Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives

International Journal of Educational Technology in Higher Education, Journal Year: 2024, Volume and Issue: 21(1)

Published: March 25, 2024

Abstract In recent years, higher education (HE) globally has witnessed extensive adoption of technology, particularly in teaching and research. The emergence generative Artificial Intelligence (GenAI) further accelerates this trend. However, the increasing sophistication GenAI tools raised concerns about their potential to automate research processes. Despite widespread on various fields, there is a lack multicultural perspectives its impact HE. This study addresses gap by examining usage, benefits, from standpoint. We employed an online survey that collected responses 1217 participants across 76 countries, encompassing broad range gender categories, academic disciplines, geographical locations, cultural orientations. Our findings revealed high level awareness familiarity with among respondents. A significant portion had prior experience expressed intention continue using these tools, primarily for information retrieval text paraphrasing. emphasizes importance integration education, highlighting both benefits concerns. Notably, strong correlation between dimensions respondents’ views related GenAI, including as dishonesty need ethical guidelines. We, therefore, argued responsible use can enhance learning processes, but addressing may require robust policies are responsive expectations. discussed offered recommendations researchers, educators, policymakers, aiming promote effective education.

Language: Английский

---

Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 69812 - 69837

Published: Jan. 1, 2024

Language: Английский

---

Large Language Models: A Comprehensive Survey of its Applications, Challenges, Limitations, and Future Prospects

Published: Nov. 16, 2023

<p>Within the vast expanse of computerized language processing, a revolutionary entity known as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to comprehend intricate linguistic patterns and conjure coherent contextually fitting responses. models are type artificial intelligence (AI) that have emerged powerful tools for wide range tasks, including natural processing (NLP), machine translation, question-answering. This survey paper provides comprehensive overview LLMs, their history, architecture, training methods, applications, challenges. The begins by discussing fundamental concepts generative AI architecture pre- trained transformers (GPT). It then an history evolution over time, different methods been used train them. discusses applications medical, education, finance, engineering. also how LLMs shaping future they can be solve real-world problems. challenges associated with deploying scenarios, ethical considerations, model biases, interpretability, computational resource requirements. highlights techniques enhancing robustness controllability addressing bias, fairness, generation quality issues. Finally, concludes highlighting LLM research need addressed order make more reliable useful. is intended provide researchers, practitioners, enthusiasts understanding evolution, By consolidating state-of-the-art knowledge field, this serves valuable further advancements development utilization applications. GitHub repo project available at https://github.com/anas-zafar/LLM-Survey</p>

Language: Английский

---

A Systematic Review of Synthetic Data Generation Techniques Using Generative AI

Electronics, Journal Year: 2024, Volume and Issue: 13(17), P. 3509 - 3509

Published: Sept. 4, 2024

Synthetic data are increasingly being recognized for their potential to address serious real-world challenges in various domains. They provide innovative solutions combat the scarcity, privacy concerns, and algorithmic biases commonly used machine learning applications. preserve all underlying patterns behaviors of original dataset while altering actual content. The methods proposed literature generate synthetic vary from large language models (LLMs), which pre-trained on gigantic datasets, generative adversarial networks (GANs) variational autoencoders (VAEs). This study provides a systematic review techniques that can be identify limitations suggest future research areas. findings indicate these technologies specific types, they still have some drawbacks, such as computational requirements, training stability, privacy-preserving measures limit usability. Addressing issues will facilitate broader adoption generation across disciplines, thereby advancing data-driven solutions.

Language: Английский

---

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

Urban Informatics, Journal Year: 2024, Volume and Issue: 3(1)

Published: Oct. 14, 2024

Abstract The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch data-driven smart city applications for efficient sustainable urban management. Despite their effectiveness, these often rely on massive amounts high-dimensional multi-domain data monitoring characterizing different sub-systems, presenting challenges in application areas that are limited quality availability, as well costly efforts generating scenarios design alternatives. As an emerging research area deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated unique values content generation. This paper aims to explore innovative integration GenAI techniques twins address planning management built environments with focuses various such transportation, energy, water, building infrastructure. survey starts introduction cutting-edge generative AI models, Adversarial Networks (GAN), Variational Autoencoders (VAEs), Pre-trained Transformer (GPT), followed a scoping review existing science leverage intelligent autonomous capability facilitate research, operations, critical subsystems, holistic environment. Based review, we discuss potential opportunities technical strategies integrate into next-generation more intelligent, scalable, automated development

Language: Английский

---

Generative AI for Cyber Security: Analyzing the Potential of ChatGPT, DALL-E, and Other Models for Enhancing the Security Space

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 53497 - 53516

Published: Jan. 1, 2024

This research paper intends to provide real-life applications of Generative AI (GAI) in the cybersecurity domain. The frequency, sophistication and impact cyber threats have continued rise today's world. ever-evolving threat landscape poses challenges for organizations security professionals who continue looking better solutions tackle these threats. GAI technology provides an effective way them address issues automated manner with increasing efficiency. It enables work on more critical aspects which require human intervention, while systems deal general situations. Further, can detect novel malware threatening situations than humans. feature GAI, when leveraged, lead higher robustness system. Many tech giants like Google, Microsoft etc., are motivated by this idea incorporating elements their make efficient dealing tools Google Cloud Security Workbench, Copilot, SentinelOne Purple come into picture, leverage develop straightforward robust ways emerging perils. With advent domain, one also needs take account limitations drawbacks that such have. some periodically giving wrong results, costly training, potential being used malicious actors illicit activities etc.

Language: Английский

---

Students’ perceived roles, opportunities, and challenges of a generative AI-powered teachable agent: a case of middle school math class

Journal of Research on Technology in Education, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Jan. 9, 2025

Ongoing advancements in generative AI (GenAI) have boosted the potential of applying long-standing "learning-by-teaching" practices form a teachable agent (TA). Despite recognized roles and opportunities TAs, less is known about how GenAI could create synergy or introduce challenges TAs students perceived application TAs. This study explored middle school students' roles, benefits, GenAI-powered an authentic mathematics classroom. Through classroom observation, focus-group interviews, open-ended surveys 108 sixth-grade students, we found that expected TA to serve as learning companion, facilitator, collaborative problem-solver. Students also expressed benefits provides implications for design educational AI-assisted instruction.

Language: Английский

---

Large language models for reticular chemistry

Nature Reviews Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Language: Английский

---

Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency

Applied Energy, Journal Year: 2025, Volume and Issue: 382, P. 125296 - 125296

Published: Jan. 13, 2025

Language: Английский

---

The Role of Generative Artificial Intelligence in Digital Agri-Food

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: unknown, P. 101787 - 101787

Published: March 1, 2025

Language: Английский

---