# data[].attributes.body
Consiste num portefólio em formato eletrónico/digital, sendo um portefólio definido de várias formas, tais como:

1. Conjunto de material gráfico utilizado em apresentações.

2. Conjunto de trabalhos ou de fotografias de trabalho de um profissional das artes.

3. Dossier ou documento com registo individual de habilitações ou de experiências.

4. Pasta ou cartão duplo para guardar papéis.

5. Conjunto de títulos que está na posse de um investidor (no ramo da economia).

Em síntese, um e-portefólio é um conjunto de provas eletrónicas (como ferramentas, ficheiros eletrónicos, imagens, website, etc.), que permite demonstrar as capacidades da pessoa que o criou. Além disso, um e-portefólio também é constituído pelas reflexões que o/a aluno/a possui, relativamente ao conteúdo e respetivo processo de aprendizagem.

# data[].attributes.body
Refere-se a qualquer ferramenta, recurso ou equipamento, que presta auxílio no processo de ensino aprendizagem, sendo que a mesma poderá ocorrer de forma física, digital, offline ou online. Portanto, a tecnologia educativa abrange, não só materiais didáticos tradicionais, como também recursos didáticos digitais.

A Tecnologia Educativa (TE) pode manifestar-se de diversas formas, como por exemplo, na gamificação. De forma sintetizada, o processo de gamificação consiste em transformar o conteúdo que se pretende abordar em formato de jogo. Neste jogo é possível acumular pontos, cumprir desafios ou missões. Este tipo de TE ajuda a tornar o processo de ensino mais fluído e natural.

# data[].attributes.attachment_link.description
This is the description of my awesome form

# data[].attributes.body
Consiste no processo de aprender e desenvolver habilidades tecnológicas, bem como também adquirir conhecimentos, valores, atitudes, regulamentos e ética sobre as Tecnologias da Informação e Comunicação, com o objetivo principal de retirar o máximo de proveito da utilização das mesmas.

# data[].attributes.body
Consiste numa técnica que utiliza a narrativa, através de recursos digitais, para transmitir alguma mensagem, uma intenção, uma ação, ou seja, a fim de atingir um objetivo. Esta técnica recorre à imagem e alia a mesma com o som (música, palavras e som). Geralmente, quem recorre a esta técnica pretende contar pequenas histórias.

# data[].attributes.body
Consiste num termo amplo, contudo, de forma sintetizada, refere-se à capacidade/habilidade que os indivíduos possuem para utilizar as diferentes Tecnologias da Informação e Comunicação, que requer tanto competências cognitivas como competências técnicas.

# data[].attributes.headline
Tarefa 4 - Questões sobre "Digital Storytelling"

# data[].attributes.subject
Tarefa 4 - Questões sobre "Digital Storytelling"

# data[].attributes.body
Arte ou processo de fixar a imagem de qualquer objeto/indivíduo através de um sensor digital com auxílio da luz. Apesar da intenção da fotografia consistir em retratar a realidade, a mesma não a retrata totalmente, uma vez que, através da fotografia realizamos um corte, ou seja, tiramos fotografia a uma parte específica da realidade.

# data[].attributes.body
Corresponde ao modo como é enquadrado o conteúdo de uma fotografia. Através do plano conseguimos escolher o que pretendemos mostrar com maior ou menor clareza, de acordo com a nossa intenção. Existem vários tipos de planos, como o plano geral, o plano de conjunto, o plano médio, o grande plano e o muito grande plano ou de pormenor.

1.     Plano Geral – não apresenta elementos particulares de atração, sendo o seu tempo de exposição muito breve.

2.     Plano de Conjunto – o ambiente continua a predominar, mas já é possível distinguir um determinado tema. Relativamente ao tempo de exposição, o mesmo é suficientemente longo para recolher os pormenores necessários para transmitir a mensagem da imagem.

3.     Plano Médio – o indivíduo sobrepõe-se ao ambiente, mas o ambiente é possível de identificar.

4.     Grande Plano – consiste num plano que possui valor expressivo, valoriza o sujeito e os seus pormenores, sendo que o seu tempo de exposição, varia de acordo com a importância que se pretender atribuir à imagem.

5.     Muito Grande Plano ou de Pormenor – a imagem apresenta um pormenor do sujeito, contudo não é suficiente ao ponto de conseguirmos focar o sujeito. O seu tempo de exposição é breve.

# data[].attributes.body
Consiste na amplitude que a fotografia é tirada. O enquadramento certo e a composição adequada da imagem são fundamentais para se conseguir obter uma boa fotografia. Existem vários tipos de ângulos fotográficos, como a tomada frontal, a perspetiva vertical ascendente e a perspetiva vertical descendente.

1.     Tomada Frontal – consiste no ângulo de frente, onde se enquadrar o objeto/indivíduo de frente.

2.     Perspetiva Vertical Ascendente ou contra-picado – consiste no ângulo de vista de baixo, ou seja, a fotografia é tirada de baixo.

3.     Perspetiva Vertical Descendente ou picado - consiste no ângulo de vista de cima, ou seja, a fotografia é tirada de cima.

# data[].attributes.body
Consiste num ambiente que é gerado através de um computador com objetos e ações que parecem ser reais, permitindo aos usuários que se sintam dentro dessa realidade. Normalmente este ambiente é compreendido através de uns óculos ou capacete de RV. Esta realidade tem a capacidade de envolver os indivíduos de forma ativa e dinâmica. De forma sintetizada, a RV cria um “mundo” onde as pessoas se envolvem através de óculos específicos, tratando-se de um ambiente totalmente imersivo em que tudo o que se vê faz parte de uma realidade construída de forma artificial.

# data[].attributes.body
Consiste numa tecnologia que nos permite sobrepor elementos virtuais à nossa visão sobre a realidade. Muitas vezes confundida com a Realidade Virtual, a RA tem a capacidade de transformar a nossa realidade através da implementação de objetos, imagens, sons, entre outros, sem que a nossa realidade “desapareça” totalmente. Isto é, tudo o que se vê no momento é real, sendo que existem implementações de ações e objetos, sem recorrer a óculos ou capacetes. Como exemplo mais comum de RA temos o mainstream e o Pokémon Go.

# data[].attributes.attachment_link.readable_text
No âmbito da sua estratégia digital, a União Europeia (UE) pretende regulamentar a inteligência artificial (IA) para garantir melhores condições para o desenvolvimento e a utilização desta tecnologia inovadora.

A IA pode trazer muitos benefícios, como melhores cuidados de saúde, transportes mais seguros e mais limpos, produções mais eficientse e energia mais barata e mais sustentável.

Em abril de 2021, a Comissão Europeia propôs o primeiro quadro regulamentar da UE para a IA. Propõe que os sistemas de IA, que podem ser utilizados em diferentes setores, sejam analisados e classificados de acordo com o risco que representam para os utilizadores.

