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Artificial Intelligence technologies are becoming ubiquitous, transforming the workforce by altering or creating jobs and influencing decisions that affect minority communities. The necessity of AI literacy, comprising knowledge and skills for critical interaction with AI, is increasingly important. Multilingual learners, engaging with both every day and domain-specific vocabulary and syntax, must acquire AI literacy, necessitating tailored pedagogical practices. This article outlines three strategies implemented for multilingual middle school students during a summer camp in Southern California. We argue for a mutually beneficial relationship between acquiring second language proficiency and AI literacy. Supporting these processes, we present three pedagogical strategies: (1) storytelling to model AI decision-making, (2) role-playing as an AI to demonstrate programmability and learning from data, and (3) programming text-to-speech-to-text AI to illustrate sensor functionality and action-reaction concepts. Additionally, we discuss their alignment with AI competencies. These strategies potentially foster linguistic scaffolding and translanguaging, aiding multilingual learners in acquiring new literacies.

The presented work provides a comprehensive evaluation of the current availability of education programs and courses related to of AI the field of Information Technologies and Computer Science in higher education institutions (HIEs) in the Republic of Bulgaria. More specifically, this study examines 163 bachelor's and 239 master's degree programs from 28 HEIs available during the 2023/24 academic year in four professional fields: (1) Electrical Engineering, Electronics, and Automation; (2) Communication and Computer Technologies; (3) Informatics and Computer Science; and (4) Mathematics. The conducted evaluation shows that 41.1% of evaluated BSc programs and 26.4% of MSc programs include at least one AI-dedicated course. Results indicate a significant presence of AI-focused education, particularly in degrees related to Informatics and Computer Science, where 47.8% of AI courses are concentrated. However, a notable disparity exists in the inclusion of AI subjects across other technical fields, particularly in Electrical Engineering and related degrees, which contain only 8% of the identified AI courses for BSc degree programs. The findings highlight the need for a broader and more accelerated integration of AI education to meet the evolving demands of both students and the labor market. This work underscores the importance of strategic curriculum adaptation to enhance the readiness of Bulgarian HEIs to support the development and application of AI technologies, addressing the skills gap and fostering a workforce capable of navigating the AI-driven future. [ABSTRACT FROM AUTHOR]

Digital technology is profoundly transforming various aspects of life, thus highlighting the need to enhance digital literacy on a national scale. In primary and secondary schools, artificial intelligence (AI) education plays a pivotal role in fostering digital literacy. To comprehensively investigate the variables influencing AI education in primary and secondary schools, a dynamic system model was constructed based on the CIPP (Context, Input, Process, and Product) education evaluation model. We employed a mixed-method research approach, combining both qualitative and quantitative methods, to thoroughly investigate the influencing factors and perform predictive and simulation experiments. Firstly, we construct a system framework for AI education in primary and secondary schools based on CIPP model, utilizing qualitative methods such as literature review and expert interviews to identify influencing factors and define the system's boundaries. Secondly, we collect 814 questionnaire responses from 12 primary and secondary schools and employ Structural Equation Modeling (SEM) for quantitative analysis to explore the relationships between these influencing factors. Finally, these relationships are utilized to construct a System Dynamics model, allowing for an in-depth exploration of the development trends in AI education in primary and secondary schools through predictive analysis and control simulation. Controlled simulations allow us to predict and validate factors that influence the level of AI education development, aiding in the identification of high-leverage elements. Our findings underscore that AI education in Chinese primary and secondary schools is still in its initial stage with insufficient developmental momentum. Through controlled simulation results, it is found that the development of AI education in primary and secondary schools is influenced by both external systems and internal student systems, with national policy serving as the fundamental driving force. Among these influencing factors, societal factors play a predominant role in the external environment, followed by school factors, while the students' level of learning engagement is identified as the most crucial factor within the internal system. Based on the results of prediction and simulation studies, recommendations for improvement are proposed, including policy reforms and collaboration between schools and enterprises. [ABSTRACT FROM AUTHOR]

