Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework
In diverse classrooms, one of the challenges educators face is creating assessments that reflect the different cultural backgrounds of every student. This study presents a novel approach to the automatic generation of cultural and context-specific science assessments items for K-12 education using g...
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MDPI AG
2024-11-01
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| author | Matthew Nyaaba Xiaoming Zhai Morgan Z. Faison |
| author_facet | Matthew Nyaaba Xiaoming Zhai Morgan Z. Faison |
| author_sort | Matthew Nyaaba |
| collection | DOAJ |
| description | In diverse classrooms, one of the challenges educators face is creating assessments that reflect the different cultural backgrounds of every student. This study presents a novel approach to the automatic generation of cultural and context-specific science assessments items for K-12 education using generative AI (GenAI). We first developed a GenAI Culturally Responsive Science Assessment (GenAI-CRSciA) framework that connects CRSciA, specifically key cultural tenets such as indigenous language, Indigenous knowledge, ethnicity/race, and religion, with the capabilities of GenAI. Using the CRSciA framework, along with interactive guided dynamic prompt strategies, we developed the CRSciA-Generator tool within the OpenAI platform. The CRSciA-Generator allows users to automatically generate assessment items that are customized to align with their students’ cultural and contextual needs. We further conducted a pilot demonstration of item generation between the CRSciA-Generator and the base GPT-4o using standard prompts. Both tools were tasked with generating CRSciAs that aligned with the Next Generation Science Standard on predator and prey relationship for use with students from Ghana, the USA, and China. The results showed that the CRSciA-Generator output assessment items incorporated more tailored cultural and context assessment items for each specific group with examples, such as traditional stories of lions and antelopes in Ghana, Native American views on wolves in the USA, and Taoist or Buddhist teachings on the Amur tiger in China compared to the standard prompt assessment items within the base GPT-4o. However, due to the focus on nationality in the pilot demonstration, the CRSciA-Generator assessment items treated the countries as culturally homogeneous, overlooking subcultural diversity in these countries. Therefore, we recommend that educators provide detailed background information about their students when using the CRSciA-Generator. We further recommend future studies involving expert reviews to assess the cultural and contextual validity of the assessment items generated by the CRSciA-Generator. |
| format | Article |
| id | doaj-art-e91b8bc9bcc848e2a61c846c0e90b6c3 |
| institution | DOAJ |
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| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Education Sciences |
| spelling | doaj-art-e91b8bc9bcc848e2a61c846c0e90b6c32025-08-20T02:53:41ZengMDPI AGEducation Sciences2227-71022024-11-011412132510.3390/educsci14121325Generative AI for Culturally Responsive Science Assessment: A Conceptual FrameworkMatthew Nyaaba0Xiaoming Zhai1Morgan Z. Faison2AI4STEM Education Center, University of Georgia, Athens, GA 30602, USAAI4STEM Education Center, University of Georgia, Athens, GA 30602, USADepartment of Educational Theory and Practice, University of Georgia, Athens, GA 30602, USAIn diverse classrooms, one of the challenges educators face is creating assessments that reflect the different cultural backgrounds of every student. This study presents a novel approach to the automatic generation of cultural and context-specific science assessments items for K-12 education using generative AI (GenAI). We first developed a GenAI Culturally Responsive Science Assessment (GenAI-CRSciA) framework that connects CRSciA, specifically key cultural tenets such as indigenous language, Indigenous knowledge, ethnicity/race, and religion, with the capabilities of GenAI. Using the CRSciA framework, along with interactive guided dynamic prompt strategies, we developed the CRSciA-Generator tool within the OpenAI platform. The CRSciA-Generator allows users to automatically generate assessment items that are customized to align with their students’ cultural and contextual needs. We further conducted a pilot demonstration of item generation between the CRSciA-Generator and the base GPT-4o using standard prompts. Both tools were tasked with generating CRSciAs that aligned with the Next Generation Science Standard on predator and prey relationship for use with students from Ghana, the USA, and China. The results showed that the CRSciA-Generator output assessment items incorporated more tailored cultural and context assessment items for each specific group with examples, such as traditional stories of lions and antelopes in Ghana, Native American views on wolves in the USA, and Taoist or Buddhist teachings on the Amur tiger in China compared to the standard prompt assessment items within the base GPT-4o. However, due to the focus on nationality in the pilot demonstration, the CRSciA-Generator assessment items treated the countries as culturally homogeneous, overlooking subcultural diversity in these countries. Therefore, we recommend that educators provide detailed background information about their students when using the CRSciA-Generator. We further recommend future studies involving expert reviews to assess the cultural and contextual validity of the assessment items generated by the CRSciA-Generator.https://www.mdpi.com/2227-7102/14/12/1325culturally responsive assessmentassessmentprompt engineeringgenerative AI (GenAI)AIscience education |
| spellingShingle | Matthew Nyaaba Xiaoming Zhai Morgan Z. Faison Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework Education Sciences culturally responsive assessment assessment prompt engineering generative AI (GenAI) AI science education |
| title | Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework |
| title_full | Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework |
| title_fullStr | Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework |
| title_full_unstemmed | Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework |
| title_short | Generative AI for Culturally Responsive Science Assessment: A Conceptual Framework |
| title_sort | generative ai for culturally responsive science assessment a conceptual framework |
| topic | culturally responsive assessment assessment prompt engineering generative AI (GenAI) AI science education |
| url | https://www.mdpi.com/2227-7102/14/12/1325 |
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