Title: AI Literacy in Early Childhood Education

Author Name: Anique Welcome

Selected Case (Published Article): Su, J., & Yang, W. (2023). Artificial Intelligence (AI) literacy in early childhood education: an intervention study in Hong Kong. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2023.2217864

1. Introduction

Artificial Intelligence (AI), a branch of computer science devoted to developing systems capable of performing tasks requiring human intelligence (i.e., learning, reasoning, problem-solving, and creative thinking), has become a prominent component of everyday life (Bailey, 2024). Though not a new phenomenon, AI has recently garnered significant attention as an emerging learning technology in education. Artificial Intelligence’s dynamic nature and promising capabilities have the potential to transform modern-day education, emphasizing the need for learners across all educational sectors—from early childhood to higher education—to become AI literate. AI literacy refers to the knowledge and skills that prepare and support humans to communicate, collaborate, and interact with other people and machines in an AI-dominated world (Long and Magerko, 2020). For younger learners ages 3–5, AI literacy encompasses both the cognitive understanding of AI concepts and the practical use of AI tools (Su, Ng, et al., 2023).

Previous research by scholars posits that implementing AI literacy programs in early childhood yields positive effects on young learners, improving their AI literacy and enhancing their inquiry skills (Williams, Park & Breazeal, 2019; Kewalramani et al., 2021). Additionally, Su & Yang (2023) cite that exposing young children to AI literacy using age-appropriate teaching methods and activities can enhance their digital competencies and prepare them to navigate a technology-driven world. Despite these benefits, Su, Ng, et al. (2023) state that little is known about the impact of AI literacy programs on learners ages 3–5. Recent research has been centered around AI literacy programs for older learners in primary through tertiary educational settings (Su et al., 2022). This study, therefore, aims to bridge the gap in the current literature by examining the impact of an AI literacy program in early childhood.

AI in Early Childhood Education

2. Overview of the Case

The subjects of this study comprised 26 children, ages 3–5, who were currently enrolled in two public kindergartens in Hong Kong. The study, which ran from August to November 2022, aimed to examine the impact and effectiveness of a targeted eight-week AI Literacy intervention program in an early childhood setting. The research was designed to (1) address two significant research gaps by providing insights into how key AI concepts and skills can be effectively introduced to young children using interactive and age-appropriate instructional methods such as hands-on activities and storytelling and (2) examine the effects of early AI literacy programs on young learners’ AI literacy and AI-related creativity, and to gauge their perceptions of the AI4KG program.

3. Solutions Implemented

An AI4KG curriculum was designed and taught to students in two kindergarten classes over the course of eight weeks.

The curriculum was taught by the researchers and teachers, with 15 teachers undergoing face-to-face training prior to the implementation of the intervention program. The AI4KG curriculum was broken down into eight 30-minute modules delivered weekly as an afterschool curriculum. Based on Scott’s (2007) curriculum design model, the AI4KG curriculum was structured around four key components: learning goals, content, teaching methods, and assessment. It incorporated three AI learning tools: AI for Oceans, Teachable Machine, and Quick, Draw!

To meet the learning and developmental needs of the young learners, the modules in the AI4KG curriculum were taught using interactive and age-appropriate teaching methods, including play-based learning, project-based learning, and the Story Approach to Integrated Learning (SAIL) (Su & Yang, 2023). Thus, the curriculum was implemented in two phases:

Foundational phase (Modules 1 – 3)
In this phase, learners were introduced to powerful AI ideas and learned about potential ethical considerations for using AI technologies. In Module 1, the children examined how robots learn to glean an understanding of AI inference using key data/features. Module 2 focused on illustrating the need for AI robots to undergo continuous learning and improvement. Module 3 used the SAIL pedagogy to improve student learning using a picture book titled “Supervised Machine Learning for Kids” by Dr. Dhoot.

