Unpacking Epistemic Insights of Artificial Intelligence (AI) in Science Education: A Systematic Review

Cheung, Kason Ka Ching; Long, Yun; Liu, Qian; Chan, Ho-Yin

doi:10.1007/s11191-024-00511-5

Sci & Educ

2024

DOI: 10.1007/s11191-024-00511-5

|View full text |Cite

Unpacking Epistemic Insights of Artificial Intelligence (AI) in Science Education: A Systematic Review

Kason Ka Ching Cheung,

Yun Long,

Qian Liu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite1

Independent1

Authors

Journals

Cited by 2 publications

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Exploring Students’ Multimodal Representations of Ideas About Epistemic Reading of Scientific Texts in Generative AI Tools

Cheung,

Pun,

Kenneth-Li

et al. 2024

J Sci Educ Technol

View full text Add to dashboard Cite

As students read scientific texts created in generative artificial intelligence (GenAI) tools, they need to draw on their epistemic knowledge of GenAI as well as that of science. However, only a few research discussed multimodality as a methodological approach in characterising students’ ideas of GenAI-science epistemic reading. This study qualitatively explored 44 eighth and ninth graders’ multimodal representations of ideas about GenAI-science epistemic reading and developed an analytical framework based on Lemke’s (1998) typology of representational meaning, namely presentational, organisational, and orientational meanings. Under each representational meaning, several categories were inductively generated while students expressed preferences in using drawn, written, or both drawn and written mode to express certain categories. Findings indicate that a multimodal approach is fruitful in characterising students’ semiotic resources in meaning-making of ideas about GenAI-science epistemic reading. We suggested implications regarding future intervention studies on tracking students’ ideas about GenAI-science epistemic reading using the analytical framework developed in this study.

show abstract

Exploring Students’ Multimodal Representations of Ideas About Epistemic Reading of Scientific Texts in Generative AI Tools

Cheung,

Pun,

Kenneth-Li

et al. 2024

J Sci Educ Technol

View full text Add to dashboard Cite

show abstract

Students’ Holistic Reading of Socio-Scientific Texts on Climate Change in a ChatGPT Scenario

Cheung,

Pun,

2024

Res Sci Educ

Self Cite

View full text Add to dashboard Cite

ChatGPT becomes a prominent tool for students’ learning of science when students read its scientific texts. Students read to learn about climate change misinformation using ChatGPT, while they develop critical awareness of the content, linguistic features as well as nature of AI and science to comprehend these texts. In this exploratory study, we investigated students’ reading performance in comprehending two ChatGPT-generated socio-scientific texts, with one focusing on cognitive-epistemic aspects of climate science and another one focusing on social-institutional aspects of climate science. We theorized such reading of ChatGPT-generated outputs as encompassing the content-interpretation, genre-reasoning and epistemic-evaluation domains. Combining Rasch partial-credit model and qualitative analysis, we explored and investigated how a total of 117 junior secondary students (grades 8 to 9) read such texts. Moreover, we also examined how 55 students’ holistic reading of socio-scientific texts on climate change in a ChatGPT scenario changes after a reading-science intervention. Our findings indicate that the content-interpretation was the easiest while the epistemic-evaluation domains were the most difficult. Interestingly, after the reading-science intervention, many students developed their tentative view on nature of science when they evaluated ChatGPT’s claims; while a small increase in number of students discussed reliability and non-epistemic nature of AI when they evaluated ChatGPT’s claims in relation to climate change. The findings also drive a pedagogical model that improves students’ holistic reading of socio-scientific texts generated by ChatGPT.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Unpacking Epistemic Insights of Artificial Intelligence (AI) in Science Education: A Systematic Review

Cited by 2 publications

References 94 publications

Exploring Students’ Multimodal Representations of Ideas About Epistemic Reading of Scientific Texts in Generative AI Tools

Exploring Students’ Multimodal Representations of Ideas About Epistemic Reading of Scientific Texts in Generative AI Tools

Students’ Holistic Reading of Socio-Scientific Texts on Climate Change in a ChatGPT Scenario

Contact Info

Product

Resources

About