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Using hierarchical multiple regression to model the impact of AI-powered adaptive testing on student academic fortunes and test anxiety in the Ghanaian context

Crossref DOI link: https://doi.org/10.1007/s44217-025-00524-4

Published Online: 2025-05-27

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Ntumi, Simon https://orcid.org/0000-0001-7874-4454
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Text and Data Mining valid from 2025-05-27

Version of Record valid from 2025-05-27
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Article History

Received: 25 July 2024

Accepted: 2 May 2025

First Online: 27 May 2025

Declarations

:

: This study was approved by the Institutional Review Board (IRB) at University of Education, Winneba. The research was conducted in accordance with the guidelines and ethical standards of the University of Education, Winneba IRB. All procedures involving human participants were performed in compliance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all participants before data collection. Participants were informed of the study’s purpose, procedures, and their right to withdraw at any time without penalty. Confidentiality of participants’ data was strictly maintained throughout the study.

: The author declares that there are no conflicts of interest related to this study. The research was conducted independently, and the findings are presented without any external influence or bias.

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