Embrace, Don’t Avoid: Reimagining Higher Education with Generative Artificial Intelligence DOI Creative Commons
Teuku Rizky Noviandy, Aga Maulana, Ghazi Mauer Idroes

и другие.

Journal of Educational Management and Learning, Год журнала: 2024, Номер 2(2), С. 81 - 90

Опубликована: Ноя. 28, 2024

This paper explores the potential of generative artificial intelligence (AI) to transform higher education. Generative AI is a technology that can create new content, like text, images, and code, by learning patterns from existing data. As tools become more popular, there growing interest in how improve teaching, learning, research. Higher education faces many challenges, such as meeting diverse needs preparing students for fast-changing careers. offers solutions personalizing experiences, making engaging, supporting skill development through adaptive content. It also help researchers automating tasks data analysis hypothesis generation, research faster efficient. Moreover, streamline administrative tasks, improving efficiency across institutions. However, using raises concerns about privacy, bias, academic integrity, equal access. To address these issues, institutions must establish clear ethical guidelines, ensure security, promote fairness use. Training faculty literacy are essential maximize benefits while minimizing risks. The suggests strategic framework integrating education, focusing on infrastructure, practices, continuous learning. By adopting responsibly, inclusive, practical, demands technology-driven world.

Язык: Английский

Diffusion-based artificial genomes and their usefulness for local ancestry inference DOI Creative Commons
Antoine Szatkownik,

Léo Planche,

Maïwen Demeulle

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Окт. 31, 2024

Abstract The creation of synthetic data through generative modeling has emerged as a significant area research in genomics, offering versatile applications from tailoring functional sequences with specific attributes to generating high-quality, privacy-preserving silico genomes. Notwithstanding these advancements, key challenge remains: while some methods exist evaluate artificially generated genomic data, comprehensive tools assess its usefulness are still limited. To tackle this issue and present promising use case, we test artificial genomes within the framework population genetics local ancestry inference (LAI). Building on previous work deep for introduce novel, frugal diffusion model show that it produces high-quality data. We then performance downstream machine learning LAI trained composite datasets comprising both real and/or Our findings reveal achieves comparable when exclusively versus Moreover, highlight how augmentation using significantly benefits model, particularly is Finally, compare conventional single dataset robust ensemble approach, wherein multiple models diverse datasets, their predictions aggregated. study highlights potential diffusion-based integration genomics. This approach could improve fair representation across populations by overcoming accessibility challenges, ensuring reliability analyses conducted

Язык: Английский

Процитировано

0
Embrace, Don’t Avoid: Reimagining Higher Education with Generative Artificial Intelligence DOI Creative Commons
Teuku Rizky Noviandy, Aga Maulana, Ghazi Mauer Idroes

и другие.

Journal of Educational Management and Learning, Год журнала: 2024, Номер 2(2), С. 81 - 90

Опубликована: Ноя. 28, 2024

This paper explores the potential of generative artificial intelligence (AI) to transform higher education. Generative AI is a technology that can create new content, like text, images, and code, by learning patterns from existing data. As tools become more popular, there growing interest in how improve teaching, learning, research. Higher education faces many challenges, such as meeting diverse needs preparing students for fast-changing careers. offers solutions personalizing experiences, making engaging, supporting skill development through adaptive content. It also help researchers automating tasks data analysis hypothesis generation, research faster efficient. Moreover, streamline administrative tasks, improving efficiency across institutions. However, using raises concerns about privacy, bias, academic integrity, equal access. To address these issues, institutions must establish clear ethical guidelines, ensure security, promote fairness use. Training faculty literacy are essential maximize benefits while minimizing risks. The suggests strategic framework integrating education, focusing on infrastructure, practices, continuous learning. By adopting responsibly, inclusive, practical, demands technology-driven world.

Язык: Английский

Процитировано

0