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Article

Development of STEM-Based AI Education Program for Sustainable Improvement of Elementary Learners

by
Junhyeok Jang
†,
Jaecheon Jeon
† and
Soon Ki Jung
*
School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2022, 14(22), 15178; https://doi.org/10.3390/su142215178
Submission received: 30 September 2022 / Revised: 11 November 2022 / Accepted: 12 November 2022 / Published: 16 November 2022
(This article belongs to the Section Sustainable Education and Approaches)
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Abstract

:
Recent years have seen tremendous technology and research development in the field of artificial intelligence (AI). Initiatives are being made to build or employ AI in various domains of society. The field of education is experiencing the same trend. Being able to solve various issues by utilizing AI and varied learning materials is a crucial fundamental skill for students of all ages. The purpose of this study is to (1) develop STEM-based artificial intelligence education for K–6 grade students and (2) measure the effectiveness of the developed program in terms of creative problem-solving ability, AI literacy, and attitude toward AI. Experimental studies show an improvement in the average score and that all three elements are statistically significant. Additionally, it is established that the view of the study’s program and AI is good as a result of the emotional analysis of the comments freely expressed by the students. This study reflects on the need for establishing effective and various AI educational programs.

1. Introduction

In the field of computer science, artificial intelligence (AI) has experienced several golden-age periods after the concept was first agreed at the Dartmouth Conference in 1956, but faced a dark age due to the limitations in research because of the underlying hardware technology and difficulties in commercialization [1]. However, since mid-2010, we have been facing a new revival due to the development of neural networks and parallelization processing technology, and a significant amount of research based on this is being conducted in academia as understanding and using AI is a very important factor in future human life and national prosperity [2,3]. It is causing many changes and innovations in all areas, such as politics, economy, culture, and education in modern society [4]. In line with this trend, countries around the world consider AI as their major future project for their country and are sparing investment and support, and this is also the case in education [5]. Education for adolescent students is a process to prepare for 10–20 years into the future, when these students will participate in economic activities. In other words, it is necessary to consider not only the immediate changes in the present, but also in the future, 10 to 50 years after the learner becomes an adult and a member of society [6]. Students’ current learning experiences can have a profound impact on their lives and help guide their overall life. Therefore, there is no shortage of artificial intelligence education (AIED), even if it plays a pivotal role in forming the future of human society and contributing to the national economic development. Therefore, providing an appropriate approach to AIED and an effective learning experience is an important task for education officials.
What is the goal of education? It is to achieve the learning goals according to the specific subject and learning elements, to gather these achievements to logically solve the problems encountered in one’s life, to feel the beauty of life, and to nurture a holistic human being who enjoys life [7]. Therefore, educators and administrators must provide specific and meaningful education so that the knowledge, skills, and attitudes that students have experienced and achieved in school education can be used meaningfully in the lives of learners beyond simply experiencing them. Various teaching and learning methodologies have been proposed to ensure the significance of education and help students to be truly useful in their lives, and STEM education is also one of the effective methods. STEM education provides an experience that allows students to acquire knowledge and skills in science, technology, engineering, and mathematics in the course of solving real-life problem situations and use them meaningfully in their lives [8]. Numerous studies have confirmed that STEM-based learning has a positive effect on learners’ knowledge utilization, proving the importance and effectiveness of STEM education in the modern curriculum [9].
As a content element of learning, AI education is essential from the perspective of sustainable education to prepare for the future, and in terms of learning methods, STEM education is an effective way to help learners use the knowledge acquired in education meaningfully in their lives. This study aims to develop a STEM-based AIED plan by linking the importance of the content of AIED with the method efficiency of STEM education and apply it to analyze the educational effect. Despite the effectiveness and importance of STEM-based AIED, very few related studies have been conducted, and research results on elementary school students targeted in this study are harder to find.
The research objectives that guide this study are as follows:
  • Develop a STEM-based AIED program using backward design for K-12 students.
  • Measure the effectiveness of the developed program on students’ creative problem-solving ability, literacy of AI, and attitude toward AI.

