Introduction
Artificial Intelligence (AI) will profoundly alter our lives in ways we cannot yet predict, with numerous applications in health, education, culture, security, defense, and other fields, affecting every aspect of our lives (UNESCO, 2018). AI has evolved from a concept of science fiction to an indispensable part of modern society. With technological advancements, AI has demonstrated its revolutionary potential across various domains, from altering business operations to shaping everyday human life. AI facilitates the extraction and application of data through a “data-driven” approach and is considered capable of addressing key challenges the world faces in critical areas such as health, finance, housing, transportation, and education (Nemorin et al., 2023). Particularly in education, AI is set to thoroughly revolutionize the sector, from teaching tools to learning methodologies to knowledge acquisition and the professional development of teachers, every element will undergo significant changes (UNESCO, 2018). AI has unlocked unprecedented possibilities and offered new pathways for learning and educational innovation.
A report from the University of London has shown that AI tools centered around teachers are widely used in courses for automated data analysis and information organization (Chaudhry and Kazim, 2022). This detailed analysis of student performance enhances work efficiency and saves time, optimizing online teaching to reduce teacher workload (Ahmad et al., 2022). Furthermore, the application of AI in education also brought numerous positive impacts for students. AI technologies, through intelligent algorithms and data analysis, can offer personalized learning experiences (Huang et al., 2023), adapting to the unique needs and learning paces of each student. This customized approach helps increase student engagement and learning efficiency (Huang et al., 2023; Hwang et al., 2020) and can identify and address specific challenges faced by students, thus providing more comprehensive educational support. This demonstrates that these tools not only enrich learning resources but also make learning more flexible and accessible. However, the application of AI technology in education also introduces certain risks, such as security, protective measures, and privacy issues (Limna et al., 2022). Additionally, the accuracy of subsequent categorizations and recommendations by AI tools relies on the depth and scope of the training data (Gupta et al., 2022). Therefore, any imbalance or flaw in the data can lead to systemic biases related to gender or race (Berendt et al., 2020). These issues serve as a reminder that when applying AI technology in education, it is crucial to handle data sources cautiously and protect student information to maximize the positive impacts of technology while effectively controlling its negative effects.
With the rapid development of AI technology, literacy in AI and computer science will become as essential as classical literacy (reading/writing) (Kandlhofer et al., 2016). AI has become a new skill that everyone should learn. In recent years, the teaching of AI from kindergarten to high school (K-12) classrooms has attracted the attention of scholars and teachers (Su, Guo, et al., 2023; Su, Ng, et al., 2023; Touretzky, Gardner-McCune, Breazeal, et al., 2019; Touretzky, Gardner-McCune, Martin, et al., 2019). Against this backdrop, how to integrate the basic concepts and skills of AI and computer science into early education is a question all scholars are facing. This is not only about learning the technical skills of AI but more importantly, cultivating children’s in-depth understanding and critical thinking abilities regarding these technologies (Goksel and Bozkurt, 2019). Moreover, AI education is of significant importance in promoting children’s interest in Science, Technology, Engineering, Arts, and Mathematics (STEAM) subjects (Xie and Zhang, 2023). By incorporating AI and computer science into teaching, children can experience the joy and challenges of these fields from an early age, which helps to inspire their long-term interest in these subjects and may promote their choice of related career paths in the future. Many organizations in Europe and America have developed and implemented a series of AI education programs aimed at comprehensively enhancing students’ AI knowledge and skills. Platforms such as the International Society for Technology in Education (https://iste.org/ai), Day of AI (https://dayofai.org/), Code.org® (https://code.org/), AI4T (https://www.ai4t.eu/), and AI+ (https://aiplus.udc.es/) offer a rich array of teaching resources and curriculum modules. They offer comprehensive educational resources and curriculum modules that cater to various educational levels, spanning from basic programming to advanced machine learning. However, in recent years, Asia has begun to actively integrate AI into education, with governments proactively establishing policies and guidelines (UNESCO, 2023). For instance, Korean scholars Kim et al. (2021) highlighted the importance of cultivating AI literacy in elementary students, emphasizing AI knowledge, skills, and attitudes. Similarly, Su and Zhong (2022) from Hong Kong developed an AI curriculum framework suitable for early childhood education, which includes components of AI knowledge, skills, and attitudes. These efforts reflect the progress Asia is making in the field of AI education and demonstrate educators’ commitment to integrating AI into early educational stages, preparing students to navigate a technologically advanced future effectively.
As AI technology continues to evolve rapidly, schools must adjust their educational strategies to accommodate these changes and provide suitable educational programs (Benvenuti et al., 2023). This includes developing up-to-date curricula that ensure educational content not only reflects current technological trends but also anticipates future developments. Consequently, creating adaptive AI education content and curricula tailored for Taiwanese children becomes particularly crucial, providing them with a robust learning environment to thrive in a future technology-driven society. This involves a thorough exploration of how AI courses can enhance children’s capabilities across various aspects, as well as their digital literacy and adaptability to technology. Against this backdrop, the primary goal of this research is to design AI courses for children in Taiwan that not only investigate and evaluate the impact on children’s learning but also specifically aim to impart a deep understanding and mastery of generative AI technologies from a user perspective. These courses are designed to equip children with the knowledge necessary to navigate and excel in a technologically driven future.
Based on the purpose of the study, this study has designed an AI curriculum tailored for children aged 9 to 12. Consequently, we have formulated three research questions:
RQ1: What content should be included in an AI curriculum designed for children aged 9–12 in Taiwan?
RQ2: How does participation in the AI curriculum affect the AI knowledge and skills of children aged 9–12 in Taiwan?
RQ3: How does participation in the AI curriculum influence the attitudes of children aged 9–12 in Taiwan towards AI technology?
Literature review
Applications of AI in the field of education
In recent years, with the increase in computational speeds and advancements in machine learning algorithms, AI has undergone rapid development. AI technology is revolutionizing traditional learning methodologies, making education more personalized, interactive, and efficient (Chen et al., 2020). Currently, one of the AI systems frequently used in education is Generative AI (Chiu, 2023), which refers to the application of AI algorithms to create new content, such as text, images, music, or other data.
AI systems for images often use deep learning models, especially Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). In contrast, AI systems for language typically rely on the Transformer model for Natural Language Processing (NLP) (Babcock and Bali, 2021). These systems produce new and unique outputs by learning and modeling the distribution of existing data. In the educational domain, applications of Generative AI include the automatic generation of learning content (Madhavi et al., 2023), language learning tools (Kessler, 2023; Rachels and Rockinson-Szapkiw, 2018), creative writing (Jauhiainen and Guerra, 2023; Javaid et al., 2023; Josephine, 2023), artistic creation (Vartiainen et al., 2023; Yin et al., 2023), and virtual experiments and simulations (Lee and Kim, 2023; Makransky and Petersen, 2019; Tsirulnikov et al., 2023). The following will introduce five applications of Generative AI:
Automatic generation of learning content: This approach creates and adapts learning content based on NLP and machine learning generative models (Jayapal, 2024). Generative AI can produce customized learning materials, generating exercises, instructional texts, or explanatory materials tailored to specific course objectives or learner needs. Common systems include Quizlet and Kahoot. The application of automatic content generation has been widely adopted in the field of language education, where technology-integrated learning systems have shown potential to enhance student engagement and improve language learning outcomes (Baxtiyorova, 2023). AI technologies aids in enhancing students’ English oral communication skills (Madhavi et al., 2023).
Language learning tools: NLP technologies based on the Transformer model are capable of understanding and generating natural language to accommodate the needs of diverse language learners (Winaitham, 2022). Generative AI can create simulated dialogs or texts to assist students in practicing language skills. This includes generating natural conversations, grammar exercises, and even adjusting difficulty levels according to students’ abilities. Common systems in education include Duolingo. In recent years, Duolingo has been extensively applied in language education (Kessler, 2023; Rachels and Rockinson-Szapkiw, 2018). Previous studies have shown that using Duolingo enhances students’ metacognitive awareness (Kessler, 2023), and gamification effectively boosts language skills and learners’ beliefs in their language learning capabilities (Rachels and Rockinson-Szapkiw, 2018).
