Author | Professor Li Jiahou, Director of the Department of Educational Technology, College of Mathematics and Information, Shanghai Normal University, Leader of the Educational Technology Master’s Program
In August last year, the “Interim Measures for the Management of Generative Artificial Intelligence Services” officially came into effect, leading to the rapid development of generative AI in China, providing services to the public and offering technical support for every teacher to use generative AI, making its application in teaching possible.
The door to generative AI in education has been opened.
01Generative AI and the Transformation of Learning: Generative Inquiry LearningEvery teacher being able to use generative AI software is the first step in applying generative AI in teaching. It is recommended that everyone try it out, register on their mobile phone or computer to use the AI large model approved by the National Cyberspace Administration, and send your ideas through prompts.Regardless of the educational issue you are concerned about, generative AI can provide unexpected responses; the more specific and precise your prompt design, the more specific and precise its responses will be. This human-computer dialogue can broaden your original thinking, but it may also be inaccurate, even “seriously talking nonsense,” with a lot of uncertainty, requiring you to think and judge independently.This learning process based on generative AI is very similar to the process of playing chess:As one side of the chess game, you provide prompts to the AI, just like placing pieces on the chessboard; the AI does not know how you will ask beforehand;It will calculate based on your prompts and give a response, and you cannot predict how the AI will respond;You ask further questions based on the AI’s response, providing the next move, which the AI also cannot predict;It continues to calculate based on your current pieces and provides a response… and so on in a cyclical iteration.
This kind of uncertain and deeply interactive generative learning activity can continuously stimulate the imagination and interest of both parties, which is why chess has maintained its charm throughout human history, and also why generative AI is different from past teaching methods and applications of information technology.When generative AI is applied in the field of education, regardless of the learning theories and teaching needs you study and practice, it ultimately comes down to its biggest difference from other technologies: AI-generated content (AIGC).Due to the sudden emergence of generative AI, there are currently no related discussions in classic works such as pedagogy, teaching theory, curriculum theory, educational psychology, and learning science. In current teacher training activities across various regions, to explain the new developments in the application of generative AI in teaching to frontline teachers, we call this new type of learning method generative inquiry learning.What is generative inquiry learning? Generative inquiry learning refers to a teaching method where teachers and students strictly follow national regulations, ethical standards, and information security requirements to reasonably apply generative AI to assist teaching, and promote optimized learning through learners’ independent inquiry, critical thinking, and creative thinking.Generative inquiry learning is a student-centered teaching method in the generative AI environment, emphasizing students’ independent exploration, practical operation, and the construction of knowledge systems through human-machine interactive dialogue and cooperation and communication between teachers and students. It requires students to use generative AI to assist teaching under the guidance, support, and control of teachers, aiming to cultivate students’ comprehensive qualities such as critical thinking, problem-solving ability, teamwork spirit, and innovative thinking. This is a way of learning that combines human wisdom with machine wisdom.The generative inquiry learning model includes the following six steps, the most attractive and challenging of which is the dialogue exploration stage.
Generative Inquiry Learning ModelStimulating Interest. Teachers stimulate students’ learning interest in various ways, such as creating problem situations to guide students’ learning interest. The problem situation should closely relate to real life and connect with the course content and learning tasks. If the problem is an open-ended question based on real-life situations, it can further stimulate students’ initiative to explore.Task Assignment. Teachers clearly assign tasks for generative inquiry learning based on curriculum standards and teaching objectives, guiding and controlling the students’ learning process through task-driven approaches.Dialogue Exploration. Under the guidance of teachers, students aim to complete learning tasks and interact in dialogue with generative AI, recording their critical thinking in a chain of thought dialogue and exploring through various means. For example, five basic search types (Baidu and Bing searches, WeChat article searches, Bilibili video searches, CNKI journal paper searches, library book searches), group peer communication, consulting teachers, parents, and field experts, conducting experiments, and natural and social investigations. They record their learning experiences and fill out learning process sheets. The learning sheet is a scaffold prepared by teachers before class to guide students’ learning processes, specifying the specific steps and process records for students to conduct generative inquiry learning under the defined teaching objectives, which can be used for formative assessment.Transfer Practice. Students independently think and solve problems in new situational problems, completing assignments and exercises assigned by teachers.Conclusion Sharing. Students summarize their learning gains, integrating their experiential insights into rational cognitive structures, and share their learning gains and research conclusions with their group members. The last step is evaluation feedback. Teachers provide learning evaluations and suggestions for further development.Unlike familiar teaching methods from the past, generative inquiry learning allows learners to interactively learn with AI in a chain of thought dialogue.The chain of thought is a strategy for artificial intelligence to handle complex tasks. This technology breaks down complex large tasks into smaller tasks containing multiple intermediate steps through a series of interrelated instructions, with each small task guided by relatively simple instructions to aid the model in generating and solving complex logical reasoning tasks.In teaching, the chain of thought dialogue between teachers, students, and generative AI refers to users asking a series of continuous questions and iterative follow-up questions, from superficial to deep, gradually forming a human-machine dialogue for solving complex problems, like squeezing toothpaste or peeling an onion.This game-like thinking training allows teachers and students to gain a completely different learning experience from past computer-assisted teaching and the use of digital educational resource platforms in their interactions with generative AI.For example, in generative inquiry learning activities, teachers and students cultivate independent thinking, critical thinking, decision-making thinking, challenging thinking, human-machine collaborative thinking, systemic thinking considering the whole, resilience qualities of “not being greedy for victory,” rigorous reasoning thinking, unconventional thinking, innovative thinking, and contextual thinking that considers the development of things over time and space, deep learning thinking of “calculating more wins than calculating less,” and philosophical thinking of “playing chess like life”—all of which cannot be learned through other teaching methods.
