Assessing the best art design based on artificial intelligence and machine learning using GTMA

Xu, Weifeng; Cai, Zilu

doi:10.1007/s00500-022-07555-1

Cited by 10 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Initial analyses showed that the study participants could not reliably discriminate between human and AIgenerated images, which suggests that AI-generated art has become highly human-like, consistent with the increasing sophistication of AI technologies 29,77,78 . Follow-up exploratory analyses found that highly empathic people may be more sensitive than people on average in discriminating between AI-and human-generated art.…”

Section: Discussionmentioning

confidence: 97%

Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks

Grassini,

Koivisto

2024

Sci Rep

View full text Add to dashboard Cite

The study primarily aimed to understand whether individual factors could predict how people perceive and evaluate artworks that are perceived to be produced by AI. Additionally, the study attempted to investigate and confirm the existence of a negative bias toward AI-generated artworks and to reveal possible individual factors predicting such negative bias. A total of 201 participants completed a survey, rating images on liking, perceived positive emotion, and believed human or AI origin. The findings of the study showed that some individual characteristics as creative personal identity and openness to experience personality influence how people perceive the presented artworks in function of their believed source. Participants were unable to consistently distinguish between human and AI-created images. Furthermore, despite generally preferring the AI-generated artworks over human-made ones, the participants displayed a negative bias against AI-generated artworks when subjective perception of source attribution was considered, thus rating as less preferable the artworks perceived more as AI-generated, independently on their true source. Our findings hold potential value for comprehending the acceptability of products generated by AI technology.

show abstract

Section: Discussionmentioning

confidence: 97%

Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks

Grassini,

Koivisto

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Information technologies facilitate the creation of high-quality works of art in a shorter time and with less effort. Digital technologies contribute to the accuracy of emotion transmission and the improvement of artistic design (Wenjing & Cai, 2023 ). The use of modern technologies shapes the learning process due to communication elements and means of expression.…”

Section: Discussionmentioning

confidence: 99%

Team-based learning in online education: the development of students’ creative thinking skills in digital art

2023

Educ Inf Technol

View full text Add to dashboard Cite

A person cultivates their creative skills influenced by various types of activities. The purpose of the work is to study the peculiarities of the development of students’ creative thinking skills, which are interrelated with the development of appropriate stages of team teaching, as well as to determine the impact of creative thinking on the indicators of academic performance and motivation to learn. The authors used the methods of sociological survey, which allowed them to determine that the largest number of students had mastered the skills of discipline (27%) and emotionality (21%) to a greater extent at the initial stage of the study. The results showed that 11% of the students in creative subjects (painting, digital art) and 7% of students in general disciplines (history, sociology, pedagogy, mathematics, physics, Chinese, cultural studies) had their academic performance at a high level before the start of online learning. The process of online teaching to paint was based on teamwork using the technologies of online education through educational online platform in the context of digital art. The results of the survey showed that the students had developed their creative skills significantly after training. The development of creative manner (29%) and analytical thinking (28%) were most popular. The authors determined that 88% of the students had received high grades in creative disciplines and 83% of the students in general academic disciplines after training. Most students were characterized by a high level of knowledge. The results are valuable for researchers who study the relationship between the development of creative skills and knowledge in general academic subjects, as well as strive to develop new curricula.

show abstract

“…China's teaching, education have significantly enhanced against backdrop of recent curriculum reform, which is directly tied to the advancement of information technology (IT) [1][2][3]. Technology is continuously being used in education and training to maximize instruction in the classroom and increase its effectiveness [4].…”

Section: Introductionmentioning

confidence: 99%

Optimized Hamiltonian Deep Neural Network-Based Art Education Teaching Content and Evaluation System

Qiuyu Long

2024

jes

View full text Add to dashboard Cite

The organized educational processes and experiences created to promote a comprehension and appreciation of the visual arts are referred to as art education. It includes a variety of exercises designed to foster artistic abilities, the ability to express oneself creatively, and a greater understanding of artistic ideas and background. There are several places where art instruction can take place, such as private studios, museums, community centers, and schools. In this manuscript, Optimized Hamiltonian Deep Neural Network-Based Art Education Teaching Content and Evaluation System (HDNN-AETC-ES) is proposed. Initially the data is collect from MADS dataset. To execute this, input data is pre-processed using Adaptive Distorted Gaussian Matched Filter (ADGMF) and it removes the noise from collected data. Then, data is fed to Hamiltonian Deep Neural Network (HDNN)for effectively categorized Technology-Based Art Teaching Platform. Generally, HDNN doesn’t express adapting optimization approaches to determine optimal parameters to ensure accurate art teaching. Hence, the Tasmanian Devil Optimization Algorithm (TDOA) to optimize Hamiltonian Deep Neural Network which accurately evaluated the art teaching. Then the proposed HDNN-AETC-ES is implemented and the performance metrics likes accuracy, precision, recall, F1-score, computation time, ROC are analyzed. Performance of the HDNN-AETC-ES approach attains 18.41%, 24.08% and 32.57% higher accuracy, 20.31%, 21.08% and 22.57% higher Precision and 21.41%, 22.08% and 23.55% higher recall when analyzed through existing techniques like art education teaching content using optimization of digital art teaching platform depend on information technology with deep learning (DAT-IT-DL), building arts education policy utilizing tools of out-of-school time youth arts organizations (BAEP-ST-YAO) and investigating arts education effects on school engagement with climate (IAE-ES-EC) methods respectively.

show abstract

Assessing the best art design based on artificial intelligence and machine learning using GTMA

Cited by 10 publications

References 22 publications

Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks

Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks

Team-based learning in online education: the development of students’ creative thinking skills in digital art

Optimized Hamiltonian Deep Neural Network-Based Art Education Teaching Content and Evaluation System

Contact Info

Product

Resources

About