Creative writing: The most representative systems are Open AI’s ChatGPT-3 and ChatGPT-4, based on Google’s Transformer large language models (Brown et al., 2020). These models excel at understanding and generating natural language, making them powerful tools for creative writing. Teachers and students can use these systems to inspire students’ creative writing (Javaid et al., 2023), such as automatically generating stories or texts. Past research has shown that ChatGPT, as personalized and customized learning material, can generate personalized learning materials based on students’ knowledge and interests (Jauhiainen and Guerra, 2023), and even enhance learning (Josephine, 2023). However, ChatGPT reduces the originality of work through simple text input, thus limiting the display of creativity (Shidiq, 2023). Moreover, scholars have pointed out that AI-generated writing raises ethical issues for society (Javaid et al., 2023). Therefore, integrating large language models into educational applications requires cautious and critical evaluation.
Artistic creation: These systems can generate high-quality, refined, and imaginative images through natural language text prompts. The systems use GANs, convolutional neural networks, and deep learning algorithms to create realistic images (Çelik, 2023). Additionally, VAEs can be used, which are known for their rapid image synthesis and achieving a good diversity of samples (Sebestyen et al., 2023). Common systems include Midjourney, Leonardo, and DALL-E, which are text-driven image generation tools. Among them, Midjourney tools have successfully integrated into the design innovation process based on collaborative design with AI, effectively enhancing innovation capabilities (Yin et al., 2023). Past research indicates that AI creation provides students with an opportunity to reflect on the differences between traditional and AI art creation, exploring their autonomy in the artistic process (Vartiainen et al., 2023). However, these text-generated systems, while iterative, lack opportunities for users to directly create and modify images. The emergence of Artbreeder offers a new perspective for AI art creation. Artbreeder’s Collage system, through shape and image collage creation followed by AI-generated artwork, allows for broader image creation and modification by users, truly realizing co-design with AI.
Virtual experiments and simulation: The most representative system is Labster, which is a laboratory learning system that integrates AI. It provides real-time feedback for students’ experiments, helping them enhance their understanding of concepts (Elmesalawy et al., 2021). Labster employs Virtual reality technology to offer scientific experiment simulations, allowing students to safely practice complex experiments (Lee and Kim, 2023). The new Labster 2.0 has added Dr. One, Labster’s new AI drone lab assistant. (Labster, 2022), providing real-time feedback and adaptive learning experiences, assisting students in learning within virtual experiments, and motivating skills development, problem-solving, and application of experiments. Labster has seen extensive application in educational settings. Previous research shows that immersive virtual lab simulations can improve student learning outcomes and motivation (Tsirulnikov et al., 2023) VR technology has been proven to significantly enhance emotional engagement by elevating learners’ sense of immersion, thereby fostering stronger intrinsic motivation, self-efficacy, and learning outcomes (Makransky and Petersen, 2019).
Generative AI has been widely applied in education, significantly impacting student learning. It not only offers personalized learning pathways, providing students with access to more resources and expertise, but also enhances learning experiences and motivation, thereby improving student learning outcomes. Most of these Generative AI tools generate outputs based on inputs provided by humans, such as text and feedback. While each tool holds value in education, it cannot entirely replace traditional teaching methods and the role of human teachers. The most effective approach may be to use AI as a supplementary tool within the educational process, in conjunction with traditional teaching methods. By doing so, a more comprehensive and effective learning environment can be created.
Teaching and assessment of AI
AI has become a body of knowledge that everyone must understand in the future, with the use of AI as a tool being one of the essential skills that everyone must possess (Yang, 2022). The previous section summarizes the current state of Generative AI tools in education, where AI has already had many successful cases in specific subject education, bringing effective improvements and enhancements to students’ learning. In the United States, the AI4K12 (2024) initiative has developed a framework consisting of five key concepts for AI education: Perception, Representation & Reasoning, Learning, Natural Interaction, and Societal Impact. This structure aids educators in deeply understanding the various dimensions of AI and integrating them into educational programs effectively. Consequently, this approach fosters comprehensive capabilities among students in grasping and applying AI technologies. According to Korean scholars Kim et al. (2021), an AI curriculum should encompass three key skills: AI Knowledge (should identify AI technologies in their daily lives), AI Skill(should be equipped with programming skills to use the technology in real world situations), and AI Attitude (should be able to consider potential ethical issues of using AI technologies). Among them, the knowledge of AI encompasses five indicators: (1) definition and types of AI, (2) problem-solving and search, (3) reasoning, (4) data and machine learning, and (5) applications. Additionally, AI skills include two indicators: (1) using AI tools, (2) computational thinking and programming. Lastly, AI attitudes comprise two indicators: (1) social impact, (2) collaborate with AI. Kim et al. (2021) comprehensively constructed the content structure of an AI literacy curriculum and implemented the curriculum in the education of 5th and 6th graders, successfully enhancing the students’ AI literacy. Based on the three skills outlined by Kim et al. (2021), Su and Zhong (2022) designed four modules, five activities, and one project, which are an introduction to AI, machine learning, speech recognition, and the flaws and biases of AI, recommending the implementation of this curriculum in the education of 3rd and 5th graders. However, to understand whether students’ AI literacy improved after actual learning requires assessment. Kim et al. (2021) assessed changes in students’ AI learning by measuring it with a subjective questionnaire before and after the program. In the study by Su and Zhong (2022), it is suggested that pre-tests and post-tests on AI knowledge can be used to understand how much students’ knowledge of AI has improved after learning. Furthermore, many researchers understand students’ learning outcomes through mixed assessment methods (Li et al., 2023), which can more comprehensively understand children’s subjective and objective learning outcomes. Therefore, this study will follow the curriculum suggestions of Su and Zhong (2022) and Kim et al. (2021), designing an AI course and understanding the changes in students after learning through a mixed assessment.
Methods
This study was conducted in Taiwan from July 17 to July 21, 2023. The course was delivered over five days, with six hours of instruction per day, totaling 30 h. A quasi-experimental single-group design was used, incorporating both pre- and post-tests to assess the outcomes. The experimental procedure, as shown in Fig. 1, began with an explanation of the experiment’s purpose and procedure to the participants, followed by an inquiry into their willingness to participate. Only after obtaining participants’ agreement did the experiment commence. Initially, all participants completed their consent forms and the pre-test (knowledge test), which covered topics on AI knowledge, skills, and attitudes. The aim of this test was to understand the participants’ knowledge, skills, and attitudes towards AI, requiring 20 min for completion. Subsequently, all participants engaged in the 5-day AI course, which amounted to 30 h in total. Upon completion of the 5-day course, participants were required to take the post-test, filling out two questionnaires (knowledge test, pupils’ attitude toward technology), which took 30 min. Additionally, a subset of students underwent semi-structured interviews after the course. Finally, four experts were invited to evaluate the students’ projects to understand their learning outcomes.
Fig. 1 [Images not available. See PDF.]
Experimental procedure.
Participants
This study employed an experimental approach, recruiting 30 elementary school students from the third and fourth grades. Participants included 17 boys and 13 girls, aged between 9 and 12 years old. Laptop computers were the primary tool used for learning AI skills. Students in Taiwan begin learning computer skills from the third grade of elementary school; this was taken into consideration when selecting our experiment participants. Additionally, proficiency in traditional Chinese was a criterion for participation, as the course was conducted in Chinese and all materials were provided in traditional Chinese. This study received approval from the medical ethics committee of the First People’s Hospital of Xuzhou. Since the participants were minors, consent was obtained from their legal guardians before the experiment. Guardians signed informed consent forms to indicate their child’s voluntary participation. All participants agreed to take part in the study, and the questionnaires used during the experiment were anonymous.
Research assessment
The instruments used in this study included AI knowledge tests, AI knowledge self-assessment questionnaires, pupils’ attitude toward technology questionnaires, and expert consensual assessment scales, employing both subjective and objective, qualitative and quantitative research methods to understand participants’ learning outcomes in the AI course. Subjective assessments comprised AI knowledge self-assessments and open-ended questions, while objective assessments included AI knowledge tests and expert consensus evaluations. For data analysis, this study utilized a statistical package for social science (IBM SPSS; Version 20) to conduct descriptive and inferential analysis of the quantitative data. The following section will introduce the instruments used in the study.
AI knowledge score test
This study developed an “AI knowledge, skills, and attitudes” test questionnaire to measure students’ learning outcomes in the AI course, serving as the basis for pre-test and post-test assessments. Our research team and AI course experts compiled the test questionnaire. The questions were drafted based on the course content and recommendations by Kim et al. (2021) and were divided into three knowledge domains: AI knowledge, AI skills, and AI attitudes. After completing the question drafting, three experts (STEAM education expert, AI course expert, and elementary school teacher) reviewed the test questionnaire for its validity. The test comprises 40 questions (Table 1), including 20 true/false questions and 20 multiple-choice questions, with each question worth 2.5 points for a total of 100 points. The same questions were used for both the pre-test and post-test, allowing for an objective evaluation of students’ learning outcomes in this course.
