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

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
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

Citations

1