Artificial Intelligence Literacy: Background, Definition, and Components

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Artificial Intelligence (AI) is profoundly changing the ways we produce, live, and organize society. How to enhance individuals’ adaptability and creativity in the intelligent era has become a new topic in public literacy research. This article interprets the background of AI literacy from the perspectives of conceptual evolution and technological development, pointing out that AI literacy is a continuation and expansion of information literacy and digital literacy in the context of the paradigm shift of human-computer interaction technology. Based on literature review, an exploratory definition of AI literacy is proposed, emphasizing that AI literacy is a diverse and multi-layered concept, and how to collaborate and coexist with AI constitutes the differentiated connotation of AI literacy; referencing the KSAVE model, a general framework for AI literacy composed of AI awareness, AI cognition, AI skills, AI thinking, and AI ethics is identified and constructed, systematically elaborating on the connotations, significance, and content structure of each element.

Introduction

Artificial Intelligence (AI) literacy is one of the most challenging topics today. The application of AI is rapidly penetrating various industries, accelerating the process of social evolution, and driving a series of changes in knowledge production methods, product manufacturing models, social connection forms, and leisure scenes. In March 2024, the Chinese government’s work report first proposed to carry out the “AI +” action, aiming to deepen the research and application of big data, AI, and create a digitally competitive industrial cluster. While bringing epoch-making opportunities and great conveniences, AI also poses numerous challenges to today’s human society. Intelligent machines will undertake most of the production work, and the competitive pressure between people and between humans and machines is increasing; AI technology also poses significant risks in terms of fairness, privacy protection, security, and ethical morality, leading to the phenomenon of “AI anxiety.” If the public’s awareness, understanding, and skills regarding AI do not improve universally, society may experience an exacerbated digital divide and create a new AI gap that is detrimental to the labor market and economic growth. There are increasing questions about whether the public is ready to understand, accept, trust, or adapt to the AI world, and how to comprehensively enhance the adaptability and creativity of social members has become a new topic in public literacy research. To date, academia has not fully explored how to define general artificial intelligence literacy (hereinafter referred to as “AI literacy”). Therefore, this article attempts to interpret the background of AI literacy from the evolution of relevant technological literacy concepts and the development of AI technology, to sort out and define the concept of AI literacy, propose a general framework for AI literacy, and systematically elaborate on the connotations, significance, and content structure of the core components of AI literacy, providing a systematic perspective for understanding and enhancing public AI literacy.01