Table 1. “AI knowledge, skills, and attitudes” test questionnaire.
True/False Questions | |
1 | Artificial Intelligence (AI) is a technology that can simulate human intelligent behavior. |
2 | AI can only be used in computer science and not in other fields. |
3 | Deep learning is a more complex machine learning technique that uses multi-layer neural networks to analyze data. |
4 | Machine learning is a branch of AI that enables computers to learn and improve through experience. |
5 | AI can learn from its own mistakes through reinforcement learning. |
6 | Python is a commonly used programming language for AI and machine learning. |
7 | All AI systems can automatically generate data analysis reports without any human intervention. |
8 | In machine learning, a training dataset is used to teach AI systems how to complete tasks. |
9 | Writing AI algorithms does not require any programming skills. |
10 | All AI systems are implemented through manual programming rather than learning from data. |
Multiple-Choice Questions | |
1 | Which of the following is an application of AI? (a) Medical diagnosis (b) Autonomous driving (c) Speech recognition (d) All of the above |
2 | Which term describes the ability of AI systems to learn from data? (a) Machine learning (b) Algorithm (c) Programming (d) Artificial Intelligence |
3 | When did AI technology first start to develop? (a) 1950s (b) 1960s (c) 1970s (d) 1980s |
4 | Which model is commonly used in deep learning? (a) Convolutional Neural Network (b) Decision Tree (c) K-Nearest Neighbors (d) Linear Regression |
5 | Natural Language Processing (NLP) is a subfield of AI that mainly deals with what? (a) Image processing (b) Speech synthesis (c) Understanding and generating language and text (d) Robot control |
6 | Which programming language is often used for AI development? (a) CSS (b) HTML (c) Python (d) SQL |
7 | Which step is the most important in AI development? (a) Web design (b) Data collection (c) Report generation (d) Documentation writing |
8 | In deep learning, what does it mean to “train” a model? (a) Deploying the model (b) Testing model performance (c) Providing data for the model to learn (d) Storing data |
9 | Which of the following is incorrect? (a) AI technology can make our lives more convenient (b) Learning AI technology is important for my future development (c) AI technology will replace some human jobs (d) We should reject the use of AI tools |
10 | Which of the following is a deep learning framework used for image recognition? (a) TensorFlow (b) Django (c) React (d) Angular |
AI knowledge self-assessment questionnaires
The AI Knowledge self-assessment questionnaire is designed to subjectively measure students’ capabilities in AI knowledge. Numerous studies have utilized self-assessment questionnaires to measure changes in students’ understanding through AI courses (Kandlhofer et al., 2016; Kim et al., 2021; Park and Kwon, 2023). The AI knowledge self-assessment questionnaire was modified based on previous research (Table 2) and then reviewed by three experts, including one elementary school teacher, one STEAM education expert, and one AI course expert. It comprises 11 items, with all responses measured using a 7-point Likert scale ranging from (1) strongly disagree to (7) strongly agree.
Table 2. Constructs, items, question, and references in AI knowledge self-assessment questionnaire.
Constructs | Number of items | Question | References |
|---|---|---|---|
AI knowledge | 4 | I know the definition of AI. | (Kandlhofer et al., 2016; Kim et al., 2021; Park and Kwon, 2023) |
I know how AI is applied in our lives. | |||
I know how AI works. | |||
I can explain what machine learning is and its role in AI. | |||
AI skills | 3 | I can use AI-based system. | |
I can approach real world problems with AI. | |||
I am proficient in operating generative AI systems. | |||
AI attitude | 4 | I think AI can be helpful in my life. | |
I think AI can help with my study. | |||
I will further study AI technologies. | |||
I think AI have postive effect on the society. |
Expert consensual assessment
Amabile (1983) revised the Creativity Scale in the field of art and introduced an assessment method through expert consensus evaluation. In recent years, this method has been widely used to evaluate students’ work (Li et al., 2022), assessing the creativity, aesthetic appeal, and technical advantages value of the work through a group of experts (Amabile et al., 2018), to understand students’ learning outcomes and creativity. In this study, students utilized AI tools to create and ultimately complete an animated film as a course outcome. To better assess the learning outcomes and creativity of the students’ work, expert consensus evaluation was employed to gain a deeper understanding of the results of the students’ work. We used the evaluation criteria of Li et al. (2022) and invited four design experts to review the students’ work. The evaluation scale covered 12 items, all measured using a 7-point Likert scale ranging from (1) strongly disagree to (7) strongly agree. Through expert consensus evaluation, this study thoroughly examines the performance of students’ AI-created works in terms of creativity, aesthetics, and technical aspects.
Students’ attitudes towards AI technology
Measuring attitudes towards AI is a crucial factor in the success or failure of AI education (Suh and Ahn, 2022). When students have a high regard for AI technology, their intention to learn improves. If students do not develop a positive attitude towards learning professional skills, they are unlikely to master these skills, regardless of the effectiveness of the education (Fredrickson, 2001). Therefore, assessing students’ attitudes towards AI technology can serve as an indicator of their overall learning performance. Bame et al. (1993) developed the pupils’ attitude toward technology (PATT) scale to measure students’ attitudes towards AI technology, which encompasses six dimensions: interest in technology, perceived gender patterns in technology, perceived difficulty in technology, consequences of technology, technology and curriculum, and career aspirations in technology. Park and Kwon (2023) used two dimensions of Bame et al. (1993) ‘s PATT (interest in technology, career aspirations in technology) as indicators to assess students’ attitudes towards technology in their study on AI education among middle school students in Korea. Additionally, Ardies et al. (2013) reconstructed the PATT questionnaire for students aged 12–14, with six dimensions: technological career aspirations, interest in technology, tediousness towards technology, technology is for both, boys and girls, consequences of technology, technology is difficult. This questionnaire has shown good reliability in previous research, with Cronbach’s Alpha ranging between 0.69 and 0.88 (Boeve-de Pauw et al., 2022), indicating good reliability. Since the subjects of previous studies were middle school students, and the participants of this study are elementary school children, the questionnaire was adapted based on previous research (Table 3). Six experts were invited to review and modify the wording of the items to measure students’ attitudes towards technology. All items were measured on a seven-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree.
Table 3. Constructs, items, and references in PATT questionnaire.
Constructs | Number of items | References |
|---|---|---|
Technological career aspirations | 4 | (Ardies et al., 2013; Boeve-de Pauw et al., 2022; Park and Kwon, 2023) |
Interest in technology | 6 | |
Tediousness towards technology | 4 | |
Technology is for both, boys and girls | 3 | |
Consequences of technology | 4 | |
Technology is difficult | 4 |
Semi-structured interviews
To gain a deeper understanding of students’ feedback on the AI course, this study collected participants’ subjective opinions through qualitative interviews, aiming to understand their learning experiences and attitudes towards AI after the course. The questionnaire comprised four questions:
Before the course, the teacher asked you: What is my ambition? After the five-day course, do you think AI will influence your future ambition? What changes might it bring?
After the five-day course, do you have a better understanding of AI? Which part do you think you gained the most knowledge from?
During the five-day course, what was your favorite lesson? Why did you like this lesson?
After this course would you consider learning more AI knowledge or skills?
AI in children’s education: course design
Course description
AI course is designed to provide students with foundational knowledge and practical skills in the field of AI. The course begins with the history and basic concepts of AI, introducing students to the developmental background and core theories of AI. It then delves into key technologies such as machine learning, deep learning, natural language processing, and computer vision, enabling students to understand how these technologies work and their applications in the real world. Students will engage in hands-on projects to learn how to create with AI, divided into four units: drawing with AI, storytelling with AI, making music with AI, and animating with AI. The practical sessions utilize systems developed based on Generative AI, such as Artbreeder, ChatGPT, cutout.pro, and Boomy, to help students build actual project experience. The course will also explore the application of AI across various industries, including healthcare, finance, autonomous vehicles, and customer service. Moreover, the course not only focuses on imparting technical knowledge but also discusses AI ethics and social impacts, allowing students to fully understand the responsible use and potential effects of AI technology. By combining theoretical learning with practical application, this course aims to cultivate students’ critical thinking and innovation abilities.
Classroom materials
This course utilizes “AI Illustrated, Artificial Intelligence for Everyone” by Kang and Li (2022) as the textbook, complemented by PowerPoint presentations. Each student is required to prepare a laptop or tablet and a Google account. Additionally, students are asked to have a parent or guardian assist with registering a ChatGPT account before the course begins, as phone verification is needed for registration.