Os diferentes níveis de risco implicarão mais ou menos regulamentação.

Sabe mais sobre o que é a inteligência artificial e como é utilizada.
O que o Parlamento pretende com a legislação sobre IA

A prioridade do Parlamento Europeu consiste em garantir que os sistemas de IA utilizados na UE sejam seguros, transparentes, rastreáveis, não discriminatórios e respeitadores do ambiente. Os sistemas de IA devem ser supervisionados por pessoas, em vez de serem automatizados, para evitar resultados prejudiciais.

O Parlamento quer também estabelecer uma definição uniforme e neutra em termos tecnológicos para a IA, de modo a ser aplicada em futuros sistemas de IA.

Lei da inteligência artificial: regras diferentes consoante os níveis de risco

As novas regras estabelecem obrigações para os fornecedores e utilizadores em função do nível de risco da IA. Embora muitos sistemas de IA representem um risco mínimo, é necessário avaliá-los.

Risco inaceitável

Os sistemas de IA de risco inaceitável são sistemas considerados uma ameaça para as pessoas e serão proibidos. Estes sistemas incluem:

manipulação cognitivo-comportamental de pessoas ou grupos vulneráveis específicos: por exemplo, brinquedos ativados por voz que incentivam comportamentos perigosos nas crianças;

pontuação social: classificação de pessoas com base no comportamento, estatuto socioeconómico, caraterísticas pessoais;

identificação biométrica e categorização de pessoas singulares;

sistemas de identificação biométrica em tempo real e à distância, como o reconhecimento facial.

Podem ser permitidas algumas exceções para fins de aplicação da lei. Os sistemas de identificação biométrica remota "em tempo real" serão permitidos num número limitado de casos considerados graves, enquanto sistemas de "pós-identificação" biométrica à distância, em que a identificação ocorre após um atraso significativo, só serão permitidos para a repressão de crimes graves e após aprovação do tribunal.

Risco elevado

Os sistemas de IA que afetam negativamente a segurança ou os direitos fundamentais serão considerados de elevado risco e serão divididos em duas categorias.

1. Sistemas de IA que são utilizados em produtos abrangidos pela legislação da UE em matéria de segurança dos produtos. Isto inclui brinquedos, aviação, automóveis, dispositivos médicos e elevadores.

2. Sistemas de IA que se enquadram em áreas específicas que terão de ser registados numa base de dados da UE:

gestão e funcionamento de infraestruturas essenciais,

educação e formação profissional,

emprego, gestão dos trabalhadores e acesso ao trabalho por conta própria,

acesso e usufruto de serviços privados essenciais e de serviços e benefícios públicos,

gestão da migração, do asilo e do controlo das fronteiras,

assistência na interpretação jurídica e na aplicação da lei.

Todos os sistemas de IA de risco elevado serão avaliados tanto antes de serem colocados no mercado como durante todo o seu ciclo de vida. Os cidadãos terão o direito de apresentar queixas contra os sistemas de IA junto das autoridades nacionais competentes.

Requisitos de transparência

A inteligência artificial generativa, tal como o ChatGPT, não será classificada como sendo de risco elevado mas terá de cumprir os requisitos de transparência e a legislação da UE em matéria de direitos de autor, ou seja:

divulgar que o conteúdo foi gerado pela IA;

conceber o modelo para evitar que este gere conteúdos ilegais;

publicar resumos dos dados protegidos por direitos de autor utilizados para a formação.

Os modelos de inteligência artificial de uso geral de alto impacto que possam representar risco sistémico, como o modelo de IA mais avançado GPT-4, teriam de ser submetidos a avaliações exaustivas e comunicar a ocorrência de quaisquer incidentes graves à Comissão Europeia.

O conteúdo que é gerado ou modificado com recurso à inteligência artificial como, por exemplo as imagens, os arquivos de áudio ou vídeos (como é o caso dos deepfakes), precisa de ser claramente rotulado como sendo gerado através de inteligência artificial para que os utilizadores tenham consciência disso quando se veem confrontados com conteúdo desse género.

A lei tem como objetivo oferecer às PMEs e start-ups oportunidades para desenvolverem e treinarem modelos de IA antes da sua apresentação perante o público em geral.

E é por esse motivo que as regras exigem que as autoridades nacionais forneçam às empresas a possibilidade de efetuar testes de simulação com condições aproximadas às do mundo real.

Próximas etapas

O texto acordado será formalmente aprovado numa próxima sessão plenária do Parlamento. Será aplicável na íntegra 24 meses após a entrada em vigor. Algumas das suas disposições serão aplicáveis antes desse período, incluindo:

a proibição de sistemas de IA que apresentem riscos inaceitáveis - aplicável seis meses após a entrada em vigor da legislação;

os códigos de prática- aplicáveis nove meses após a entrada em vigor;

as regras sobre sistemas de IA de uso geral que precisam de cumprir os requisitos de transparência - serão aplicáveis 12 meses após a entrada em vigor.

Os sistemas de risco elevado dispõem de mais tempo para cumprir os requisitos, uma vez que as obrigações que lhes dizem respeito serão aplicáveis 36 meses após a entrada em vigor da legislação sobre inteligência artificial.

Espera-se que o texto acordado seja adotado em abril de 2024.

Mais sobre a ação digital da UE:

- Os perigos da criptomoeda e as vantagens da legislação da UE
- Luta contra a cibercriminalidade: explicação da nova legislação da UE em matéria de cibersegurança
- Aumentar a partilha de dados na UE: quais são os benefícios?
- Lei dos Mercados Digitais e Lei dos Serviços Digitais da UE
- Cinco formas de o Parlamento Europeu proteger os jogadores de vídeo em linha

# data[].attributes.attachment_link.title
Lei da UE sobre IA: primeira regulamentação de inteligência artificial | Temas | Parlamento Europeu

# data[].attributes.attachment_link.description
A utilização da inteligência artificial na UE será regulada pela lei da IA, a primeira lei abrangente do mundo sobre a IA. Sabe como esta lei te vai proteger.

# data[].attributes.attachment_link.readable_text
A Comissão Nacional de Proteção de Dados (CNPD) decidiu suspender, por 90 dias, a recolha de dados biométricos da íris e rosto realizada pela Worldcoin Foundation, para salvaguardar o direito à proteção de dados pessoais, especialmente de menores.

Em comunicado, a CNPD explica que a empresa já foi informada desta suspensão temporária, que decorre até que seja concluída a averiguação e emitida a decisão final sobre a matéria.

A adoção desta medida provisória urgente surge na sequência de "largas dezenas de participações" recebidas na CNPD no último mês, dando conta da recolha de dados de menores de idade sem a autorização dos pais ou outros representantes legais, bem como de deficiências na informação prestada aos titulares, na impossibilidade de apagar os dados ou revogar o consentimento.

Na sequência destas queixas, a CNPD já tinha aconselhado, no início do mês, os cidadãos a ponderarem muito bem antes de ceder estes dados.