The Background of AI Literacy

Literacy can be understood as the mastery of knowledge or skills in specific fields, and its terminology system has evolved continuously with social development. Since the third industrial revolution, with the in-depth development of information technology, the degree of government information disclosure and data openness has continuously improved, and various market-oriented online information media and digital tools have rapidly become popular. Concepts such as computer literacy, information literacy, media and information literacy, digital literacy, data literacy, algorithm literacy, and AI literacy have emerged. From the perspective of public education, authoritative institutions such as UNESCO and the European Union have a high recognition of information literacy and digital literacy. In 1974, Zurkowski, then president of the American Information Industry Association, first proposed the concept of information literacy in a report submitted to the American Library and Information Science Committee titled “Information Services Environment: Relationships and Advantages.” In response to the diverse trends in social information dissemination at that time, Zurkowski proposed that information literacy is “the skills and techniques to use various information tools and major information resources to solve problems,” covering multiple aspects including information retrieval, information evaluation, and information utilization. The report emphasized various relationships and advantages in the information environment and pointed out the importance of information ethics, providing valuable theoretical resources and practical guidelines for understanding and addressing the challenges of the information society. Subsequently, with the promotion of institutions such as the American Library Association’s Information Literacy President’s Committee and the National Information Literacy Forum, the theory and practice of information literacy developed rapidly and entered the international perspective. In 2013, UNESCO’s “Media and Information Literacy Policy and Strategy Guidelines” gathered the achievements of academia and practice, first merging information literacy and media literacy, defining it as “the cognitive, skills, and attitudes exhibited by individuals and groups in accessing, understanding, evaluating, creating, and communicating various forms of media and information.” Since the 1990s, personal computers began to enter more households and enterprises, leading to the emergence of a more comprehensive literacy framework. In 1994, Alkalai first proposed the term “digital literacy” and established a theoretical model of digital literacy consisting of five elements: “image-literacy,” reproductive literacy, branching literacy, information literacy, and “social-emotional literacy” in 2004. Since the 21st century, more and more scholars, institutions, and governments have begun to adopt the concept of digital literacy, with the EU’s policy influence being the most widespread. In 2006, the EU proposed in the “Key Competences for Lifelong Learning: European Framework” that digital literacy is “the ability of individuals to use various technologies of the information society confidently and critically, possessing basic skills in information and communication technology.” As an important achievement of the “European Digital Agenda,” the EU has released multiple versions of the “European Digital Competence Framework,” with the latest version, DigComp2.2, released in March 2022. In the EU framework system, information literacy and data literacy are sub-dimensions of digital literacy, and AI-related capability requirements are reflected in multiple sub-fields. Since OpenAI launched ChatGPT-3.5 in November 2022, generative AI has rapidly penetrated various industries, with the number of global generative AI users growing exponentially, marking the official start of the large-scale commercialization era of AI. Currently, the transition from “artificial human intelligence” to a more advanced “general artificial intelligence” is an inevitable trend, with AI surpassing human performance in benchmark tests such as image classification, basic reading comprehension, visual reasoning, and natural language reasoning, continuously narrowing the gap in complex cognitive tasks. AI is reshaping the interaction between humans and the physical, digital, and machine worlds. The three significant characteristics that distinguish AI technology from non-AI technology—autonomy, learning, and non-explainability—render the core assumptions about human-technology interaction in traditional information system theory invalid. For instance, AI technology breaks the assumption of functional consistency because it can learn from the data it processes, thereby exhibiting different behaviors over time; natural interaction methods such as voice and vision eliminate the need for artificial interfaces, making it possible for humans to be unaware that they are interacting with AI. The shift in the paradigm of human-computer interaction technology has made the need to revise the technological literacy system urgent. Against this backdrop, as a continuation and expansion of information literacy and digital literacy, AI literacy has entered the academic discussion and research domain. 02

Definition of AI Literacy

Since its formal proposal in the mid-1950s, AI has undergone nearly seventy years of development, witnessing several major technological paradigm shifts. In contrast, AI literacy is still a nascent concept. In 2015, the term AI literacy was first mentioned in an online article submitted by Konishi. Konishi believed that AI literacy is not only the mastery of technology but also an ability to think and respond to future technological developments. Subsequently, Kandlhofe et al. defined AI literacy as the ability to understand the basic technologies and concepts behind different products and services of AI, identifying seven themes: automation, intelligent agents, graphs and data structures, sorting, problem-solving through search, classical planning, and machine learning. At this stage, due to the complexity of technology, AI literacy education mainly targeted higher education institutions, with low public awareness. In recent years, with the continuous maturity of technology and the expansion of application scenarios, technologies such as graphical modular programming and virtual simulation have emerged, significantly lowering the threshold for AI learning, and AI literacy has transformed into a terminology system aimed at non-professionals, focusing more on the basic AI knowledge and comprehensive abilities that the public should possess. The definition proposed by Long and Magerko in 2020 has been widely cited—”AI literacy encompasses a set of abilities that enable individuals to critically assess AI technology, communicate and collaborate effectively with AI, and use AI as a tool in online, home, and workplace contexts.” This research marks the entry of AI literacy research into the mainstream academic view, with increasing studies focusing on AI literacy education. Since 2020, several representative definitions of AI literacy have emerged (see Table 1). Table 1 Representative Definitions of AI Literacy (sorted by publication year)From the table, it can be seen that earlier definitions focused more on concepts, technologies, data, processes, and skills, while recent definitions have begun to emphasize individuals’ attitudes and their ability to respond to complex demands in specific contexts. According to the National Research Council of the United States, to adapt to society and the times, a concept of technological literacy should include at least three independent yet interdependent dimensions: knowledge, thinking and action modes, and abilities. AI literacy not only involves professional skills but also includes understanding and reflecting on ethical, social responsibility, and global governance issues behind AI technology, requiring the construction of a systematic cognitive ability that allows individuals to comprehensively understand how AI works, its application scenarios, and its impact on society. Based on existing research, this article defines AI literacy from an explanatory perspective as: the comprehensive abilities, behavioral modes, and ethical values exhibited by individuals in their daily lives, learning, and working contexts when encountering, understanding, evaluating, and utilizing AI technologies and applications. From a functional perspective, individuals with AI literacy should be able to: (1) be aware of and understand the mechanisms of social operation driven by AI, and understand the social impacts and development trends of AI; (2) understand the basic principles of AI, be familiar with mainstream AI tools and applications, flexibly utilize AI to solve problems, and grasp individual subjectivity and control in a human-machine symbiotic environment; (3) possess AI ethical awareness and actively participate in AI policy issues. The connotations and characteristics of AI literacy are mainly reflected in the following two aspects:First, AI literacy is a diverse and multi-layered concept. The AI literacy needs of different user groups, including students and teachers at different educational stages, corporate management, various job employees, and government officials, are embedded in their respective application scenarios. In the context of social educational practice and individual lifelong learning, the complexity of cultivating and enhancing AI literacy is also reflected in the depth of understanding of technology, the breadth of application scenarios, and the sensitivity to ethical issues. When a specific group is not defined, “AI literacy” usually refers to public AI literacy aimed at non-professional backgrounds or universal AI literacy, synonymous with “general AI literacy.” The closest definition is provided by the U.S. National Artificial Intelligence Advisory Committee, which states that the focus should be on individuals from community colleges, universities, non-traditional students, the workforce, underrepresented communities, and adults most likely to be replaced by AI.

