Every teacher being able to open and use generative artificial intelligence software is the first step in utilizing generative artificial intelligence in teaching. Readers are encouraged to try it themselves by logging in and registering to use the generative artificial intelligence model approved by the National Cyberspace Administration that is open to the public in China. You can send your thoughts to generative artificial intelligence through prompts, and the AI will respond instantly.

It is not difficult to find that regardless of the educational issue you are concerned about, generative artificial intelligence can provide you with unexpected responses. The more specific and precise your prompt design, the more specific and precise its responses will be. This human-machine dialogue can broaden your original thinking, but it may also be inaccurate, or even “seriously nonsense,” with a great deal of uncertainty, requiring you to think and judge independently.

This learning process based on generative artificial intelligence is very similar to the process of playing chess: you, as one side of the game, give AI prompts, just like placing a piece on the chessboard. The AI does not know how you will ask beforehand; then it will calculate based on your prompts and respond to you. For you, how the AI responds is also unpredictable and uncertain; then you can ask follow-up questions based on the other party’s response and place another piece for them. For the AI, this is also unpredictable; it will calculate based on your current piece and respond again… This iterative cycle is full of uncertainty and deep interaction, continuously stimulating the imagination and interest of both parties. This is why chess has maintained its charm throughout human history and why generative artificial intelligence differs from past teaching methods and applications of information technology.

When generative artificial intelligence is applied to the field of education, regardless of the learning theories and teaching needs you study and practice, it ultimately falls on the biggest difference between generative artificial intelligence and other technologies: Artificial Intelligence Generated Content (AIGC). Due to the sudden emergence of generative artificial intelligence, there are currently no discussions about the application of generative artificial intelligence in teaching in classic works on pedagogy, teaching theory, curriculum theory, educational psychology, and learning science. Currently, in various teacher training activities, to explain the new developments of generative artificial intelligence in teaching to frontline teachers, we refer to this new learning method as Generative Inquiry-Based Learning.

What is Generative Inquiry-Based Learning? Generative Inquiry-Based Learning refers to a teaching method where teachers and students, in teaching activities, strictly follow national laws, ethical standards, and information security requirements to reasonably apply generative artificial intelligence to assist teaching, and promote optimized learning through learners’ independent inquiry, critical thinking, and creative thinking.

Generative Inquiry-Based Learning is a student-centered teaching method in the environment of generative artificial intelligence, emphasizing students’ independent inquiry, practical operations, and constructing knowledge systems through human-machine interactive dialogue and cooperation and communication between teachers and students. It requires students to use generative artificial intelligence to assist teaching under the guidance, support, and control of teachers, aiming to cultivate students’ critical thinking, problem-solving ability, teamwork spirit, and innovative thinking. This is a human-machine wisdom learning method that combines human wisdom with machine wisdom. The English term for Generative Inquiry-Based Learning is Generative Quest Learning, abbreviated as GenQuest or GQL.

The Generative Inquiry-Based Learning model includes the following six steps, as shown in Figure 1, among which the dialogue exploration step is the most attractive and challenging.

First, Stimulating Interest. Teachers stimulate students’ learning interests in various ways, such as creating problem situations to guide students’ learning interests. The problem situations should be closely related to real life and connected with curriculum content and learning tasks. If the problems are open-ended and based on real-life situations, they can better stimulate students’ initiative to explore.Second, Task Assignment. Teachers clearly assign tasks for generative inquiry-based learning based on curriculum standards and teaching objectives, guiding and controlling students’ learning processes through task-driven methods.Third, Dialogue Exploration. Under the guidance of teachers, students focus on the learning tasks to interact with generative artificial intelligence in dialogue, recording their critical thinking in a chain of thought dialogue and exploring through various methods. For example, five basic search types (Baidu, Bing, WeChat article search, Bilibili video search, CNKI journal paper search), peer communication in groups, consulting teachers, parents, and domain experts, conducting experiments, and natural and social surveys. They record their learning experiences and fill out learning sheets. Learning sheets are scaffolds prepared by teachers in advance to guide students through the learning process, specifically detailing the steps and process records for generative inquiry-based learning under the defined teaching objectives, which can be used for process evaluation.Fourth, Transfer Practice. Students independently think and solve problems in new situational problems, completing assignments and exercises assigned by teachers.Fifth, Conclusion Sharing. Students summarize their learning gains, integrating their experiential learning into rational cognitive structures and sharing their learning outcomes and research conclusions with group members. Sixth, Evaluation Feedback. Teachers provide learning evaluations and suggestions for further development.

Generative Inquiry-Based Learning allows learners to interactively learn with AI through chain-of-thought dialogues. A thought chain is a strategy used by artificial intelligence to process complex tasks, breaking down a complex large task into smaller tasks containing multiple intermediate steps through a series of sequentially related instructions, each small task guided by relatively simple instructions to assist the model in generating and solving complex logical reasoning tasks. In teaching, the chain-of-thought dialogue between teachers, students, and generative artificial intelligence refers to the user asking a series of continuous questions that progress from superficial to deeper, like “squeezing toothpaste” or “peeling an onion,” gradually forming a human-machine dialogue approach to solve complex problems.