With the push to integrate Artificial Intelligence (AI) from kindergarten to twelfth-grade levels comes a need for equipped teachers. However, there needs to be more initiative in professional learning opportunities, which demands the required effort to ensure teachers learn the AI content they will be teaching. To design an effective professional development program, understanding teachers' existing knowledge, interest and disposition towards AI is crucial to devise strategies that could best support the teachers. As a result, this study aims to investigate in-service teachers' perspectives of teaching AI in schools from the perspective of planned behavior theory. Using a 7-factor scale of AI Anxiety, AI Readiness, AI Relevance, Attitude towards using AI, AI for Social Good, Confidence in AI and Behavioral Intention. This study sampled 159 in-service teachers in Namibia. The teachers' responses were analyzed with SmartPLS using Structural Equation Modelling and various Multigroup Analysis techniques. This study found that teachers' behavioral intention to teach AI depends on a combination of factors, including the relevance of AI, attitude towards using AI, the use of AI for social good and confidence. Meanwhile, AI Anxiety and readiness could not be linked to the intention to teach AI. We discussed our findings, highlighted the study implication, and suggested future directions. [ABSTRACT FROM AUTHOR]

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In the artificial intelligence (AI) era, K-12 AI education has attracted great attention. With the increasing prevalence of AI, students are likely to have misconceptions about AI starting in elementary school. Hence, it is crucial to explore an effective approach to change elementary students' misconceptions and facilitate their scientific understanding of AI. The two-tier test is an effective method to diagnose students' misconceptions, but it needs to be combined with specific learning models to further promote conceptual change in teaching practice. The dual-situated learning model (DSLM) is an instructional approach used to facilitate students' conceptual change. Therefore, this study proposed a DSLM-based two-tier test (DSLM-TT) approach for AI courses, and explored its effects on elementary students' conceptual change, learning motivation, and self-efficacy in AI courses. 160 sixth-grade students participated in this experiment: the experimental group adopted the DSLM-TT approach, while the control group adopted the conventional learning model-based two-tier test approach (CLM-TT). Results showed that the DSLM-TT group performed better on conceptual change of AI, learning motivation, and self-efficacy than the CLM-TT group. Moreover, the interview results revealed two groups' learning experiences and perceptions. Accordingly, this study can provide a baseline for future research on conceptual change in AI education. [ABSTRACT FROM AUTHOR]

Copyright of Digital Education Review is the property of University of Barcelona, Virtual Teaching & Learning Research Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to advances in Artificial Intelligence (AI), computer science education has rapidly started to include topics related to AI along K-12 education. Although this development is timely and important, it is also concerning because the elaboration of the AI field for K-12 is still ongoing. Current efforts may significantly underestimate the role of data, the fundamental component of an AI system. If the goal is to enable students to understand how AI systems work, knowledge of key concepts related to data processing is a prerequisite, as data collection, preparation, and engineering are closely linked to the functionality of AI systems. To advance the field, the following research provides a comprehensive collection of key data-related concepts relevant to K-12 computer science education. These concepts were identified through a theoretical review of the AI field, aligned through a review of AI curricula for school education, evaluated through interviews with domain experts and teachers, and structured hierarchically according to the data lifecycle. Computer science educators can use the elaborated structure as a conceptual guide for designing learning arrangements that aim to enable students to understand how AI systems are created and function.

In recent years, Game-Based Learning (GBL) has been widely adopted in various educational settings. This paper aims to review empirical studies that adopt GBL in the field of AI education and explore its future research perspectives. After a systematic keyword search in the online database and a snowballing approach, a total of 125 empirical papers with 133 studies were targeted as samples. Results indicated that the games in AI education are mainly fell into five categories: Puzzle games are the most used in the curriculum (27.07%), followed by Reasoning strategy games (23.31%), Robot games (18.05%), Role-playing games (9.02%) and Simulation games (6.77%). Among them, 22.39% of games were with real characters, 11.94% were with virtual characters and 64.18% were with no characters. Besides, games were used in three main forms in AI education: games as teaching tools (78.95%), games as student works (12.03%), and games as a competing mechanism (9.02%). Researchers mainly paid attention to the effect of GBL on students' Opinions and Attitude (52.96%) and Learning achievement (24.04%), while the other three categories such as Skills and ability, Interaction, and Cognition were not extensively measured. The cross-sectional analysis, research gaps, and potential directions for future research were also discussed. [ABSTRACT FROM AUTHOR]

This study investigates how 5-year-old children perceive and understand Artificial Intelligence (AI). The participants of the study were 45 children from a private kindergarten in South Korea. The research employs drawings and small group interviews to explore the children's conceptualizations of AI. Research results were as follows. First, 73.3% of children associated AI with positive feelings. Although they generally viewed AI as a convenience in their lives, they also expressed concerns related to health and costs. Secondly, they indicated household items, smart devices, robot and machinery, personal computer and other things as images of AI. The most frequently indicated images of AI included items that they had used themselves or seen people using in daily lives. Children primarily view AI as a tool for adult convenience and entertainment, with their perceptions shaped by their developmental stage and daily interactions with technology. These findings underscore the need for a more comprehensive approach to AI education in early childhood, one that addresses ethical considerations and is tailored to children's developmental needs. This study aims to contribute to the ongoing dialogue and development of strategies for effectively introducing AI to young learners in a way that supports their growth and development. [ABSTRACT FROM AUTHOR]

