AI‐Assisted Assessment of Inquiry Skills in Socioscientific Issue Contexts

ABSTRACT Background Study Assessing learners' inquiry‐based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of scienc...

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Published inJournal of computer assisted learning Vol. 41; no. 1
Main Authors Zhang, Wen Xin, Lin, John J. H., Hsu, Ying‐Shao
Format Journal Article
LanguageEnglish
Published Chichester, UK John Wiley & Sons, Inc 01.02.2025
Wiley Subscription Services, Inc
Subjects
Active Learning
Artificial intelligence
auto scoring
Data collection
Education
Inquiry
inquiry‐based learning
learning assessment
Learning Modules
Neural networks
Science Education
Science Instruction
Skills
socioscientific issues
Student Evaluation
Students
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Abstract ABSTRACT Background Study Assessing learners' inquiry‐based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education. Objectives The present study evaluated the SSI inquiry skills of students in an AI‐enabled scoring environment. An AI model for socioscientific issues that can assess students' inquiry skills was developed. Responses to a learning module were collected from 1250 participants, and the open‐ended responses were rated by humans in accordance with a designed rubric. The collected data were then preprocessed and used to train an AI rater that can process natural language. The effects of two hyperparameters, the dropout rate and complexity of the AI neural network, were evaluated. Results and Conclusion The results suggested neither of the two hyperparameters was found to strongly affect the accuracy of the AI rater. In general, the human and AI raters exhibited certain levels of agreement; however, agreement varied among rubric categories. Discrepancies were identified and are discussed both quantitatively and qualitatively.
AbstractList Background StudyAssessing learners' inquiry‐based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education.ObjectivesThe present study evaluated the SSI inquiry skills of students in an AI‐enabled scoring environment. An AI model for socioscientific issues that can assess students' inquiry skills was developed. Responses to a learning module were collected from 1250 participants, and the open‐ended responses were rated by humans in accordance with a designed rubric. The collected data were then preprocessed and used to train an AI rater that can process natural language. The effects of two hyperparameters, the dropout rate and complexity of the AI neural network, were evaluated.Results and ConclusionThe results suggested neither of the two hyperparameters was found to strongly affect the accuracy of the AI rater. In general, the human and AI raters exhibited certain levels of agreement; however, agreement varied among rubric categories. Discrepancies were identified and are discussed both quantitatively and qualitatively.
ABSTRACT Background Study Assessing learners' inquiry‐based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education. Objectives The present study evaluated the SSI inquiry skills of students in an AI‐enabled scoring environment. An AI model for socioscientific issues that can assess students' inquiry skills was developed. Responses to a learning module were collected from 1250 participants, and the open‐ended responses were rated by humans in accordance with a designed rubric. The collected data were then preprocessed and used to train an AI rater that can process natural language. The effects of two hyperparameters, the dropout rate and complexity of the AI neural network, were evaluated. Results and Conclusion The results suggested neither of the two hyperparameters was found to strongly affect the accuracy of the AI rater. In general, the human and AI raters exhibited certain levels of agreement; however, agreement varied among rubric categories. Discrepancies were identified and are discussed both quantitatively and qualitatively.
Author Hsu, Ying‐Shao
Lin, John J. H.
Zhang, Wen Xin
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This work was supported by National Science and Technology Council; NSTC 110‐2511‐H‐003‐025‐MY3, and NSTC 113‐2410‐H‐003‐049‐MY3, and the “Institute for Research Excellence in Learning Sciences” of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
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Snippet ABSTRACT Background Study Assessing learners' inquiry‐based skills is challenging as social, political, and technological dimensions must be considered. The...
Background StudyAssessing learners' inquiry‐based skills is challenging as social, political, and technological dimensions must be considered. The advanced...
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SubjectTerms Active Learning
Artificial intelligence
auto scoring
Data collection
Education
Inquiry
inquiry‐based learning
learning assessment
Learning Modules
Neural networks
Science Education
Science Instruction
Skills
socioscientific issues
Student Evaluation
Students
Title AI‐Assisted Assessment of Inquiry Skills in Socioscientific Issue Contexts
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fjcal.13102
https://www.proquest.com/docview/3160149383
Volume 41
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