Second, AI is increasingly exhibiting social and ethical attributes, and how to collaborate and coexist with AI constitutes the differentiated connotation of AI literacy. The new generation of AI will highlight disruptive significance in its intelligent form, showing broader penetration and more universal empowerment space in its social impact. In the interaction process between individuals and AI, social logic transcends machine interaction logic, with AI more often acting as a collaborator and co-creator with advanced intelligence. This difference leads to different standards for users to evaluate AI products compared to traditional digital products, making it inappropriate to directly use the content related to previous digital literacy to describe AI literacy. AI possesses superhuman intelligence in certain specific fields, and humans and intelligent machines can collaborate to complete more complex tasks in intricate environments. The flexibility, imagination, and creativity of humans can complement the stability and logic of machines. Veloso et al. argue that the relationship between humans and intelligent systems will be one of “symbiotic autonomy.” Future social communication is likely to extend beyond mere interactions between human individuals to interactions between humans and AI. The cultivation of AI literacy must transcend a technical perspective, maintaining human initiative, subjectivity, and leadership in human-machine symbiosis.

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The Components of AI Literacy

Corresponding to the conceptual research of AI literacy, there is currently no widely recognized general framework for AI literacy. A relatively representative framework proposed by Long and Magerko, based on the perspective of human-computer interaction, includes 17 elements of AI literacy capabilities; based on this, Heyder and Posegga structured the capability into three conceptual blocks: functional AI literacy, critical AI literacy, and socio-cultural AI literacy; Ng et al. constructed a core AI literacy coding framework based on Bloom’s taxonomy of educational objectives, identifying four aspects of cultivating AI literacy through literature review: knowing and understanding AI, using and applying AI, evaluating and creating AI, and AI ethics; Wang et al. developed a core AI literacy framework based on the “technology-cognition-ethics” model, focusing on awareness, use, evaluation, and ethics; Chiu et al. proposed a framework that includes five key components: technology, impact, ethics, collaboration, and self-reflection; UNESCO defined AI literacy during the “AI in Education” themed conference held in December 2021 as knowledge, understanding, skills, and values regarding AI. Therefore, current research on AI literacy frameworks covers various elements including awareness, knowledge, skills, thinking, attitudes, ethics, values, evaluation, creation, and socio-cultural aspects. The meanings and extensions of the same element may vary in different research and practical contexts, and the logical relationships between the elements are still not very clear. Based on learning progression theory and social network perspectives, Wilson et al. proposed the KSAVE Information and Communication Technology (ICT) Literacy Model, consisting of knowledge, skills, attitudes, values, and ethics. Here, the “attitude” refers to a way of thinking that aligns with the “modes of thought” emphasized in the National Research Council’s view of technological literacy, which can be understood as a way of thinking adapted to a dynamic technological environment; values and ethics reflect humanistic factors, generally governed by the concept of “ethics” in authoritative policy documents from institutions like UNESCO and the EU. Referring to the aforementioned literature and the KSAVE model, this article proposes a general framework for AI literacy consisting of five main components: AI awareness, AI cognition, AI skills, AI thinking, and AI ethics (see Figure 1). These five elements have their relatively independent connotations and boundaries while being interrelated and mutually influential. Among them, AI awareness is the precursor and foundation; AI cognition achieves a deep and comprehensive understanding of AI concepts and principles, and mainstream applications based on AI awareness; AI skills transform AI awareness and cognition into practical abilities to solve problems; AI thinking emphasizes the construction of high-level thinking modes and methodologies to support innovative collaboration, decision-making, and complex decision-making; AI ethics runs through all elements, forming the underlying support for all components, ensuring that individuals adhere to the principle of “human-centered” in enhancing AI awareness, cognition, skills, and thinking, achieving a unity of self-development and social value. In line with the dynamic development of AI, individuals should maintain lifelong learning, and the enhancement of AI cognition, skills, and thinking triggers a higher level of AI awareness, further driving a new round of cognitive learning, practical application, thinking growth, and behavior formation, creating a spiral upward cycle of AI literacy development.Figure 1 AI Literacy Framework3.1 AI AwarenessAI awareness refers to the sensitivity to AI development and the basic perception and understanding