Application phase (Modules 4–7):
This phase of the curriculum focused on the principles and processes of machine learning. In Module 4, the kg students learned the basics of machine learning and how to use the Quick, Draw! AI tool. They also looked at how to improve the accuracy of computer recognition of paintings. Module 5 integrated AI for Oceans to help learners ascertain what an ocean looks like by differentiating between fish and garbage, training fish with diverse characteristics, and selecting a new word to teach AI. Modules 6 and 7 focused on building the young learners’ problem-solving and communication skills through machine-learning activities designed with Teachable Machine. While Module 8 was used to summarize and reinforce concepts covered in all the learning modules.

The students’ learning outcomes were measured using a series of independent evaluations in the form of an oral post-test assessment, observations, and drawings from the students. The AI literacy assessment comprised 18 multiple-choice questions from five dimensions covered by the AI4KG curriculum and took approximately 20 minutes to complete. Additionally, the students were observed and videotaped twice weekly. During this period, the researchers utilized printing and drawing robots to evaluate the students’ creativity skills and interacted with them to foster a deeper understanding of AI robots, which allowed them to clearly describe their drawings at the end of the intervention program. As a culminating activity, the KG students were tasked with drawing pictures of their most memorable unit and attaching their biographical data at the back.

4. Outcomes

The results of the study revealed compelling insights into the young learners’ interactions with artificial intelligence. Most of the kindergarten students had little to no prior knowledge about AI concepts or prior experience with AI tools, highlighting the critical need for early AI literacy programs. Despite this, the research indicated that young children possess the ability to acquire basic AI concepts and skills. In relation to AI-related creativity, younger learners, ages 3 – 4, were better able to use their imaginations to design a chatting robot, while older learners, ages 4–5, were better able to develop high-level AI robots capable of helping people draw. Similarly, the learners’ perception of the AI4KG curriculum varied by age. The older children were fonder of training AI, while the younger children preferred more creative expressions like drawing and storytelling. Ultimately, the findings underscore the positive impact of AI literacy education in preparing young children for an AI-driven future.

5. Implications

The findings of this study provide significant implications for AI literacy programs in Early Childhood Education, an emerging and underexplored area of study. Owing to the novelty of this field, researchers often possess limited knowledge and lack guidance in designing AI curricula for young learners. This study addresses that gap by presenting a valuable curriculum framework with suggested learning activities and appropriate pedagogical methods that can be adopted and adapted by researchers and educators alike.

A noteworthy contribution of the study is the identification and implementation of three open-source AI learning tools that can effectively support AI literacy at the kindergarten level and beyond. Furthermore, the study’s use of a comprehensive assessment questionnaire to evaluate the students’ AI literacy offers a structured approach that can be used to measure the learning outcomes of younger learners. This approach can contribute valuable insights into the topic and close the existing research gap in this field.

All in all, the research demonstrates the potential for integrating AI education in early childhood learning environments, proposing that young learners are capable of becoming AI-literate with the support of a well-designed AI literacy curriculum and age-appropriate instructional strategies.

References 

Bailey, J. (2024, November 19). AI in Education. Education Next. https://www.educationnext.org/a-i-in-education-leap-into-new-era-machine-intelligence-carries-risks-challenges-promises/

Kewalramani, S., Kidman, G., & Palaiologou, I. (2021). Using artificial intelligence (AI)-interfaced robotic toys in early childhood settings: A case for children’s inquiry literacy. European Early Childhood Education Research Journal, 29(5), 652–668. https://doi.org/10.1080/1350293X.2021.1968458

Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In R. Bernhaupt (Ed.), Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). Association for Computing Machinery.

Scott, D. (2007). Critical essays on major curriculum theorists. Routledge.

Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial intelligence (AI) literacy in early childhood education: The challenges and opportunities. Computers and Education: Artificial Intelligence, 4, 100124.

Su, J., & Yang, W. (2023). Artificial Intelligence (AI) literacy in early childhood education: an intervention study in Hong Kong. Interactive Learning Environments, 32(9), 5494–5508. https://doi.org/10.1080/10494820.2023.2217864

Williams, R., Park, H. W., & Breazeal, C. (2019). A is for artificial intelligence: The impact of artificial intelligence activities on young children’s perceptions of robots. In S. Brewster & G. Fitzpatrick (Eds.), Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–11). Association for Computing Machinery.

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