Na nota emitida esta terça-feira, a CNPD refere ainda que a cobertura noticiosa feita pelos órgãos de comunicação social trouxe ao seu conhecimento que mais de 300 mil pessoas em Portugal já tinham fornecido os seus dados biométricos, condição indispensável para receber um determinado valor em criptomoeda, designada por Worldcoin.

O plano da Worldcoin para as autenticações do futuro através da leitura da íris e com moeda virtual à mistura. CNPD já fez fiscalização

Numa declaração escrita enviada ao Observador, Jannick Preiwisch, responsável pela proteção de dados na Worldcoin Foundation, diz que a empresa "está em total conformidade com todas as leis e regulamentos que regem a recolha e transferência de dados biométricos, incluindo o Regulamento Geral de Proteção de Dados da Europa".

"A Worldcoin Foundation tem o maior respeito pelo papel e responsabilidades das autoridades de proteção de dados, incluindo a CNPD em Portugal", assegura. A companhia refere que tem sido "completamente transparente" e demonstrado disponibilidade "para responder às questões ou preocupações da CNPD".

"O relatório da CNPD é o primeiro contacto que temos da parte deles sobre muitos destes assuntos, incluindo o tema de verificações por menores em Portugal, para o qual temos tolerância zero e estamos a trabalhar para resolver em todas as instâncias, mesmo que sejam poucas situações", continua.

A empresa começou há meses, em diversos países, a fazer imagens digitais da íris de pessoas que voluntariamente se submeteram a esse registo em troca de uma compensação em criptomoedas (uma moeda virtual usada na internet) equivalente a cerca de 70 euros.

Os locais de recolha de dados através do dispositivo "Orb", situados em grandes superfícies comerciais, quase que duplicaram em seis meses. "A grande afluência de pessoas, incluindo de menores, para aderir ao projeto Worldcoin e receber 'tokens' (criptomoeda) levou à necessidade de marcação prévia do processo de registo e recolha dos dados biométricos", recorda a CNPD, sublinhado que não há "qualquer mecanismo de verificação de idade dos aderentes".

Os dados biométricos "são qualificados como dados especiais no RGPD (Regulamento Geral sobre a Proteção de Dado)" e gozam, por isso, de "uma proteção acrescida", lembra a CNPD, insistindo que os riscos do tratamento deste tipo de dados são elevados.

Por outro lado - sublinha - "os menores são pessoas especialmente vulneráveis e são também objeto de uma proteção especial por parte do legislador europeu e nacional, por poderem estar menos cientes dos riscos, consequências e garantias do tratamento dos seus dados pessoais, bem como dos seus direitos". Assim, a CNPD justifica esta "intervenção urgente" para "prevenir danos graves ou irreparáveis" com o facto de o risco para os direitos fundamentais dos cidadãos ser elevado.

Citada em comunicado, a presidente da CNPD, Paula Meira Lourenço, considerou que "esta ordem de limitação temporária da recolha de dados biométricos pela Worldcoin Foundation é, neste momento, uma medida indispensável e justificada para obter o efeito útil da defesa do interesse público na salvaguarda dos direitos fundamentais, sobretudo dos menores".

A CNPD refere que prossegue a sua averiguação, com "diligências adicionais" e a análise das participações que diariamente continua a receber, tendo em vista a conclusão do processo.

A Agência Espanhola de Proteção de Dados ( AEPD) ordenou no início do mês a suspensão da atividade da empresa. A AEPD tinha anunciado em 20 de fevereiro a abertura de uma investigação à atividade da empresa na sequência de quatro queixas que tinha recebido. Na altura, a Worldcoin acusou a AEPD, de "contornar a lei" da União Europeia e divulgar "afirmações imprecisas e equivocadas" sobre a sua tecnologia.

Espanha suspende atividade da Worldcoin, a empresa que digitaliza a íris em troca de criptomoedas
(Atualizado com reação da Worldcoin)

# data[].attributes.attachment_link.title
Comissão de Proteção de Dados suspende recolha de dados da íris pela Worldcoin

# data[].attributes.attachment_link.description
A medida deriva de "largas dezenas de participações" de recolha de dados de menores. A suspensão, da qual a empresa já tem conhecimento, está em vigor até ser concluída a averiguação.

# data[].attributes.attachment_link.description
Os professores que não souberem usar a IA ficarão obsoletos e sem qualquer tipo de competências de gestão de uma aula no presente.

# data[].attributes.attachment_link.readable_text
Implementing artificial intelligence education for middle school technology education in Republic of Korea

Abstract

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.

Similar content being viewed by others

Introduction

As the field of education emphasizes career education, each provincial education office tries to develop students' dreams and talents through various programs and teaching-learning methods in Republic of Korea. Career experience is implemented from the elementary school level while high schools aim to deepen career education. Meanwhile, in middle schools, the Ministry of Education implemented a nationwide free semester and free year system (from now on "free semester") in 2016 (Ministry of Education, 2015a). As a result, quantitative grading was removed, leaving only qualitative evaluations in the free semester system. Freed from paper-based evaluations, students can explore career paths of their dreams and talents through various programs (Kim & Hong, 2021).

On the other hand, despite good intentions, there are critical differences of opinion between the Ministry of Education and the Office of Education, teachers in the field and students. Developing curriculum for free semester classes involves significant research and it may be challenging to conduct fair evaluations (Shin et al., 2015). Thus, teachers struggle to provide quality classes to students, and students perceive the free semester system as a "play semester," and do not concentrate on classes. As a result, difficult situations for teachers and students often arise in class operations, which also cause parental dissatisfaction (Shin et al., 2018).

In the results of a study on Korean teachers' perceptions related to AI education, the demand for using AI in real life and learning situation was high, but the demand for learning the concept and principle of AI were relatively low (Kim & Han, 2020). Additionally, teachers recognized the necessity of integrating AI to the problem-solving process with other subjects (Kim & Han, 2020). This finding shows that teachers recognize "real-life problem-solving and inter-subject convergence education through AI" as the direction of AI education. On the other hand, the study found low teacher awareness of the need for education in specialized fields such as "the concept and principle of AI" and "AI development."

However, research on systematically applying AI and machine learning in education is insufficient (Tedre et al., 2021). In addition, Lee's ( 2021) questionnaire-based study showed a very low level of confidence among in-service teachers, with 65.4% of elementary school teachers in charge of AI education answering, "I do not know how to apply AI convergence education in the subject." It is not expected that this pattern will be significantly different in middle school.

Despite the high demand for AI, there is not enough education or support for field teachers or preservice teachers, creating a gap between the policy and field atmosphere. Even though the Ministry of Education and related ministries actively promote free semester and AI education projects, there are still many difficulties in applying them in school. Moreover, research in AI and software-related fields or free semester research in technology education is insufficient (Cho, 2017; Lee, 2018). With the above background, this study developed an AI program that can be applied immediately by technology teachers teaching the free semester, measuring its effectiveness after application with students.

