Future expectations for faculty roles at Yarmouk University in light of AI-based learning DOI Open Access

Miesam Fawzi Motiar Al Azam

International Journal of ADVANCED AND APPLIED SCIENCES, Journal Year: 2024, Volume and Issue: 11(11), P. 19 - 27

Published: Nov. 1, 2024

This study aimed to examine future expectations for faculty roles at Yarmouk University in the context of artificial intelligence (AI)-based learning. Using a descriptive approach, researchers employed questionnaire as primary tool, with sample 140 members from College Education. Results indicated that first category, related teaching methods, received weighted average 4.55, indicating strong agreement. Similarly, second category communication scored 4.57, which also reflects The third focusing on technical performance, achieved 4.59, showing agreement, while fourth addressing educational activities, 4.58, Overall, combined categories had an score suggesting agreement within AI-based learning environment. Additionally, significant differences emerged among respondents based gender, college affiliation, and years experience; however, no were found academic rank.

Language: Английский

Relevance and Impact of Generative AI in Vocational Instructional Material Design: A Systematic Literature Review DOI Creative Commons
Fadhli Ranuharja, Ganefri Ganefri, Fahmi Rizal

et al.

Salud Ciencia y Tecnología, Journal Year: 2025, Volume and Issue: 5, P. 1336 - 1336

Published: Jan. 1, 2025

This study examines the relevance and impact of Generative Artificial Intelligence (GenAI) in design instructional materials for vocational education through a systematic literature review following PRISMA guidelines. The draws from reputable databases, including Scopus, Web Science (WoS), ERIC, to identify peer-reviewed articles published between 2019 2024. After applying inclusion exclusion criteria, 28 eligible were analyzed. findings highlight that GenAI significantly enhances material by supporting personalized learning, automating content creation, improving accessibility. It enables development adaptive high-quality resources tailored diverse learner needs education. Furthermore, visualizes research trends using bibliometric analysis, providing insights into evolution distribution GenAI-related across time, regions, themes. However, challenges such as need digital competency among educators, ethical concerns regarding bias quality, potential over-reliance on AI tools are identified. underscores importance balancing AI-driven innovation with human-centered ensure effective sustainable educational practices. Practical recommendations include targeted professional programs frameworks guide integration

Language: Английский

Citations

0
Relevance and Impact of Generative AI in Vocational Instructional Material Design: A Systematic Literature Review DOI
Fadhli Ranuharja, Ganefri Ganefri, Fahmi Rizal

et al.

Salud Ciencia y Tecnología, Journal Year: 2025, Volume and Issue: 5, P. 1636 - 1636

Published: March 4, 2025

This study examines the relevance and impact of Generative Artificial Intelligence (GenAI) in design instructional materials for vocational education through a systematic literature review following PRISMA guidelines. The draws from reputable databases, including Scopus, Web Science (WoS), ERIC, to identify peer-reviewed articles published between 2019 2024. After applying inclusion exclusion criteria, 28 eligible were analyzed. findings highlight that GenAI significantly enhances material by supporting personalized learning, automating content creation, improving accessibility. It enables development adaptive high-quality resources tailored diverse learner needs education. Furthermore, visualizes research trends using bibliometric analysis, providing insights into evolution distribution GenAI-related across time, regions, themes. However, challenges such as need digital competency among educators, ethical concerns regarding bias quality, potential over-reliance on AI tools are identified. underscores importance balancing AI-driven innovation with human-centered ensure effective sustainable educational practices. Practical recommendations include targeted professional programs frameworks guide integration

Language: Английский

Citations

0
The Role of Artificial Intelligence in Computer Science Education: A Systematic Review with a Focus on Database Instruction DOI Creative Commons

