Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework DOI Creative Commons
Matthew Nyaaba, Xiaoming Zhaı, Morgan Z. Faison

et al.

Education Sciences, Journal Year: 2024, Volume and Issue: 14(12), P. 1325 - 1325

Published: Nov. 30, 2024

In diverse classrooms, one of the challenges educators face is creating assessments that reflect different cultural background every student. this study presents a novel approach to automatic generation and context-specific science items for K-12 education using generative AI (GenAI). We first developed GenAI Culturally Responsive Science Assessment (GenAI-CRSciA) framework connects CRSciA, specifically key tenets such as indigenous language, Indigenous knowledge, ethnicity/race, religion, with capabilities GenAI. Using CRSciA framework, along interactive guided dynamic prompt strategies, was used develop CRSciA-Generator tool within OpenAI platform. The allows users automatically generate assessment item are customized align their students’ contextual needs. conducted pilot demonstration between base GPT-4o (using standard prompts), both tools were tasked generating CRSciAs aligned Next Generation Standard on predator prey relationship students from Ghana, USA, China. results showed output incorporated more tailored context each specific group examples, traditional stories lions antelopes in Native American views wolves Taoist or Buddhist teachings Amur tiger China compared GPT-4o. However, due focus nationality demonstration, treated countries culturally homogeneous, overlooking subcultural diversity these countries. Therefore, we recommend provide detailed information about when CRSciA-Generator. further future studies involving expert reviews assess validity generated by

Language: Английский

Generative AI in Education: Perspectives Through an Academic Lens DOI Open Access
Iulian Întorsureanu, Simona‐Vasilica Oprea, Adela Bârã

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(5), P. 1053 - 1053

Published: March 6, 2025

In this paper, we investigated the role of generative AI in education academic publications extracted from Web Science (3506 records; 2019–2024). The proposed methodology included three main streams: (1) Monthly analysis trends; top-ranking research areas, keywords and universities; frequency over time; a keyword co-occurrence map; collaboration networks; Sankey diagram illustrating relationship between AI-related terms, publication years areas; (2) Sentiment using custom list words, VADER TextBlob; (3) Topic modeling Latent Dirichlet Allocation (LDA). Terms such as “artificial intelligence” “generative artificial were predominant, but they diverged evolved time. By 2024, applications had branched into specialized fields, including educational research, computer science, engineering, psychology, medical informatics, healthcare sciences, general medicine surgery. sentiment reveals growing optimism regarding education, with steady increase positive 2023 to while maintaining predominantly neutral tone. Five topics derived based on an most relevant terms by LDA: Gen-AI’s impact research; ChatGPT tool for university students teachers; Large language models (LLMs) prompting computing education; (4) Applications patient (5) ChatGPT’s performance examinations. identified several emerging topics: discipline-specific application LLMs, multimodal gen-AI, personalized learning, peer or tutor cross-cultural multilingual tools aimed at developing culturally content supporting teaching lesser-known languages. Further, gamification involves designing interactive storytelling adaptive games enhance engagement hybrid human–AI classrooms explore co-teaching dynamics, teacher–student relationships classroom authority.

Language: Английский

Citations

3
Developing and validating an instrument for teachers’ acceptance of artificial intelligence in education DOI
Shuchen Guo, Lehong Shi, Xiaoming Zhaı

et al.

Education and Information Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Language: Английский

Citations

1
Generative artificial intelligence in teacher training: a narrative scoping review DOI Creative Commons
Andrii O. Kolhatin

CTE Workshop Proceedings, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

The emergence of generative artificial intelligence (GenAI) has transformed various sectors, including education. This narrative scoping review examines how GenAI is being integrated into teacher training programs, exploring its applications, benefits, challenges, and implementation frameworks. By synthesizing findings from recent literature (2022-2025), we identify key themes the development AI literacy among teachers, impact on pedagogical content knowledge, ethical considerations in implementation. Our analysis reveals significant benefits enhancing teaching performance facilitating personalized learning, while also highlighting challenges such as technical limitations, concerns, resistance to change. We gaps current research, particularly non-STEM subjects framework development, suggest directions for future research advance responsible integration

Language: Английский

Citations

1
Generative AI in teacher education: Teacher educators’ perception and preparedness DOI Creative Commons
Bismark Nyaaba Akanzire, Matthew Nyaaba, Macharious Nabang

et al.