Course content and educational objectives
This course, designed for children’s education, blends theory with practice to cultivate a comprehensive understanding and application ability of AI among children. The course includes 10 units, covering a wide range of content from basic AI knowledge to specific applications (Table 4). In the first lesson, children will learn about the brief history and working principles of AI, laying the foundation of knowledge. Subsequent lessons will guide students through hands-on operations, using various AI tools for creative projects, such as drawing with Artbreeder, storytelling with ChatGPT, making music with Boomy, and producing animations with cutout.pro. Beyond developing technical skills, the course also covers AI applications in different fields, such as robotics classification, image recognition, speech recognition, machine translation, autonomous driving, and smart surveillance, helping students understand the diversity and practicality of AI. The final lessons focus on discussing the societal impacts of AI, including ethics, biases, and future development trends, aiming to foster children’s correct attitudes and critical thinking towards AI. Through these lessons, students will not only acquire AI knowledge and skills but also develop a profound understanding and positive outlook towards the future development of AI.
Table 4. The guideline of Lesson and learning objectives.
Unit topic | Content summary | Learning objectives | |
|---|---|---|---|
Lesson 1 | What is AI ? | 1. A brief history of AI. 2. How does AI work? 3. Classifications of AI. | • Understand the concept of AI • Recognize what AI is. |
Lesson 2 | Drawing with AI | 1. Image-generative AI. 2. The AI tool “Artbreeder”. | • Understand the concept of generative AI. • Learn about AI tools. |
Lesson 3 | Application of AI | 1. Classification of robots. 2. Image recognition—How AI sees the world. 3. Voice recognition—Listening to the world. 4. Machine translation—Barrier-free communication. 5. Autonomous driving—Everyone can be an expert driver. 6. Intelligent surveillance—Observing the world with a thousand eyes. 7. The future of AI in education. | • Recognize robots. • Understand the applications of image recognition, voice recognition, machine translation, autonomous driving, and intelligent surveillance. • Understand the future of AI education. |
Lesson 4 | Story telling with AI | 1. AI creating miracles in the literary field. 2. The AI tool “ChatGPT”. | • Learn about AI tools. |
Lesson 5 | AI creativity | 1. AI painting—Everyone is an “artist”. 2. AI design—Everyone is a “designer”. 3. AI photography—How AI changes photography. 4. AI media—How AI does the news. 5. AI awakening? The philosophical dialog between LaMDA and engineers. | • Understand the application of AI in various industries. |
Lesson 6 | Animating with AI | 1. Special virtual idols. 2. AI tools “Gooey.AI Docs, cutout.pro”. | • Learn about AI tools. |
Lesson 7 | The future of AI and humanity | 1. The impact of AI on society. 2. AI: Ethics, biases, and trust. 3. The relationship between AI and work life. 4. The future of AI. 5. The future of humanity. | • Understand the flaws and biases of AI. |
Lesson 8 | Make music with AI | 1. The infinite music prodigy. 2. The AI tool “Boomy”. | • Learn about AI tools. |
Lesson 9 | Making project | 1. Combine what has been learned in the AI course to write a story text, and create a theme poster, animation, and soundtrack. | • Learn to integrate skills acquired throughout the course. |
Lesson 10 | Presentation | 1. Present the project to the audience and introduce the AI tools used. Additionally, share reflections on the course. |
Lesson 1: What is AI?
In the first lesson, the teacher begins by posing two questions for the children to answer: (1) What is AI? and (2) What is your ambition? Following this, the teacher introduces the brief history of AI, how AI works, and the classification of AI. This lesson primarily focuses on imparting knowledge and theory, which might be somewhat dry for the students. Hence, the teaching method largely involves using case studies to explain the working principles of AI, particularly the concepts of machine learning and deep learning. Through the use of images and case studies, students’ knowledge retention and understanding are deepened. Figure 2 showcases the presentation of the lesson and the students’ learning process during the first class.
Fig. 2 [Images not available. See PDF.]
Course PowerPoint and classroom photos in lesson 1.
a Presentation for Lesson 1, which includes a brief history of AI, how AI works, and the classification of AI. b Photos of the teacher and students in Lesson 1.
Lesson 2: Drawing with AI
The second lesson primarily teaches students to draw pictures using AI tools. This class is divided into two parts; the first part involves students operating an AI image generation system. Students prepare their photos in advance, and through the AI system, future versions of themselves can be generated. The second part involves learning the “artbreeder” software, which requires initial drawing input, followed by entering prompts and adjusting parameters. Students can experiment with different parameter combinations to achieve satisfactory results. Figure 3 below displays the presentation of the lesson and the students’ learning process during the second class.
Fig. 3 [Images not available. See PDF.]
Course PowerPoint and classroom photos in lesson 2.
a Presentation for Lesson 2, the operational process of AI image generation systems. b Photo of a student learning about AI image generation in the class.
Lesson 3: Application of AI
The third lesson begins with the teacher showing a video about AI robots, followed by an introduction to the three laws of robotics and the types of robots (industrial robots, military robots, educational robots, etc.). Finally, the current five applications of AI in daily life are introduced, including image recognition, speech recognition, machine translation, autonomous driving, and intelligent surveillance. Through video explanations and case study analysis, students learn about the widespread application of AI technology in daily life and its indispensable role in modern living. Figure 4 shows the presentation of the lesson along with photos of students learning and interacting with the teacher.
Fig. 4 [Images not available. See PDF.]
Course PowerPoint and classroom photos in lesson 3.
a Presentation for Lesson 3, which includes an introductory video on AI robots, the three laws of robotics, and types of robots. b Photos of the teacher and students in Lesson 3.
Lesson 4: Story telling with AI
In the fourth lesson, students learn to write stories using AI. Initially, the teacher introduces the earliest applications of AI technology in literature, followed by an explanation of the process and working principles of AI content generation. Subsequently, the three-act structure and types of stories are discussed, acquainting children with the principles of story writing. Finally, the ChatGPT tool is taught, allowing students to learn basic operations of ChatGPT and use the tool to write stories. Figure 5 shows the presentation of the lesson and photos of students during the learning process.
Fig. 5 [Images not available. See PDF.]
Course PowerPoint and classroom photos in lesson 4.
a Presentation for Lesson 4, about the three-act structure and types of stories. b Photos of the teacher and students in Lesson 4.
Lesson 5: AI creativity
In the fifth lesson, the teacher explains through a presentation how AI can enhance human creativity in everyday life. The course is divided into five parts: AI painting, AI design, AI photography, AI media, and AI awakening. Through case analysis, students realize the limitless potential of AI technology. Friendly use can improve work performance in our lives and also increase creativity. Figure 6 shows the presentation of the lesson and photos of students during the learning process.
Fig. 6 [Images not available. See PDF.]
Course PowerPoint and classroom photos in lesson 5.
a Presentation for Lesson 5, about how artificial intelligence can enhance human creativity in everyday life, including five parts: AI painting, AI design, AI photography, AI media, and AI awakening. b Photos of the teacher and students in Lesson 5.
Lesson 6: Animating with AI
The sixth lesson mainly focuses on learning AI tools “Gooey.AI Docs, cutout.pro.” In previous lessons, students wrote stories through ChatGPT; in this lesson, the story is transformed into animated images using AI imaging tools. The teacher explains the tool’s usage, guiding students step by step to complete an animation. Figure 7 shows the presentation of the lesson and photos of students learning during the sixth class.
Fig. 7 [Images not available. See PDF.]
Course PowerPoint and classroom photos in lesson 6.
a Presentation for Lesson 6, about the operation of AI tools ‘Gooey.AI Docs, cutout.pro’. b Photos of the teacher and students in Lesson 6.
Lesson 7: The future of AI and humanity
The seventh lesson primarily explores the flaws and biases of AI. This class discusses the role of AI in modern society and the ethical, social, and professional challenges it brings. The course aims to provide students with a comprehensive understanding of AI’s potential and limitations, offering insights into AI and its multidimensional impacts on society, and understanding how to deal with and shape a future driven by AI. Figure 8 shows the presentation of the lesson and photos of students during the learning process.
Fig. 8 [Images not available. See PDF.]
Course PowerPoint and classroom photos in lesson 7.
a Presentation for Lesson 7, about the role of AI in modern society and the ethical, social, and professional challenges it brings. b Photos of the teacher and students in Lesson 7.