2. Theoretical Background

2.1. The Reseach Trend of STEM and AI Education

VOS viewer, a research bibliographic visualization program, was used to identify research trends in STEM and AIED. The keywords “artificial intelligence education,” “STEM,” and “STEAM” were searched in the WoS database by limiting the publication year from 2019 to 2022 and the publication type to article, and using this, the main keywords of related papers and their relationships were visualized Figure 1. Irrelevant terms were removed manually from the searched database, and the minimum frequency of occurrence was set to 5 or more to structurally indicate the trend in the research field. Research from the database, the VOSviewer generated 10 clusters with 185-word items, 1601 links, and 3065 total link strength. Of the 185-word items, the following words have high values of the total link strength: “artificial intelligence” (total link strength 1101), “education” (total link strength 420), “machine learning” (total link strength 314), “deep learning” (total link strength 145), “higher education” (total link strength 120), “big data” (total link strength 110), “STEM” (total link strength 90), “technology” (total link strength 90), “STEAM” (total link strength 81), “internet of things” (total link strength 75), and “e-learning” (total link strength 62) as can be seen in Table 1.

2.2. AI Education

Since the mid-2000s, AI once again entered a period of revival after 2010, and the demand for AIED has also begun to emerge in society. Although the academic perspective on AIED is being studied in various ways [10], this study intends to systematize AIED using the South Korean classification system, which is as follows: AI-based education, AI-using education, and AI-understanding education [11]. First, AI-based education refers to the use of AI as a learning management tool, analyzes learner achievement to find strengths and weaknesses, and provides appropriate feedback through this. In other words, from the perspective of realizing individualized education, AI is used as a starting point analysis tool for education. Second, AI-using education refers to the perspective of using AI services as a tool to achieve the objectives of the class in the teaching and learning scene. This is a way for students to pay more attention to how they use AI to solve their problems. For example, when students whose native language is not English learn English, AI will be used as a learning tool when they practice the accuracy of English pronunciation using artificial intelligence speakers. Third, AI understanding education refers to education that develops students’ problem-solving ability by understanding the concept of AI, implementing its principles in computer programs, or including AI in other programs. By understanding the basic concepts of AI and the principles of modern AI led by machine learning and deep learning, students can improve their problem-solving ability in the process of creating their own models through public open neural networks or developing programs using them.
Major foreign countries are also paying attention to education by increasing their investment in AI, which is a major foundational element of future industries. The United Kingdom, through the AI Roadbook announced by the AI Council, speaks of the need to develop a national AI strategy and will work to improve AI and data literacy for all, which will enable people to understand the risks and benefits of AI with confidence and information. It was argued that the user should be able to obtain this understanding [12]. In the United Kingdom, there is no direct mention of AI, but in the computer science area, the basic learning of algorithms and programming provides the foundation for the understanding and knowledge of AI [13]. The European Union announced a plan to distribute free online lectures on Elements of AI, an artificial intelligence learning program developed by the University of Helsinki, to improve the basic AI competency of the EU citizens. The program consists of six themes, including “What is AI, AI problem solving, real-world AI, machine learning, neural networks, and social impact,” and is available in 29 official EU languages [14]. In the United States, the Association for the Advancement of Artificial Intelligence (AAAI) and Computer Science Teachers Association, and CSTA announced AI4K12 (AI for K-12 students), an AI education guideline for elementary school students [15]. In AI4K12, the five big ideas for artificial intelligence are perception, expression and reasoning, learning, interaction, and social impact [16]. South Korea has established a basic plan for AI education context led by government-related institutions and advocates linking with computer science and software education. The three areas, including the understanding of artificial intelligence, the principles and application of artificial intelligence, and the social impact of artificial intelligence, each include sub-areas [17]. Table 2 shows the main elements of AI education in the curriculum of the European Union, the United States, and South Korea.
AIED, which is intended to be developed in this study, is an education for elementary school students about AI itself, understanding the principles of AI at a rudimentary level, and using it to self-directly experience the process of solving real problems. It focuses on improving overall understanding about AI and improving problem-solving ability.

