A Study on the Direction of Technology Education in the Age of Artificial Intelligence

Lim, Mika

doi:10.24062/kpae.2020.33.4.81

Cited by 6 publications

(12 citation statements)

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“…Artificial intelligence (AI) technology is applied to promote learning and teaching effectiveness (Lim, 2020). AI technology is being used to monitor teaching processes and to perform academic analysis and academic level diagnosis (Zhai et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory

Zhang²,

Li³

et al. 2022

Front. Psychol.

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Artificial intelligence (AI) technology has been widely applied in many fields. AI-assisted learning environments have been implemented in classrooms to facilitate the innovation of pedagogical models. However, college students' willingness to accept (WTA) AI-assisted learning environments has been ignored. Exploring the factors that influence college students' willingness to use AI can promote AI technology application in higher education. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the theory of perceived risk, this study identified six factors that influence students' willingness to use AI to analyze their relationships with WTA AI-assisted learning environments. A model including six hypotheses was constructed to test the factors affecting students' WTA. The results indicated that college students showed “weak rejection” of the construction of AI-assisted learning environments. Effort expectancy (EE), performance expectancy (PE), and social influence (SI) were all positively related to college students' WTA AI-assisted learning environments. Psychological risk (PR) significantly negatively influenced students' WTA. The findings of this study will be helpful for carrying out risk communication, which can promote the construction of AI-assisted learning environments.

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Section: Introductionmentioning

confidence: 99%

Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory

Zhang²,

Li³

et al. 2022

Front. Psychol.

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“…The revolutionary change in human life occurs due to an automated labor force or intelligence is called the industrial revolution. Right now, artificial intelligence is at the forefront of the 4th industrial revolution, an intelligent information society that is explained by technologies such as IoT, big data, and augmented reality (Lim, 2020). The Oxford English Dictionary defines AI as the ability of computers or other machines to display or imitate intelligent behavior (OED, 2018).…”

Section: Introductionmentioning

confidence: 99%

Learner Model of Technology Education for AI Convergence Individualized Learning

2022

pnr

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The purpose of this study was to derive a learner model from technological education for AI convergence individualized learning. The learning model devised in this study reflects the characteristics of technological problem-solving as it considers all areas of cognition, definition, and psychodynamics. Among the dimensions of recognition, judgment, creativity, execution, attitude, and interaction of technological problem-solving learning styles, the dimensions that acted mainly according to the content of the problem and the learning type could be selected and constructed as a model. In this study, a creativity-execution model was devised in consideration of the 'adaptationinnovation type' of the creativity level and the 'reflection-action type' of the execution level. In addition, an interactive-attitude model was devised in which the 'independent-cooperative type' in the interaction dimension and the 'avoidance-participation type' in the attitude dimension act dynamically. Furthermore, a three-factor learner model was developed considering the dimensions of cognitive judgment, creativity, and execution. Also, a five-factor learner model for technological problem-solving was devised and presented, with all five dimensions of cognition-judgment, creativity, execution, interaction, and attitude of the technological problem-solving learning style as factors. The learner characteristics defined through the model design suggested the direction of AI convergence education considering individual characteristics of learners and provided basic data for it. In the future, it is suggested that specific individualized teaching and learning development research be conducted in AI convergence education, considering the characteristics of learners according to the learning style of technological problem-solving.

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“…When a revolutionary change in human life occurs due to an automated labor force or intelligence, it is called the industrial revolution. And now, artificial intelligence is at the forefront of the 4th industrial revolution, an intelligent information society that is explained by technologies such as IoT, big data, and augmented reality (Lim, 2020) [1]. The Oxford English Dictionary defines AI as 'the ability of computers or other machines to display or imitate intelligent behavior' (OED, 2018) [2].…”

Section: Introductionmentioning

confidence: 99%

“…The fields of AI and education are still in the early stages of research, so there are many areas where concepts and terms are not clearly defined. Lim (2020) [1] classified AI into 'education about AI' and 'education using AI'. It is expected that AI convergence education will be able to achieve quantitative and qualitative expansion of education by enabling education beyond the limitations of time and space.…”

Section: Introductionmentioning

confidence: 99%

Learning Style for AI Convergence in Technology Education

Lim¹

2021

JHSTI

Self Cite

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The purpose of this study was to devise a model of learning style for individualized learning of AI convergence in technological problem solving, which is the main teaching-learning method of technology subject. Among the dimensions of recognition-judgment, creativity, execution, attitude, and interaction of technological problem-solving learning styles, the dimensions that act mainly according to the problem content and learning form were selected and constructed as a model. The method of research for this purpose was Kolb's research method, which derives four learning styles by synthesizing the correlation between two types of information perception methods and two types of information processing methods. Therefore, in this study, a creativity-execution model was devised in consideration of the 'adaptation-innovation type' of the creativity level and the 'reflection-action type' of the execution level. In addition, an interactive-attitude model was devised in which the 'independent-cooperative type' in the interaction dimension and the 'avoidance-participation type' in the attitude dimension act dynamically. In addition, a five-factor learning style model for technological problem solving was devised and presented, with all five dimensions of recognition-judgment, creativity, execution, interaction, and attitude of the technological problem-solving learning style as factors. The learner characteristics defined through the model design suggested the direction of AI convergence education considering individual characteristics of learners and provided basic data for it. In the future, it is suggested that specific individualized teaching and learning development research be conducted in AI convergence education, taking into account the characteristics of learners according to the learning style of technological problem solving.

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A Study on the Direction of Technology Education in the Age of Artificial Intelligence

Cited by 6 publications

References 0 publications

Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory

Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory

Learner Model of Technology Education for AI Convergence Individualized Learning

Learning Style for AI Convergence in Technology Education

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