Special Issue: Generative Models in Artificial Intelligence and Their Applications

Castelli, Mauro; Manzoni, Luca

doi:10.3390/app12094127

Cited by 18 publications

(8 citation statements)

References 10 publications

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“…The literature suggests that every new technology generates satisfaction based on its utility to the user's needs. Generative AI is more capable of being utilized for students' various needs, such as learning, simplification, and task fulfilment [43]. Thus, perceived utility becomes a significant predictor of satisfaction with generative AI tools.…”

Section: Discussionmentioning

confidence: 99%

“…The perceived utility can better be defined as a person's subjective and objective assessment of any product, service, and information system, such as generative AI, and its output, such as content, to be useful, valuable, and effective for any specific purpose [42]. Regarding generative AI tools, various elements can determine the perceived utility [43]. However, one key element is that overall features and attributes of content can satisfy users' underlying needs.…”

Section: Perceived Utilitymentioning

confidence: 99%

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Determinants of Students’ Satisfaction with AI Tools in Education: A PLS-SEM-ANN Approach

Almufarreh

2024

Sustainability

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The emergence of Artificial Intelligence (AI) technology has significantly disrupted the educational landscape. The latest development in AI, generative AI that can generate new and tailored to specific content, has significantly impacted education. Given the value of AI technology in general and generative AI specific to users in education, such as students, the adaptability of these technologies has significantly increased. However, continuing and productive usage of AI tools depends upon students’ satisfaction with these tools. Drawing from the existing research, the present research has developed factors that affect students’ general satisfaction with AI tools. The research collected the data using a survey questionnaire from a Saudi Arabian university. The two-stage method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) have been employed. The two-stage method is applied in a way that PLS-SEM is used for testing the hypothesis and significance of the factor’s influence on satisfaction, and ANN is used to determine the relevant importance of the factor. The PLS-SEM results have shown that factors such as content quality, emotional wellbeing and perceived utility determine student satisfaction with AI tools. The ANN results show that emotional wellbeing is the most critical factor in satisfaction, followed equally by content quality and perceived utility.

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Section: Discussionmentioning

confidence: 99%

Section: Perceived Utilitymentioning

confidence: 99%

Determinants of Students’ Satisfaction with AI Tools in Education: A PLS-SEM-ANN Approach

Almufarreh

2024

Sustainability

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“…By reviewing fundamental approaches, paradigm-shifting models, tangible applications and open questions, this paper surveys the machinery powering generative AI' [5]s ascendance as one of the most rapidly developing and seminal fields within modern artificial intelligence research.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Generative Artificial Intelligence Techniques

Sakirin,

Kusuma

2023

Babylonian Journal of Artificial Intelligence

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Generative artificial intelligence (AI) refers to algorithms capable of creating novel, realistic digital content autonomously. Recently, generative models have attained groundbreaking results in domains like image and audio synthesis, spurring vast interest in the field. This paper surveys the landscape of modern techniques powering the rise of creative AI systems. We structurally examine predominant algorithmic approaches including generative adversarial networks (GANs), variational autoencoders (VAEs), and autoregressive models. Architectural innovations and illustrations of generated outputs are highlighted for major models under each category. We give special attention to generative techniques for constructing realistic images, tracing rapid progress from early GAN samples to modern diffusion models like Stable Diffusion. The paper further reviews generative modeling to create convincing audio, video, and 3D renderings, which introduce critical challenges around fake media detection and data bias. Additionally, we discuss common datasets that have enabled advances in generative modeling. Finally, open questions around evaluation, technique blending, controlling model behaviors, commercial deployment, and ethical considerations are outlined as active areas for future work. This survey presents both long-standing and emerging techniques molding the state and trajectory of generative AI. The key goals are to overview major algorithm families, highlight innovations through example models, synthesize capabilities for multimedia generation, and discuss open problems around data, evaluation, control, and ethics. Please let me know if you would like any clarification or modification of this proposed abstract.

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“…By using synthetic data, we can avoid privacy concerns stemming from real data. Another study, [2] Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning, discussed how maximizing the use of generative AI tools such as ChatGPT can also improve education, resulting in more personalized, interactive learning (Baidoo-Anu and Owusu Ansah, 2023). Companies such as Github have also capitalized on this new wave by releasing their own generative AI products such as Github Copilot.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Generative AI on Human Productivity in Creative Writing

Bhimavarapu

2023

J Stud Res

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With the innovative field of generative artificial intelligence having advanced at an incredibly rapid rate, its applications are of the utmost priority to study. The purpose of this study is to determine whether or not generative AI can help increase human productivity, specifically in writing. We hypothesized that generative AI will have a positive impact on human productivity. In order to test this, we employed the use of participants to write short fictional stories, one with the help of AI and one without. They were provided with survey questions that helped assess any changes in productivity levels. The productivity of the participants was also analyzed in terms of grammar, spelling, and consistency while compared against time . With the results obtained, we hoped to assess how AI can impact productivity in creative tasks (i.e., writing, art). We also hoped to understand its broader applications for human use and potential benefits and caveats to using generative AI. Based on the results, we concluded that using generative AI did indeed improve writing productivity as it lowered the amount of errors and shortened the time taken. However, how productive the individual was in producing quality work of their own merit also depended on how much work they delegated to the generative AI as well as how they perceived it.

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Special Issue: Generative Models in Artificial Intelligence and Their Applications

Abstract: In recent years, artificial intelligence has been used to generate a significant amount of high-quality data, such as images, music, and videos [...]

Cited by 18 publications

References 10 publications

Determinants of Students’ Satisfaction with AI Tools in Education: A PLS-SEM-ANN Approach

Determinants of Students’ Satisfaction with AI Tools in Education: A PLS-SEM-ANN Approach

A Survey of Generative Artificial Intelligence Techniques

The Impact of Generative AI on Human Productivity in Creative Writing

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