2.3. STEM Education

STEM education is a convergence educational method that allows students to learn the learning elements of science, technology, engineering, and mathematics in the process of solving real-life problem situations, thereby cultivating students’ practical problem-solving capabilities and composing meaningful knowledge that connects knowledge and life [8]. Discussion on the educational effect and methodology of STEM education has been carried out in numerous studies, and it is a method of innovative and meaningful teaching and learning activities for educational members—administrators, teachers, and students—in the school education field.
Research on STEM education is being conducted in various ways to synthesize its effects from the perspective of students. It has previous studies that STEM education improves students’ cognitive, emotional, and psychodynamic areas and has a positive effect on students’ career interest and maturity [18,19,20,21]. STEM education also contributes to linking ethnic and gender differences, and students can easily solve big challenges that have yet to be solved by developing 21st century skills such as adaptability, problem-solving skills, complex communication, and system thinking skills [22,23,24].
In particular, Batdi et al. (2019) emphasized that as a result of qualitatively synthesizing related papers, STEM classes induce active participation of students and provide meaningful learning to students, contributing to cognitive improvement. As a result of analyzing the impact of STEM education by school level, it was found that the cognitive improvement in elementary schools was higher than that of other schools, and the educational effect in elementary schools was much higher than that of middle and high schools [25].
Research on the improvement of the affective domain through STEM education has been verified and mentioned internationally through various countries. Kanadli (2019) synthesizes changes in students’ affective domains based on 22 studies in Turkey and emphasizes that STEM classes are a good way to improve students’ learning aspirations and motivation, especially attention and interest [26]. In addition, according to a study by Kang (2019), the impact of integrated STEAM education in Korea is effective in both the cognitive and definitive learning of students, but it is especially more effective in the affective domain [27]. Finally, Murphy et al. (2019) analyzed Australia’s STEM-related documents and emphasized that STEM education should have an educational strategy in the direction of improving curiosity and confidence [28].
Even in the psychodynamic area, STEM education consists of active participation and activities of students, so it can be expected to improve students’ psychodynamic functions [25]. This can develop students’ ability to plan and practice through project classes or problem-solving classes, and can also develop engineering skills. These activities can also improve research skills, communication skills, cooperation, and problem-solving skills [25,28].
Looking at the empirical research case, it was found that the STEM-based engineering education targeting non-engineering students in a study targeting university students increased the students’ AI literacy and AI ethics [29]. In addition, STEM education in science subjects was effective in enhancing students’ problem-solving ability and critical thinking [30,31], and had a positive effect on spatial sense and geographic knowledge development [32].
STEM education positively affects not only the intellectual development of the subjects—science, technology, engineering, mathematics—constituting it, but also the student’s learning attitude toward each subject and the learning process. Rather than being fragmented and isolated knowledge, each learning content is internalized in the learner’s cognitive process and expanded and applied in various situations. In other words, what is learned at school goes beyond memorizing and becomes living knowledge that can be used in the lives of students, which has a developmental effect on students’ learning attitudes, which becomes an especially important learning experience in their lives. In some countries, STEM education has also been expanded under the name STEAM education, which has added art that means literary, artistic, and moral elements.
Based on the above discussion, STEM education and AI are complementary; hence, students can use STEM education to develop AI literacy, and AI must play a role to learn STEM knowledge in an interesting way. It should be interactive and connect students’ real life with what they learn [33].

3. Methods

3.1. Backward Design

Tyler’s curriculum design method, which has been used as a method for setting up an education plan for a long time, consists of goal defining, learning experience selection, learning experience organization, and evaluation, which is called forward design [34]. In the past century, this forward instructional design has been practiced by many schools and teachers and has produced excellent educational cases and research results, but the learning goals and the contents of the evaluation plan are not connected to each other, and there is a gap or the results of evaluation are not satisfactory. It has also caused problems that could not be utilized for intellectual growth. In the teaching and learning scene, the purpose of evaluation is not to classify and rank who is good at what, but to help each learner achieve their learning goals by examining student achievement and understanding the level of each student. If the viewpoint of evaluation focuses on the growth of students in the process of learning rather than the learning outcome, the organic relationship between learning content and evaluation becomes important.
The backward method of instructional design can focus more on the educational use of evaluation. The backward instructional design [35] direction proceeds by identifying desired results, determining acceptable evidence, and planning learning experiences and instruction. It is different from forward teaching and learning design in the order of the evaluation plan, and it is also called reverse teaching and learning design. This has the advantage of focusing the content of the evaluation thoroughly only on the learning goal and preventing the provision of a learning experience that is not related to the achievement of the goal [36]. As shown in Figure 2, the STEM-based AIED program developed in this study was also developed through backward design to focus on the achievement of students’ achievement of AIED learning goals.