of the existence, application, and social impact of AI technology, closely related to forming attitudes towards AI. Given the impact of the shift in the human-computer interaction paradigm mentioned earlier, this psychological readiness is necessary for the public to actively use AI as a tool to solve real-life problems, which previous studies often overlooked. AI is fundamentally changing the way human society, economy, and life operate, and the explosive and discontinuous growth of related applications has led to a serious disconnect at the social awareness level, where early adopters coexist with ignorance. Enhancing public AI awareness, avoiding blind rejection or excessive dependence on AI technology, is a key step in building a more equitable, sustainable, and innovation-driven intelligent society. (1) AI Impact Awareness. Individuals should maintain curiosity about AI applications and adopt an open and inclusive mindset. The deep integration of AI applications into electronic products, home devices, office software, social media, transportation tools, and production equipment has become part of the social ecosystem. Only by feeling, paying attention to, and being aware of the universality and impact of AI in the current economy and society can individuals proactively enhance AI cognition and possess the motivation to use AI to solve learning, life, and work problems. (2) AI Demand Awareness. As mentioned in the discussion of the connotations of AI literacy, AI is an enabling construct. Individuals should possess a subject awareness, collaborative awareness, and co-creation awareness that complements the advantages of AI, being adept at linking AI with real-life, learning, and work. The continuous and successful use of AI to solve real problems can enhance self-efficacy. (3) AI Safety Awareness. Related to the subsequent discussion on AI ethics, individuals should be aware of the limitations and potential risks of AI, be vigilant about the negative impacts of AI, and protect their own and others’ interests, privacy, and safety. For instance, AI algorithms in short videos and social media are often designed to maximize dopamine, which can lead to addiction and pose significant threats to the physical and mental health of minors, requiring attention from society, families, and minors themselves. (4) AI Self-Assessment Awareness. AI awareness also includes metacognitive literacy; a self-reflective mindset should be incorporated into the concept of AI literacy. Individuals should be able to recognize their level and state of AI literacy and appropriately assess the degree of alignment between their AI literacy and the demands of their professional, learning, and living environments. 3.2 AI CognitionAI cognition is the core component of AI literacy, involving understanding the basic concepts, technical principles, and application scenarios of AI, as well as insights into the development trends of AI technology, requiring individuals to possess deeper levels of understanding, analysis, and prediction of AI technologies and applications. (1) Understanding Basic Concepts and Technical Principles of AI. Conceptual understanding is the foundation for the public to apply AI in daily life. In addition to being AI users, individuals should understand the underlying technical principles. The level of understanding is crucial in distinguishing professional education from public education, and in principle, AI concepts that do not require any prior knowledge constitute the most basic level of AI literacy. The following three aspects of basic concepts and technical principles should be focused on: ① AI Models. Individuals should be able to understand the basic logic of AI algorithms, unsupervised learning algorithms, and neural networks in mainstream technological paradigms; comprehend the non-explainability, learning capability, and degree of autonomy of different AI models; understand how AI models operate, make decisions, and respond to environments; recognize how knowledge is represented in AI and the limitations of these representations; and understand the advantages and disadvantages of different types and sizes of AI models. ② Data for AI. With the introduction of AI, the relationship between data and technology has changed, as AI can learn from data rather than relying solely on expert coding. AI is increasingly capable of handling unstructured data, which presents new challenges for data governance. The basic knowledge of data includes fundamental concepts like data structure and data cleaning, as well as understanding the significance of data for different AI applications. Individuals should understand how to collect and process suitable data; be able to appropriately interpret the data output by AI; and recognize potential biases in the output data. ③ AI Interfaces. Individuals should understand the basic principles of how AI interacts with its environment; recognize the types, functions, performance, and distribution of sensors that serve as virtual or physical interfaces for AI interaction; and understand how different AI technologies receive and output data through their respective interface types. (2) Understanding AI Tools and Application Scenarios. Various AI tools are increasingly applied in fields such as industry, education, transportation, healthcare, finance, research, and cultural entertainment. Individuals should be able to understand the connections between different AI models and common AI tools; comprehend the basic principles, functional scope, and typical cases of AI application tools in different fields; understand how to operate AI tools correctly; be able to keenly identify the existence and operational status of AI tools; and understand how to manage their data and privacy when interacting with AI tools. (3) Understanding AI Trends. AI technology is developing at an unprecedented speed; to adapt to the ever-changing environment and demands, individuals should continuously learn to maintain insights into emerging technologies and be able to judge the main trends in AI technology and application development. 