Behavioral-pattern exploration and development of an instructional tool for young children to learn AI

Hsu, Ting Chia; Abelson, Harold; Lao, Natalie; Tseng, Yuh‐Min; Lin, Yi-Ting

doi:10.1016/j.caeai.2021.100012

Cited by 24 publications

(8 citation statements)

References 15 publications

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“…Thus, students learn by experiencing the different roles of AI. Other frequently used approaches include collaborative learning, which explicitly involves group work or paired work, game-based learning [64,65], and inquiry-based learning [35,66].…”

Section: Experiential Learningmentioning

confidence: 99%

“…In addition to the commonly used activities that have been mentioned in Tables 4 and 5 when describing learning theories and pedagogical approaches, some unique T&L activities merit additional discussion. First, in unplugged activities, as adopted in study [64], students learned data management and decision tree algorithms by practicing their computational thinking through group work prior to actual programming; this process involved decomposition, pattern recognition, abstraction, and algorithms, thus cultivating logical thinking and problem-solving skills. Another unplugged example is "Be the machine" [34], a team role-playing game that taught how ML works; in this game, each team member assumed a different role to manually train an ML model.…”

Section: Experiential Learningmentioning

confidence: 99%

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Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review

2022

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In response to the growing popularity of artificial intelligence (AI) usage in daily life, AI education is increasingly being provided at the K-12 level, with relevant initiatives being launched worldwide. Examining how these programs have been implemented and summarizing useful experiences is thus imperative. Although prior reviews have described the characteristics of AI education programs in publications, the papers reviewed were mostly nonempirical reports, and the analysis typically only involved a descriptive summary. The current review focuses on the most recent empirical studies on AI teaching programs in K-12 contexts through a systematic search of the Web of Science database from 2010 to 2022. To provide a comprehensive overview of the status of AI teaching and learning (T&L), 32 empirical studies were analyzed both descriptively and thematically. We analyzed (1) the research status, (2) the pedagogical design, and (3) the assessments and outcomes of the AI teaching programs. An increasing number of studies have focused on AI education at the K-12 stage, but most of them have a small sample size. Moreover, the data were mostly collected through interviews and self-reports. We reviewed the pedagogical design of AI teaching programs by using Gerlach and Ely’s pedagogical design model. The results comprehensively delineated current AI teaching programs through nine dimensions: learning theory, pedagogical approach, T&L activities, learning content, scale, teaching resources, prior knowledge prerequisite, aims and objectives, assessment, and learning outcome. The results highlighted the positive impact of current AI teaching programs on students’ motivation, engagement, and attitude. However, we observed a lack of sufficient research objectively measuring students’ knowledge acquisition as learning outcomes. Overall, in this paper, we discussed relevant findings in terms of research trends, learning content, teaching units, characteristics of the pedagogical design, and assessment and evaluation by providing illustrations of exemplary designs; we also discussed future directions for research and practice in AI education in the K-12 context.

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Section: Experiential Learningmentioning

confidence: 99%

Section: Experiential Learningmentioning

confidence: 99%

Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review

2022

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“…Realizing that AI occupies an important position in the future development of society, investing in AI from the primary education level is an issue that needs urgent attention [14]. Many countries have listed AI application as the essential part in information technology (IT) curriculums of primary and secondary schools [14]. Presently, there are many studies devoted to the development of AI learning content.…”

Section: Artificial Intelligence Educationmentioning

confidence: 99%

Is It Possible for Young Students to Learn the AI-STEAM Application with Experiential Learning?

et al. 2021

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This study attempted to evaluate the learning effectiveness of using the MIT App Inventor platform and its Personal Image Classifier (PIC) tool in the interdisciplinary application. The instructional design was focused on applying PIC in the integration of STEAM (i.e., Science, Technology, Engineering, Art, and Mathematics) interdisciplinary learning, so as to provide sustainable and suitable teaching content based on the experiential learning theory for 7th grader students. Accordingly, the sustainable AI-STEAM course with the experiential learning framework has been implemented and verified, so as to confirm that the AI-STEAM course is not too difficult for young students. Many basic concepts involved in the AI-STEAM course, regarding programming logic, electromechanical concepts, interface design, and the application of image recognition, were measured in this study. The results showed that the students not only made significant progress in learning effectiveness, but also in particular made significant improvements in two parts: electromechanical concepts and image recognition knowledge. In the end, this study further provides some advice on the sustainable AI-STEAM course based on the survey of some important factors including active learning, and self-efficacy after confirming that it is not a barrier for the young students to learn the sustainable AI-STEAM course developed in this study.

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“…With the fast-paced, continual development of computer science, including huge gains in artificial intelligence (AI) and machine learning, the application of AI has become popular in our daily lives due to the high-speed development of hardware (Hsu et al, 2021). One rapidly-growing subfield includes conversational AI, which is the ability of machines to converse with humans, including voice-based technologies such as Amazon's Alexa.…”

Section: Introductionmentioning

confidence: 99%

The Effects on Secondary School Students of Applying Experiential Learning to the Conversational AI Learning Curriculum

Hsu

Abelson

2022

IRRODL

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The purpose of this study was to design a curriculum of artificial intelligence (AI) application for secondary schools. The learning objective of the curriculum was to allow students to learn the application of conversational AI on a block-based programming platform. Moreover, the empirical study actually implemented the curriculum in the formal learning of a secondary school for a period of six weeks. The study evaluated the learning performance of students who were taught with the cycle of experiential learning in one class, while also evaluating the learning performance of students who were taught with the conventional instruction, which was called the cycle of doing projects. Two factors, learning approach and gender, were taken into account. The results showed that females’ learning effectiveness was significantly better than that of males regardless of whether they used experiential learning or the conventional projects approach. Most of the males tended to be distracted from the conversational AI curriculum because they misbehaved during the conversational AI process. In particular, in their performance using the Voice User Interface with the conventional learning approach, the females outperformed the males significantly. The results of two-way ANCOVA revealed a significant interaction between gender and learning approach on computational thinking concepts. Females with the conventional learning approach of doing projects had the best computational thinking concepts in comparison with the other groups.

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Behavioral-pattern exploration and development of an instructional tool for young children to learn AI

Cited by 24 publications

References 15 publications

Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review

Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review

Is It Possible for Young Students to Learn the AI-STEAM Application with Experiential Learning?

The Effects on Secondary School Students of Applying Experiential Learning to the Conversational AI Learning Curriculum

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