Copyright of e-Education Research is the property of Northwest Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to improve the quality of AI education for the AI era by developing an educational dataset library and exploring its applicability. Reflecting the needs of teachers engaged in AI educational activities, the dataset library emphasizes the diversity of topics, forms, and sizes of datasets provided. Additionally, it is designed with a feature to generate outliers and missing values suitable for students' accessibility and educational purposes. The library developed in this research is based on Python and employs the random forest modeling method to generate high-quality synthetic datasets. The functionality and suitability of this library for AI education have been evaluated by an expert panel, which has endorsed its applicability in the field. In detailed assessments of the synthetic datasets generated, the library demonstrated its capability to accurately mirror the statistical characteristics of original datasets, achieving high levels of accuracy and cosine similarity in the modeling results. These outcomes confirm the library's efficacy in reconstructing educational datasets specifically for AI education purposes and crafting high-quality synthetic datasets. This approach offers a practical solution to the existing shortage of educational datasets and substantially enhances the overall quality of education. This research proves immensely beneficial for educators and learners, laying a foundation for ongoing and future research focused on creating and utilizing educational datasets in AI. This paves the way for expanding the possibilities and scope of their application in the educational field, potentially transforming AI education practices. [ABSTRACT FROM AUTHOR]

AI education aims to nurture convergence talents equipped with various knowledge and AI capabilities. However, the educational programs developed so far are designed for less than 10 sessions. This is insufficient time for students to understand artificial intelligence algorithms, utilize the learned principles of artificial intelligence, and expand by converging with various knowledge. Since the developmental characteristics of students in the lower grades of elementary school are different, it is difficult to apply them as they are. Therefore, it is necessary to study the following AI education teaching and learning methods suitable for lower grade students. In this study, we develop a teaching system design model for artificial intelligence-based subject convergence education for elementary school students in the lower grades, and based on this, design and apply an artificial intelligence-based subject convergence education program. Experiments were conducted with 47 second-year elementary school students, and students' responses were better in the AI-based convergence education program than in general subject classes in terms of interest, understanding, and expectations for classes. [ABSTRACT FROM AUTHOR]

The purpose of this study is multifold: First, to develop an educational program using artificial intelligence (AI) in middle school free semester system of South Korea. Second, to verify the program's effectiveness, the study clarified the definition of AI and AI education and considered their meaning in technology education. This study used three steps: preparation, development, and improvement. In the preparation step, this study set the theme and purpose of the AI program and selected the free semester activity type "theme selection activity." After analyzing the technology curriculum and extracting AI-related elements in the development step, this study laid out the program for 16 h of class time. In the improvement step, to augment the program's validity, the researcher revised and supplemented it as a whole through expert consultation. This research differentiated the developed program from the AI education program of other subjects and specialized it, focusing on the specificity of technology education. The study emphasized the social impact of the latest technology, ethics of AI, physical computing using AI, and technological problem-solving activity using AI. The final developed program was applied to the students, and students participated in a pretest and posttest. The study used the PATT and AI competency test tools. The PATT results showed a significant increase in the mean of both constructs in "interest in technology" and "career aspirations in technology." In AI competency, the mean of two constructs increased significantly in "social impact of AI" and "AI performance." In particular, "AI performance" showed the largest increase. There was no statistically significant change in "interaction with AI." The study results confirmed that the developed AI program was effective in technology education and career exploration, which is the primary purpose of the free semester. In addition, it was possible to confirm the technology educational value of the AI education program centered on technological problem-solving. These research results have implications for bringing AI into technology education. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has emerged as a pivotal technology for enhancing national and industrial competitiveness in the digital transformation era. Consequently, the cultivation of specialized talent in AI has garnered significant attention. This study analyzed AI-related department curricula at major universities worldwide, identifying critical courses for each academic semester. The data we collected included course titles, syllabi, and learning objectives, which were refined and analyzed afterward. Furthermore, we comparatively examined university AI education programs based on the content of Computer Science Curricula 2023, a widely recognized framework for computer science education. The insights gleaned from our analysis revealed that AI curricula are built upon a foundation of computer science, emphasizing the importance of a deep understanding of various related domains within the field of computer science. Based on these findings, we proposed a curriculum for AI departments, considering the need for a comprehensive understanding of computer science alongside specialized AI courses. This study aims to provide foundational data for advancing AI education and guide educational program improvements. Ultimately, it aspires to contribute to developing specialized professionals in the AI field, thereby bolstering national and industrial competitiveness in the rapidly evolving digital landscape. [ABSTRACT FROM AUTHOR]