This game-like thinking training allows teachers and students to gain a completely different learning experience in their interactions with generative artificial intelligence compared to past computer-assisted teaching and the use of digital educational resource platforms. For example, in generative inquiry-based learning activities, teachers and students develop independent thinking, critical thinking, decision-making thinking, challenging thinking, human-machine collaborative thinking, holistic system thinking, resilience qualities of “not being greedy for victory,” rigorous reasoning thinking, unconventional thinking, innovative thinking, and contextual thinking that considers the development of things in time and space, deep learning thinking of “calculating more wins than less,” and philosophical thinking of “chess is like life,” gaining insights that cannot be learned through other teaching methods.

Mr. Li Bingde summarizes teaching activities into seven elements, as shown in Figure 2. What changes will occur if each teaching element is combined with generative artificial intelligence?

Every Teacher Has an Intelligent Teaching Assistant. By mastering prompt design skills, teachers can issue appropriate prompts to AI, and generative artificial intelligence can assist teachers in many tasks, just like a wise and capable teaching assistant. For example, grading assignments, writing lesson plans, designing exercises, creating PPT presentations, writing research articles, designing micro-videos, drafting various work documents, writing program code, designing and managing experiments, etc., thus improving teachers’ work efficiency and reducing their burdens while enhancing quality. In the near future, any teacher can have a personal assistant driven by artificial intelligence, far surpassing today’s information technology.

Every Student Has an Intelligent Mentor and Study Partner. Students using generative artificial intelligence can ask knowledge-related questions one-on-one and receive help; it can provide writing ideas, polish essays, enhance imagination in writing; offer varied practice assignments, enriching multiple solution approaches; provide learning suggestions for project-based learning; offer examples for writing program code; serve as a partner and mentor for English conversational learning; participate in large unit and interdisciplinary learning activities, with AI becoming an intelligent partner that answers students’ questions, provides learning reference resources, and promotes personalized teaching and tailored instruction. Recently, I saw reports from remote schools in western Guizhou and Sichuan where teachers organized students to interact with generative dialogue, allowing children in the mountains to see a different world. Generative artificial intelligence has brought personalized mentors and study partners to students, effectively helping to narrow educational inequalities and urban-rural gaps.

Every Principal Has an Intelligent Assistant. Generative artificial intelligence assists principals in designing various educational management documents; providing reference strategies for educational governance; searching for educational resource websites; building campus culture; aiding in writing educational research papers; suggesting work for the school; providing reference plans for school environment design; offering suggestions for the development of educational information technology; and even assisting schools in drafting management measures for the application of generative artificial intelligence in education. Various assistants in schools can share large and small model data, connect with each other, forming an educational assistant group capable of providing intelligent services around the clock.

Every Textbook is Accompanied by a Generative Artificial Intelligence Mini-Model Assistant. Reshaping educational publishing with generative artificial intelligence is a new track for various textbook publishers in the digital textbook publishing transformation. Based on past multimedia textbooks and cloud-based digital textbooks, and grounded in the print version of textbooks, generative artificial intelligence mini-models tailored to different textbook versions are developed and trained, combined with digital human technology, metaverse virtual learning space technology, speech synthesis technology, etc., to provide intelligent learning assistants for students. This requires collaboration between publishers and large model developers to provide a “mini-model editing training system” for textbook publishing editors, assisting users in customizing scenarios; using proprietary data to train mini-models, allowing users to attach exclusive knowledge bases, and optimizing/evaluating prompts, relying on large models to provide algorithms and computing power to enhance the efficiency of users training their exclusive mini-models. Similar to how Microsoft Office has become a basic productivity tool for knowledge workers worldwide, the mini-model editing training system will become a productivity tool for editors engaged in book publishing to train textbook mini-models, transforming publishers from traditional print book publishing institutions into new publishing organizations that produce multimedia textbooks + intelligent learning assistants. This will be a disruptive revolution in textbook construction.

The Curriculum System is Developing Toward Generative Courses. In domestic classic curriculum teaching literature, there are references to “curriculum” in ancient Chinese texts. A curriculum is the totality of educational content beneficial to physical and mental development, obtained under the planned and organized guidance of educators through interaction between the educated and the educational context. The curriculum is the sum of teaching content and processes. It encompasses the outlines and goal systems of classroom teaching, extracurricular learning, and self-study activities, representing an overall plan and process for teaching and various learning activities. It is evident that the common characteristic of curricula is that they are pre-designed, planned, and compiled learning content. Now, the sudden emergence of generative artificial intelligence has broken the conventional strict planning and pre-setting of curricula that have lasted for centuries, creating a new form of curriculum through generation, which we call generative curriculum.

The generative curriculum in the era of artificial intelligence refers to the dynamic generation of new learning content, including text, images, audio, video, code, etc., through the chain-of-thought dialogue between teachers and students with generative artificial intelligence, changing the modes of teaching and learning activities, and reshaping curriculum resources and teaching structures. The learning content and curriculum resources of generative courses are generated during the human-machine interactive dialogue process, which cannot be predicted by the pre-planned curriculum compilation and print textbook publishing processes. This intelligent generation method subverts people’s cognitive models of traditional curriculum materials. Table 2 compares the differences between traditional curriculum materials and generative courses.