3.2. Research Participants

To investigate the effect of the STEM-based AI education program developed in this study on learners’ creative problem-solving ability, literacy of AI, and attitude toward AI, 120 students in five classes in grades K–6 of elementary school were targeted. It was applied for one semester. Curriculum during this period consisted of the application of development materials as well as classes of general other subjects individually. All the participating students (62 males, 58 females) were subjected to verification and statistical analysis of the application results. Instead of conducting classes in a separate computer room, the smart devices provided in the classroom were actively used, and hence, an environment for using one device per person was prepared. Teachers with more than 10 years of experience held a pre-training session and a monthly meeting to discuss the progress of the class and effective learning process to improve their competency to operate the developed program.
Prior to the experiment, participating students responded to two types of preliminary questionnaires and the results are shown in Table 3. The first survey was conducted to determine the use of information devices during class activities as a question asking students’ ICT using ability, and the average result was 3.89 (5-point Likert scale). The second survey is a question that asks students about their experiences with various teaching and learning methods, such as cooperative learning, convergence class, and inquiry learning, to choose effective learning ways during the STEM class. They answered that they had experience in various teaching and learning methods, and that more learning occurs in the process of exploring and discussing with peer learners rather than transferring knowledge from the teacher. In addition, the students subject to the experiment had sufficient experience in computer programming education using Scratch and using ICT devices and were familiar with teaching methods, such as cooperative learning and convergence education, as well as traditional classes. The actual class was conducted as a method of collaborative learning with peer learners and inquiry learning that guides them to solve problems on their own.
Students participating in this study were informed that they would participate in the pre-post-developmental test in the form of a questionnaire, and that the questionnaire had complete anonymity and non-identification. In addition, it was announced that the results of the test are not analyzed for each individual, but values such as the average score and variance are calculated for each group. As such, the contents of the test were thoroughly guaranteed anonymity and there was no room for infringement of individual rights, but the experiment was conducted on non-adult students, so written consent was obtained from the students’ parents or guardians regarding the experiment and participation guide.

3.3. Data Collection and Analysis

In this study, students were asked to respond to two types of questionnaires. The first was a questionnaire for quantitative analysis. It consists of a creative problem-solving ability test tool (2004, MI Research Team, Psychology Lab, Seoul National University) and two types of AI-related questionnaires (AI literacy, AI attitude). The reliability of Cronbach’s alpha for the creative problem-solving ability test tool is 0.93 and consists of four sub-domains: self-confidence and independence of learners, divergent thinking, critical logical thinking, and motivational thinking. Creative problem-solving ability is an important competency in a learner’s life, and the sub-elements of this test tool are good indicators to check how they affect the development of problem-solving ability, which is very important to students. Therefore, it can be confirmed whether the effect of STEM-based AIED is a stepping stone for sustainable growth and meaningful learning in a student’s lifetime. The AI-related questionnaire used the test tool of Chang-mo Yang (2022), and this test has a total of 17 questions, with a total of 5 questions on AI literacy, consisting of a 1-point to 4-point Likert scale and its Cronbach’s alpha is 0.72. Scores of 1 were “I do not know at all”, 2 points were “I have a memory but I don’t know”, 3 points were “I know but I can’t explain it”, and 4 points was “I know well and can explain it well”. The AI literacy area asks how well students understand the meaning of AI and how it affects our lives, and how students can use and apply AI in the process of solving their own problems. The remaining 12 items are about AI-related attitudes and consisted of a Likert scale ranging from 1 to 5, with 1 being “not at all”, 2 being “not at all”, 3 being “normal”. A score of 4 was measured as “agree” and a score of 5 as “strongly agree”. Attitude towards AI is an ethical area of AI, which is about the positive and negative effects of using AI, and the tendency to use AI in life. Details of the questionnaire for quantitative research can be found in Appendix A. The results of these surveys are statistically processed in SPSS to prove their significance.
The second type of survey is a satisfaction survey for qualitative surveys. We received participants’ opinions about the STEM-based AIED program in a free form and qualitatively analyzed participants’ tendencies, interest, and immersion in the development program using ORAGNE3’s text mining technique. All questionnaires were presented in Korean and responses were received in Korean in consideration of the age of the test subjects, and for qualitative analysis, they were translated into English that can contain the same meaning as much as possible.

