The AI General Education Course and Information Technology

Artificial intelligence is usually realized on computers, and the main subject matter of the information technology course is precisely computers and related knowledge. A common view therefore holds that AI can be regarded as a part of information technology, as "the complex processing of information". In curricular practice, too, AI content is making its way into the information technology classroom.

Yet to conclude from this that the AI general education course is merely an extension of the information technology course is not logically coherent; nor is adding a few AI modules to the information technology classroom enough to fulfil the mission that general education bears in our time.

One qualification is needed: "information technology" here refers mainly to the primary and secondary school curriculum, not to "information science" as a professional discipline.

1. The Disciplinary Foundations and Founding Motivations behind the Two Courses

To discuss the relationship between courses, we first need to understand the disciplinary foundations and founding motivations behind them.

Artificial intelligence has deep intellectual roots. Ancient logic studied how to reason validly according to rules; modern mathematical logic went on to turn reasoning into an object that can be treated formally; and the modern theory of computation laid the foundation for carrying out these processes mechanically. Whether certain human thinking activities can be explicitly described and executed according to rules became an important thread connecting these explorations.

At the same time, probability and statistics provided methods for forming judgments from incomplete information and limited experience, while neuroscience and psychology offered inspiration for understanding learning, memory and behaviour. Several intellectual traditions gradually converged, leading AI to take shape as a distinct field of research in the mid-twentieth century. The modern discipline of artificial intelligence is thus only a few decades old, yet its intellectual sources can be traced back thousands of years.

This history shows that AI has a fundamental question of its own, one it has pursued continuously: how to simulate human intelligent behaviour. Behind it lies a series of key questions: how knowledge is represented, how reasoning is carried out, how experience gives rise to learning, how existing regularities apply to new situations, and how a system perceives its environment and takes action.

Around these questions, AI has formed a relatively independent scientific system. Logic, probability and statistics, and the theory of computation provide the foundations; knowledge representation, search, reasoning and machine learning constitute the methods; language understanding, visual recognition, planning and decision-making form specific research directions. The different parts are interconnected and together support how intelligent behaviour is described, realized and tested.

Today, the rapid development of AI requires everyone to understand AI and to live alongside it, while preserving their own cognitive autonomy. This calls for a course with a complete body of knowledge and clear educational goals, so that we and the next generation can enter the intelligent society with confidence. The Tsinghua Redbook defines this course as the AI general education course, and its goal is "cultivating the capacity for cognitive autonomy".

The information technology curriculum, by contrast, took shape against the background of the spread of computers. In the 1990s, computers began to enter society on a large scale; schools needed to help students master the relevant knowledge and skills, and the early information technology courses began. As networks and digital technology spread further, the content of the curriculum kept expanding, from computer basics, programming and software use to data processing, network applications, digital learning and responsibility in the information society. The building of the information technology curriculum in China's primary and secondary schools advanced precisely on the basis of the gradual spread of computer education.

The 2025 revision of the new curriculum standards places still greater emphasis on combining scientific principles, computational thinking and practical application: it gives equal weight to "science" and "technology", stresses the explanation of principles, strengthens the body of knowledge, and attaches importance to frontier technologies such as artificial intelligence.

The information technology curriculum therefore has a distinct task for its time: to help students understand how information is represented, processed and transmitted, to master the basic principles of digital technology, and to be able to take part in learning and life in the information society.

This curriculum does of course have disciplinary foundations as well, including the logic, algorithms and theory of computation of computer science, and the theory of coding and transmission in information theory. But the primary and secondary school information technology curriculum is not organized around the theoretical systems of computing and information theory. Its selection of knowledge mainly serves students' need to understand and use digital technology, and it has a clear orientation toward application.

From the discussion above, we can see that the two courses each have their own disciplinary foundations and founding motivations. They may be close and connected in such respects as "computer use" and "information processing", but artificial intelligence is an independent discipline and cannot be regarded as an extension or derivative of information technology.

2. Why Information Technology Has Taken In Artificial Intelligence

Within the existing curriculum system, information technology and artificial intelligence are related in knowledge, and information technology has therefore become an important course for taking on AI education.