This systematic review investigates learning task design for K-12 AI education, aiming to provide an overview of the status of AI education and identify trends, challenges, and opportunities. Through an analysis of 47 empirical studies, the review presents, synthesizes, and evaluates the educational theories underpinning the learning task design, the content, the pedagogies used for teaching, as well as the measurement and outcomes of the existing literature on AI education programs in K-12 settings. The principal findings reveal a diverse landscape of learning task design for teaching AI to K-12 students. Positive outcomes underscore the effectiveness of well-crafted hands-on tasks in fostering deep understanding and engagement. Challenges include addressing initial teacher and student apprehension, enhancing deep conceptual explanations of AI concepts, and hardware-related obstacles. The review encourages deep conceptual knowledge, holistic AI education, collaborative knowledge-sharing across nations, and co-design of learning tasks and resources across sectors.

We present CNNExplorer, a generalized visualization system designed to assist beginners in understanding Convolutional Neural Networks (CNNs). CNNExplorer provides an intuitive interface and detailed visualizations at both the module and layer levels, enabling users to explore and understand the complex structures and operations of CNN models without requiring extensive coding knowledge. The module view abstracts the complexity by grouping layers into modules, while the layer view shows detailed transformations within each module. Our system supports a wide range of CNN models, including those available on Hugging Face, allowing users to visualize and analyze a variety of pre-trained models. To demonstrate the potential effectiveness of CNNExplorer in practical applications, we proposed specific application scenarios. Furthermore, we conducted a user study with participants and found the system is effective in enhancing their understanding of deep learning models and appreciated the interactive features. In future work, we will focus on supporting the latest vision models and expanding visualization capabilities to other deep learning architectures.

The presented work provides a comprehensive evaluation of the current availability of education programs and courses related to of AI the field of Information Technologies and Computer Science in higher education institutions (HIEs) in the Republic of Bulgaria. More specifically, this study examines 163 bachelor’s and 239 master’s degree programs from 28 HEIs available during the 2023/24 academic year in four professional fields: (1) Electrical Engineering, Electronics, and Automation; (2) Communication and Computer Technologies; (3) Informatics and Computer Science; and (4) Mathematics. The conducted evaluation shows that 41.1% of evaluated BSc programs and 26.4% of MSc programs include at least one AI-dedicated course. Results indicate a significant presence of AI-focused education, particularly in degrees related to Informatics and Computer Science, where 47.8% of AI courses are concentrated. However, a notable disparity exists in the inclusion of AI subjects across other technical fields, particularly in Electrical Engineering and related degrees, which contain only 8% of the identified AI courses for BSc degree programs. The findings highlight the need for a broader and more accelerated integration of AI education to meet the evolving demands of both students and the labor market. This work underscores the importance of strategic curriculum adaptation to enhance the readiness of Bulgarian HEIs to support the development and application of AI technologies, addressing the skills gap and fostering a workforce capable of navigating the AI-driven future.

Artificial Intelligence (AI) has triggered profound reforms across industries, including education. These developments necessitate the inclusion of AI as a subject in K-12 classrooms. However, the need for students to learn AI demands that educators pay increasing attention, believe in its relevance and intend to promote it among their students and colleagues. This paper aimed to explore teachers' perceptions of and behavioral intention to teach AI. We specifically considered the association of AI anxiety, perceived usefulness, attitude towards AI, AI relevance, AI readiness, and behavioral intention factors. This research further aims to examine the moderator effect of AI for social good and confidence on the relationship in our hypothesized research model. To address this purpose, a quantitative methodology with the use of structural equation modeling was utilized. Data were retrieved through an online questionnaire from 320 lower and upper secondary school in-service teachers, mostly in STEM-related fields. Our findings reveal that teacher perceptions of AI for social good and confidence will affect most relationships in the model. Teacher professional programs should include the benefits and risks of AI and good practice sharing. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI) continues its rapid and relentless progression, the necessity for a comprehensive AI education has become increasingly evident. While South Korea has initiated various policies related to AI education, recent research has underscored the potential adverse repercussions of current instructional approaches on learners. In response to this pressing concern, the present study delves into integrating design thinking principles into AI education and meticulously assesses its impact on learning outcomes. To achieve this objective, we seamlessly amalgamated design thinking principles with AI problem-solving techniques, developing a tailor-made AI education curriculum explicitly crafted for middle school students. Subsequently, this innovative curriculum was implemented among middle school students, and their Computational Thinking (CT) competence was rigorously evaluated. The findings unequivocally establish that the infusion of design thinking into AI education significantly augmented the CT skills of the participating students. In comparison to the control group, it was discerned that middle school students who underwent AI education integrated with design thinking exhibited a statistically substantial enhancement in their Computational Thinking (CT) proficiencies. This study furnishes compelling empirical evidence that unequivocally endorses design thinking as a potent instructional approach within the domain of AI education, particularly for middle school students. Furthermore, it underscores the necessity of embracing innovative pedagogical methodologies in AI education to equip the younger generation with the indispensable skills to adeptly navigate the perpetually evolving landscape of an AI-driven future. [ABSTRACT FROM AUTHOR]