4. Results

4.1. Development of STEM Program for AI Education

In this study, we developed a STEM-based Aid program for an elementary K–6 school. The program is organized around three topics. The developed educational program was written based on the backward design for effective education and practical development of students. This allows more goal-oriented activities for students, and teachers can focus on giving students appropriate feedback. The specific details of the contents are shown in Figure 3.
In class, students focused on what problems they could find in each situation. Some students had difficulties in finding problems, but they were observed to identify a problem-solving plan and participate in the solution process with the help of the teachers or discussions with fellow students. Students experienced immersion in the learning process by not only accepting the teacher’s explanation, but also leading their own learning, and actively used various media to acquire new information and knowledge. This will be the process in which the knowledge learned at school is integrated into the learner and becomes meaningful in the student’s life. Figure 4 shows some scenes from the class of the STEM-based AI program.
Teaching K–6 students about the operation principle and computational process of artificial intelligence models, such as classification, regression, and natural language recognition, is not appropriate for the developmental stage. For example, even if students searched for the operating principle of the CNN network through internet searches, they did not learn in detail, and the teacher guided the students with easy content that AI finds feature maps of images through simple but numerous calculations.

4.2. Change in Creative Problem-Solving Ability, Literacy of AI, and Attitude toward AI

Students were asked to respond to a total of three types of questionnaires related to creative problem-solving ability, AI literacy, and AI attitude. There were significant differences between the pre-result and the post-result in all sub-domains of the questionnaire on creative problem-solving ability. In all areas of self-confidence and independence, divergent thinking, critical and logical thinking, and motivation, the average score of the post-results improved compared to the pre-results, and it was confirmed that they were statistically significant. This means that in teaching AI to elementary school students (K–6), the STEM-based educational method is one of the effective methods to help students develop creative problem-solving ability using AI. In learning about AI, it goes beyond simple experience or imitative education, that is, by presenting real-life situations, students discover problems on their own, explore content elements of various disciplines, and solve problems according to their own plans. It can be analyzed that the STEM education process is meaningful in the development of learners’ creative thinking skills. The specific survey results for the experiment are shown in Table 4.
In addition, in attitude toward AI, as shown in Table 5, the score of the post-test improved compared to the pre-test. The mean score improved in all sub-domains, and the significance probability was also less than 0.05, confirming that it was statistically significant. Great progress has been made in the field of social impact of AI characteristics of AI, which analyzed that the students recognized that AI is better than humans and it can be applied to many parts of society by using it.
Lastly, the AI literacy domain, as shown in Table 6, it was also confirmed that the results of the post-test improved compared to the pre-test and that it was statistically significant. The questionnaire on AI literacy is about “Can you explain to others?” the concepts, functions, principles, ethics, and perceptions of AI. Students made progress in all areas, which means that students’ level of understanding of AI has been further improved through STEM-based AIED.

4.3. Sentiment Analysis of Learning Exprience

Students participating in the experiment wrote free-form opinions for qualitative analysis. They were allowed to freely write their feelings about the educational program and their opinions on AI, and the responses of students were analyzed using various sentiment analysis methods, such as Liu-hu, Vader, Multilingual sentiment, and SentiArt, and the results are shown in Table 7.
First, Liu-hu is a lexicon-based sentiment analysis tool that supports English and Slovenian [37] and returns the standardized score difference for positive expressions (maximum value 100) and negative expressions (maximum value −100). Students’ opinions showed that the average Liu-hu score was 31.10, which means that the STEM-based AIED program developed and applied in this study provided students with a positive learning experience.
Second, in the analysis through VADER, a lexicon and rule-based sentiment analysis tool [38], the positive score (M = 0.33, SD = 0.23) greatly outweighed the negative score (M = 0.01, SD = 0.05), and between −1 and 1, the average of the compound score with the range was also 0.43, indicating that the STEM-based AIED program had a positive effect on the students. Figure 5 is a heat map of the sentiment analysis result through VADER. The blue area representing negative emotions is very small, while the green and yellow areas representing neutral and positive emotions are very wide.
Third, an average score of 22.93 was also output in multilingual sentiment, a lexicon-based sentiment analysis tool [39]. Finally, in SentiArt, based on the vector space models’ returning text valence [40], the happiness score (M = 1.32, SD = 0.81) and surprise score (M = 0.58, SD = 0.84) were higher than the anger score (M = −0.09, SD = 0.59), meaning negative, and the overall score was also 0.78, indicating a positive result. Through these emotional analysis results, the STEM-based AIED program was recognized as a positive and enjoyable learning experience for students, which can be expected to have a lasting and positive impact on students’ lifelong education.