02Two Major Challenges of Generative AI in Classroom TeachingAn important technological innovation supporting generative inquiry learning is the upgrade from the previously mature course management system in the mobile Internet era to the “Generative Inquiry Learning Course Management System.”Frontline teachers report two major challenges in the application of generative AI in classroom teaching:First, the AI large model may “seriously talk nonsense,” making it unusable in teaching;Second, according to the Ministry of Education’s “Five Management Measures” and the UNESCO “Guidelines for the Use of Generative Artificial Intelligence in Education and Research,” minors under 13 cannot independently use generative AI, and the entire class of students cannot register to use generative AI with their mobile phones in class.First, let’s discuss the accuracy issue of large models. The “large” of large models makes it unable to delve deeply into specialized subject areas. Developing focused “small models” for specific subjects and teaching scenarios is a new way to solve the first challenge.Small Language Models (SLM) are the future direction for applying generative AI in education: small language models are highly customizable and can be tailored to user needs, focusing on specific goals. For example, small models can be customized for a subject, a textbook, or even a specific teaching application scenario. They are lower in cost, more efficient, safer, and more accurate, which is especially important for education.Regarding the large-scale use by students, teachers suggest two approaches: first, students can form small groups, and teachers can register a large model account with their own mobile phone, so the whole class only needs a few teacher accounts; second, large model providers can directly offer generative AI services to local education departments, allowing teachers and students to access large models through the intelligent education systems developed by the companies.03The Impact of Generative AI on TeachingMr. Li Bingde summarizes teaching activities into seven elements. What changes would occur if each element were combined with generative artificial intelligence?
The Relationships of Teaching ElementsEvery teacher has an intelligent teaching assistant. Teachers mastering prompt design skills can leverage generative AI to assist in many tasks, like a smart and capable teaching assistant. For example, grading assignments, writing lesson plans, designing assignment exercises, creating PPT presentations, writing research articles, designing micro-videos, drafting various work documents, writing program code, and managing experiments, thus improving teachers’ work efficiency and reducing their workload while enhancing quality. In the near future, any teacher could have an AI-driven personal assistant available 24/7, far exceeding current information technology capabilities.Every student has an intelligent mentor and learning partner. Students using generative AI can ask knowledge-related questions one-on-one, receive help; provide writing ideas, polish composition examples, enhance creativity in writing; offer varied practice assignments, enriching multiple solution approaches; provide project-based learning suggestions; offer examples for writing program code; act as partners and mentors in language dialogue learning; participate in large unit and interdisciplinary learning activities, answer students’ questions, provide learning resources, and promote personalized teaching and tailored instruction.Every principal has an intelligent assistant. Generative AI assists principals in designing various educational management documents; providing educational governance reference strategies; searching educational resource websites; building campus culture; aiding in writing educational research papers; offering suggestions for school operations; providing design reference plans for school environments; and suggesting developments in educational information technology. Various assistants in schools can share data from large and small models, connecting with each other to form an educational assistant group, providing intelligent services around the clock.Every textbook is paired with a generative AI small model assistant. Reshaping educational publishing with generative AI is a new path for various textbook publishers in their digital textbook publishing transformation. Based on past multimedia textbooks and cloud-based digital textbooks, AI small models tailored for different textbook versions can be developed and trained, combined with digital human technology, metaverse virtual learning space technology, and voice synthesis technology to provide intelligent learning assistants for students that accompany textbooks. This requires collaboration between publishers and large model developers to provide a “small model editing training system” for textbook publishing editors, assisting users in customizing scenarios; utilizing proprietary data to train small models, allowing users to attach exclusive knowledge bases, and optimizing/evaluating prompt words, relying on large models to provide algorithms and computing power to enhance the efficiency of users editing and training exclusive small models.The small model editing training system will prompt publishers to transform from past printed book publishing institutions to new publishing organizations that produce multimedia textbooks and intelligent learning assistants. This will be a revolutionary change in textbook construction.The curriculum system is evolving towards generative courses. The generative course in the era of artificial intelligence refers to teachers and students dynamically generating new learning content through chain dialogue with generative AI, including text, images, audio, video, code, etc., changing the way teaching and learning activities are conducted, reshaping curriculum resources and teaching structures. The learning content and curriculum resources of generative courses are generated during the process of human-machine interactive dialogue, which cannot be predicted in advance by the process of traditional course preparation and printed textbook publishing. This intelligent generation method subverts people’s cognitive models of traditional curriculum materials.