Purpose of research

This study developed a program to bring AI into the technology education free semester in middle school. In addition, the study measured the effectiveness of AI education programs in technology education, attitudes toward technology, and AI-related competencies. The two objectives of the study are:

First, develop an artificial intelligence theme selection activity for application in technology education during the free semester.

Second, analyze the effect of the artificial intelligence theme selection activity on students' attitudes toward technology and AI competencies.

Background

Free semester in Korean middle school

The free semester is an educational policy introduced to improve public education in South Korea. Similar examples abroad include the "transition year programme" in Ireland, "prao" in Sweden, "afterschool" in Denmark, and "gap year" in the United Kingdom (Kim & Choi, 2014). These educational policies reduce the pressure on learners to evaluate and provide experience-centered classes. As a result, students can explore their career paths and enable self-development according to their aptitudes. Furthermore, at the national level, governments applied free semesters to improve public education and to increases the reliability of school education.

The Ministry of Education ( 2015a) presents the purpose of the free semester as "finding dreams and talents by exploring aptitudes and future," "transitioning to future core competency-enhancing education," and "happiness education that satisfies all students, parents, and teachers." The activities of the free semester in South Korea are composed of four types as shown in Table 1. Generally, "career exploration activity" and "club activity" have been implemented for career guidance teachers. In practice, middle school teachers take "theme selection activity" and "art-physical activity." Moreover, "art-physical activity" occurs in arts and physical education subjects.

There are contrasting studies on the effectiveness of the free semester. Previous studies related to the career effect, which is one of the basic purposes of the free semester, confirmed various effects such as career decision self-efficacy, career competency, and self-efficacy (Hwang, 2019; Lee et al., 2016). Meanwhile, one longitudinal study of multiple middle schools showed that the free semester did not affect career maturity and self-efficacy (Lee, 2020). Another study confirmed "interest in science, technology, engineering, and mathematics" and "self-efficacy and career effects" of inquiry learning that applied convergence education in science free semester classes (Jeong & Lee, 2017). In addition, a qualitative study targeting secondary school teachers looked at the difficulties encountered in the science free semester classes (Kim & Choi, 2019). In the subject of mathematics, a study confirmed the change in "career exploration ability" and "attitude toward statistics" through the free semester statistics program (Kang et al., 2017). Additionally, another study looked at the link between convergence education research and engineering tools and 3D printers to mathematics education (Cho et al., 2014; Kang et al., 2016).

Artificial intelligence in education

Artificial intelligence education

Seymour Papert, a mathematician and computer scientist who co-wrote "Perceptrons" with Marvin Minsky, greatly influenced AI education. Influenced by Jean Piaget, Seymour Papert significantly contributed to computer education and maker education and was interested in mathematics education using computers (Sung, 2018). Papert ( 1980) discusses his ideas about education in his book "Mindstorms: Children, Computers, and Powerful Ideas." He said that learning spontaneously by interacting with the environment is the core of Piaget's education theory and that computers make learning efficient. Therefore, he paid attention to computer-use education, simultaneously discussing AI education in his book. He defined AI education in terms of cognitive science, arguing for materializing metaphysical thinking in order to create intelligent machines (artificial intelligence). He expected students' improved thinking processes through clarification of their thinking processes. For this reason, Seymour Papert argued the necessity of AI education.

Most see AI as a tool, but some educate purely on AI, approaching AI education as an extension of science, technology, engineering, and mathematics (STEM) education (Hong et al., 2020). The main concern is understanding AI and educating students about AI-related technology according to their level. For example, "5 big idea in AI" of AI4K12, which studied AI education standards, is representative. AI4K12 presents "perception, representation & reasoning, learning (machine learning, deep learning), natural interaction, and social impact" in AI as five core ideas (Touretzky et al., 2019). In addition, the topics of AI education involve AI ethics. Coeckelbergh ( 2019) pointed out the biases created by machine learning go beyond much-discussed privacy protection and discussed various ethical issues, such as the attribution of responsibility for these issues and the problem of unemployment, extending beyond the individual level to social issues. This point of view is similar to that of the Ministry of Education ( 2020). They added the subjects of "Artificial Intelligence Ethics" as well as "Artificial Intelligence Mathematics" and "Artificial Intelligence Fundamentals" to educate on AI itself. Meanwhile, AI ethics deals with the impact of AI technology on society, and there is an intersection with seeing AI as a tool of education.

In addition, educational researchers are actively conducting studies in AI-related fields, such as "computational thinking" and "SW competency," reflecting competency-based evaluation (Choi, 2019). However, AI-related research has only recently emerged as a research topic, and investigation of the definition and evaluation of AI competency is insufficient (Min & Shim, 2021). Kim and Lee ( 2021) conducted a study to measure AI literacy for basic research related to students' AI evaluation. Koh ( 2020) considered the mathematics curriculum to cultivate AI competency in mathematics education. A previous study related to AI evaluation developed a tool that can test the attitude toward AI to suit each middle and high school (Kim & Lee, 2020a, 2020b). Han ( 2020) developed an instrument to measure changes in attitude and efficacy toward AI in AI project classes. Another study developed and evaluated the fourth industrial revolution-related technology education program, including AI in technology education (Lee et al., 2019). Although there have been several studies related to AI evaluation, there have been few studies that actually dealt with AI education and evaluation in depth in Korea (Kim et al., 2021; Min & Shim, 2021).

As mentioned above, different subjects apply AI education in various ways. For example, in science education, a study intervention helped learners understand scientific principles through construction practice by building an AI-based mixed reality system (Yannier et al., 2020). Further, a study analyzed AI tools for application in computation and AI tutoring in mathematics education (Van-Vaerenbergh & Pérez-Suay, 2021). In addition, researchers frequently conduct studies using translation services, chatbots, and AI speakers in English education, according to the subject characteristics (Kaharuddin, 2021; Li et al., 2021; Wang & Petrina, 2013). Although not secondary education, a study of STEM-based AI education programs for non-engineering college students showed improvement in AI literacy (Lin., 2021). While research in AI education is primarily about either "learning with AI" or "learning about AI," studies of science and mathematics education show that approaches with both characteristics are also possible.

Artificial intelligence and technology education

Studies on education methods using AI tools and AI education in STEM are underway. However, the number of studies in technology subjects is small. Software education and computational thinking related to AI also lack the number of studies in technology education compared to other subjects (Lee, 2018, 2019). Kim ( 2021) analyzed research trends related to AI in elementary and secondary education through topic modeling and showed that all keywords included "technology." Still, there were very few studies on "technology education." Lim ( 2020) said that technology education justifies AI education and that in the age of AI, technology education needs direction and active research. For example, in a study of elementary and secondary teachers, teachers answered that "AI education is using AI in life." Furthermore, teachers answered that it is most important for students to integrate AI with other subjects or apply it to problem-solving processes (Kim & Han, 2020). In a cognitive analysis study of AI-using education, elementary school teachers said that "used to aid in class" is appropriate for AI in education (Han, 2020). In addition, educators recognized the "problem-centered learning approach" as the most appropriate (Han et al., 2020). The core goal of technology education is to solve technological and practical problems in the real world through a convergence approach (Custer, 1995). Thus, centering technology education is very effective for AI education.