5. Discussion and Conclusions

With the innovative increase in computing power, large-scale parallel processing can be efficiently processed; therefore, artificial intelligence technologies, such as classification, regression, and natural language recognition, are being developed explosively. In the future, AI computational ability will be used more actively in various fields of human society, and creative problem-solving ability using AI is an essential competency for students who will live in future society.
STEM education is an effective educational methodology that helps students not only know what they have learned, but also applies them to real life. There are various views on AI education, but in this study, it was viewed from the perspective of providing an experience of problem solving using AI. Therefore, in this study, a STEM-based AIED education program for elementary school students was developed and applied to present problem situations that students can encounter in real life and to solve problems by linking various academic contents, including AI. The theme of the developed program took into consideration the interests of the students and their social issues. The selected topics are about creating a program for the socially disadvantaged, an infectious disease prevention system based on mask recognition, and conservation of the ecosystem, and the details were explained in Section 4.
This study aimed to investigate the change in creative problem-solving ability, attitude toward AI, and AI literacy. In addition, by receiving students’ free opinions, sentiment analysis was conducted on the student’s thoughts on the development program and AI. The experiment was conducted for K–6 students, and the results of this study are as follows.
First, an AI education program based on STEM education was developed for students to use AI. The level of difficulty was set for the program to suit the developmental level of the students, and it was structured to proceed through discussion and cooperative activities from setting up a plan to solve problems on their own, to implementing them, and organizing the results.
Second, to review the effectiveness of the development program, pre- and post-tests were conducted on creative problem-solving ability, attitude toward AI, and AI literacy. In all three items, the post-test results were improved compared to the pre-test, and statistical significance was confirmed. In an age based on AI, creative solving ability that thinks about solutions to problems in various ways is very important, and education should also be set in the direction of using AI and enhancing creativity. The results of our study could be the basis for these claims.
Third, as a result of the emotional analysis of free-form responses to questions about the development program and one’s own thoughts on AI, positive results were found in all analysis methods, and through this, it was confirmed that the STEM-based AIED education program provided students with a positive perception of AI and a learning experience. The fact that students have a positive perception of AI can also be said to have increased their tendency to accept AI technology in the future. In other words, by using AI in problems where AI can be used in students’ lives, attitudes to make their lives more enriching and convenient for themselves are formed.
Looking at the above results, the STEM-based AIED education program developed by us had a positive effect on students’ creative problem-solving ability and basic literacy and attitude toward AI. Therefore, education officials should consider providing real-life-oriented problem-solving experiences using AI among various educational contents when composing the curriculum and teachers should also recognize the importance and value of convergence education that includes the use of AI.
This study is a STEM-based AIED program for K–6 students, and it has specificity and differentiation compared to other studies. However, it cannot be denied that the classification model consisting of text and images is mainly used among various AI fields and that the main content is simulation programming to solve problems. Research to develop various AI-related educational programs for students who are the future leaders and to confirm their effectiveness should be continuously conducted. This study—the development and effectiveness analysis of STEM-based AI education materials for elementary school students conducted—serves as a beacon of a new field of AI education and STEM education, and we hope this article can inspire you to research practice.

