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DTSTAMP:20261012T210907Z
LOCATION:Summit Pavilion
DTSTART;TZID=America/Los_Angeles:20261021T173000
DTEND;TZID=America/Los_Angeles:20261021T183000
UID:HFESAM_ASPIRE 2026_sess257_POST184@linklings.com
SUMMARY:Enhancing Statistical Numeracy Measurement Through a Human-in-the-
 Loop, AI-Assisted Item Development Approach
DESCRIPTION:Alejandra Sanroman and Edward Cokely (University of Oklahoma),
  Saima Ghazal (Missouri University of Science and Technology), Adam Feltz 
 (University of Oklahoma), and Rocio Garcia-Retamero (University of Granada
 )\n\nRisk misunderstanding is a costly and preventable cause of decision-m
 aking errors for experts and non-experts alike (Cokely et al., in press; G
 igerenzer et al., 2007; Reyna et al., 2009). While numerous factors contri
 bute to decision vulnerability, tests of statistical numeracy like the Ber
 lin Numeracy Test have been found to be among the strongest general predic
 tors of decision quality and risk literacy (e.g., the ability to evaluate 
 and understand risk; Cokely et al., 2012; 2018). Recently, some researcher
 s have suggested there is a need to expand the pool of validated numeracy 
 questions across the range of difficulty (≈ 90 items) to support advanced 
 applications (e.g., personalized training and assessment protocols; enhanc
 e precision of IRT-based latent-skill measurement models). Can traditional
  and structured human-in-the-loop, AI-assisted workflows be used to genera
 te valid and robust items to expand the Berlin Numeracy Test item pool?\n\
 n
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