Generative artificial intelligence is integrated into various aspects of the curriculum, and the course learning content resources generated through AI and teacher-student interactive dialogue possess characteristics of generativity, uncertainty, and richness, allowing teachers to link educational theories, teaching strategies, and subject teaching training datasets to curriculum design, resource generation, and teaching activity management, reinforcing AI’s understanding of curriculum teaching content and its adaptive learning resource generation capabilities, forming a complementary relationship with traditional curriculum resources. This is the new track for future curriculum construction.

(1) Top-Level Design

Every school must formulate management measures for the application of generative artificial intelligence in education. This is the premise that schools must first consider in light of the rapid development of generative artificial intelligence. Due to the powerful content generation capabilities of generative artificial intelligence, there are also unpredictable risks and dangers. The UNESCO’s “Guidelines for Using Generative Artificial Intelligence in Education and Research” repeatedly emphasizes that researchers, teachers, and learners need to be aware that generative artificial intelligence does not understand the texts it generates and often produces incorrect statements. Users need to take a critical approach to everything it generates and enhance the regulation of generative artificial intelligence applications in education.

Governments, educational institutions, generative artificial intelligence providers, school administrators, and teachers must seriously assess and regulate the potential risks of artificial intelligence, establish basic principles, procedures, measures, and regulations for using generative artificial intelligence in education, ensure information security, and evaluate and strictly control the potential impacts of AI-generated content on the development of human abilities such as critical thinking and creativity, implementing specific regulations regarding the ethics and morals of artificial intelligence. Readers can try to let AI assistants collaborate in drafting management measures for the application of generative artificial intelligence in schools. Using the prompt, “You are an artificial intelligence education expert, please formulate management measures for the application of generative artificial intelligence education in XXX school, outlining 8 management measures.” You will find that the draft AI provides is indeed worth referencing.

(2) Teacher Training

Enhancing the generative artificial intelligence literacy of all teachers, including basic skills for using generative artificial intelligence, prompt design skills, management capabilities for using generative artificial intelligence in teaching, changes in teaching design, development of assignments and evaluations, and even how teachers speak in class, and changes in classroom language, all need to be relearned.

Readers can try asking generative artificial intelligence to demonstrate how teachers should guide students in using generative artificial intelligence in classroom language. Example prompt 1: If students use generative artificial intelligence to assist learning in the classroom, what changes will occur in the language used by teachers during teaching activities? Please provide 10 examples of teacher classroom language. Example prompt 2: How should students ask AI questions during learning activities? Please provide 20 examples of prompts that students can use to ask generative artificial intelligence. Example prompt 3: How to improve the skills of teachers and students in using generative artificial intelligence in teaching? Please provide 10 suggestions. Follow-up: How to avoid negative risks for students using generative artificial intelligence in learning? Please provide 10 suggestions for teachers.

(3) Teaching Research

Readers are encouraged to experience having generative artificial intelligence as their educational research assistant. Example prompts are as follows: Suppose you are a high school Chinese teacher, and you need to research how to apply generative artificial intelligence to assist teaching in the teaching activities of the People’s Education Press high school Chinese curriculum (you can replace it with your own course name) in large unit thematic teaching activities. Please provide 10 innovative ideas for educational research topics, including the topic title, significance of the research, and specific implementation plans, outputting in a table format. When you see the desired research topic ideas appearing row by row in the table, how will you feel?

(4) Overall Reform

Education must prepare to welcome the knowledge industrial revolution of the artificial intelligence era, adapting to the challenges posed by the rapid development of artificial intelligence to future talent cultivation methods. The application of generative artificial intelligence in teaching represents a profound transformation in teaching and learning methods, requiring a re-evaluation from both teachers and students, from curriculum and textbook construction to the transformation of learning resources. This is a systemic change that requires comprehensive reforms and developments across educational philosophy, institutional construction, organizational development, teacher training, curriculum teaching reform, and educational evaluation.

Today, AI is creating a brand new world through generative methods, and this revolution is forcing all knowledge economy industries to join the ranks of generative artificial intelligence, with all social services facing digital and intelligent transformation. The new generation of intelligent ecological systems in future society is beginning to take shape.

At this critical juncture of the world transitioning from a knowledge economy to an intelligent economy, the Ministry of Education has proposed to accelerate the strategic development of education digital transformation. We must fully utilize digital technology to seek new developments in education according to the national principle of “balancing development and security, promoting innovation and lawful governance”: First, change students’ learning through digital education, initiating a learning revolution; Second, empower teachers’ teaching through digital means, promoting a teaching revolution; Third, drive school management changes through data, accelerating the reform of precise educational governance; Fourth, lead the reshaping of a new educational ecology through educational digitalization. Chinese education needs to comprehensively analyze the impact and changes brought by generative artificial intelligence on society and education, conduct transformation research on curriculum, textbooks, and teaching methods, carry out new teaching practices of generative inquiry-based learning, and proactively adapt to the arrival of the generative artificial intelligence era.