Content area
Currently, generative AI technologies represented by ChatGPT are experiencing rapid development and, with their powerful data processing and content generation capabilities, are penetrating various industries, significantly altering traditional in-dustry ecosystems. The education sector is no exception. With the support of gen-erative AI technology, intelligent tutoring and answering, adaptive learning path optimization, and automated homework assessment and feedback have become possible, providing strong technical support for personalized learning. However, this also brings about issues such as educational equity and the cultivation of think-ing skills. Based on this, this article first analyzes the positive impacts of genera-tive AI technology on the education sector, including personalized learning expe-riences, enriched teaching resources, and improved teaching efficiency. Then, it delves into several key points of the application of generative AI technology in the education sector, aiming to maximize the educational value of generative AI tech-nology, effectively avoid potential risks, and enable students to acquire knowledge of high quality, promoting the modernization of education.
Abstract
Currently, generative AI technologies represented by ChatGPT are experiencing rapid development and, with their powerful data processing and content generation capabilities, are penetrating various industries, significantly altering traditional in-dustry ecosystems. The education sector is no exception. With the support of gen-erative AI technology, intelligent tutoring and answering, adaptive learning path optimization, and automated homework assessment and feedback have become possible, providing strong technical support for personalized learning. However, this also brings about issues such as educational equity and the cultivation of think-ing skills. Based on this, this article first analyzes the positive impacts of genera-tive AI technology on the education sector, including personalized learning expe-riences, enriched teaching resources, and improved teaching efficiency. Then, it delves into several key points of the application of generative AI technology in the education sector, aiming to maximize the educational value of generative AI tech-nology, effectively avoid potential risks, and enable students to acquire knowledge of high quality, promoting the modernization of education.
Keywords
Generative AI technology; ChatGPT; Primary and secondary education
Generative AI is an emerging technology, represented by tools such as ChatGPT, Wenxin Yiyan, and Claude. Its most notable advantages lie in data processing and content generation capabilities. Its deep application in the edu-cation sector has not only reshaped the knowledge transmission process, learning assessment methods, and educa-tional management measures but also made it possible for educational resources to be generated automatically and for personalized tutoring. This is of great significance for promoting the all-around development of students.
1. The Positive Impacts of Generative AI Technology on the Education Sector
1.1 Personalized Learning Experience
In the traditional education model, teachers usually set a unified teaching plan and arrange teaching content. Due to the large number of students in a class, it is difficult to take into account the learning pace and cognitive level of each student. This "one-size-fits-all" teaching model fails to fully meet the learning needs of students at different ability levels, and some students may even develop a negative and reluctant attitude towards learning due to longterm inability to keep up with the learning progress (Shi, 2024). The emergence and deep application of generative AI technology in the education sector have brought about significant changes to primary and secondary education, effectively making up for the shortcomings of the traditional teaching model and truly achieving individualized teaching. This technology has outstanding data processing and analysis capabilities, which can collect students' learning behavior data to accurately identify their learning needs, cognitive characteristics, and weak points, and then push learning content and customize learning paths for them (Kong, Zhao, & Song, 2025). Additionally, gen-erative AI also has the function of intelligent tutoring. Simply put, when students encounter difficult problems in their studies, they only need to input the problem or upload the question, notes, screenshots of wrong answers, etc. into the system, and ChatGPT will provide an immediate answer, offering solution steps, related knowledge points, common misunderstandings, and chart analysis to help students smoothly overcome learning bottlenecks and firmly grasp knowledge on the basis of understanding.
1.2 Enriched Teaching Resources
The smooth and orderly implementation of teaching activities cannot be separated from the support of educational resources. Rich and sufficient teaching resources often help to stimulate students' interest in learning and their desire to explore, further optimizing teaching quality. In the past, teachers usually needed to invest a lot of time and energy in writing lesson plans, making courseware, and designing exercises during the preparation stage, which greatly increased the burden on teachers (Jauhiainen & Guerra, 2024). The application of generative artificial intelligence technology can effectively solve the above problems. When teachers prepare lessons, they only need to input the unit theme and core knowledge points into the system, and the system will automatically generate a clear-structured teaching plan, helping teachers quickly sort out teaching objectives, key points, difficulties, strategies, and interac-tive activities. At the same time, relying on the deep learning technology framework and large language learning models, generative artificial intelligence can intelligently generate course outlines, knowledge point explanations, exercises, and other texts, as well as generate corresponding videos, audios, animations, and other multimedia teach-ing resources based on specific teaching content, enriching teaching resources and improving their quality and ap-plication depth (Liu & He, 2024). With the assistance of generative artificial intelligence, teachers can significantly reduce the time spent on lesson preparation and resource development, liberating them from mechanical and repet-itive work and allowing them to invest the saved time in more educationally valuable areas, continuously improving the overall quality of teaching.