A study by Lim ( 2020) suggested the direction in technology education and discussed the justification of AI education in technology curriculum. In addition, Kim's ( 2020) study applied maker education methodology based on AI and physical computing in program development. Furthermore, Lee and Lee ( 2021) developed a program to select the core concepts of AI and apply them to technology education. In addition to AI, a study also selected the core interlocking technology of the fourth industrial revolution for application in middle school technology education, developed a program, and measured the effect (Lee et al., 2019). Although it is not direct, there have also been studies on robot education and developing bio-mimicking robot education programs in technology education (Kim, 2019; Kim & Jeong, 2006). Research on these AI-related technologies can serve as a reference for the direction of technology education.

As mentioned above, studies related to AI education can have many implications for technology education. For example, Papert ( 1980) expected that learners would improve their thinking in clarifying the thinking process to create AI. His research suggests that AI education can affect mathematical thinking and learners' cognitive ability. In addition, although education can approach AI as a tool in computer science or educational methods, AI education from the STEM perspective can also affect technology education. Educators can teach AI as cutting-edge technology, and students can learn social changes in technology education. In addition to the technology aspect, the ethical problems of AI pointed out by Coeckelbergh ( 2019) are highly related to technology education's ICT ethics. Moreover, research on AI conducted to realize each subject's purpose in STEM-related subjects (science, mathematics) can indirectly provide directions for technology education.

Method

Research design

Based on the research purpose and literature review, this study developed an AI education program as a free semester theme selection activity in middle school technology education. The study applied the intervention as an actual free semester class in a middle school in Seoul. The program was implemented for eight weeks at two hours per week for 16 h. Most of the courses were face-to-face, but due to the COVID-19 virus, some courses (weeks 1 and 2) were real-time interactive video classes. The study applied the AI education program to 23 middle school students who participated in the experiment, conducting "technology attitude" and "AI competency" tests. The study used the experimental method of a single group pretest and posttest.

Participants

The study implemented the program as a free-semester theme selection activity, allowing students to select it in the same way as the general free-semester program selection method. Although each school's situation is different, students generally have four theme selection programs to choose from depending on their career path, and this program was chosen as one of them by the participants. Before the program started, the researcher informed the students and parents of the study's research ethics. The Institutional Review Board (task management number 2021-73) reviewed and approved this study. There were a total of 23 volunteer participants. The students were all first graders (12 ~ 13 years old) attending the middle school free year system, and there were 19 male students and 4 female students, for a total of 23 students. The students have experience in SW education in elementary school practical subjects. Students had a learning experience (2-h class) about the impact of AI on society with the subject of future technology and social change in the technology class in the first year of middle school. Educators introduced information subjects in the second year, so students did not receive education on related content.

Instruments

This research conducted a pretest and posttest of a single group to investigate the study's effect. The study performed quantitative tests through two instruments on "technology attitude" and "AI competency" before and after the program. After completing the program agreement, the researcher conducted a pretest before the program application and a posttest after the end of the program. The environment did not include the researcher (i.e., a teacher in charge); thus, students completed the test remotely at any time from one to three days after the last class.

The study piloted the test tool on 200 first-year middle school students to verify its reliability before application to study participants. As a result, the reliability of 19 items of The Pupils' Attitude toward Technology (PATT) was 0.930. In the AI competency test that measured four constructs, the reliability of 12 items of three constructs was 0.812. Meanwhile, the reliability of one construct was lower than 0.7 (a characteristic of AI), so this study excluded it from test tool.

PATT (The Pupils' attitude toward technology)

The PATT tool, developed by Bame et al. ( 1993), tests students' attitudes toward technology. In South Korea, the study of Lee ( 2008) applied a five-point Likert scale after translation. This tool consists of six constructs, each with "interest in technology," "perceived gender patterns in technology," "perceived difficulties in the technology," "consequences of technology," "technology and curriculum," and "career aspirations in technology."

This study measured two constructs among the six constructs in the original instrument after the reviews of previous studies on PATT. This study measured "interest in technology" and "career aspirations in technology." as shown in Table 2.

Artificial intelligence competency

This study used the AI competencies instrument by mixing two test tools related to AI. The first is an attitude test tool for AI for middle school students developed by Kim and Lee ( 2020a). The AI attitude test tool consists of five constructs, each with "social impact of AI," "interaction with AI," "communication with AI," "emotional exchange with AI," and "characteristics of AI."

This study used an eight-item test tool with two constructs, "Social impact of AI" (four items) and "Interaction with AI" (four items) as shown in Table 3. The pilot test included three factors, excluding two out of five factors. The pilot test measured the "characteristics of AI" construct, but it was difficult for students to understand and answer accurately due to ambiguous questions; the reliability was less than 0.7. Accordingly, the study measured only two constructs, "social impact of AI" and "interaction with AI," excluding the "characteristics of AI" construct.

In addition to the two constructs of "AI attitude," the researcher used the second test tool to measure "AI performance" in solving technology problems. This study used Choi's ( 2014) test developed for computational thinking in robot utilization education. Choi's test tool consists of three constructs, "computational thinking concept," "computational thinking performance," and "computational thinking perspective." In particular, "computational thinking performance" aims to measure computational thinking centered on problem-solving in robot education. Therefore, it was necessary to modify the construct to fit this study to measure the AI competency implemented in technology education. The researcher modified the test tool of "computational thinking performance" and used it in terms of AI performance competency, focusing on technological problem-solving.

Data Analysis

The number of participants in this study was 23. Since the sample was small ( n < 30), the researcher performed a normalization test to examine normality according to the central limit theorem (Kish, 1965). However, most constructs showed normality less than 0.05 in the Kolmogorov-Smirnov, and Shapiro-Wilk tests. Therefore, the researcher conducted the Wilcoxon signed-rank nonparametric test.

Results

Program development

To develop the middle school AI education theme selection activity for technology education in the free semester, the study used the three steps of "preparation, development, and improvement" suggested by Mager and Beach ( 1967). The researcher modified and applied each step according to the purpose of the study. The figure below shows the steps of development (See Fig. 1).

In the preparation step, the researcher set the topic and purpose of the AI program based on the literature review. After that, the researcher selected the most suitable program type for study from among the four free semester models, "theme selection activity," "art-physical activity," "career exploration activity," and "club activity" (Ministry of Education, 2015a). In addition, the study did not select learners separately since the free semester is currently implemented only for the first year of middle school.