3.3 AI SkillsThe cultivation of AI skills focuses on practical operational aspects, requiring individuals to apply theoretical knowledge to identify, evaluate, use, and integrate AI technologies, tools, and resources to achieve specific goals, solve practical problems in digital life, learning, work, and social scenarios, enhance personal efficiency, and optimize decision-making processes, becoming active participants in the AI world. (1) Evaluating AI Selection. AI products are gradually diversifying. Based on practical problems, individuals should be able to evaluate the functions, advantages, and limitations of different AI tools; comprehensively consider various factors such as models, data, tools, interfaces, and hardware discussed in the previous AI cognition section to identify and select suitable AI tools for their needs; assess the risks and benefits of AI projects, and develop reasonable management strategies. (2) Interactive Use of AI. Due to the technical characteristics of AI, the efficiency of AI output largely depends on the user’s practical skill level. For example, when using the same large language model dialogue application, constructing prompts in different ways can guide AI along different paths in the dataset, resulting in varying levels of output. Individuals should be able to skillfully use mainstream AI intelligent assistants, language processing, office assistance, multimedia production, recommendation systems, smart devices, and other applications closely related to their daily life and work scenarios; be able to integrate different types of AI tools. (3) Evaluating AI Output. The complexity of AI technology and the “black box” nature of AI operations reinforce the importance of output evaluation. After executing AI-based solutions, individuals should be able to carefully measure and assess results, forming clear insights into the strengths and weaknesses of the solutions, to improve current solutions or design new ones, continuously enhancing their AI interaction and problem-solving abilities. 3.4 AI ThinkingAI thinking is the advancement of AI cognition on the logical and methodological levels. In a narrow sense, AI thinking refers to the application of algorithms and models, belonging to a subset of AI technology; the basic concepts and principles have been covered in the aforementioned AI cognition elements. Here, AI thinking primarily refers to the thinking patterns and methodologies that individuals should possess when using AI technology and adapting to the AI social environment, emphasizing innovative thinking and collaborative co-creation using underlying logic and systems thinking to support adaptive and complex decision-making. (1) Computational Thinking and Data Thinking. The two are two sides of the same coin. Computational thinking, under the AI literacy framework, is mainly a cross-disciplinary general way of thinking, analyzing and processing information logically and systematically through a series of steps and rules to find optimal solutions, including core elements such as decomposition, pattern recognition, abstraction, and algorithm design. Computational thinking encompasses algorithmic thinking; understanding the information processing process supported by algorithms helps individuals manage, control, and express information in the intelligent era. Data is the “oil” of the new generation of AI deep learning. Breaking away from traditional intuitive or experience-driven methods, data thinking requires individuals to understand and apply data-driven decision-making methods. Citizens should be able to understand data quality, sources, and the potential uses and limitations of data. (2) Critical Thinking. Although AI performs excellently in certain tasks, it is still limited by design intentions, development levels, and the quality of training datasets. Recent large language models often have billions, trillions, or even tens of trillions of parameters, making it increasingly difficult to explain their operations, output processes, and reliability. The “hallucinations” generated by systems and errors could have serious consequences; the boundaries between physical reality and digital virtuality are becoming increasingly blurred, posing significant challenges to application security and social governance. Individuals with AI thinking should be able to critically assess the needs, processes, and results of interactions with AI, maintaining independent judgment. (3) Design Thinking. Compared to human individuals, AI has unlimited learning and cognitive abilities, heavily reliant on historical data, and is a typical continuous learning model. Its greatest advantage lies in the breadth of knowledge and output speed, while its intelligent “emergence” ability still needs development. Humans learn from experience and also create, which is jump learning. Design thinking refers to treating AI as a partner in design innovation, fully leveraging the advantages of both subjects to creatively generate various ideas and solutions to problems. 