| 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; Language models; Test anxiety; Educational technology; Long-term memory; Learning; Artificial intelligence; 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] |
| Database: |
Supplemental Index |