This study explores the effects of pre-service teachers' digital literacy and self-efficacy on their perception of AI education for young children in early childhood education settings. Digital literacy refers to the ability to communicate with others by using digital technology, including the ability for sharing knowledge and information after user's creation. A survey was conducted on 212 pre-service early childhood teachers at two Universities located in the eastern United States, and the collected data were analyzed by descriptive statistics, correlation analysis, and hierarchical multiple regression. As a result of the study, first, pre-service teachers had high critical thinking among the sub-factors of digital literacy, but relatively low problem-solving skills. Second, the relationship between digital literacy, self-efficacy, and the perception of AI education for young children showed a positive correlation. Third, among the sub-factors of digital literacy of pre-service early childhood teachers, technical application, learning interaction, and problem-solving with teachers' self-efficacy were found to have a significant effect on the perception of AI education for young children. This study proposes that developing a pre-service teacher education program that can improve their digital literacy and self-efficacy is necessary for guiding early childhood teachers to use artificial intelligence technology with young children in early childhood education classrooms in educationally meaningful ways. [ABSTRACT FROM AUTHOR]

A growing number of courses seek to increase the basic artificial-intelligence skills ("AI literacy") of their participants. At this time, there is no valid and reliable measurement tool that can be used to assess AI-learning gains. However, the existence of such a tool would be important to enable quality assurance and comparability. In this study, a validated AI-literacy-assessment instrument, the "scale for the assessment of non-experts' AI literacy" (SNAIL) was adapted and used to evaluate an undergraduate AI course. We investigated whether the scale can be used to reliably evaluate AI courses and whether mediator variables, such as attitudes toward AI or participation in other AI courses, had an influence on learning gains. In addition to the traditional mean comparisons (i.e., t-tests), the comparative self-assessment (CSA) gain was calculated, which allowed for a more meaningful assessment of the increase in AI literacy. We found preliminary evidence that the adapted SNAIL questionnaire enables a valid evaluation of AI-learning gains. In particular, distinctions among different subconstructs and the differentiation constructs, such as attitudes toward AI, seem to be possible with the help of the SNAIL questionnaire. [ABSTRACT FROM AUTHOR]

This paper presents a proposal of specific curriculum in Artificial Intelligence (AI) for high school students, which has been organized as a two-year subject. The curriculum was designed based on two premises. The first one is that, although the proposal is targeted to scientific programmes, the involved students and teachers do not have any previous knowledge about AI. Accordingly, the teaching units have been designed with the aim of supporting teachers in a new discipline for them and, in addition, providing an introductory level to students. The main didactical objective is to establish the fundamentals of AI from a practical perspective, learning technical concepts by using them to solve specific problems. The approach that has been followed in the teaching units is focused on developing embedded intelligence solutions, that is, programming real-world devices which interact with real environments. To this end, and to address a second fundamental premise of low investment capability at schools, it has been decided to use Smartphones as the central technological element to implement such embedded intelligence at classes. This curriculum has been developed within the Erasmus + project entitled "AI + : Developing an Artificial Intelligence Curriculum adapted to European High School". The project was carried out by a team of AI experts and high school teachers who created the teaching units, and a group of students that tested them for three years, providing feedback to make the curriculum feasible for its introduction in schools in the short-term. The main results obtained from its implementation within the project scope are presented and discussed here, with the aim of contributing to the AIEd community progress by means of a practical pilot experience. Although the curriculum has been designed and tested at European level, it has been created with a general perspective of AI education, so it can be applied worldwide. [ABSTRACT FROM AUTHOR]