Characteristics of Generative CoursesGenerative AI integrates into every aspect of the curriculum, generating course learning content resources through AI interactions with teachers and students, characterized by generativity, uncertainty, and richness, allowing teachers to connect educational theories, teaching strategies, and subject teaching training datasets to course design, course resource generation, and teaching activity management, enhancing AI’s understanding of course teaching content and the adaptive learning capabilities of generated course resources, forming a complementary relationship with traditional course resources. This is the new path for future curriculum construction.04Suggestions for Applying Generative AI in Teaching
· Top-Level DesignEvery school should establish management measures for the educational application of generative AI. Due to the powerful content generation capabilities of generative AI, which come with unpredictable risks and hazards, the UNESCO “Guidelines for the Use of Generative Artificial Intelligence in Education and Research” repeatedly emphasize that researchers, teachers, and learners need to be aware that generative AI does not understand the text it produces; it can, and often does, generate incorrect statements and necessitates a critical approach to everything it generates, strengthening the regulation of generative AI applications in education.Governments, educational institutions, generative AI providers, school administrators, and teachers must carefully assess and regulate the potential risks of artificial intelligence, formulating basic principles, procedures, measures, and regulations for the use of generative AI in education to ensure information security, evaluate, and strictly control the potential impact of AI-generated content on the development of human capabilities such as critical thinking and creativity, and implement specific regulations on AI ethics.· Teacher TrainingEnhance the generative AI literacy of all teachers, including basic skills for using generative AI, prompt design skills, management capabilities for using generative AI in teaching, changes in teaching design, developments in assignments and evaluations, and even how teachers speak in class, requiring a re-learning of classroom language changes.Everyone can try to guide students on how to use AI features in class.Prompt 1: If students use generative artificial intelligence to assist learning in class, what changes will occur in the teacher’s language during teaching activities? Please provide 10 examples of teacher classroom language.Prompt 2: How should students ask questions to AI during learning activities? Please provide 20 examples of prompts used by students to ask generative artificial intelligence.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 can we avoid the negative risks of students using generative artificial intelligence in learning? Please provide 10 suggestions for teachers.· Educational ResearchIt is recommended to try using generative AI as an educational research assistant. The prompt is as follows:If you are a high school Chinese teacher, how to apply generative artificial intelligence to assist teaching in the thematic teaching activities of the People’s Education Press high school Chinese curriculum (which can be replaced with your course name), please provide 10 innovative ideas for educational research topics, listing the topic name, research significance, specific implementation plan, and output in a table.· Overall ReformEducation must prepare for the knowledge industrial revolution in the AI era, adapting to the challenges posed by the rapid development of artificial intelligence on future talent cultivation methods. The application of generative AI in teaching represents a profound transformation in teaching and learning methods, requiring a re-evaluation from both teachers and students, from curriculum textbook construction to the transformation of learning resources. This is a systemic reform that requires comprehensive reform and development in educational philosophy, institutional construction, organizational development, teacher training, curriculum and teaching reform, and educational evaluation.Today, AI is creating an entirely new world through generative means, and this revolution is forcing all knowledge economy sectors to join the ranks of generative AI, with all social services facing digital and intelligent transformation, and the new generation of intelligent ecological systems in society is beginning to take shape.At this critical juncture of the world’s transition from a knowledge economy to an intelligent economy, the Ministry of Education has proposed a strategy for accelerating the digital transformation of education. We must follow the national principle of “balancing development and security, promoting innovation and legal governance,” and fully utilize digital technology to seek new developments in education:First, change students’ learning through digital education, initiating a learning revolution; second, empower teachers’ teaching through digital means, driving a teaching revolution; third, transform school management through data-driven approaches, accelerating precise educational governance reform; fourth, lead educational digitalization to reshape the new ecology of education and teaching.Chinese education needs to comprehensively analyze the impacts and changes brought by generative AI on society and education, conduct research on the transformation of curriculum, textbooks, and teaching methods, and carry out new teaching practices of generative inquiry learning, proactively adapting to the arrival of the generative AI era.NewCourseRecommendations
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