Artificial intelligence in early childhood education: A scoping review

Su, Jiahong; Yang, Weipeng

doi:10.1016/j.caeai.2022.100049

Cited by 120 publications

(68 citation statements)

References 21 publications

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“…In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al, 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.…”

Section: Resultsmentioning

confidence: 99%

“…More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al, 2012 ), metacognitive learning outcomes (Verschaffel et al, 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al, 2022 ; Su & Yang, 2022 ; Su et al, 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al, 2022 ; Quah & Ng, 2022 ).…”

Section: Resultsmentioning

confidence: 99%

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Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

et al. 2022

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Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

et al. 2022

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“…Resulting from our approach, we discovered that most of the reviewed papers of studies leaned towards teaching ML to high schoolers. Literature (Su & Yang, 2022) has shown that limited discussion exists about AI activities appropriate for younger children, however, studies have been carried out to introduce ML at early years of pupils' generation of ML knowledge (Druga et al, 2019;Vartiainen et al, 2020aVartiainen et al, , 2020b. Nevertheless, no basis could be identified to assume that high schools are the levels that requires ML knowledge, it is essential to conduct research on ML projects suitable for kindergarten, elementary and middle schools.…”

Section: Discussionmentioning

confidence: 99%

“…It is worthy of note that this work was founded on forty-three (43) paper identified with definite search principles in six databases. Previous studies have concluded based on lesser articles such as Barreto and Benitti, (2012) that reviewed 10 articles and Su and Yang (2022) who concluded based on 17 articles. Though other criteria, strategy and online bibliographic databases may have generated more papers.…”

Section: Limitationsmentioning

confidence: 99%

A systematic review of teaching and learning machine learning in K-12 education

et al. 2022

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The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field.

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“…Given the enormous impact of modern technology tools on the education system, numerous teaching staff are becoming more willing to incorporate them into their teaching activities ( Blackwell et al, 2014 ; Bourbour Hosseinbeigi, 2020 ; Undheim, 2021 ). It is necessary to surround the child with such an environment and such a system of relationships that stimulates the most diverse independent activities of the child and to form exactly what the child at the right time is able to perform ( Plumb and Kautz, 2016 ; Yilmaz, 2016 ; Su and Yang, 2022 ).…”

Section: Digital Instruments In Preschool Educationmentioning

confidence: 99%

A Radial Basis Function Neural Network Approach to Predict Preschool Teachers’ Technology Acceptance Behavior

et al. 2022

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With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function (RBF) neural network (NN) modeling technique to predict preschool instructors’ technology usage in classes based on recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers’ technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model (TAM) with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models’ dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN’s prediction on psychological data, opening up a new interdisciplinary field of research.

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Artificial intelligence in early childhood education: A scoping review

Cited by 120 publications

References 21 publications

Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

A systematic review of teaching and learning machine learning in K-12 education

A Radial Basis Function Neural Network Approach to Predict Preschool Teachers’ Technology Acceptance Behavior

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