Author Contributions

Conceptualization, J.J. (Junhyeok Jang) and J.J. (Jaecheon Jeon); methodology, J.J. (Junhyeok Jang) and J.J. (Jaecheon Jeon); formal analysis, J.J. (Junhyeok Jang) and J.J. (Jaecheon Jeon); investigation, J.J. (Junhyeok Jang) and J.J. (Jaecheon Jeon); data curation, J.J. (Junhyeok Jang) and J.J. (Jaecheon Jeon); writing—original draft preparation, J.J. (Junhyeok Jang) and J.J. (Jaecheon Jeon); writing—review and editing, J.J. (Junhyeok Jang) and J.J. (Jaecheon Jeon); visualization, J.J. (Junhyeok Jang) and J.J. (Jaecheon Jeon); supervision, S.K.J.; project administration, S.K.J. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the BK21 FOUR project (AI-driven Convergence Software Education Research Program) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (4199990214394).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Preliminary questionnaires.
Table A1. Preliminary questionnaires.
QuestionsResult
ICT Using AbilityI have studied using ICT devices.1, 2, 3, 4, 5
You can study more effectively by studying using ICT devices.1, 2, 3, 4, 5
ICT devices can be used for the purpose of my desired purpose.1, 2, 3, 4, 5
Experience in various teaching and learning methodsIt is effective to discuss with fellow learners.1, 2, 3, 4, 5
It is fun to think about solving problems and find relevant information.1, 2, 3, 4, 5
I have worked with my fellow learners to solve the problem.1, 2, 3, 4, 5
I know that various knowledge and information are needed to solve the problems in real life.1, 2, 3, 4, 5
Table
Table A2. Questionnaires to measure AI literacy.
Table A2. Questionnaires to measure AI literacy.
SubfactorsQuestions
Q01. What is AI?Concept
Q02. What can AI do?Function
Q03. How does AI work?Principle
Q04. How we should us AI?Ethics
Q05. Whether is it Ai or not?Recognition
Table
Table A3. Questionnaires to measure AI attitude.
Table A3. Questionnaires to measure AI attitude.
SubfactorsQuestions
Social impact of AIQ06. It seems that if technology advances and AI acts like a human being, it can be helpful to humans.
Q07. If AI has emotions, it will be useful for humans.
AI and communicationQ08. I can continue the conversation with AI.
Q09. I can communicate fluently with AI.
Q10. I can predict what action AI will do.
Interaction with AIQ11. It would be useful for AI to decide.
Q12. It would be convenient to study where AI is used.
Emotional exchange with AIQ13. If AI has emotions, I think I can be friends with AI.
Q14. I am not comfortable living with an AI that has emotions.
Q15. I am comfortable talking to AI.
Characteristics of AIQ16. I know how fast AI can work.
Q17. I understand how useful AI is.
Table
Table A4. Questionnaires to measure creative problem-solving ability.
Table A4. Questionnaires to measure creative problem-solving ability.
SubfactorsQuestions
Self-confidence and independence1. Keep asking questions with a lot of work in class.
2. Find a variety of answers to a given problem and sometimes give a unique answer.
3. I freely express my doctor in class, and sometimes I do not have any opinions.
4. I usually have abundant humor and make others funny even when others are not ridiculous.
5. I like to use my head in my study time.
Divergent thinking6. I can say novel and extraordinary thoughts.
7. I can solve the problem in a new way that is already known.
8. What I made is new and is very different from what other friends made.
9. I create a variety of ideas to solve the problem.
10. I connect what I don’t care about.
Critical and logical thinking11. I know how to distinguish between the facts and imagination.
12. I can trim the ideas and conclusions in class in class.
13. I know whether it is right for study time or wrong.
14. I can draw conclusions by myself based on various information with my friends.
15. I can find information related to a given problem.
Motivation16. I try to do it to the end without giving up hard and difficult.
17. I want to know more about other topics of this subject.
18. I have a lot of fun studying at this time.
19. I try to achieve my goals if I think I have not achieved my goal.
20. If I think I have achieved my goal, I set the next step goal.

