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 |
| 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 |
| 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. |
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| ISSN: | 0022-4308 1098-2736 |
| DOI: | 10.1002/tea.70009 |
