Integrating Large Language Models into Accessible and Inclusive Education: Access Democratization and Individualized Learning Enhancement Supported by Generative Artificial Intelligence DOI Creative Commons
Iñigo López-Gazpio

Information, Journal Year: 2025, Volume and Issue: 16(6), P. 473 - 473

Published: June 3, 2025

This study explores the integration of large language models (LLMs) into educational environments, emphasizing enhanced accessibility, inclusivity, and individualized learning experiences. The evaluates trends in transformative potential artificial intelligence (AI) technologies their capacity to significantly mitigate traditional barriers related diversity, disabilities, cultural differences, socioeconomic inequalities. result analysis highlights how LLMs personalize instructional content dynamically respond each learner’s emotional needs. work also advocates for an instructor-guided deployment as pedagogical catalysts rather than replacements, educators’ role ethical oversight, sensitivity, support within AI-enhanced classrooms. Finally, while recognizing concerns regarding data privacy, biases, implications, argues that proactive responsible by educators is necessary democratizing access education foster inclusive practices, thereby advancing effectiveness equity contemporary frameworks.

Language: Английский

University Students’ Usage of Generative Artificial Intelligence for Sustainability: A Cross-Sectional Survey from China DOI Open Access
Xiao Lin, How Shwu Pyng, Ahmad Fauzi Mohd Ayub

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(8), P. 3541 - 3541

Published: April 15, 2025

The rapid development of generative artificial intelligence (GenAI) technology has triggered extensive discussions about its potential applications in sustainable higher education. Based on the acceptance model (TAM) and task–technology fit (TTF) theory, this research aimed to investigate current situations challenges Chinese university students using GenAI four typical task scenarios. This was performed a cross-sectional design. data were collected via questionnaire, with 486 undergraduates from participating. analysis methods include descriptive statistics, inferential content analysis. results show that more than 70% actively use GenAI, but nearly half them are not very proficient use. Doubao ERNIE Bot tools they prefer most. primary functions text production information retrieval. They mainly learn relevant knowledge skills through self-media knowledge-sharing platforms. Among scenarios, is widely used course learning activities, while application daily life job search relatively limited. demographic variables shows grade major have significant impact students’ GenAI. In addition, suggest universities should offer courses or lectures provide comprehensive technical support improve popularity operability study provides suggestions for universities, education administration departments, departments services. It will help optimize allocation educational resources promote equity sustainability.

Language: Английский

Citations

0

Generative Artificial Intelligence for Sustainable Learning Development DOI
Eleni Meletiadou

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 23 - 42

Published: March 7, 2025

Research indicates that generative artificial intelligence (Gen AI) and its integration into higher education (HE) classes can support the developments in sustainability, lead to improvements educational practices, promoting for social justice, fostering inclusion accessibility all students, irrespective of their background, environmental, i.e., climate awareness sustainability. These are only some ways Gen AI enhance creation more effective, inclusive, sustainable contexts HE. The current study aimed explore students' perspectives on tools inform changes should be implemented HE effective use by students. One hundred sixty-three students from universities Greece, Albania, UK participated this study. It is suggested policies regulations modified reformulated development, while empirical research incorporation a necessity (there limited focus practice-oriented/empirical research).

Language: Английский

Citations

0

The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching DOI Creative Commons
Yao Xu, Ying Zhong, Weiran Cao

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 28, 2025

This work examines the application of Generative Artificial Intelligence (GAI) technology in animation teaching, focusing on its role enhancing teaching quality and learning efficiency through innovative instructional strategies. Compared to traditional methods, GAI introduces a novel pedagogical paradigm characterized by adaptive personalized pathways, intelligent resource optimization, immersive interactive models. A mixed-methods research approach is adopted, integrating quantitative analysis (experimental data questionnaire surveys) qualitative (behavioral observations) systematically assess educational effectiveness technology. The experiment, conducted over 12 weeks, involved 120 students divided into an experimental group control group. Data sources included pre- post-test evaluations, feedback surveys, classroom behavior analysis. results indicate that, compared conventional significantly enhances outcomes, knowledge abilities, motivation, student satisfaction. pathway dynamically adjusts content based students' progress, improving their mastery foundational skill transferability. Intelligent resources automatically generate high-quality examples provide dynamic mechanisms, fostering creative expression practical efficiency. model effectively increases engagement, teamwork skills, problem-solving abilities. These findings demonstrate that has potential transform optimizing experience advancing methodologies. Beyond offering solutions, plays crucial cultivating creativity, critical thinking, autonomous provides theoretical support guidance for digital transformation while underscoring broader applicability education sector, new directions future development education.

Language: Английский

Citations

0

Integrating Large Language Models into Accessible and Inclusive Education: Access Democratization and Individualized Learning Enhancement Supported by Generative Artificial Intelligence DOI Creative Commons
Iñigo López-Gazpio

Information, Journal Year: 2025, Volume and Issue: 16(6), P. 473 - 473

Published: June 3, 2025

This study explores the integration of large language models (LLMs) into educational environments, emphasizing enhanced accessibility, inclusivity, and individualized learning experiences. The evaluates trends in transformative potential artificial intelligence (AI) technologies their capacity to significantly mitigate traditional barriers related diversity, disabilities, cultural differences, socioeconomic inequalities. result analysis highlights how LLMs personalize instructional content dynamically respond each learner’s emotional needs. work also advocates for an instructor-guided deployment as pedagogical catalysts rather than replacements, educators’ role ethical oversight, sensitivity, support within AI-enhanced classrooms. Finally, while recognizing concerns regarding data privacy, biases, implications, argues that proactive responsible by educators is necessary democratizing access education foster inclusive practices, thereby advancing effectiveness equity contemporary frameworks.

Language: Английский

Citations

0