In the development step, the study extracted AI learning components based on the achievement standards of the practical (technology and home-economics) curriculum. After that, the researcher subdivided the goal of the theme selection activity by each class, selecting and organizing the program contents according to student and teacher roles.

Experts reviewed the program at the improvement stage, and the researcher revised and supplemented the program according to the expert review.

Preparation

Setting the program topic and purpose

As a technology, the trend of patent applications for the latest AI technology is increasing in all fields of industry (Lim et al., 2019). Therefore, considering AI as applied technology, it is not appropriate to confine the topic of an AI education program to a specific unit of technology education.

Based on the literature review, it would be appropriate to develop an AI program in the form of a problem-solving program integrated with a subject (Kim & Han, 2020). For instance, information subject, which is actively researching AI, is approaching the data to realize the purpose of the curriculum. It is significant for technology practitioners to analyze the curriculum and goals of technology education in developing educational program in a technology subject. If one applies AI in technology problem-solving activities, various approaches and convergence will be possible in the technology curriculum.

In this study, students learned the basics, history, and social impact of AI in the same context and suggested problem-solving activities. This study emphasized the central role of problem-solving, thus excluding excessive professional or vocational education to preserve the uniqueness of technology education. Additionally, per the purpose of the free semester, it should not be a theory-centered or a practice-centered. Therefore, the program aims to cultivate technological literacy to realize the purpose of technology in general subjects.

Selecting a free semester activity type

The free semester consists of four activities: theme selection, art-physical, career exploration, and club. The researcher reviewed all four types to effectively apply the AI program. As discussed above, including the literature review, only theme selection activity and art-physical activity types remain in the practical application of AI education in the technology education curriculum. The theme selection activity aims to provide a systematic and professional program tailored to students' interests. The examples provided by the Ministry of Education ( 2015a) also include elements related to the fourth industrial revolution; therefore, they are appropriate. Art-physical activity occurs mainly in the arts and physical subjects, and other subjects must include convergence elements. The media sometimes shows examples of applying AI to works of art. However, as this is an AI application field, there are not many cases, and the difficulty level is not appropriate because it requires a high level of programming.

Therefore, considering the organization and operation of the free semester system, theme selection activity is the most appropriate to apply in AI education. Through theme selection activity, teachers provide a professional AI program, and students have opportunities to explore their career paths through various experiences. Thus, this study developed a program in the form of a theme selection activity as a free semester activity.

Development

Selecting program learning contents

After setting the program's purpose, the researcher determined the learning content for each class. Finally, the researcher extracted content elements related to AI education and organized them from the 2015 revised curriculum (technology and home economics) as shown in Table 4

This study could extract elements related to AI directly or indirectly across a wide area of technology education. For example, in the technology system area, manufacturing technology includes content elements associated with AI such as "the fourth industrial revolution, system design and automation (including physical computing)." Physical computing, automation, etc., are not directly related to AI but can apply AI indirectly as a system to solve problems. Physical activities in the real world can motivate learners to explore and manufacture these systems.

Using computers and AI can link information and communication technology in technology systems to "communication technology problem-solving activities." In problem-solving activities, learners can experience machine learning. For example, a learner can recognize and learn an image or voice using sensors such as cameras or microphones. In addition, students can learn about data and information and ethical issues of AI with creative activities.

In technology utilization, students can recognize the social impact of AI as a future technology along with occupations. Additionally, in connection with the technology system area, it is possible to find problems in that can be solved by the use of AI.

Organizing program learning contents

After arranging the learning contents based on the program's purpose, the researcher organized the learning content for the 16 h of instruction (See Fig. 2), structured similarly to general technology education courses based on previous studies (Lee & Lee, 2021; Lee et al., 2019; Ministry of Education, 2015b).

Improvement

Expert consultation

In order to review the applicability and validity of the program developed in this study, the researcher asked for consulting from four teachers (educators) and one AI expert with expertise in technology education and convergence education. Table 5 shows the contents of the expert consultation.

Revision and supplementation

The researcher finally completed the program by modifying and supplementing the program based on the expert consultations. Figure 3 shows the completed program (See Fig. 3).

In the first and second classes, the researcher added the activity of investigating occupations and social changes in the fourth industrial revolution. In addition, the program explained the origins of AI, emphasizing the technological aspect by introducing AI as the latest technology.

Recently, the field of education mainly pursued the latest technology and did not try to deal with the impact of the latest technology on society. However, in the first and second classes, since this is a very familiar area in technology education, the emphasis was on preserving the uniqueness of technology education such as the social change affected by technology (Barlex et al., 2020). Especially, we focused on cutting-edge technology. After that, the program intended to cultivate the students' ability to evaluate the impact of the latest technology themselves objectively.

In the third and fourth classes, the study changed the term "AI service" to "AI technology." Additionally, the program added a brief technological problem-solving activity using AI, utilizing AI Experiments; AI drawing; AI music; AI machine learning.

The program maintained the basic content in the fifth and sixth session classes, introducing data analysts, data engineers, and data scientists as the latest promising jobs (Barlex et al., 2020). In addition, it provided an indirect opportunity to experience the jobs by collecting data and creating an app. Open data was used for data learning, and an app development program was used for app creation. (See Fig. 4)

In the seventh and eighth classes, in AI Ethics, issues such as autonomous driving, AI bias, and privacy were addressed and discussed. Physical computing dealt with Micro:bit coded by educational programming language (EPL) like Scratch block coding.

In the ninth to twelfth classes, the students studied remote control technology with Micro:bit (radio communication technology) in physical computing to learn communication technology elements. The students learned how to code the transmitter and receiver using Micro:bit and communicate with friends using radio waves, making it possible to design high-quality projects. In addition, the researcher reorganized the class to enable cooperative learning by adding "group composition and professional distributing roles." The students were assigned the roles of "team leader," "engineer," and "recorder," thus giving them a more professional experience and a sense of responsibility. Afterward, the students were given an unstructured package, and each team selected a project theme using AI. The program added interim presentations in the eleventh and twelfth classes to share ideas in the results presentation and the process. (See Fig. 5)

In the thirteenth to sixteenth classes, projects were carried out based on the theme chosen by each team. Various projects have been created, such as the AI that plays an OST when a character is censored, the AI that recognizes friends' faces, and the AI that represent the famous K-drama. During the final presentation, ideas were shared based on the project portfolio. (See Fig. 6)

Program implementation and effects

Students' attitudinal transition toward technology

The PATT test measured the attitude toward technology in two constructs, "interest in technology" and "career aspirations in technology," finding a significant increase in both areas (See Table 6). Cronbach's α of the survey tool was 0.903 in the pretest and 0.886 in the posttest, indicating reliability; "interest in technology" rose from 3.96 to 4.17, and "career aspirations in technology" rose from 3.83 to 4.12. In the Wilcoxon signed-rank test, "interest in technology" and "career aspirations in technology" showed Z values of − 2.047 ( p < 0.05) and − 2.907 ( p  < 0.01), respectively.

