Supervised ML based Model to Predict Satisfaction of Student Performance During the Pandemic DOI

Prajkta P. Chapke,

Anjali B. Raut

Published: May 3, 2024

Language: Английский

Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis DOI Open Access
Zied Bahroun, Chiraz Anane, Vian Ahmed

et al.

Sustainability, Journal Year: 2023, Volume and Issue: 15(17), P. 12983 - 12983

Published: Aug. 29, 2023

In the ever-evolving era of technological advancements, generative artificial intelligence (GAI) emerges as a transformative force, revolutionizing education. This review paper, guided by PRISMA framework, presents comprehensive analysis GAI in education, synthesizing key insights from selection 207 research papers to identify gaps and future directions field. study begins with content that explores GAI’s impact specific educational domains, including medical education engineering The versatile applications encompass assessment, personalized learning support, intelligent tutoring systems. Ethical considerations, interdisciplinary collaboration, responsible technology use are highlighted, emphasizing need for transparent models addressing biases. Subsequently, bibliometric is conducted, examining prominent AI tools, focus, geographic distribution, collaboration. ChatGPT dominant tool, reveals significant exponential growth 2023. Moreover, this paper identifies promising directions, such GAI-enhanced curriculum design longitudinal studies tracking its long-term on outcomes. These findings provide understanding potential reshaping offer valuable researchers, educators, policymakers interested intersection

Language: Английский

Citations

350

A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour DOI Creative Commons
Melissa Bond, Hassan Khosravi, Maarten de Laat

et al.

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

Published: Jan. 19, 2024

Abstract Although the field of Artificial Intelligence in Education (AIEd) has a substantial history as research domain, never before rapid evolution AI applications education sparked such prominent public discourse. Given already rapidly growing AIEd literature base higher education, now is time to ensure that solid and conceptual grounding. This review reviews first comprehensive meta explore scope nature (AIHEd) research, by synthesising secondary (e.g., systematic reviews), indexed Web Science, Scopus, ERIC, EBSCOHost, IEEE Xplore, ScienceDirect ACM Digital Library, or captured through snowballing OpenAlex, ResearchGate Google Scholar. Reviews were included if they synthesised solely formal continuing published English between 2018 July 2023, journal articles full conference papers, had method section 66 publications for data extraction synthesis EPPI Reviewer, which predominantly (66.7%), authors from North America (27.3%), conducted teams (89.4%) mostly domestic-only collaborations (71.2%). Findings show these focused on AIHEd generally (47.0%) Profiling Prediction (28.8%) thematic foci, however key findings indicated predominance use Adaptive Systems Personalisation education. Research gaps identified suggest need greater ethical, methodological, contextual considerations within future alongside interdisciplinary approaches application. Suggestions are provided guide primary research.

Language: Английский

Citations

153

Empowering education development through AIGC: A systematic literature review DOI
Xiaojiao Chen, Zhebing Hu, Chengliang Wang

et al.

Education and Information Technologies, Journal Year: 2024, Volume and Issue: 29(13), P. 17485 - 17537

Published: Feb. 29, 2024

Language: Английский

Citations

54

A Survey of Machine Learning Approaches for Mobile Robot Control DOI Creative Commons
Monika Rybczak, Natalia Popowniak, Agnieszka Lazarowska

et al.

Robotics, Journal Year: 2024, Volume and Issue: 13(1), P. 12 - 12

Published: Jan. 9, 2024

Machine learning (ML) is a branch of artificial intelligence that has been developing at dynamic pace in recent years. ML also linked with Big Data, which are huge datasets need special tools and approaches to process them. algorithms make use data learn how perform specific tasks or appropriate decisions. This paper presents comprehensive survey have applied the task mobile robot control, they divided into following: supervised learning, unsupervised reinforcement learning. The distinction methods wheeled robots walking presented paper. strengths weaknesses compared formulated, future prospects proposed. results carried out literature review enable one state different tasks, such as position estimation, environment mapping, SLAM, terrain classification, obstacle avoidance, path following, walk, multirobot coordination. allowed us associate most commonly used robotic tasks. There still exist many open questions challenges complex limited computational resources on board robot; decision making motion control real time; adaptability changing environments; acquisition large volumes valuable data; assurance safety reliability robot’s operation. development for nature-inspired seems be challenging research issue there exists very amount solutions literature.

Language: Английский

Citations

15

Generative Artificial Intelligence in Latin American Higher Education: A Systematic Literature Review DOI
Alejandra de‐la‐Torre, María Baldeon-Calisto

Published: April 29, 2024

The utilization of Artificial Intelligence (AI) and Generative AI (GenAI) in higher education has increased importantly the last years. Studies show that holds promise enhancing learning experiences for both students educators, offering personalized assessment opportunities. This study conducts a systematic review on application within Latin American education. To this end, we synthesized 25 papers published between 2021 2023, encompassing AI's Mexico, Colombia, Ecuador, Brazil, Peru, Chile, Argentina, Bolivia. analysis addresses three key inquiries: prevalent applications education, perceptions GenAI models among educators students, particular challenges encountered by institutions implementation. offers an updated understanding role with emphasis latest technologies.