1.3 Enhancing Teaching Efficiency
In educational and teaching activities, efficiency is one of the key indicators, closely related to the timeliness of knowledge transmission and the effectiveness of student learning. In the traditional teaching model, teachers spend a lot of time on tasks such as grading, explaining knowledge points, and answering questions, resulting in low overall teaching efficiency. The application of generative artificial intelligence technology can assist teachers in automatically handling routine teaching tasks, comprehensively optimizing the teaching process, and significantly improving teaching efficiency, allowing teachers to have more time for personalized tutoring. Take homework grading as an example (Ke, Mi, & Bao, 2024). In practical applications, generative artificial intelligence technology, based on natural language processing and machine learning algorithms, can not only quickly determine the correct-ness of answers but also break down the problem-solving steps, associate knowledge points, and point out logical flaws, promoting students to correct mistakes in a timely manner and achieve autonomous deep learning. For teach-ers, they can use generative artificial intelligence to check students' homework completion online at any time, ena-bling them to promptly identify individual and common problems and make appropriate adjustments to subsequent teaching plans and strategies, further enhancing the attractiveness of the classroom and striving to create an efficient classroom.
2. Key Points of the Application of Generative Artificial Intelligence Technology in Education
2.1 Balancing Technology and Humanities
In the field of education, the application of generative artificial intelligence has brought significant changes to the traditional education model. In the practice of technology application, the teaching community must consciously find a balance between technology and the humanities, and must not neglect the essence of education by overly pursuing the advancement of technology. In educational practice, teachers should take the all-round development of students as the core, closely monitor students' growth from perspectives such as knowledge and skills, emotional attitudes, and values, and promote the deep integration of technological rationality and humanistic care (Zhou, Wei, & Zhang, 2024). For example, in personalized learning recommendations, teachers need to use generative artificial intelligence technology to analyze students' learning behavior data to understand their knowledge mastery, including whether there are cognitive breakpoints, correct answer rates, and thinking bottlenecks. Based on this, they can recommend learning content to students in a targeted manner to better adapt to their learning progress and needs, effectively cultivating students' higher-order thinking. Education is not only a means of transmitting knowledge but also a necessary process of shaping values and cultivating a healthy personality. When teachers carry out educational and teaching activities with the assistance of generative artificial intelligence, they must reposition themselves and actively play the role of "growth mentors", integrating care for students into daily teaching details, proactively communicating with students, and listening attentively to their inner thoughts to deeply understand the difficulties they encounter in learning. At the same time, through daily observation, classroom interaction, and after-class com-munication, teachers should be sensitive to students' subtle emotional changes and abnormal behaviors, so as to identify potential psychological problems in students in a timely manner and provide appropriate psychological counseling.
2.2 Strengthen Data Protection
Currently, generative artificial intelligence technology is increasingly applied in the field of education. Various intelligent teaching platforms and personalized learning systems have been deeply integrated into all aspects of education and teaching, significantly improving the overall quality and efficiency of education. However, due to the ambiguous boundaries of data collection, inconsistent storage standards, and insufficient security measures, there are also issues regarding the security of students' data, greatly increasing the risks of data leakage and abuse, as well as malicious attacks. In response to this phenomenon, government departments and relevant units must fully play their functional roles and promptly introduce regulations related to the security management of educational data. Based on the educational context, clear boundaries for data collection by generative artificial intelligence should be defined, such as prohibiting the collection of sensitive family information (home address, parents' occu-pation, income status, marital status), personal biometric data (fingerprints, facial recognition, iris scanning, voice-print), health and mental health data (medical records, psychological assessment results, disability status), and other unnecessary data. Emphasis should be placed on detailing technical standards for data storage, storage periods, and cleaning mechanisms, and urging all platforms and schools to enhance data protection efforts to minimize the risk of data leakage. Educational authorities should completely abandon the passive compliance mindset (Sun & Zhou, 2024). In addition to building a three-level protection system covering data collection, storage, and usage, they also need to emphasize the principle of minimum necessity at the institutional level, centering on data collection, and grant differentiated data access rights to teachers and staff. Their data access behaviors should be monitored in real-time to effectively safeguard students' digital rights and gradually build a solid data security barrier.
