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A Multimodal Interactive Framework for Science Assessment in the Era of Generative Artificial Intelligence

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Title: A Multimodal Interactive Framework for Science Assessment in the Era of Generative Artificial Intelligence
Language: English
Authors: Yizhu Gao (ORCID 0000-0002-7791-3700), Xiaoming Zhai (ORCID 0000-0003-4519-1931), Min Li, Gyeonggeon Lee, Xiaoxiao Liu
Source: Journal of Research in Science Teaching. 2025 62(9):2014-2028.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 15
Publication Date: 2025
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R305C240010
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Secondary Education
Descriptors: Artificial Intelligence, Computer Software, Science Education, Integrity, Risk, Outsourcing, Student Evaluation, Authentic Learning, Evaluation Methods, Semiotics, Barriers, Guidelines, Science Tests, Scientific Concepts, Concept Formation, Learner Engagement, Test Format, Interaction Process Analysis, Academic Standards, Elementary Secondary Education
DOI: 10.1002/tea.70009
ISSN: 0022-4308
1098-2736
Abstract: The rapid evolution of generative artificial intelligence (GenAI) is transforming science education by facilitating innovative pedagogical paradigms while raising substantial concerns about scholarly integrity. One particularly pressing issue is the growing risk of student use of GenAI tools to outsource assessment tasks, potentially compromising authentic learning and evaluations. Addressing these challenges requires reflection on existing assessment practices and features. This position paper advances a conceptual framework for science assessment through the lens of "multimodality" and "interactivity." Multimodality emphasizes the use of diverse, organized semiotic resources for meaning making, while interactivity characterizes assessment environments where outcomes are shaped by students' actions. With the two dimensions, our multimodal interactive framework classifies assessments into four categories, with varying degrees of modality and interactivity. We argue that tasks with higher modality and interactivity can potentially overcome the concerns of GenAI on academic integrity. To further articulate this point, we provide concrete assessment examples for each category and explain how the prompt and response affordances in each assessment category help gauge students' understandings of key science constructs and identify tasks that are resistant or susceptible to AI-based outsourcing. We conclude by discussing how the framework serves as a meaningful analytical tool for educational researchers and practitioners.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2025
Accession Number: EJ1486547
Database: ERIC
FullText Text:
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  Data: A Multimodal Interactive Framework for Science Assessment in the Era of Generative Artificial Intelligence
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  Data: <searchLink fieldCode="AR" term="%22Yizhu+Gao%22">Yizhu Gao</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7791-3700">0000-0002-7791-3700</externalLink>)<br /><searchLink fieldCode="AR" term="%22Xiaoming+Zhai%22">Xiaoming Zhai</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4519-1931">0000-0003-4519-1931</externalLink>)<br /><searchLink fieldCode="AR" term="%22Min+Li%22">Min Li</searchLink><br /><searchLink fieldCode="AR" term="%22Gyeonggeon+Lee%22">Gyeonggeon Lee</searchLink><br /><searchLink fieldCode="AR" term="%22Xiaoxiao+Liu%22">Xiaoxiao Liu</searchLink>
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 15
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  Data: 10.1002/tea.70009
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  Data: The rapid evolution of generative artificial intelligence (GenAI) is transforming science education by facilitating innovative pedagogical paradigms while raising substantial concerns about scholarly integrity. One particularly pressing issue is the growing risk of student use of GenAI tools to outsource assessment tasks, potentially compromising authentic learning and evaluations. Addressing these challenges requires reflection on existing assessment practices and features. This position paper advances a conceptual framework for science assessment through the lens of "multimodality" and "interactivity." Multimodality emphasizes the use of diverse, organized semiotic resources for meaning making, while interactivity characterizes assessment environments where outcomes are shaped by students' actions. With the two dimensions, our multimodal interactive framework classifies assessments into four categories, with varying degrees of modality and interactivity. We argue that tasks with higher modality and interactivity can potentially overcome the concerns of GenAI on academic integrity. To further articulate this point, we provide concrete assessment examples for each category and explain how the prompt and response affordances in each assessment category help gauge students' understandings of key science constructs and identify tasks that are resistant or susceptible to AI-based outsourcing. We conclude by discussing how the framework serves as a meaningful analytical tool for educational researchers and practitioners.
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