Alkmini Gaitantzi,

Ioannis Kazanidis

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 3960 - 3960

Published: April 3, 2025

The integration of artificial intelligence (AI) into computer science (CS) education is evolving, yet its specific application in database instruction remains underexplored. This systematic review analyzes 31 empirical studies published between 2020 and 2025, examining how AI applications support teaching learning CS, with an emphasis on education. Following the PRISMA methodology, categorizes according to instructional design models, roles, actions, benefits, challenges. Findings indicate that tools, particularly chatbots, intelligent tutoring systems, code generators, effectively personalized instruction, immediate feedback, interactive problem-solving across CS database-specific contexts. However, challenges persist, including inaccuracies, biases, student dependency AI, academic integrity risks. also identifies a shift programming as reshapes software development practices, prompting need align curricula evolving industry expectations. Despite growing attention education, database-related research limited. highlights necessity for further investigations specifically more extensive addressing AI-driven pedagogical strategies their long-term impacts. results suggest careful tools can complement traditional emphasizing critical role human educators achieving meaningful effective outcomes.

Language: Английский

Citations

0
An Automated Hierarchy Method to Improve History Record Accessibility in Text-to-Image Generative AI DOI Creative Commons

Hui-Jun Kim,

Jaeseong Park,

Young-Mi Choi

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1119 - 1119

Published: Jan. 23, 2025

This study aims to enhance access historical records by improving the efficiency of record retrieval in generative AI, which is increasingly utilized across various fields for generating visual content and gaining inspiration due its ease use. Currently, most AIs, such as Dall-E Midjourney, employ conversational user interfaces (CUIs) creation retrieval. While CUIs facilitate natural interactions between complex AI models users making process straightforward, they have limitations when it comes navigating past records. Specifically, require numerous interactions, must sift through unnecessary information find desired records, a challenge that intensifies volume grows. To address these limitations, we propose an automatic hierarchy method. method, considering modality characteristics text-to-image applications, implemented with two approaches: vision-based (output images) prompt-based (input text) approaches. validate effectiveness method assess impact approaches on users, conducted 12 participants. The results indicated enables more efficient than traditional CUIs, preferences varied depending their work patterns. contributes overcoming linear existing CUI systems development It also enhances accessibility, essential function effective tool, suggests future directions research this area.

Language: Английский

Citations

0
AIGC-enabled Education Information Technology Integration Application and Research--Taking Information Technology Teaching of Preschool Education Major as an Example DOI Open Access

Qiming Qiao

Applied Mathematics and Nonlinear Sciences, Journal Year: 2025, Volume and Issue: 10(1)

Published: Jan. 1, 2025

Abstract Artificial Intelligence Generated Content (AIGC) technology has become an important driver of educational innovation with its content creation capability and personalized learning experience. In this paper, taking the teaching information in preschool education as example, a method generating mind maps based on videos is proposed to be used design. For task resource recommendation, DB-CGAT model proposed, which combines knowledge graph context processing dual behavior aggregation method. Yelp 2018, Amazon-Book, CoLR datasets are for recommendation performance experiments. comparison six mainstream baseline methods, can achieve better most cases. When τ = 0.3, best Recall@20 performance.

Language: Английский

Citations

0
Future expectations for faculty roles at Yarmouk University in light of AI-based learning DOI Open Access

Miesam Fawzi Motiar Al Azam

International Journal of ADVANCED AND APPLIED SCIENCES, Journal Year: 2024, Volume and Issue: 11(11), P. 19 - 27

Published: Nov. 1, 2024

This study aimed to examine future expectations for faculty roles at Yarmouk University in the context of artificial intelligence (AI)-based learning. Using a descriptive approach, researchers employed questionnaire as primary tool, with sample 140 members from College Education. Results indicated that first category, related teaching methods, received weighted average 4.55, indicating strong agreement. Similarly, second category communication scored 4.57, which also reflects The third focusing on technical performance, achieved 4.59, showing agreement, while fourth addressing educational activities, 4.58, Overall, combined categories had an score suggesting agreement within AI-based learning environment. Additionally, significant differences emerged among respondents based gender, college affiliation, and years experience; however, no were found academic rank.

Language: Английский

Citations

0