2.3 Enhance Teachers' Competence
As a new technology, generative artificial intelligence has reshaped educational models and learning paradigms, and accordingly, it has put forward stricter requirements for teachers' technical literacy and application capabilities (Cheng, 2024). To maximize the application effectiveness of generative artificial intelligence technology and truly serve the all-around development of students, schools must make more efforts in enhancing teachers' capabilities. Regularly organize teachers to participate in technical training activities, inviting AI technology experts, educational technology experts, and front-line AI education practitioners to introduce the core principles, development status, application prospects, and technical boundaries of generative artificial intelligence technology, especially its appli-cation in teaching scenarios such as intelligent lesson preparation, personalized learning support, and automated assessment. This should be combined with educational theories such as constructivism and adaptive learning to deeply explain the underlying logic of technology-enabled education. At the same time, lead teachers to conduct practical training based on real teaching cases, covering project design, test generation, homework grading, multi-media generation, and interdisciplinary integration, and provide professional guidance. Common problems should be analyzed in depth to help the teacher group firmly master the technology and continuously improve their skills. Encourage teachers to make full use of their spare time to conduct self-study through online platforms, watching video tutorials, case collections, operation manuals, and taking knowledge tests and simulation exercises, gradually becoming proficient in using generative artificial intelligence technology.
2.4 Pay Attention to Educational Equity
Currently, generative artificial intelligence technology is developing rapidly and is increasingly applied in the field of education. However, while the popularization of technology can improve educational efficiency, enrich teaching methods, and promote personalized learning to a certain extent, it may also exacerbate educational inequality. In some economically underdeveloped regions, many schools have insufficient network coverage and limited hardware conditions, making it difficult for students to access high-quality educational resources. Especially for students from poor families, they may not even have the opportunity to come into contact with related technologies. In contrast, in regions with better economic development or high-income families, they have advanced equipment and learning tools, and can make the most of generative artificial intelligence for efficient learning. In response to this situation, when promoting generative artificial intelligence on a large scale, the issue of educational equity must be taken into account. Through multi-level intervention, educational inequality should be alleviated to ensure that generative ar-tificial intelligence truly becomes a tool for promoting educational equity. For students from low-income families, in addition to providing purchase subsidies for designated model terminal devices, it is also necessary to offer free borrowing services for generative artificial intelligence learning devices. The aim is to break down the technological barriers for students in remote areas and from low-income families, enabling them to equally access high-quality educational resources.
2.5 Strengthening Evaluation and Feedback
When applying generative artificial intelligence technology in the field of education, to ensure the maximum effec-tiveness of the technology and truly promote teaching optimization and learning improvement, it is necessary to establish a sound and quantifiable evaluation and feedback mechanism. In practice, this should start from three aspects: academic performance (knowledge mastery, higher-order thinking ability, and personalized learning adapt-ability), learning behavior (learning participation, interest changes, and autonomous learning ability), and teaching adaptability (teachers' usage experience, classroom interaction quality, and teaching efficiency improvement). Multi-dimensional evaluations of the application effectiveness of generative artificial intelligence technology in the field of education should be conducted. Structured learning data, such as correct answer rates, response times, dis-tribution of incorrect options, and frequently missed knowledge points, as well as unstructured data such as class-room videos and online learning behaviors, should be widely collected. With the support of educational data mining technology, students' learning patterns can be deeply analyzed to assist them in adjusting their learning paths and optimizing classroom interactions under data-driven conditions, making individualized teaching at scale possible. At the same time, effective feedback channels should be established to encourage teachers, parents, and students to promptly provide feedback on technical issues, educational adaptability issues, and ethical and security issues dur-ing the application process of generative artificial intelligence technology. This will help technicians accurately identify problem areas and provide a basis for technical improvements, content adjustments, and product optimiza-tions of generative artificial intelligence in education. Through this process, a complete closed loop can be formed among usage, feedback, and optimization.
3. Conclusion
In summary, the in-depth and extensive application of generative artificial intelligence technology in the field of education has reshaped the educational ecosystem, bringing about significant changes to teaching models and learn-ing methods. It not only promotes personalized learning but also enriches teaching resources and continuously im-proves teaching efficiency. However, while enjoying the convenience brought by technology, it is also necessary to remain highly vigilant about issues such as data leakage and academic ethics, consciously balance technology and humanity, and strengthen data protection. Regular teacher training, close attention to educational equity, and en-hanced evaluation and feedback should be carried out. Only in this way can generative artificial intelligence tech-nology truly serve the all-round development of students and promote the modernization of education.
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