According to the 2021 and 2022 Horizon Report, AI is emerging in all areas of education, in various forms of educational aids with various applications, and is carving out a similarly ubiquitous presence across campuses and classrooms. This study explores a user-centered approach used in the design of the AI educational software by taking the redesign of the user interface of AI educational software Blockly–Electron as an example. Moreover, by analyzing the relationship between the four variables of software usability, the abstract usability is further certified so as to provide ideas for future improvements to the usability of AI educational software. User-centered design methods and attribution analysis are the main research methods used in this study. The user-centered approach was structured around four phases. Overall, seventy-three middle school students and five teachers participated in the study. The USE scale will be used to measure the usability of Blockly–Electron. Five design deliverables and an attribution model were created and discovered in the linear relationship between Ease of Learning, Ease of Use, Usefulness and Satisfaction, and Ease of use as a mediator variable, which is significantly different from the results of previous regression analysis for the USE scale. This study provides a structural user-centered design methodology with quantitative research. The deliverables and the attribution model can be used in the AI educational software design. Furthermore, this study found that usefulness and ease of learning significantly affect the ease of use, and ease of use significantly affects satisfaction. Based on this, the usability will be further concretized to facilitate the production of software with greater usability. [ABSTRACT FROM AUTHOR]

The study of causal inference has seen recent momentum in machine learning and artificial intelligence (AI), particularly in the domains of transfer learning, reinforcement learning, automated diagnostics, and explainability (among others). Yet, despite its increasing application to address many of the boundaries in modern AI, causal topics remain absent in most AI curricula. This work seeks to bridge this gap by providing classroom-ready introductions that integrate into traditional topics in AI, suggests intuitive graphical tools for the application to both new and traditional lessons in probabilistic and causal reasoning, and presents avenues for instructors to impress the merit of climbing the “causal hierarchy” to address problems at the levels of associational, interventional, and counterfactual inference. Finally, this study shares anecdotal instructor experiences, successes, and challenges integrating these lessons at multiple levels of education.

Abstract This research explored the creative thinking, learning achievement, and engagement of students when they integrated the application of the personal audio classifier (PAC) into the competition of a computational thinking (CT) board game (i.e., the experimental group), or did not integrate it into the competition but only collaborated with peers to test the function of the program which they had developed (i.e., the control group). The students had experienced popular speech recognition usage in their daily life, such as Siri and Google Assistant; therefore, this study developed instructional material for university freshmen to learn to develop their own artificial intelligence (AI) application (app) on a smart phone with PAC in MIT App Inventor. The PAC platform and the learning material cultivated students to train their own voice classification model, which is a form of supervised machine learning in the AI domain. The results showed that both groups, who had successfully trained computers to distinguish received voice commands with PAC receiving the human voice spectrogram via the cloud platform developed by MIT, made significant progress in their learning effectiveness in AI education. When the students employed the AI app on smartphones in the CT board game, the students’ voice commands could be classified, and then the corresponding command could be executed through the program to control the action of the robot car on the map, regardless of whether they were competing or not. This study not only successfully provided the students with simple AI learning material, but also cultivated their creative thinking, as identified in the survey of the computational thinking self-efficacy scale. During the process of completing a mobile phone application with AI, students should know and use the function of voice classification to achieve goals and expand their cognition of AI applications. This study concluded that the AI learning material for general students rather than students in the department of computer science facilitated the students’ engagement.

The professional training model in Colombia is moving from a competency-based approach to learning outcomes. This path is enriched with contributions both at the level of national policies and each institution, as well as the teaching experiences and interests of the students. In this environment, this research presents the impact on the motivational level, and on the development of research skills, in undergraduate students of the electrical area generated by implementing a training scheme that involves developing research formulated and guided by the students themselves. The students come from a model based on labor competencies, and the current curricular reform proposes a new model based on learning outcomes that demands a closer follow-up of the training process of each individual. The experimental group consisted of students in a new academic space in artificial intelligence (AI), for which specific instructional material was designed so that students could build deep learning classification models for real problems while implementing a scientific development scheme that includes the generation of scientific publications. The control group maintained a traditional training scheme based on content tracking and periodic written evaluations. The results showed that although both groups achieved a high level of learning effectiveness, the experimental group demonstrated more remarkable development in their creative thinking and more significant appropriation of the academic space and the training process, with a solid self-critical position. The research concluded that the follow-up model, as well as the material developed, manages to increase the student’s commitment to their training process and to generate positions on how this process is developed. [ABSTRACT FROM AUTHOR]