Language: Английский

Citations

7

Could ChatGPT get an engineering degree? Evaluating higher education vulnerability to AI assistants DOI Creative Commons
Beatriz Borges, Negar Foroutan,

Deniz Bayazit

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(49)

Published: Nov. 26, 2024

AI assistants, such as ChatGPT, are being increasingly used by students in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges assessment learning outcomes. We conceptualize through the lens of vulnerability, potential university assessments outcomes to be impacted student use generative AI. investigate scale this vulnerability measuring degree which assistants can complete questions standard university-level Science, Technology, Engineering, Mathematics (STEM) courses. Specifically, we compile a dataset textual from 50 courses at École polytechnique fédérale de Lausanne (EPFL) evaluate whether two GPT-3.5 GPT-4 adequately answer questions. eight prompting strategies produce responses find that answers an average 65.8% correctly, even correct across least one strategy 85.1% When grouping our program, systems already pass nonproject large numbers core various programs, posing risks accreditation will amplified models improve. Our results call revising program-level design light advances

Language: Английский

Citations

6

Impact of ChatGPT Usage on Nursing Students Education: A cross-sectional Study DOI Creative Commons
A. García, David Bermejo‐Martínez, Ana Isabel López Alonso

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 11(1), P. e41559 - e41559

Published: Dec. 31, 2024

Language: Английский

Citations

5

Exploring prompt pattern for generative artificial intelligence in automatic question generation DOI Creative Commons
Lili Wang, Ruiyuan Song, Weitong Guo

et al.

Interactive Learning Environments, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 26

Published: Oct. 11, 2024

The construction of questions is an essential component in educational assessment and student learning processes. However, manually constructing a complex task that requires not only professional training, substantial experience, extensive resources from teachers but also time-consuming. This article introduces Automatic Question Generation (AQG) technology based on prompt pattern to alleviate this burden address the ongoing need for new education. essence method lies grounded collective knowledge base derived teachers, thereby enhancing quality produced. Practical applications expert evaluations demonstrate integrating with into Large Language Models (LLMs) results high-quality statistically significant results. These meet standards approach constructed by certain aspects. Our research further emphasizes feasibility AI-teacher collaboration

Language: Английский

Citations

4

Utilizing Emerging Technology Trends and Artificial Intelligence in Higher Education DOI Open Access
Hugo Luis Moncayo Cueva,

Giovanna Cuesta-Chávez,

Andrea Ramírez

et al.

Journal of Higher Education Theory and Practice, Journal Year: 2024, Volume and Issue: 24(3)

Published: Feb. 29, 2024

Today’s higher education is characterized by accelerated technological advances and a growing need for adaptation. This research focuses on the utilization of emerging trends artificial intelligence (AI) as innovative solutions to enhance quality effectiveness teaching learning. The problem lies in necessity strategically harnessing potential technologies AI education. objective thoroughly examine implementation within realm A comprehensive systematic review was conducted, involving analysis 240 articles selected from searches Scopus, SpringerLink, Web Science databases selection process employed rigorous inclusion exclusion criteria, achieve this, we utilized PRISMA methodology with approach. Consequently, our findings indicate that integration offers valuable guidance decision-making enhances educational strategies digital era.

Language: Английский

Citations

3

Tendencias de IA para la educación universitaria: un enfoque bibliométrico DOI Creative Commons
Hernán Ramiro Pailiacho Yucta, Alex Armando Chiriboga Cevallos, Jessica Wendy Espinoza Toala

et al.

Esprint Investigación, Journal Year: 2025, Volume and Issue: 4(1), P. 154 - 171

Published: Feb. 17, 2025

En los últimos años, la inteligencia artificial (IA) ha revolucionado radicalmente el ámbito educativo al ofrecer herramientas innovadoras que transforman tanto enseñanza como aprendizaje. No obstante, comprensión sobre su impacto específico en educación universitaria sigue siendo limitada. Por ello, este estudio tiene objetivo analizar las tendencias emergentes de IA superior utilizando un enfoque bibliométrico. lo tanto, se recopiló información base datos Scopus mediante una estrategia búsqueda específica permitió obtener total 4146 documentos para análisis. Se utilizó paquete Bibliometrix R y software RStudio procesar visualizar datos, identificó patrones producción científica, así principales actores influyentes áreas investigación predominantes. Los resultados indican crecimiento exponencial número publicaciones, con particular aplicación aprendizaje personalizado automatización procesos educativos. Además, análisis temporal palabras clave reveló cambio significativo investigativas, destacando aumento exploración enfoques basados machine learning Sin embargo, persisten desafíos adopción e implementación esta tecnología entornos educativos, relacionados aspectos seguridad, ética disponibilidad recursos. Este proporciona visión integral panorama actual relevante futuras investigaciones campo.

Citations

0