Both constructs showed a significant increase, with a relatively high increase in the "career aspirations in technology" construct. As for the study's purpose, it was possible to see an increase in the "attitude towards technology" through the AI program. In particular, "interest in technology" and "recognition of job aspects:" rose simultaneously. These findings showed that the developed free semester theme selection activity matched the original purpose of the free semester system.

Students' transition toward AI competency

For AI competencies, the study measured three constructs, "social impact of AI," "interaction with AI," and "AI performance." Cronbach's α of the survey tool was 0.850 in the pretest and 0.866 in the posttest, demonstrating reliability.  Table 7  shows that the average score significantly increased in two of the three areas through the tests before and after the program: "social impact of AI" improved from 3.43 to 4.09, "interaction with AI" improved from 3.74 to 4.00, and "AI Performance" improved from 2.96 to 4.04. In the Wilcoxon signed-ranking test, "social impact of AI" and "AI performance" generated Z values of− 3.763 ( p < 0.001) and − 4.023 ( p < 0.001), respectively, allowing meaningful observation. On the other hand, "interaction with AI" was − 1.196 ( p ≧ 0.05), with a higher p-value than 0.05, which was not statistically significant as it had a high probability of significance.

The analysis made it possible to observe that the AI competencies improved after applying the program. In particular, AI performance showed the largest increase. It was possible to confirm that the program developed in this study improved the AI competencies after 16 h of instruction. Simultaneously, the experiment confirmed its value as an AI program.

Discussion

Because the current technology education lacks research on free semesters and AI education, this study developed an AI program for implementing free semester technology education in schools and analyzed its effectiveness. Technology in-service teachers have difficulty teaching free semester classes (Kim, 2017). In addition, although educational research on AI is active, it is relatively rare in technology education (Kim, 2021).

In this study, starting from an awareness of the above problem, the research purpose was to develop an AI program for implementing in technology education. This study's program development applied Mager and Beach's ( 1967) three steps of "preparation, development, and improvement." In the preparation step, based on prior research on AI-related technology education, the purpose was to learn AI in general, focusing on problem-solving activities. In particular, the intent was to include technological problem-solving and technological elements to maintain differentiation from other subjects. The researcher analyzed the technology education curriculum in the development step and extracted and organized AI-related elements. In addition, the researcher developed an AI program for learning over 16 h of instruction.

The first class begins by dealing with the impact of the latest technology on society from various perspectives. Recently, the field of education mainly pursued the latest technology and did not try to deal with the impact of the latest technology on society. On the other hand, this is a very familiar area in technology education, and it can preserve the uniqueness of technology education. In addition to the learning topics of general machine learning programs, the program includes system design and automation (physical computing) and communication technology. Although physical computing and communication technology are not directly related to AI, students could use them as tools to solve problems during technological problem-solving activities. On the other hand, the study attempted to expand the information and communication ethics in the previous information and communication technology by adding AI ethics. Although the emphasis on information and communication ethics was not high in domestic technology education, the researchers recognize and emphasize its importance as a new ethical issue in the AI era.

As a result of data collection and analysis after the program's application, it was possible to observe the average change in all measured constructs. The observations show the effectiveness of this AI program both in AI education and technology education at the same time. First, "interest in technology" has increased, and in particular, the "career aspirations in technology" construct has changed highly. As a theme selection activity of the free semester, career exploration is an essential element for inclusion. The change in "career aspirations in technology" shows the value of this program as a free semester program. Second, in the AI competency test, the researcher observed an increase in the "social impact of AI, AI performance." In particular, AI performance shows the largest increase. The AI performance items consist of four questions related to problem-solving, including "problem identification, repair, problem-solving, and problem-solving thinking." Lim ( 2020) showed similarities between AI problem-solving processes and technological problem-solving processes. This program showed the effectiveness as a technology education of an AI program developed with a focus on technological problem-solving by applying a similar problem-solving process. In addition, it shows the possibility and validity of technology education in AI education.

This study's greatest significance is in bringing AI into technology education. As a result of the study, it was possible to confirm the value of the AI free semester program for implementation in technology education as a research purpose. Furthermore, as in the study of Lim ( 2020), it was possible to confirm the validity of AI education in technology education in the age of AI. In addition, as suggested by Lim ( 2020), it was possible to confirm the suitability of the AI problem-solving approach integrated with the center of technology education. Similarly, the study could confirm meaningful results of other AI program studies in technology education studies. However, the study's small sample size and short duration are limitations. Future research could focus on a more in-depth and long-term study with more participants to confirm learners' change.

Conclusion and recommendations

Under the "Middle School Free Semester Implementation Plan," the free semester started nationwide in 2016. From 2020, the free semester expanded to the free year system (Ministry of Education, 2015a). The free semester is a flexible curriculum that improves student-participatory classes so that students can discover their dreams and talents, enabling a variety of career-related experiential activities. However, because the in-service teachers need to spend more time on research and have an increased workload due to the new teaching and evaluation methods, they avoid taking charge of the free semester (Shin et al., 2015).

This study, starting from the awareness of the above problem, tried to bring AI into technology education by creating an AI education program for immediate implementation. For this, it was necessary to develop an AI program and confirm the value in technology education, confirming the effect of AI. Therefore, the researcher first developed a free semester program in technology education. Second, after implementing the program, the study checked the attitude toward technology and the effectiveness of AI competencies.

Conclusion

This study developed an AI program for theme selection activity in the free semester for implementation in technology education. After implementing the program, the study's results led to the following conclusions.

First, the AI program developed as a free semester theme selection activity is effective for career exploration, which is one of the main purposes of the free semester. In the program, learners can explore social changes in the future society, focusing on AI and learning about promising jobs. Students can learn various latest technologies and job changes related to the fourth industrial revolution and experience data-related jobs (data analyst, data scientist, data engineer, and app developer). In addition, as technology developers, students can professionally divide their roles and experience self-directed cooperative development projects. Students can experience various career paths within technology education and positively change attitudes toward technology-related jobs.

Second, this AI program, developed with a focus on technology education, improves AI competencies. The research extracted AI elements dealing with technology education and developed an AI program through expert consultation based on previous research. In the AI program, students learn AI basics, the social impact of AI, data relating to AI, AI ethics, and AI-based problem-solving. In particular, learners can do overall AI elements and cultivate AI competency through technological problem-solving-based projects. Through this program, students can improve their AI competencies, such as their attitude toward AI and AI performance.

Third, the free semester theme selection AI program has value as technology education. AI programs improve AI competencies and attitudes toward technology. In addition, it provides opportunities for career exploration to realize the purpose of the free semester. Thus, the developed free semester theme selection AI program is effective in technology education. Therefore, this program is a free semester AI program should be implemented in technology education.