Lesson 8: Make music with AI
Lesson 8 aims to teach students how to create music using AI, specifically to compose soundtracks for their previously made animations. Initially, the teacher introduces the basic concepts and principles of AI music creation. By understanding how AI can simulate and learn the music creation process, students discover AI’s potential in the music domain. Next, the course covers how to use Boomy, an AI tool for music creation. Boomy is a platform that allows users to quickly generate original music pieces without the need for professional knowledge or complex musical theory. Students gradually master the skill of creating music with AI, from selecting styles and adjusting elements to generating musical works, and then creating soundtracks for animations. Figure 9 shows the presentation of the lesson and the process of students making soundtracks.
Fig. 9 [Images not available. See PDF.]
Course PowerPoint and classroom photos in lesson 8.
a Presentation for Lesson 8, about the operation of AI music composition tools ‘Boomy’. b Photos of students creating music using the ‘Boomy’ tool.
Lesson 9: Making project
In Lesson 9, students will integrate the knowledge and skills learned throughout the course to create a complete story project with the aid of AI. By integrating what has been learned in previous lessons, students ultimately complete an animated film that includes story creation, visual design, animation, and music, showcasing their unique perspectives and creativity. Figure 10 displays the presentation of the lesson and the process of students making the soundtrack.
Fig. 10 [Images not available. See PDF.]
Course PowerPoint and classroom photos in lesson 9.
a Presentation for Lesson 9, about how to use ChatGPT to write stories. b Photos of students using AI tools to create a story project.
Lesson 10: Presentation
The final lesson involves student presentations, where students report on stage, sharing their stories and reflections on the course learning. Figure 11a displays the course outcome poster, Fig. 11b shows a silhouette of a student’s report, and Fig. 11c, d feature videos produced by students using generative techniques.
Fig. 11 [Images not available. See PDF.]
Students’ works and classroom photos in lesson 10.
a Presentation for Lesson 10, the students’ project results story poster. b Photos of students during the project reporting process. c Video from the project titled ‘Adventurer’. d Video from the project ‘I Became a Mouse’.
Results
Reliability of the scale
This study conducted a reliability analysis on the assessment instrument, and the results revealed that the AI knowledge self-assessment questionnaire has a Cronbach’s Alpha coefficient of internal consistency of 0.904, the expert consensual assessment questionnaire has a Cronbach’s Alpha of 0.735, and the pupils’ attitude toward technology questionnaire has a Cronbach’s Alpha of 0.878. These figures indicate that the tools used for evaluation in this study possess good internal consistency reliability, as shown in Table 5.
Table 5. Reliability analysis of assessment instrument.
Items | Cronbach’s Alpha |
|---|---|
AI knowledge self-assessment | 0.904 |
Expert consensual assessment | 0.735 |
Pupils’ attitude toward technology | 0.878 |
Analysis of AI knowledge score test
AI knowledge scores, determined through objective test results, reflect students’ learning outcomes. Tests were administered to students both before and after the course. The study used paired sample t-tests to analyze students’ AI knowledge scores, to determine if there was a significant difference in scores before and after the course. Table 6 shows that there was a statistically significant difference in students’ scores before and after learning AI (p < 0.001).
Table 6. Paired sample t-test of pre-test and post-test scores for AI knowledge.
Pre-test | Post-test | ||||||
|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | df | T- Value | P- Value | |
Pre- and Post-Test | 45.16 | 19.67 | 73.50 | 13.07 | 29 | −6.58 | 0.000 |
Analysis of AI knowledge self-assessment
Questionnaires were administered to students both before and after the course, with the subjective AI knowledge self-assessment used to understand changes in students’ learning outcomes regarding AI knowledge. The study analyzed students’ AI knowledge self-assessments with paired sample t-tests, and the results showed a significant difference in AI knowledge self-assessment before and after the test (p < 0.001). Table 7 indicates that the AI course effectively enhanced students’ knowledge about AI.
Table 7. Paired sample t-test of pre-test and post-test scores for AI knowledge self-assessment.
Pre-test | Post-test | ||||||
|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | df | T- Value | P- Value | |
Knowledge | 3.63 | 0.85 | 5.45 | 0.42 | 29 | −10.05 | 0.000 |
Skills | 3.92 | 1.08 | 5.81 | 0.47 | 29 | −8.13 | 0.000 |
Attitude | 3.85 | 1.06 | 5.82 | 0.48 | 29 | −9.34 | 0.000 |
Analysis of expert consensual assessment
To understand students’ learning outcomes from the AI course, experts were invited to evaluate students’ AI projects after the course using Expert Consensual Assessment. A one-sample t-test analysis was conducted, utilizing a 7-point Likert scale with 4 (neutral) as the test value for comparison. Table 8 displays the results. The results showed significant differences when compared to the neutral value of 4 in aspects of creativity, aesthetic appeal, and technical advantages (p < 0.001). This indicates that students’ project outcomes post-course performed well in creativity, aesthetic appeal, and technical advantages, with creativity scoring the highest.
Table 8. One sample t-test of expert consensual assessment.
95% Confidence interval | |||||||
|---|---|---|---|---|---|---|---|
Mean | SD | df | T- Value | P- Value | Lower | Upper | |
creativity | 5.82 | 0.89 | 29 | 20.50 | 0.000 | 1.64 | 2.00 |
aesthetic appeal | 5.22 | 0.74 | 29 | 16.50 | 0.000 | 1.07 | 1.37 |
technical advantages | 5.57 | 0.11 | 29 | 16.06 | 0.000 | 1.63 | 2.11 |
Analysis of pupils’ attitude toward technology
To understand students’ attitudes towards AI, the study assessed students’ attitudes towards AI technology using the pupils’ attitude toward technology scale. A one-sample t-test analysis was conducted, using a 7-point Likert scale with 4 (neutral) as the test value for comparison. Table 9 shows the results. The results indicated significant differences when compared to the neutral value of 4 in aspects of technological career aspirations, interest in technology, tediousness towards technology, technology is for both boys and girls, consequences of technology, and technology is difficult (p < 0.001). This indicates that students had a highly positive evaluation of their attitudes towards AI technology post-course.
Table 9. One sample t-test of Pupils’ attitude toward technology.
95% Confidence interval | |||||||
|---|---|---|---|---|---|---|---|
Mean | SD | df | T- Value | P- Value | Lower | Upper | |
Technological career aspirations | 5.45 | 0.42 | 29 | 18.98 | 0.000 | 1.30 | 1.61 |
Interest in technology | 5.83 | 0.33 | 29 | 30.35 | 0.000 | 1.71 | 1.96 |
Tediousness towards technology | 5.50 | 0.43 | 29 | 19.08 | 0.000 | 1.33 | 1.66 |
Technology is for both, Boys and Girls | 5.71 | 0.31 | 29 | 30.00 | 0.000 | 1.59 | 1.82 |
Consequences of technology | 5.84 | 0.48 | 29 | 20.81 | 0.000 | 1.66 | 2.02 |
Technology is difficult | 5.58 | 0.29 | 29 | 29.29 | 0.000 | 1.47 | 1.69 |
Analysis of semi-structured interviews
Through semi-structured interviews, we explored students’ learning experiences, attitudes toward AI, and their suggestions for the course. The first question posed by the teacher before the course was: What is your ambition? After the five-day course, do you think AI will influence your future ambition? What changes might it bring? Student 2 stated, “My ambition is to be a doctor. AI will definitely impact my future work; it could help me provide more precise emergency care to patients, potentially making me more efficient in surgeries. However, I am also concerned that robots might replace my profession in the future.” Additionally, Student 12 mentioned, “I want to be a teacher, and I believe AI could assist me in managing students, such as identifying which student is not paying attention. If AI develops very rapidly, it could become everyone’s teacher, leaving me without a job.” Furthermore, Student 20 expressed, “I aspire to be a traffic police officer, maintaining order in traffic. If AI could assist me in monitoring vehicles that violate regulations, it could reduce my workload, which I hope can be realized.” These responses reflect the students’ expectations and concerns about the application of AI technology in various professional fields. They recognize the potential of AI and the challenges it may bring, demonstrating their foresight into the transformations in the future professional world.