Background: Group size is one of the important factors that affect collaborative learning, however, there is no consensus in the literature on how many students should the groups be composed of during the problem‐solving process. Objectives: This study investigated the effect of group size in a K‐12 introductory Artificial Intelligence course by comparing the students' cognitive load, learning motivation, collaborative problem‐solving quality, and in‐classroom interaction between two‐ and three‐student groups. Methods: Forty‐eight high school students were randomly assigned to two kinds of groups (i.e., the two‐student group, and the three‐student group, each consisting of 24 students). During the experiment, Xiaofei robots were used to teach the theoretical and practical content of five AI topics over 6 weeks for 1.5 h each week. Results: The ANOVA results indicated that group size mattered in the AI course, the two‐student group was more effective in terms of enhancing students' learning motivation and collaborative problem‐solving quality, as well as imposing more cognitive load than the three‐student group. The advantage was more obvious in the practical problem context. The Lag Sequential Analysis results indicated that more collaborative learning behavioural sequences existed in the two‐student group than in the three‐student groups. Contribution: This research provides empirical evidence and potential guidance for group design in Artificial Intelligence Education. Although group size did not affect students' learning achievement, it affected learning motivation, cognitive load, and problem‐solving quality and processes. Two‐student groups work better than three‐student groups in the AI course. Lay Description: Group size affected students' learning motivation, cognitive load, and problem solving process, but not their learning achievement.The two‐student group was more effective in terms of enhancing students' learning motivation and collaborative problem‐solving quality, while imposing more cognitive load than the three‐student groups.The Lag Sequential Analysis results indicated that more collaborative learning behavioural sequences existed in the two‐student group than in the three‐student groups. The tendency was more obvious in the practical problem context than in the theoretical problem context.Overall, in terms of problem‐solving and learning, group size should be considered in instructional design of AI courses. [ABSTRACT FROM AUTHOR]

A growing number of courses seek to increase the basic artificial-intelligence skills (“AI literacy”) of their participants. At this time, there is no valid and reliable measurement tool that can be used to assess AI-learning gains. However, the existence of such a tool would be important to enable quality assurance and comparability. In this study, a validated AI-literacy-assessment instrument, the “scale for the assessment of non-experts’ AI literacy” (SNAIL) was adapted and used to evaluate an undergraduate AI course. We investigated whether the scale can be used to reliably evaluate AI courses and whether mediator variables, such as attitudes toward AI or participation in other AI courses, had an influence on learning gains. In addition to the traditional mean comparisons (i.e., t-tests), the comparative self-assessment (CSA) gain was calculated, which allowed for a more meaningful assessment of the increase in AI literacy. We found preliminary evidence that the adapted SNAIL questionnaire enables a valid evaluation of AI-learning gains. In particular, distinctions among different subconstructs and the differentiation constructs, such as attitudes toward AI, seem to be possible with the help of the SNAIL questionnaire.

Artificial Intelligence (AI) and Computer Vision (CV) have rapidly permeated various industries, increasing demand for professionals well-versed in these disciplines. In response to this need, this study evaluates the effectiveness of a novel undergraduate course that aims to equip students with foundational AI and CV knowledge and skills. Unlike existing software-centric courses, this class capitalizes on drones as a practical application area for AI and CV, fostering an active learning environment. The course was conducted over five semesters at a U.S. research institution, with 153 participating students. Data were collected using diverse methods, including formative and summative assessments, surveys of learning gains, focus group interviews, and analysis of capstone project process videos. The course design included Intended Learning Outcomes (ILOs) tailored to meet the requirements of contemporary AI/CV professionals, weekly AI-based curricular modules, unrestricted access to computational resources, and diverse assessment strategies. This study also delves into students’ perceptions of this unique learning approach and challenges, with students from varied academic majors. Initial findings suggest that employing active learning techniques significantly enhances student engagement and understanding of AI/CV concepts, leading to substantial skill gains and positive attitudes toward the subject.

