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Considering AI for Generating Mnemonics to Support Learning in Introductory Business Statistics.

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Title: Considering AI for Generating Mnemonics to Support Learning in Introductory Business Statistics.
Authors: Mocko, Megan E.1, Lesser, Lawrence M.2, Lugo, Alejandra2, Shein, Megan1
Source: Ohio Journal of School Mathematics. Fall2025, Vol. 101, p142-168. 28p.
Subject Terms: *Mnemonics, *Test anxiety, *Educational technology, *Learning, *Artificial intelligence, Language models, Long-term memory, Commercial statistics
Abstract: We present results from using a three-part, multiday SMART (Statistics Mnemonics Assembled with Reflection and Technology) activity designed to help students in an introductory business statistics course explore mnemonic use with and without large language models (LLMs), to support memory retention, and conceptual understanding. Preliminary findings (from analysis of = 70 student reflections) suggest that mnemonics created without the aid of LLMs felt more personal to students, while LLM-supported efforts helped students work more efficiently. Seventy five percent of 108 sentences in the reflections about anxiety reported feeling a reduction in anxiety including having more confidence, feeling better prepared, and improved recall when the mnemonic exit ticket structure was used. [ABSTRACT FROM AUTHOR]
Copyright of Ohio Journal of School Mathematics is the property of Ohio Council of Teachers of Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22Ohio+Journal+of+School+Mathematics%22">Ohio Journal of School Mathematics</searchLink>. Fall2025, Vol. 101, p142-168. 28p.
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  Data: We present results from using a three-part, multiday SMART (Statistics Mnemonics Assembled with Reflection and Technology) activity designed to help students in an introductory business statistics course explore mnemonic use with and without large language models (LLMs), to support memory retention, and conceptual understanding. Preliminary findings (from analysis of = 70 student reflections) suggest that mnemonics created without the aid of LLMs felt more personal to students, while LLM-supported efforts helped students work more efficiently. Seventy five percent of 108 sentences in the reflections about anxiety reported feeling a reduction in anxiety including having more confidence, feeling better prepared, and improved recall when the mnemonic exit ticket structure was used. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Ohio Journal of School Mathematics is the property of Ohio Council of Teachers of Mathematics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              Text: Fall2025
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