The first reason is closeness in knowledge. Current AI systems run mainly on computers, and their implementation involves data, algorithms, programs and networks. The information technology curriculum already teaches part of this basic knowledge, so students who enter the study of AI from there are well placed to make the connection.

The second is the curriculum's own need to develop. Digital tools that used to serve retrieval, editing, calculation and communication are acquiring capabilities such as recognition, prediction, recommendation and generation. The information environment that students encounter is changing as well. The information technology curriculum needs to explain these new phenomena and help students understand how digital technology is developing.

From the standpoint of computer applications and information processing, the entry of AI into the information technology curriculum is a natural continuation. Extending the original curriculum from conventional information processing to more complex intelligent processing, and from operating software to a preliminary understanding of models and systems, keeps the teaching content connected to real life.

In addition, information technology teachers, computer rooms, programming environments and experimental platforms provide ready-made support for teaching AI. For schools that are just beginning AI education, these conditions are especially important.

So the inclusion of AI in the information technology curriculum is reasonable in terms of its knowledge base, and also in terms of curricular renewal and the practicalities of teaching. This reasonableness is plain to see.

However, a relationship of curricular accommodation cannot be read backwards into a relationship of disciplinary origin. That AI has entered the information technology classroom does not mean that AI is a derivative of information technology, still less that information technology can fully achieve the educational goals of AI general education.

3. Why Including AI Content Still Does Not Amount to Accomplishing AI General Education

Since the information technology curriculum already contains artificial intelligence, why do we still need to discuss building the AI general education course independently?

First, there is a clear difference between including some knowledge and establishing a complete system of learning.

A course usually selects its content according to its own goals. The information technology course can introduce data processing through image recognition, discuss network applications through intelligent recommendation, and explain digital tools through generative AI. Such content helps in understanding information technology, and can also provide some preliminary knowledge of AI.

But if AI content always appears in scattered form around other teaching themes, students may know a number of applications and yet find it hard to build a systematic understanding of AI. How data affects learning, how learning forms a model, how a model produces inferences, and how inferences enter into real-world action: these relationships need to be taught and analysed continuously.

Second, AI general education has a clear goal of cognitive cultivation.

Earlier articles in this series have explained that the AI general education course takes cognitive autonomy as its main educational goal. Learners need to understand AI, and they also need to be able to judge under what conditions they can rely on it, which results need to be checked, and which decisions should be made by themselves, and to bear responsibility for the actions that follow.

This goal requires knowledge of AI as its foundation, and also needs to be cultivated continuously through prototype-based training. The course's selection of content, arrangement of activities and assessment of learning should all be organized around this goal. The relevant knowledge and activities in the information technology curriculum contain related elements, but the presence of these elements does not mean that a complete process of cultivation has taken shape.

Finally, there are the practical matters of class hours and progression.

The information technology course already carries the teaching of data, algorithms, networks, digital applications and more. If the structure of the curriculum and the total number of class hours remain essentially unchanged, newly added AI content must share limited time with the existing content. A few units can provide an initial introduction, but can hardly take on systematic learning of knowledge, prototype analysis and sustained cognitive training all at once.

4. What Foundations for Learning Information Technology Can Provide

Once the boundaries between the courses are clear, the connection between the two actually becomes easier to arrange.

There is no need for the AI general education course to teach all computer-related knowledge over again. The computer knowledge, computational thinking and basic practical skills already established in the information technology curriculum can serve as the starting point for learning in the AI general education course.

Knowledge of computers and digital systems helps students understand the environment in which AI systems run. Knowing how programs execute, how data is stored and how networks connect helps in understanding what parts an intelligent application is made of, and how a model works together with other systems.

Algorithms and computational thinking help students turn real problems into tasks that can be processed. Training in decomposing problems, representing objects, designing steps and testing results provides a foundation for going on to understand AI methods. For example, before learning path planning, students who can already use a graph to represent places and roads will more easily understand the objects that search algorithms operate on.

Data processing and basic experimental skills can also directly support the learning of AI. Organizing samples, reading data, recording results and comparing performance under different conditions are all abilities needed when analysing intelligent systems.

These foundations are worth drawing on, but drawing on them does not mean depending on them.