IntroductionArtificial intelligence and data-driven predictive modeling have become increasingly common tools integrated in clinical practice, heralding a new chapter of medicine in the digital era. While these techniques are poised to affect nearly all aspects of medicine, medical education as an institution has languished behind; this has raised concerns that the current training infrastructure is not adequately preparing future physicians for this changing clinical landscape. Our institution attempted to ameliorate this by implementing a novel artificial intelligence in radiology curriculum, “AI-RADS,” in two different educational formats: a 7-month lecture series and a one-day workshop intensive.MethodsThe curriculum was structured around foundational algorithms within artificial intelligence. As most residents have little computer science training, algorithms were initially presented as a series of simple observations around a relatable problem (e.g., fraud detection, movie recommendations, etc.). These observations were later re-framed to illustrate how a machine could apply the underlying concepts to perform clinically relevant tasks in the practice of radiology. Secondary lessons in basic computing, such as data representation/abstraction, were integrated as well. The lessons were ordered such that these algorithms were logical extensions of each other. The 7-month curriculum consisted of seven lectures paired with seven journal clubs, resulting in an AI-focused session every two weeks. The workshop consisted of six hours of content modified for the condensed format, with a final integrative activity.ResultsBoth formats of the AI-RADS curriculum were well received by learners, with the 7-month version and workshop garnering 9.8/10 and 4.3/5 ratings, respectively, for overall satisfaction. In both, there were increases in perceived understanding of artificial intelligence. In the 7-lecture course, 6/7 lectures achieved statistically significant (P

This study aimed to provide insight into the competencies required for students to participate actively and thrive in artificial intelligence education in the K-12 context taking cognizance of ethical concerns. The problem is that AI education is new, and we have not understood the competencies required by students to understand AI effectively. Since research in this area is limited and lacking in African settings, this study focused on Nigerian K-12 students to understand the factors that support a proper grasp of AI content in the context. A quantitative methodology approach was utilized with a hardcopy survey administered to secondary school students, which yielded 605 responses. It was found that cognitive, human-tool collaboration, self-learning, skill competence, and ethics significantly influence the content of AI. It was further revealed that no moderating effect was shown while the mediating effect of ethics was significant between cognitive competence and content of AI. Surprisingly, all the propositions made show that only the relationship between teamwork competence and AI course content was not supported. This result suggests a clear need for activities that promotes collaboration in the AI curriculum. This study's findings contribute to the limited evidence on the significance of learners’ competence in understanding AI through the content provided. The identified competencies could guide the development of relevant content by teachers and other practitioners. Besides, the outcome of this study can empower students to develop the right competencies needed to navigate the world of AI.

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. [ABSTRACT FROM AUTHOR]

This research explored the creative thinking, learning achievement, and engagement of students when they integrated the application of the personal audio classifier (PAC) into the competition of a computational thinking (CT) board game (i.e., the experimental group), or did not integrate it into the competition but only collaborated with peers to test the function of the program which they had developed (i.e., the control group). The students had experienced popular speech recognition usage in their daily life, such as Siri and Google Assistant; therefore, this study developed instructional material for university freshmen to learn to develop their own artificial intelligence (AI) application (app) on a smart phone with PAC in MIT App Inventor. The PAC platform and the learning material cultivated students to train their own voice classification model, which is a form of supervised machine learning in the AI domain. The results showed that both groups, who had successfully trained computers to distinguish received voice commands with PAC receiving the human voice spectrogram via the cloud platform developed by MIT, made significant progress in their learning effectiveness in AI education. When the students employed the AI app on smartphones in the CT board game, the students' voice commands could be classified, and then the corresponding command could be executed through the program to control the action of the robot car on the map, regardless of whether they were competing or not. This study not only successfully provided the students with simple AI learning material, but also cultivated their creative thinking, as identified in the survey of the computational thinking self-efficacy scale. During the process of completing a mobile phone application with AI, students should know and use the function of voice classification to achieve goals and expand their cognition of AI applications. This study concluded that the AI learning material for general students rather than students in the department of computer science facilitated the students' engagement. [ABSTRACT FROM AUTHOR]

Many approaches have been proposed to teach the basic concepts of big data and artificial intelligence to K-12 students based on block-based programming languages, such as Scratch. Using these approaches, young students can easily experience big data and artificial intelligence through a drag-and-drop approach. However, it remains difficult for them to perform more complex tasks, such as directly collecting data from the web or exploiting custom-made machine learning algorithms. In this paper, we propose a novel Scratch extension that allows Scratch to communicate with text-based programming languages such as Python and JavaScript using WebSockets. Unlike other Scratch extensions, our extension greatly enhances the extensibility of Scratch given its use of “text-based visual blocks” so that messages can be freely exchanged through a minimum number of blocks. In order for students to use these blocks easily, the blocks are designed such that they can be used as if talking with a friend named “Tooee.” In order to show how this extension can help students create big data and artificial intelligence programs, we present eight example applications that students can easily implement. These are (1) Weather Forecast, (2) Top 5 Movies in Theaters, (3) COVID-19 Dashboard, (4) Saving Quiz Results to a CSV File, (5) Facial Image Classification, (6) Color Classification, (7) Object Classification, and (8) Handwriting Recognition. Our analyses and experimental results show that Tooee has several advantages over other educational environments.