Journal of Digital Educational Technology, Journal Year: 2025, Volume and Issue: 5(1), P. ep2508 - ep2508

Published: Jan. 28, 2025

This rapid study explores teacher educators’ perceptions of generative artificial intelligence (GenAI) in education, conducted through a descriptive survey involving 55 educators from two colleges education Ghana. A convenience sampling technique was adopted for data collection, and analysis using <i>exploratory factor analysis</i> used to identify primary factors shaping preparedness GenAI integration. Key findings reveal generally positive perception among the educators, who recognize GenAI’s potential support academic achievement, increase student engagement, improve communication within settings. The further indicate that background factors, such as age, years teaching experience, department, college, do not significantly predict their GenAI. Since none these measured were significant predictors, this suggests training resources should be broadly prioritized, accessible, heavily tailored specific demographic groups. However, identified concerns <i>barriers challenges</i> including ethical issues, fairness assessment, possible adverse effects on educator-student relationship. <i>communication independence</i> highlight need professional development, with emphasizing importance usage optimize its educational potential. concludes while benefits, there are essential practical challenges address. Recommendations include establishing clear policies guidelines guide implementation ensure usage. We recommend expansion research larger sample gather comprehensive insights acceptance levels

Language: Английский

Citations

0
Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education DOI Creative Commons
Wenhan Lyu, Shuang Zhang,

T.-H. Chuang

et al.

Computers and Education Artificial Intelligence, Journal Year: 2025, Volume and Issue: unknown, P. 100383 - 100383

Published: Feb. 1, 2025

Language: Английский

Citations

0
Intersections of Mind and Machine: Navigating the Nexus of Artificial Intelligence, Science Education, and the Preparation of Pre-service Teachers DOI Creative Commons
Grant Cooper, Kok‐Sing Tang, Angela Fitzgerald

et al.

Journal of Science Education and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Language: Английский

Citations

0
Exploring human and AI collaboration in inclusive STEM teacher training: A synergistic approach based on self-determination theory DOI
Tingting Li, Zehui Zhan, Yu Ji

et al.

The Internet and Higher Education, Journal Year: 2025, Volume and Issue: unknown, P. 101003 - 101003

Published: Feb. 1, 2025

Language: Английский

Citations

0
Artificial Intelligence and Students Happiness DOI
Shorouk Mohamed Farag Mohamed Aboudahr,

Faisal Al-Showaikh,

Manoharan Nalliah

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 351 - 370

Published: March 13, 2025

The purpose of this chapter was to examine the role self-regulation as a mediator in relationship between use artificial intelligence (AI) learning tool on student happiness among private university students Bahrain. data were collected from 171 at Using theoretical framework social cognitive theory, results showed that directly positively related perceived usefulness AI and attitude toward use. finding also, indicated significantly mediates Ai usage students' happiness. recommendation develop students′ increase positive impact their well-being overall

Language: Английский

Citations

0
Analysis of Practical Characteristics in Simulated Elementary Science Lessons Earth and Space Using ChatGPT DOI

Yoon-Sung Choi,

Jung-In Chung

Journal of the Korean earth science society, Journal Year: 2025, Volume and Issue: 46(1), P. 78 - 95

Published: Feb. 28, 2025

Language: Английский

Citations

0
ThinkBox: When gamification meets artificial intelligence: rethinking learning experiences DOI Creative Commons
Vanessa Itacaramby Pardim, Adriana Backx Noronha Viana, Pedro Isaías

et al.

Revista de Gestão, Journal Year: 2025, Volume and Issue: 32(1), P. 66 - 70

Published: March 27, 2025

Language: Английский

Citations

0