Recommendations

The following recommendations are based on the results of this study.

First, further research should include more participants to improve the study's generalizability. The current study used nonparametric statistical analysis because there were only 23 participants. Thus, future studies should supplement the limitations of statistical methods, including a longer-term study for a comprehensive understanding of the learning results.

Second, there is a need for curriculum research that can improve the perception of technology. The increase in students' interest in technology in this study suggests a need to change technology classes. Lee ( 2015) pointed out the difference in perception between technology and technology subjects and the need for various and interesting teaching-learning methods and curriculum research in technology education. Ultimately, it is necessary to study the technology curriculum itself to solve the fundamental problem. Currently, the curriculum does not contain content related to the latest technologies such as AI, metaverse, and autonomous driving. As a result, it is difficult for students to feel interested in the curriculum, and there is a limit to how much improvement can be achieved by teacher teaching-learning methods.

Third, it is necessary to study the latest technology education. Specifically, AI is a very influential field. Furthermore, when excluding research related to AI in elementary education, there were relatively few studies in secondary technology education compared to other subjects. Studies related to the free semester were also lacking. Therefore, educators should treat AI as a technology in the technology curriculum. However, there are only relevant elements, and there is no achievement standard directly related to AI in the current curriculum. Therefore, studying the curriculum aspect of the latest technologies such as AI is necessary. In addition, it is essential to conduct research on technology curriculum to carefully examine changes in the latest technologies like AI and reflect them in the curriculum. Thus, this work should not indiscriminately bring in AI technology. Instead, it is necessary to study AI education in technology education by distinguishing it from science and information subjects, focusing on the purpose of technology subjects.

Availability of data and material

All data generated or analyzed during this study are included in this full manuscript.

Abbreviations

AI:

Artificial intelligence

MIT:

Massachusetts Institute of Technology

Perception + Neuron:

Perceptron

CSTA:

Computer Science Teachers Association

AAAI:

Association for the Advancement of Artificial Intelligence

AI4K12:

Artificial Intelligence for K-12 Standards

AIED:

Artificial Intelligence in Education

ICT:

Information and Communication Technology

STEM:

Science, Technology, Engineering, and Mathematics

PATT:

Pupils' Attitude toward Technology

References

Ali, S., Williams, R., Payne B., Park H. & Breazeal C. (2019). Constructionism, ethics, and creativity: Developing primary and middle school artificial intelligence education. In International Workshop on Education in Artificial Intelligence K-12, EDUAI '19, 1-4.

Busan Metropolitan City Office of Education. (2019). AI-based education guidebook.

Busan Metropolitan City Office of Education. (2021) Full-scale promotion of education using artificial intelligence (AI). press release.

Government. (2019). Artificial intelligence national strategy. press release.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education. Center for Curriculum Redesign.

Google Scholar

Hong, S., Cho, B., Choi, I., Park, K., Kim, H., Park, Y. & Park, J. (2020). Artificial intelligence and edutech in school education. Korea Institute for Curriculum and Evaluation. RRI, 2.

Kim, M. (2006). Investigation on interest level to technology education of 2nd grade students of junior high school, [Ph. M.A., Korea National University of Education]. RISS.

Kim, S. & Lee, Y. (2021). Basic study for the development of artificial intelligence literacy instrument. Proceedings of the Korean Association for Computer Education Conference, 25(2(A)), 59-60.

Kish, L. (1965) Sampling organizations and groups of unequal sizes. American sociological review, 564-572.

Li, P., Ning, Y., & Fang, H. (2021). Artificial intelligence translation under the influence of multimedia teaching to study English learning mode. The International Journal of Electrical Engineering & Education. https://doi.org/10.1177/0020720920983528

Article Google Scholar

Luckin, R., Holmes, W., Griffiths, M. & Forcier, L., (2016) Intelligence unleashed: An argument for AI in education.

Mager, R. E., & Beach, K. M. Jr. (1967). Developing vocational instruction

Minsky, M., & Papert, S. (1969). Perceptrons

Ministry of Education. (2015b). Practical arts (technology and home economics) information curriculum. Ministry of Education Notice No. 2015b-74, Supplementary Volume 10.

Ministry of Education. (2015a). Middle school free semester implementation plan. policy report. Public Education Promotion Division

Ministry of Education. (2017). A plan to expand and develop the middle school free semester. policy report. Department of Public Education Promotion.

Ministry of Education. (2020). Held the 19th social relations ministers' meeting and the 7th human investment talent nurturing council. press release.

Papert, S. (1980). Mindstorms: Children. Computers and powerful ideas.

Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, R. (2020). Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE Access, 8, 220121-220139.

Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386.

CAS PubMed Google Scholar

Seoul Metropolitan Office of Education. (2020). Plan to operate a leading school in artificial intelligence (AI) education in 2021.

Seoul Metropolitan Office of Education. (2021). AI-based convergence innovative future education mid- to long-term development plan ('21~'25).

Um, J. (2016). Action research for the development of technology curriculum applicable during the free semester: focusing on the development of core competencies, [Ph. M.A., Yonsei University]. RISS.

Van-Vaerenbergh, S. & Pérez-Suay, A. (2021). A Classification of Artificial Intelligence Systems for Mathematics Education. arXiv preprint arXiv:2107.06015.

Download references

Acknowledgements

The authors would like to thank participants for this 'free semester program' and school administrators for giving us an approval for data collection. In addition, this manuscript was written based on the first author's master's thesis.

Funding

No funding was received to conduct this study.

Author information

Authors and Affiliations

1. Gajaeul Middle School, Seoul, Republic of Korea

Woongbin Park

2. Department of Technology & Home-Economics Education, Kongju National University, #102 Human Ecology Building, Gongjudaehak-Ro 56, Gongju-si, Chungcheongnam-do, 32588, Republic of Korea

Hyuksoo Kwon

Contributions

WP conceptualized this study, collected and analyzed the data. HK drafted the manuscript, WP and HK contributed the revisions, and the approved the final draft. Both authors finalized the final manuscript.

Corresponding author

Correspondence to Hyuksoo Kwon.

Ethics declarations

Conflict of interest

The authors have no competing interest to declare that are relevant to the content of this article.

Ethics approval and consent to participate

This study has an approval from school IRB after the ethical consideration and review for this research.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissions

About this article

Cite this article

Park, W., Kwon, H. Implementing artificial intelligence education for middle school technology education in Republic of Korea. Int J Technol Des Educ 34, 109-135 (2024). https://doi.org/10.1007/s10798-023-09812-2
Download citation

Accepted:

Published:

Issue Date:

DOI: https://doi.org/10.1007/s10798-023-09812-2

Share this article

Anyone you share the following link with will be able to read this content:

Get shareable link

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

# data[].attributes.attachment_link.title
Implementing artificial intelligence education for middle school technology education in Republic of Korea - International Journal of Technology and Design Education

# data[].attributes.attachment_link.description
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.