3.5 AI EthicsAI ethics concerns the moral use of AI concepts and applications, closely related to legal, cultural, moral, and social value issues in an intelligent society. Its complexity lies in the need to balance technological development with human values. Human behavior is essentially value-driven, and it is crucial to construct and apply AI with a human interest orientation. AI is better at outputting “what is” but has a clear value tendency when explaining “why.” The accelerated development of AI may bring unpredictable and uncontrollable dangers, posing significant risks to information security, human rights, and privacy. Fei-Fei and Etchemendy point out that, “Unlike most ‘dual-use’ technologies like nuclear energy and biotechnology, AI development and use are global and relatively easy to enter, making it impossible to control such a decentralized phenomenon.” The “AI alignment” required in the AI development system demands that the goals of AI systems align with human values; similarly, individuals using AI should fully consider its social impact. (1) Ethical Recognition. The development of AI technology has brought new ethical and legal issues, such as AI gray industries, data and network security, privacy and IP infringement, bias and discrimination, cyber fraud, fake news and public opinion manipulation, severe employment replacement, excessive virtual control over labor, damage to diversity, and abuses in creating biological, cyber, and autonomous weapons, all of which have raised widespread concerns. The penetration of intelligent assistants, digital humans, brain-computer interfaces, and service robots may alter traditional social relationships and ethical cultures. Individuals should be able to identify and describe the main ethical issues and risks related to AI mentioned above. (2) Ethical Utilization. Based on ethical recognition, individuals should adopt a positive and rational attitude towards the impacts brought by AI, exploring reasonable applications of AI, taking effective measures to mitigate, avoid, and manage negative consequences, enhancing their ability to identify false information, prevent AI fraud, protect personal data and privacy, and legally safeguard their rights, appropriately addressing security issues and moral dilemmas encountered in AI applications; strictly adhering to relevant laws and regulations such as the “Interim Measures for the Management of Generative Artificial Intelligence Services,” becoming responsible AI users, and avoiding infringing on the legitimate rights and interests of others and social welfare. (3) Participation in Ethical Policies. Due to unpredictable risks and challenges, the commercialization progress of many key AI applications, such as autonomous driving, mainly depends not on technology but on the degree of public cognition, participation, and support. Individuals should be able to understand AI ethics-related policy issues to ensure that the use and development of future AI technologies align with inclusiveness, equitable access, and minimal bias principles. As AI technology is applied in public services and people’s livelihoods in education, healthcare, transportation, and other fields, the public also needs to have the ability to participate in policy discussions and recommendations, actively engage in maintaining the ethical environment, caring for vulnerable groups, and building ethical policies, fulfilling the responsibilities of active citizens in the intelligent era.04

Conclusion

AI is the engine and important driving force leading a new round of technological revolution and industrial transformation, profoundly changing the ways of social production, life, and organization. The entire society should prepare for technological advancement, ensuring that AI technology is developed and applied responsibly and inclusively. As OpenAI CEO Altman said during a discussion at Stanford University’s Business Thought Leaders Forum, “Let society develop alongside technology. Let society tell us, whether collectively or individually, what they want to gain from this technology.” Clearly, only individuals with basic AI literacy and the communities and organizations composed of these individuals can truly participate. AI literacy will be an important embodiment of individual competitiveness in the intelligent era and a key driving force for technological progress, industrial development, and social welfare enhancement in countries and regions. It is urgent to comprehensively build an AI literacy education system aimed at all levels of society. Future research can further explore the construction of integrated models of AI literacy for diverse groups, the design of cultivation systems, and the development of assessment systems.Source | “Educational Technology” WeChat Public AccountReferences | Yin Kaiguo. Artificial Intelligence Literacy: Background, Definition, and Components [J]. Library and Information Science, 2024, (03): 60-68.

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