The second question asked students about their understanding of AI after the five-day course and which part they felt they learned the most knowledge from. Student 6 stated, “Before the course, I had no idea what AI was. After the teacher’s explanation, I came to understand the basic concepts of AI. The ChatGPT unit taught me the most skills; it’s like my encyclopedia, providing me with a lot of knowledge.” Additionally, Student 15 mentioned, “I learned a lot about using AI tools, especially the part about AI painting, which allows me to quickly realize my concepts.” Furthermore, Student 17 said, “ I learned a lot about AI, particularly the parts about AI ethics and intellectual property rights, which scared me. It turns out our lives are full of AI technology, monitoring us.” From this feedback, we can see that AI education not only enhances students’ technical knowledge and skills but also engages them in deep reflections on the ethics and social impact of AI.
The third question asked students about their favorite lesson during the five-day course and why they liked that lesson. Ten students favored Lesson 4 (Storytelling with AI), with Student 7 mentioning, “I learned how to use AI tools, especially how to converse with ChatGPT. It’s really powerful in answering many of my questions and can even help me write stories.” Additionally, eight students liked Lesson 3 (Application of AI), with Student 18 stating, “This unit expanded my understanding of AI. It can be applied in so many areas, truly broadening my horizons. AI permeates our lives, a fact I was previously unaware of.” Furthermore, five students enjoyed Lesson 2 (Drawing with AI), with Student 12 expressing, “I was extremely excited about the AI drawing unit. Through learning, I discovered that AI can help us turn imagined scenes into reality, creating amazing artworks. This process not only inspired my creativity but also made me eager to use it more, curious about the possible changes. The AI tools made me realize that even non-professional artists could create beautiful paintings with the help of AI.” Moreover, three students favored Lesson 6 (Animating with AI), with Student 15 commenting, “AI animation tools not only simplify the animation production process but also allow for the creation of high-quality animations in a short period. This makes me look forward to future animation creation, feeling like I can create any story I imagine.” In addition, two students liked Lesson 8 (Make music with AI), with Student 6 saying, “I never thought I could create music. The process was magical and fun, allowing me to produce unique music based on my mood and style. It completely changed my view of music creation.” Additionally, one student preferred Lesson 7 (The future of AI and humanity), with Student 30 noting, “I like this unit because I think it’s very important. While AI technology can improve our lives, it also poses many risks, prompting me to think about how to develop and use AI technology responsibly.” These feedbacks show that students not only have a deep understanding of the diverse applications of AI technology but also recognize the opportunities and challenges brought by AI. Each unit has influenced students’ views on AI to varying degrees and has had a positive impact on their learning and future career development.
The fourth question inquired whether students would pursue further learning in AI knowledge or skills after this activity. Twenty-eight students expressed their intention to continue studying AI knowledge and skills in the future, while two students stated they did not wish to continue using AI tools. Student 4 remarked, “AI has limitless potential; it can not only enhance our work efficiency but also inspire innovation and creativity. Therefore, I plan to delve deeper into AI-related knowledge and skills, especially in data analysis and machine learning, hoping to apply these technologies in my professional field in the future.” Additionally, Student 24 commented, “After participating in this activity, I am more convinced that everyone must understand AI in the future because it is ubiquitous. I intend to enroll in more AI courses, particularly those related to AI’s application in design and artistic creation. I believe that mastering more AI tools and technologies will open new possibilities in my creative process.” Meanwhile, Student 17, who prefers not to continue using AI tools, stated, “In this course, I saw the infinite potential of AI tools, but I feel they might limit my learning. I prefer hands-on and traditional methods of creating and learning because they provide a more direct and profound experience.” Furthermore, Student 30 mentioned, “I might not actively use AI tools, but I still maintain an open attitude towards the development of AI technology and am willing to understand its progress and impact in other fields.” These feedbacks reveal the diverse impact of AI education on students’ attitudes, as well as the personal preferences and educational methods that need to be considered when continuing to adopt and integrate AI technology.
Discussion
The transformative effects of AI education on students’ knowledge and creativity
Based on the aforementioned results, students experienced varying impacts on their AI knowledge and skills after a five-day AI course. The study results indicate that after the AI course, students achieved positive outcomes in learning AI knowledge and skills. Both objective test scores and subjective knowledge self-assessments were consistent, with post-test learning outcomes significantly better than pre-test results. This indicates that AI courses contribute to enhancing students’ AI knowledge and skills, aligning with previous research (Aung et al., 2022; Wu and Yang, 2022), which suggests AI courses can improve students’ understanding of fundamental AI concepts. Additionally, the results from semi-structured interviews also echo this finding, with students unanimously agreeing that the five-day course enriched their AI knowledge, especially in the use of AI tools. Ng et al. (2022) highlighted that AI education courses can enhance students’ awareness, understanding, and ability to use AI. It also increases motivation to learn (Josephine, 2023). Hence, the AI education program designed in this study effectively cultivated students’ AI literacy, equipping them to solve real-life problems with AI knowledge. Furthermore, analysis using the Expert Consensual Assessment Technique showed significant differences from the median score of 4, indicating students performed well in creativity in the arts, with creativity being their strongest aspect. This result suggests that AI courses have the potential to stimulate students’ creativity in design, consistent with the views of Rong et al. (2022), that applying AI technology in courses can effectively enhance students’ creativity and stimulate their learning enthusiasm. AI can facilitate creativity (Marrone et al., 2022). Similarly, semi-structured interview outcomes revealed that students believe AI tools not only inspire creativity but also enhance curiosity, encouraging continuous exploration. Moreover, AI can also spark learners’ curiosity (Gordon et al., 2015), prompting them to keep experimenting. Thus, it is evident that AI education courses can effectively improve creativity, technical application, and other comprehensive skills.
Students hold positive views on AI; AI courses not only enhance students’ engagement but also increase their focus on ethics and morality
This study employed the pupils’ attitude toward technology scale to measure students’ attitudes towards AI technology. The research findings indicate significant differences from the median score of 4 on the scale, demonstrating that the majority of students have a positive attitude towards AI technology and are willing to further explore and learn AI-related knowledge. Courses that integrate theory and practice allow students to directly perceive the practical value and creative potential of AI technology, thereby enhancing their motivation and interest in learning. These outcomes align with results from previous research, suggesting AI courses can cultivate a positive attitude towards AI literacy in students, enhancing their intrinsic motivation to learn AI (Aung et al., 2022; Lee and Kwon, 2024). Results from semi-structured interviews also reveal a similar pattern, where students exhibit a high level of acceptance towards AI technology, viewing it as an essential tool for everyone to understand and learn, and a critical component of future life. However, a minority of students hold negative attitudes, perceiving potential risks associated with AI technology that invoke fear. This phenomenon mirrors findings in the research by Kaspersen et al. (2022), indicating a need for cautious engagement with AI, as AI systems should benefit humanity rather than pose potential threats. Therefore, educators and researchers should incorporate content on AI ethics, safety, and its societal impacts within training programs. By discussing potential risks and ethical dilemmas associated with AI, students can be guided to develop critical thinking skills, understanding how to enjoy the conveniences AI offers while avoiding potential risks and impacts, ensuring a comprehensive understanding of AI’s benefits and challenges. Overall, the findings of this study underscore the crucial role of education in fostering positive attitudes towards AI technology among students. Integrating theoretical and practical content not only enhances students’ understanding and interest in AI technology but also assists them in forming a comprehensive view of AI ethics and societal impacts.
Conclusions
The objective of this study was to design an AI course for children, to explore and evaluate its impact on their learning. Over a 5-day (30-h) AI course, a total of 30 elementary school students participated. The results indicate significant effects of the AI education program in enhancing students’ AI knowledge, skills, and positive attitudes. Post-course, students not only showed marked improvements in AI-related knowledge and skills but also exhibited excellent creativity, verified by both objective test scores and subjective self-assessments. This study found that students generally hold positive attitudes towards AI technology and express a strong interest in delving deeper into AI techniques. They widely regard AI technology as an indispensable tool for the future, deserving of everyone’s learning and mastery. However, the study also notes that, despite the overwhelming positive attitudes towards AI, a minority of students expressed concerns about its potential risks. Thus, this suggests that educators and curriculum designers, in future AI educational practices, need to place more emphasis on teaching AI ethics, safety, and societal impacts. Introducing these topics not only aids students in forming a comprehensive and balanced understanding of AI technology but also fosters the development of their critical thinking skills, preparing them for responsible AI use in future societies. Given that there has yet to be a systematic application of AI education courses for elementary children, this research fills a significant gap in the field of children’s AI education and contributes to AI education. The study confirms the effectiveness of AI education courses in enhancing students’ literacy in AI, sparking interest in learning, and fostering creativity. AI knowledge, AI skills, and AI attitudes are the three competencies students need to possess for AI literacy (Kim et al., 2021). Furthermore, the study highlights the importance of strengthening ethics and social responsibility education in AI teaching, ensuring students can positively utilize AI technology while fully recognizing and addressing its potential challenges.
