SmileyCluster

Wan, Xiaoyu; Zhou, Xiaofei; Ye, Zaiqiao; Mortensen, Chase K.; Bai, Zhen

doi:10.1145/3392063.3394440

Cited by 45 publications

(25 citation statements)

References 42 publications

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“…However, most are relatively short-term courses (Figure 8). Half of the teaching units (16 of 32) in the reviewed studies comprise only one session lasting no more than 3 h, suggesting that a short lesson may be sufficient for children to develop an understanding of AI concepts e.g., [33,48,49]. The second most popular lesson duration is multiple sessions on different days, typically no more than 15 h or 7 days in total.…”

Section: Durationmentioning

confidence: 99%

“…Second, the content of most of the teaching units is centered on ML. Various types of ML are explored, including image recognition [33,42], emotion recognition [39], gesture recognition [49,51], object recognition [33], k-means clustering [48,52], supervised learning [37,38], neural networks [52], and natural language processing [53]. Most teaching units only cover the most accessible processes such as data management and practicing model learning and evaluation.…”

Section: Learning Content Tools and Materials And Prior Knowledge Pre...mentioning

confidence: 99%

“…In addition to general tools that are well-designed for novices to experience AI and ML, some teaching units include researcher-developed materials and tools. Typical examples are SmileyCluster, a hands-on and collaborative learning environment by [48], and Zhorai, a conversational platform with an online web interface by [60]. Some tangible resources such as robots, programmable toys, and diverse types of real-life sensors have also been used to teach AI and ML [8,38].…”

Section: Learning Content Tools and Materials And Prior Knowledge Pre...mentioning

confidence: 99%

“…The learning content generally focuses on one specific AI concept, mostly ML, and only targets one of the ML methods, such as supervised learning and image/sound/motion recognition. Some studies have argued that providing more lessons would not help much to improve student understanding of the basic concept of AI [48]. However, with short-term courses, a key concern is whether learning only certain parts of AI, such as ML, is sufficient for equipping students with AI literacy, especially when compared with the knowledge that can be gained in long-term courses.…”

Section: Teaching Unit Settingsmentioning

confidence: 99%

“…In primary and middle school, the focus is on basic principles of AI approaches, conveyed through experimenting and problem-solving activities. In secondary school, the focus is on core AI concepts and the exploration of advanced ML topics through hands-on artifact construction [48,52]. Only a few teaching units support a cross-age design despite using a long-term curriculum.…”

Section: Teaching Unit Settingsmentioning

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: Durationmentioning

confidence: 99%