The AI general education course still needs to make choices according to its own teaching tasks: which knowledge can be used directly, which needs to be explained anew, and which should be supplemented. For example, knowing how to build a data table does not mean a student has understood whether the samples in that table are representative; being able to run a program does not mean having understood why a model fails in a new situation.

At the same time, information technology can provide a foundation, but it is not a prerequisite course; nor can the AI general education course wait until information technology has finished teaching the basics before it begins. For the AI general education course, content already mastered in the information technology course can reduce repetition, content not yet mastered but genuinely necessary should be filled in, and the time saved should be used for learning the key knowledge of AI and for prototype-based training.

5. The Transitional Stage: Sharing Teaching Resources

That the AI general education course has an independent educational task does not mean that every school can immediately assign dedicated teachers, add class hours and set up a new teaching research system. In reality, building the course requires reasonable transitional arrangements.

First, the existing teaching resources of information technology can play an important role in this process. Teachers with the appropriate background can take on AI general education teaching, computer rooms, experimental platforms and some teaching materials can be shared, and the two courses can also coordinate the teaching of basic knowledge.

Second, if the course temporarily takes the form of modules within the information technology course, joint teaching or cross-course coordination, care should be taken over what to keep and what to give up. One situation in particular needs to be avoided: the tasks of the original course are not reduced, the new tasks come with no corresponding class hours, and in the end teachers are simply asked to "integrate AI as appropriate". Such an arrangement looks flexible, but it leaves teachers at a loss, and the teaching can only remain at the level of isolated cases and short-term activities.

Third, in course design, the goal of cultivating "cognitive autonomy" must be kept clear throughout. Only when this goal is held to can the arrangement be called a "transition" toward AI general education; otherwise it is merely an AI unit within information technology.

Fourth, teacher development likewise needs dedicated arrangements. Familiarity with computers and programming provides a favourable basis for teaching AI, but the body of knowledge of AI, the ways of cultivating cognitive autonomy and the corresponding methods of assessment still need to be studied systematically.

6. Building an Independent Course for the Future

In the long run, AI general education should be built to the requirements of an independent foundational course. The content system, learning progression, teaching methods and teacher support that it needs should all gradually take shape.

First, it needs a stable yet open structure of knowledge.

AI is developing rapidly, and specific tools and technologies will keep being updated. The course needs to organize knowledge around basic questions such as the representation, learning, reasoning and action of intelligent behaviour, so that students can come to understand new achievements on the basis of what they already understand. At the same time, the course also needs to adjust its content as the discipline develops, allowing new knowledge that has gradually matured to enter foundational teaching.

Second, it needs a learning progression that runs through the different stages of schooling.

If every stage starts from trying out chatbots and generating pictures, students may remain at much the same level of understanding even after taking AI classes many times. An independent course design should make the learning tasks of each stage explicit, so that basic concepts gradually deepen, prototype analysis gradually becomes more complex, and training in cognitive autonomy develops continuously.

Third, it needs assessment suited to its educational goals.

Course assessment should check the necessary knowledge of AI, and also observe whether students can explain a system's behaviour, analyse errors, identify the limits of applicability, and reflect on their own judgments. The four capacities proposed in the Redbook (induction, generalization, judgment, and self-awareness) can give direction to such assessment. Basic knowledge and cognitive abilities should be tested in concrete tasks.

Finally, it needs sustained support in teaching staff and teaching research.

For a course to develop steadily, it cannot depend in the long term on the personal interest of a few teachers. Teachers need to be able to keep learning the subject knowledge, to study teaching prototypes together, to compare the difficulties students have in understanding, and to improve the ways of cultivation. Information technology teachers can become an important force in building the course, and can also take part together with teachers from AI and other fields.

Conclusion

Information technology provides an important foundation for students to understand and take part in the digital society; the AI general education course bears the mission of cognitive cultivation in the intelligent society. The two courses each have their own clear responsibilities, and they also share common ground in knowledge.

For AI general education, making use of this common ground can reduce duplicated effort and support more schools in quickly beginning to teach the AI general education course. At the same time, the AI general education course must always keep its own structure of knowledge and its own educational goals, so as to fulfil its original aspiration and mission of "cultivating the capacity for cognitive autonomy".