Limitations and future research
This study has some limitations. Firstly, it targeted third and fourth graders in Taiwan as the sample; future research should consider encompassing a broader range of children from different regions and backgrounds to enhance the representativeness and generalizability of the research findings. Secondly, the study was conducted on only 30 children, limiting the general applicability and statistical power of the results; future studies could consider expanding the sample size. Lastly, in terms of creativity assessment, this study solely utilized post-test expert evaluations to measure the children’s creativity performance. While this method provides a snapshot of children’s creativity levels at the end of learning, it fails to capture changes and developments in creativity throughout the learning process. Therefore, future research is suggested to explore creativity changes through pre-post testing methods.
Acknowledgements
We thank all participants, research assistants, and others involved in conducting this study.
Author contributions
Hong-Guang Zhao contributed to the development of the AI curriculum design, collection of data, and empirical analysis. Xin-Zhu Li and Xin Kang contributed to the data analysis, design of research methods, and creation of tables. Hong-Guang Zhao and Xin-Zhu Li participated in the development of the research design, writing, and interpretation of the analysis. All authors contributed to the literature review and conclusion. All authors have read and approved the final manuscript.
Competing interests
The authors declare no competing interests.
Ethical approval
This research was approved by the medical ethics committee of the First People’s Hospital of Xuzhou (approval No. xyy11 [2023] 157). This study does not involve the collection or analysis of data that could be used to identify participants. All information is anonymized and the submission does not include images that may identify the person. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.
Informed consent
Informed consent was obtained from all participants involved in this study, which focused on educational research targeting elementary school students. Since the participants were minors, consent was obtained from their legal guardians. All guardians were thoroughly informed about the purpose and aims of the study and provided voluntary consent after being assured of their child’s anonymity and the academic use of the collected data.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
Ahmad SF, Alam MM, Rahmat MK, Mubarik MS, Hyder SI (2022) Academic and Administrative Role of Artificial Intelligence in Education. Sustainability, 14(3), 1101. https://www.mdpi.com/2071-1050/14/3/1101
AI4K12 (2024) Artificial Intelligence for K12 initiative. Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA). Retrieved 12 May 2024 from https://ai4k12.org
Amabile, TM. The social psychology of creativity: a componential conceptualization. J Personal Soc Psychol; 1983; 45,
Amabile TM, Collins MA, Conti R, Phillips E, Picariello M, Ruscio J, Whitney D (2018) Creativity in context: Update to the social psychology of creativity. Routledge
Ardies, J; De Maeyer, S; Gijbels, D. Reconstructing the pupils attitude towards technology-survey. Des Technol Educ; 2013; 18,
Aung, ZH; Sanium, S; Songsaksuppachok, C; Kusakunniran, W; Precharattana, M; Chuechote, S; Pongsanon, K; Ritthipravat, P. Designing a novel teaching platform for AI: A case study in a Thai school context. J Comput Assisted Learn; 2022; 38,
Babcock J, Bali R (2021) Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models. Packt Publishing Ltd
Bame, EA; Dugger, WE; de Vries, M; McBee, J. Pupils’ Attitudes Toward Technology—PATT-USA. J Technol Stud; 1993; 19,
Baxtiyorova, N. The impact of technology-based interactive english teaching tools on student engagement and language learning outcomes. Innov Dev Educ Act; 2023; 2,
Benvenuti, M; Cangelosi, A; Weinberger, A; Mazzoni, E; Benassi, M; Barbaresi, M; Orsoni, M. Artificial intelligence and human behavioral development: A perspective on new skills and competences acquisition for the educational context. Comput Hum Behav; 2023; 148, [DOI: https://dx.doi.org/10.1016/j.chb.2023.107903]
Berendt, B; Littlejohn, A; Blakemore, M. AI in education: learner choice and fundamental rights. Learn Media Technol; 2020; 45,
Boeve-de Pauw, J; Ardies, J; Hens, K; Wullemen, A; Van de Vyver, Y; Rydant, T; De Spiegeleer, L; Verbraeken, H. Short and long term impact of a high-tech STEM intervention on pupils’ attitudes towards technology. Int J Technol Des Educ; 2022; 32,
Brown, T; Mann, B; Ryder, N; Subbiah, M; Kaplan, JD; Dhariwal, P; Neelakantan, A; Shyam, P; Sastry, G; Askell, A. Language models are few-shot learners. Adv Neural Inform Process Syst; 2020; 33, pp. 1877-1901.
Çelik T (2023) The Role of Artificial Intelligence for The Architectural Plan Design: Automation in Decision-making. In: Proceedings of the 2023 8th International Conference on Machine Learning Technologies. https://doi.org/10.1145/3589883.3589903
Chaudhry, MA; Kazim, E. Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021. AI Ethics; 2022; 2,
Chen, X; Xie, H; Zou, D; Hwang, G-J. Application and theory gaps during the rise of Artificial Intelligence in Education. Comput Educ Artificial Intell; 2020; 1, [DOI: https://dx.doi.org/10.1016/j.caeai.2020.100002]
Chiu TKF (2023) The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interact Learn Environ 1–17. https://doi.org/10.1080/10494820.2023.2253861
Elmesalawy MM, Atia A, Yousef AMF, Abd El-Haleem AM, Anany MG, Elmosilhy NA, & El Din ES (2021) Ai-based flexible online laboratory learning system for post-covid-19 era: Requirements and design. In 2021 international mobile, intelligent, and ubiquitous computing conference (MIUCC) IEEE, (pp. 1–7). https://doi.org/10.1109/MIUCC52538.2021.9447601
Fredrickson, BL. The role of positive emotions in positive psychology: the broaden-and-build theory of positive emotions. Am Psychol; 2001; 56,
Goksel N, Bozkurt A (2019) Artificial Intelligence in Education: Current Insights and Future Perspectives. In S Sisman-Ugur & G Kurubacak (eds), Handbook of Research on Learning in the Age of Transhumanism (pp 224–236). IGI Global. https://doi.org/10.4018/978-1-5225-8431-5.ch014
Gordon G, Breazeal C, Engel S (2015) Can children catch curiosity from a social robot? In: Proceedings of the tenth annual ACM/IEEE international conference on human-robot interaction. pp 91–98. https://doi.org/10.1145/2696454.2696469
Gupta, M; Parra, CM; Dennehy, D. Questioning racial and gender bias in AI-based recommendations: do espoused national cultural values matter?. Inform Syst Front; 2022; 24,
Huang, AYQ; Lu, OHT; Yang, SJH. Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Comput Educ; 2023; 194, [DOI: https://dx.doi.org/10.1016/j.compedu.2022.104684]
Hwang, G-J; Xie, H; Wah, BW; Gašević, D. Vision, challenges, roles and research issues of Artificial Intelligence in Education. Comput Educ Artificial Intel; 2020; 1, [DOI: https://dx.doi.org/10.1016/j.caeai.2020.100001]
Jauhiainen, JS; Guerra, AG. Generative AI and ChatGPT in School Children’s Education: evidence from a School Lesson. Sustainability; 2023; 15,
Javaid, M; Haleem, A; Singh, RP; Khan, S; Khan, IH. Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. BenchCouncil Transac Benchmarks Standards Eval; 2023; 3,
Jayapal J (2024) Artificial Intelligence in Education: A Critic on English Language Teaching. In Artificial Intelligence and Knowledge Processing, CRC Press, p 348–357
Josephine, O. Benefits of artificial intelligence (ChatGPT) in education and learning: is chat GPT helpful?. Int Rev Pract Innov Technol Green Energy (IRPITAGE); 2023; 3,
Kandlhofer M, Steinbauer G, Hirschmugl-Gaisch S, Huber P (2016) Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE frontiers in education conference (FIE). IEEE, pp 1–9, https://doi.org/10.1109/FIE.2016.7757570
Kang X, Li X-Z (2022) AI Illustrated, Artificial Intelligence for Everyone [图解AI 适合所有人的人工智能]. Lulu Press
Kaspersen, MH; Bilstrup, K-EK; Van Mechelen, M; Hjort, A; Bouvin, NO; Petersen, MG. High school students exploring machine learning and its societal implications: Opportunities and challenges. Int J Child-Comput Interact; 2022; 34, [DOI: https://dx.doi.org/10.1016/j.ijcci.2022.100539]
