Artificial intelligence-supported art education: a deep learning-based system for promoting university students’ artwork appreciation and painting outcomes

Chiu, Min‐Chi; Hwang, Gwo‐Jen; Hsia, Lu‐Ho; Shyu, Fong-Ming

doi:10.1080/10494820.2022.2100426

Cited by 27 publications

(12 citation statements)

References 90 publications

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“…Instant feedback, personalised guidance and recommendations are made possible by AI technology and machine learning. In the citers to this cluster, AI-supported personalised learning is applied in art education (Chiu et al , 2022), informal learning environments (Tavakoli et al , 2022) and sports (Zhang, 2022). The cited references generally cover ITSs developed across educational fields (Mousavinasab et al , 2021), applications of AI and deep learning in a broader sense (LeCun et al , 2015; Lu, 2019) and instructional design and learning strategies (Fiorella and Mayer, 2016; Ou et al , 2019).…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in personalised learning: a bibliometric analysis

Li¹,

Wong²

2023

ITSE

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Purpose This paper aims to present a comprehensive overview of the patterns and trends of publications on artificial intelligence (AI) in personalised learning. It addresses the need to investigate the intellectual structure and development of this area in view of the growing amount of related research and practices. Design/methodology/approach A bibliometric analysis was conducted to cover publications on AI in personalised learning published from 2000 to 2022, including a total of 1,005 publications collected from the Web of Science and Scopus. The patterns and trends in terms of sources of publications, intellectual structure and major topics were analysed. Findings Research on AI in personalised learning has been widely published in various sources. The intellectual bases of related work were mostly on studies on the application of AI technologies in education and personalised learning. The relevant research covered mainly AI technologies and techniques, as well as the design and development of AI systems to support personalised learning. The emerging topics have addressed areas such as big data, learning analytics and deep learning. Originality/value This study depicted the research hotspots of personalisation in learning with the support of AI and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and the need for future work on diverse means to support personalised learning with AI, the pedagogical issues, as well as teachers’ roles and teaching strategies.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence in personalised learning: a bibliometric analysis

Li¹,

Wong²

2023

ITSE

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“…This was a restricted search for texts written in English. As conference journals are part of a well-known domain that is utilized in building, they were excluded from term searches that yielded more than one hundred articles (Chiu et al 2022). Most conference journals are published as journal articles.…”

Section: Analysis Publicationmentioning

confidence: 99%

A review of the current and future developments of artificial intelligence in the management and building sectors

Mishra,

Pareek,

Kumar

et al. 2024

Multidiscip. Rev.

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The integration of Artificial Intelligence (AI) into the management and building sector has witnessed significant advancements in recent years. Traditional methods can fall short in meeting these demands, prompting the exploration of AI solutions. This review aims to identify existing gaps, assess the efficacy of current AI applications and provide insights into potential areas for further development. A comprehensive literature review was conducted, encompassing peer-reviewed articles, industry reports and case studies. The analysis focused on AI applications such as machine learning, robotics and data analytics in the context of project management, resource optimization along with sustainable building practices. The review identifies key AI applications in project planning, risk management and construction processes, demonstrating their potential to streamline operations and improve decision-making. The analysis reveals successful implementations of AI-driven technologies, highlighting their impact on cost reduction, time efficiency and sustainability practices. Additionally, emerging trends such as generative design and smart buildings indicate promising directions for future development. The integration of Artificial Intelligence in the management and building sector demonstrates substantial benefits in efficiency, cost reduction and sustainability, while ongoing research as well as adaptation to emerging technologies is crucial for sustained progress.

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“…Chen et al [25] accelerated an intelligent bibliometricsdriven literature analysis by leveraging deep learning for automatic literature screening. By using artificial intelligence to empower education and obtain a new learning model, Chiu et al [26] launched a deep-learning-based art learning system to help increase students' art appreciation and creativity. Lin et al [27] developed learners' complex professional skills by using a virtual reality inversion learning method.…”

Section: Artificial Intelligence Educationmentioning

confidence: 99%

Artificial Intelligence-Empowered Art Education: A Cycle-Consistency Network-Based Model for Creating the Fusion Works of Tibetan Painting Styles

et al. 2023

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The integration of Tibetan Thangka and other ethnic painting styles is an important topic of Chinese ethnic art. Its purpose is to explore, supplement, and continue Chinese traditional culture. Restricted by Buddhism and the economy, the traditional Thangka presents the problem of a single style, and drawing a Thangka is time-consuming and labor-intensive. In response to these problems, we propose a Tibetan painting style fusion (TPSF) model based on neural networks that can automatically and quickly integrate the painting styles of the two ethnicities. First, we set up Thangka and Chinese painting datasets as experimental data. Second, we use the training data to train the generator and the discriminator. Then, the TPSF model maps the style of the input image to the target image to fuse the two ethnicities painting styles of Tibetan and Chinese. Finally, to demonstrate the advancement of the proposed method, we add four comparison models to our experiments. At the same time, the Frechet Inception Distance (FID) metric and the questionnaire method were used to evaluate the quality and visual appeal of the generated images, respectively. The experimental results show that the fusion images have excellent quality and great visual appeal.

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Artificial intelligence-supported art education: a deep learning-based system for promoting university students’ artwork appreciation and painting outcomes

Cited by 27 publications

References 90 publications

Artificial intelligence in personalised learning: a bibliometric analysis

Artificial intelligence in personalised learning: a bibliometric analysis

A review of the current and future developments of artificial intelligence in the management and building sectors

Artificial Intelligence-Empowered Art Education: A Cycle-Consistency Network-Based Model for Creating the Fusion Works of Tibetan Painting Styles

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