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Sustainability 14 15178 g001 550
Figure 1. The analysis of articles about AI education and STEM from 2019 to 2022.
Figure 1. The analysis of articles about AI education and STEM from 2019 to 2022.
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Figure 2. STEM-based AIED development structure diagram.
Figure 2. STEM-based AIED development structure diagram.
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Figure 3. Details of developed STEM-based AIED program.
Figure 3. Details of developed STEM-based AIED program.
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Figure 4. STEM-based AI program class scene.
Figure 4. STEM-based AI program class scene.
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Figure 5. Heat map of sentiment analysis result through VADER.
Figure 5. Heat map of sentiment analysis result through VADER.
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Table
Table 1. Co-occurrence word items from 2019–2022 publications on AI and STEM research.
Table 1. Co-occurrence word items from 2019–2022 publications on AI and STEM research.
KeywordOccurrencesTotal Link Strength
Artificial intelligence6171011
Education172420
Machine learning150314
Deep learning71145
Higher education55120
Big data42110
STEM5490
Technology3790
STEAM5081
Internet of things3275
e-learning3262
Table
Table 2. Overview of AIED Curriculum in Each Country [14,15,17].
Table 2. Overview of AIED Curriculum in Each Country [14,15,17].
CountryEuropean UnionUnited StatesSouth Korea
Elements of AIEDWhat is AIPerceptionUnderstanding of AI
AI problem solvingRepresentation and reasoning
Real world AI Principles and application of AI
Machine learningLearning
Neural networksNatural interactionSocial impact of AI
ImplicationsSocietal impact
Table
Table 3. Information on participants.
Table 3. Information on participants.
Class AClass BClass CClass D
N30303030
ICT knowledge and experiences3.92/53.88/54.01/53.78/5
Experience in various teaching and learning methods4.12/54.33/54.08/54.02/5
Table
Table 4. Results of pre/post-survey on creative problem-solving ability.
Table 4. Results of pre/post-survey on creative problem-solving ability.
Sub-DomainsCronbach’s αMeanS.D.t
(pre) Self-confidence and independence0.6382.810.74−7.153 **
(post) Self-confidence and independence0.7863.430.90
(pre) Divergent thinking0.7883.020.82−7.354 **
(post) Divergent thinking0.8953.680.94
(pre) Critical and logical thinking0.8113.260.81−7.376 **
(post) Critical and logical thinking0.8793.910.86
(pre) Motivation0.7813.570.79−6.062 **
(post) Motivation0.8274.090.78
** p < 0.01.
Table
Table 5. Results of pre/post-survey on attitude toward AI.
Table 5. Results of pre/post-survey on attitude toward AI.
Sub-DomainsCronbach’s αMeanS.D.t
(pre) Social impact of AI0.7863.271.02−8.388 **
(post) Social impact of AI0.9074.150.97
(pre) AI and communication0.7422.840.88−12.123 **
(post) AI and communication0.8603.940.92
(pre) Interaction with AI0.6353.190.97−9.081 **
(post) Interaction with AI0.8074.080.95
(pre) Emotional exchange with AI0.6323.080.74−6.936 **
(post) Emotional exchange with AI0.6133.600.79
(pre) Characteristics of AI0.7992.841.06−11.027 **
(post) Characteristics of AI0.7954.060.95
** p < 0.01.
Table
Table 6. Results of pre/post-survey on AI literacy.
Table 6. Results of pre/post-survey on AI literacy.
Sub-DomainsMeanS.D.t
(pre) Concept2.670.87−10.789 **
(post) Concept3.580.62
(pre) Function2.620.91−10.629 **
(post) Function3.540.68
(pre) Principle2.320.93−11.511 **
(post) Principle3.370.94
(pre) Ethics2.460.91−10.743 **
(post) Ethics3.440.75
(pre) Recognition2.590.84−11.973 **
(post) Recognition3.540.64
** p < 0.01.
Table
Table 7. Sentiment analysis results.
Table 7. Sentiment analysis results.
MethodClassificationMeanSDMinMax
Liu-HuSingle31.1036.11−100.00100.00
VADERPositive0.330.230.001.00
Negative0.010.050.000.50
Neutral0.660.230.001.00
Compound0.430.31−0.060.99
MultilingualSingle22.9331.21−33.33100.00
SentiArtSentiment0.780.62−0.543.31
Anger−0.090.59−1.531.58
Fear0.590.70−1.622.27
Disgust0.000.63−1.321.39
Happiness1.320.81−0.463.42
Sadness0.320.50−1.163.03
Surprise0.580.84−1.182.52
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Jang, J.; Jeon, J.; Jung, S.K. Development of STEM-Based AI Education Program for Sustainable Improvement of Elementary Learners. Sustainability 2022, 14, 15178. https://doi.org/10.3390/su142215178

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Jang J, Jeon J, Jung SK. Development of STEM-Based AI Education Program for Sustainable Improvement of Elementary Learners. Sustainability. 2022; 14(22):15178. https://doi.org/10.3390/su142215178

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Jang, Junhyeok, Jaecheon Jeon, and Soon Ki Jung. 2022. "Development of STEM-Based AI Education Program for Sustainable Improvement of Elementary Learners" Sustainability 14, no. 22: 15178. https://doi.org/10.3390/su142215178

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