Kessler, M. Supplementing mobile-assisted language learning with reflective journal writing: a case study of Duolingo users’ metacognitive awareness. Comput Assisted Lang Learn; 2023; 36,
Kim, S; Jang, Y; Kim, W; Choi, S; Jung, H; Kim, S; Kim, H. Why and What to Teach: AI curriculum for elementary school. Proc. AAAI Conf. Artificial Intell; 2021; 35,
Labster (2022) Meet Dr. One: Labster’s new lab companion. https://www.labster.com/blog/meet-dr-one
Lee, J; Kim, Y. Sustainable educational metaverse content and system based on deep learning for enhancing learner immersion. Sustainability; 2023; 15,
Lee, SJ; Kwon, K. A systematic review of AI education in K-12 classrooms from 2018 to 2023: Topics, strategies, and learning outcomes. Comput Educ Artificial Intell; 2024; 6, [DOI: https://dx.doi.org/10.1016/j.caeai.2024.100211]
Li X-Z, Chen C-C, Kang X (2022) Research on the cultivation of sustainable development ability of higher vocational students by creative thinking teaching method [Original Research]. Front Psychol 13. https://doi.org/10.3389/fpsyg.2022.979913
Li, X-Z; Chen, C-C; Kang, X. Religious diversity education: raising children’s awareness of religious diversity through augmented reality. Humanit Soc Sci Commun; 2023; 10,
Limna, P; Jakwatanatham, S; Siripipattanakul, S; Kaewpuang, P; Sriboonruang, P. A review of artificial intelligence (AI) in education during the digital era. Adv Knowl Exec; 2022; 1,
Madhavi, E; Lavanya, S; Vijayakumar, K; Rani, PBE; Vemuganti, S. Developing Learners’ English-Speaking Skills using ICT and AI Tools. J Adv Res Appl Sci Eng Technol; 2023; 32,
Makransky, G; Petersen, GB. Investigating the process of learning with desktop virtual reality: a structural equation modeling approach. Comput Educ; 2019; 134, pp. 15-30. [DOI: https://dx.doi.org/10.1016/j.compedu.2019.02.002]
Marrone, R; Taddeo, V; Hill, G. Creativity and Artificial Intelligence—A Student Perspective. J Intell; 2022; 10,
Nemorin, S; Vlachidis, A; Ayerakwa, HM; Andriotis, P. AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development. Learn Media Technol; 2023; 48,
Ng, DTK; Luo, W; Chan, HMY; Chu, SKW. Using digital story writing as a pedagogy to develop AI literacy among primary students. Comput Educ Artificial Intell; 2022; 3, [DOI: https://dx.doi.org/10.1016/j.caeai.2022.100054]
Park W, Kwon H (2023) Implementing artificial intelligence education for middle school technology education in Republic of Korea. Int J Technol Des Educ. https://doi.org/10.1007/s10798-023-09812-2
Rachels, JR; Rockinson-Szapkiw, AJ. The effects of a mobile gamification app on elementary students’ Spanish achievement and self-efficacy. Comput Assisted Lang Learn; 2018; 31,
Rong Q, Lian Q, Tang T (2022) Research on the influence of AI and VR technology for Students’ Concentration and Creativity [Original Research]. Front Psychol 13. https://doi.org/10.3389/fpsyg.2022.767689
Sebestyen, A; Hirschberg, U; Rasoulzadeh, S. Using deep learning to generate design spaces for architecture. Int J Arch Comput; 2023; 21,
Shidiq M (2023) The use of artificial intelligence-based chat-gpt and its challenges for the world of education; from the viewpoint of the development of creative writing skills. 2023, 1(1), 5. https://ejournal.unuja.ac.id/index.php/icesh/article/view/5614
Su J, Guo K, Chen X, Chu SKW (2023) Teaching artificial intelligence in K–12 classrooms: a scoping review. Interact Learn Environ 1–20. https://doi.org/10.1080/10494820.2023.2212706
Su, J; Ng, DTK; Chu, SKW. Artificial Intelligence (AI) literacy in early childhood education: the challenges and opportunities. Comput Educ Artificial Intell; 2023; 4, [DOI: https://dx.doi.org/10.1016/j.caeai.2023.100124]
Su, J; Zhong, Y. Artificial Intelligence (AI) in early childhood education: Curriculum design and future directions. Comput Educ Artificial Intell; 2022; 3, [DOI: https://dx.doi.org/10.1016/j.caeai.2022.100072]
Suh, W; Ahn, S. Development and validation of a scale measuring student attitudes toward artificial intelligence. SAGE Open; 2022; 12,
Touretzky, D; Gardner-McCune, C; Breazeal, C; Martin, F; Seehorn, D. A Year in K–12 AI Education. AI Magazine; 2019; 40,
Touretzky, D; Gardner-McCune, C; Martin, F; Seehorn, D. Envisioning AI for K-12: what should every child know about AI?. Proc. AAAI Conf. Artificial Intell.; 2019; 33,
Tsirulnikov, D; Suart, C; Abdullah, R; Vulcu, F; Mullarkey, CE. Game on: immersive virtual laboratory simulation improves student learning outcomes & motivation. FEBS Open Bio; 2023; 13,
UNESCO (2018, 15 May 2023) Audrey Azoulay: Making the most of artificial intelligence. Retrieved 1 January 2024 from https://courier.unesco.org/en/articles/audrey-azoulay-making-most-artificial-intelligence
UNESCO (2023) How generative AI is reshaping education in Asia-Pacific. Retrieved 11 May 2024 from https://www.unesco.org/en/articles/how-generative-ai-reshaping-education-asia-pacific
Vartiainen, H; Tedre, M; Jormanainen, I. Co-creating digital art with generative AI in K-9 education: socio-material insights. Int J Educ Art; 2023; 19,
Winaitham W (2022) The scientific review of AI functions of enhancement english learning and teaching. In 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). IEEE, pp 148–152. https://doi.org/10.1109/ICTC55196.2022.9952632
Wu S-Y, Yang K-K (2022) The Effectiveness of Teacher Support for Students’ Learning of Artificial Intelligence Popular Science Activities [Original Research]. Fron Psychol 13. https://doi.org/10.3389/fpsyg.2022.868623
Xie Y, Zhang X (2023) Research on the design and implementation of primary school STEM project based on VR coursewares. Int JTechnol Des Educ. https://doi.org/10.1007/s10798-023-09848-4
Yang, W. Artificial Intelligence education for young children: why, what, and how in curriculum design and implementation. Comput Educ Artificial Intell; 2022; 3, [DOI: https://dx.doi.org/10.1016/j.caeai.2022.100061]
Yin, H; Zhang, Z; Liu, Y. The exploration of integrating the midjourney artificial intelligence generated content tool into design systems to direct designers towards future-oriented innovation. Systems; 2023; 11,
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Abstract
In the digital age, the application of Artificial Intelligence (AI) has become an irreversible trend, with its potential in the field of education being particularly noteworthy. However, there are currently few AI education programs for children in Taiwan, and there is a lack of systematic teaching resources and methods, which poses a major challenge to the promotion of AI education. To address this challenge, this study designed a tailor-made AI curriculum for children in Taiwan, aimed at enhancing their foundational knowledge in the AI field and skills in using generative AI. This study was conducted in Taiwan, involving 30 elementary school students from grades 3 and 4, employing a single-group pre-test and post-test research design. Data were collected and analyzed through pre-and post-tests on AI knowledge quizzes and AI knowledge self-assessment, as well as the expert consensual assessment technique and the pupils’ attitude toward technology survey questionnaire. Research shows that students who participated in the AI course significantly improved their test scores on AI knowledge before and after the course. The students AI knowledge increased by 62.75%, demonstrating the course’s effectiveness and showing a positive attitude towards AI technology. Additionally, the student’s project outcomes demonstrated a high level of creativity. The students exhibited an enhanced interest and positive attitude towards learning AI, expressing a willingness to participate in more AI educational courses. This work provides valuable experience and guidance for the future integration of AI technology in children’s education, offering practical guidelines for teachers and researchers on how to effectively teach AI knowledge, as well as serving as a robust reference for educational policy makers in formulating strategies to promote AI education.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
; Kang, Xin 3 1 Tianjin University of Technology, School of Art and Design, Tianjin, China (GRID:grid.265025.6) (ISNI:0000 0000 9736 3676)
2 China University of Mining and Technology, School of Architecture & Design, Xuzhou, China (GRID:grid.411510.0) (ISNI:0000 0000 9030 231X)
3 NingboTech University, School of Design, Ningbo, China (GRID:grid.513221.6)