive abilities. Overall, it allows both teachers and students to gain a greater sense of achievement in the teaching process, making the teaching process no longer dull and tedious.