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Highlights AI robot image recognition technology is a method that uses computer vision and machine learning algorithms to identify and analyze objects, features, and relationships in images.Image recognition technology mainly includesimage digitization, preprocessing, feature extraction, classification, and recognition (application of image models)and other processes. By analyzing the pixel values, colors, edges, and textures of images, relevant information is extracted, and then classifiers or deep learning models are used for matching to identify different objects in the image, ultimately determining the object or scene represented by the image.
How Does Image Recognition Technology Assist Science Teaching?
The role of AI robot image recognition technology in science teaching mainly includes the following aspects:
Intuitive Understanding of Complex Scientific Concepts
Teachers can use AI image recognition technology to assist in explaining complex scientific concepts, such as cell division. By displaying cell images and dynamic simulations, students can intuitively observe the morphology of the various stages of cell division, thereby deepening their understanding of this complex process.[1]
Mastering the Principles and Applications of Image Recognition Technology
Students learn the basic principles of image recognition technology through processes such as preprocessing, feature extraction, classification, and recognition. They then engage in practical operations, such as using tools like the VIA Pixetto visual sensor, to conduct AI and machine learning projects to identify different types of cell images, thus mastering the use of image recognition technology.
Stimulating Learning Motivation and Mastering New Knowledge
Teachers can leverage image models provided by AI open platforms to guide students in identifying different categories of images. Students can use microscopes to observe cells and train the AI image recognition system to identify different stages of cell division, thereby experiencing the application of AI image recognition technology in education, deepening their understanding and mastery of new knowledge, and enhancing their interest and motivation for learning.[2]
2
Experimental Research
Teaching Effect of Image Recognition Technology
Pei-Yu Chen, Yuan-Chen Liu, and others[3] adopted a quasi-experimental research design to explore the impact of using AI robot image recognition technology system teaching on students’ learning situations and learning motivation. The experiment selected 81 seventh-grade students from four classes in a rural junior high school in Taiwan as research subjects, dividing them into an experimental group and a control group. The experimental group used AI to learn the concept of cell division equipped with visual sensors, while the control group relied on textbooks for learning. The study used the “Cell Division Two-Stage Diagnostic Test” and the “Science Learning Motivation Scale” for pre- and post-tests to assess students’ understanding of cell division and their scientific learning motivation.
Detailed Steps of the Experiment
The specific steps are as follows:
Figure 1 Operation Steps
1
Model Definition and Design
Define the machine learning model and specify it for identifying whether it is mitosis or meiosis. The model identifies and classifies various aspects of the cell division process and specifies the number of images or videos that need to be uploaded.
2
Media Upload
Students upload pictures or videos of the cell division process, choosing to upload different types of cells to enrich the training dataset and enhance the robustness of the model.
3
Start Training
After completing the media upload, users can click “Start Training” to initiate the training process. The model adjusts and refines based on the provided images and videos, enabling it to accurately identify and classify the features of cell division.
4
Student Application
Teachers first spend 10 minutes teaching the course topic and the concepts students need to learn. Then, students are divided into groups of four to learn the concept of cell division, and under the teacher’s guidance, they use robots equipped with the VIA Pixetto visual sensor to train the AI image recognition system and input pattern recognition results.
5
Practical Operation
Students engage in practical operations, such as using microscopes to observe cells and utilizing AI image recognition technology to identify different stages of cell division, thus gaining a more intuitive understanding of the cell division process.
6
Feedback and Assessment
At the end of the learning process, students are given a post-test questionnaire to evaluate their understanding of cell division and their scientific learning motivation.
3
Experimental Results and Analysis
After the experiment, the collected data were analyzed. Figure 2 shows a line graph of the changes in scientific concepts for the experimental and control groups in the first to fifth questions, indicating that the experimental group had significantly higher average scores in the post-test of the cell division concept compared to the control group, suggesting that using AI robot image recognition technology enhances students’ understanding of cell division concepts more than textbook learning.
Figure 2 “Cell Division Two-Stage Diagnostic Test” Pre- and Post-Test
(Changes in scientific concepts for each question between the two groups)
Moreover, regarding learning motivation, the experimental group also performed better than the control group. Figure 3 shows the scores of the two groups in the pre-test and post-test of the scientific learning motivation scale. The two groups showed no significant differences in the pre-test, but in the post-test, the experimental group’s average score was significantly higher than that of the control group. This indicates that this technology has a certain promoting effect on students’ motivation to learn science.
Conclusion
It can be seen that AI robot image recognition technology can promote students’ understanding of complex scientific concepts and stimulate their enthusiasm for learning. It has certain prospects in science teaching practice. With the continuous development of technology, AI will continuously empower education and inject fresh blood into education.[4]
References:
[1]Hung V, Fung D. The effectiveness of hybrid dynamic visualization in learning genetics in a Hong Kong secondary school[J]. Research in Science & Technological Education, 2017:1-22.
[2]Zawacki-Richter O, Victoria I. Marín, Bond M, et al. Systematic review of research on artificial intelligence applications in higher education – where are the educators? [J]. International Journal of Educational Technology in Higher Education, 2019.
[3]Chen Y P, Liu C Y. Impact of AI robot image recognition technology on improving students’ conceptual understanding of cell division and science learning motivation[J]. Journal of Baltic Science Education, 2024, 23(2):208-220.
[4]Chiu T K F, Xia Q, Zhou X, et al. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education[J]. Computers and Education: Artificial Intelligence, 2023, 4.
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