Female gender, higher autonomous motivation, lower prior knowledge, and higher final course grades each predict more frequent voluntary StatBot use among graduate statistics students¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 2 of 2 entries) · not reviewed by a person

Subclaims¶

q2 i? Male students used StatBot less frequently than female students (b = -0.57, p < .05). → Yuan-Hsuan Lee and Jiun-Yu Wu 2026 q2 i? Autonomous motivation positively predicted chatbot use frequency (b = 0.52, p < .05). → Yuan-Hsuan Lee and Jiun-Yu Wu 2026 q2 i? Prior knowledge negatively predicted usage frequency while final course grades positively predicted it. → Yuan-Hsuan Lee and Jiun-Yu Wu 2026 (2)

Evidence¶

Yuan-Hsuan Lee and Jiun-Yu Wu 2026¶

Yuan-Hsuan Lee and Jiun-Yu Wu. (2026). Differential engagement with generative artificial intelligence in higher education: Gender, motivation, and achievement trajectories. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-026-00615-8

q2 · i? · associational · r2

ZINB count-component regression of 97 graduate students' semester-long voluntary StatBot use found males used the chatbot less often than females, with the printed coefficient b = -0.57, p < .05, corresponding to an IRR of about 0.57. Autonomous motivation was also a significant positive predictor (b = 0.52, p < .05).

"female students were more likely than males to use StatBot frequently. The coefficient for Male (vs. Female) was significant and negative ( b = -0.57, p < .05)."

Yuan-Hsuan Lee and Jiun-Yu Wu 2026 (2)¶

Yuan-Hsuan Lee and Jiun-Yu Wu. (2026). Differential engagement with generative artificial intelligence in higher education: Gender, motivation, and achievement trajectories. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-026-00615-8

q2 · i? · associational · r2

In the same ZINB count model of StatBot use, prior knowledge carried a negative coefficient (b = − 0.02, p < .001) and final course grades a positive one (b = 0.04, p = .004), so students with stronger initial understanding used the chatbot less while higher final performers used it more.

"prior knowledge was negatively associated with usage ( b = − 0.02, p < .001), implying that students with stronger initial understanding had an expected usage frequency about 2% lower per unit increase in baseline knowledge"

Discussion¶

Learner Variables¶

• Gender — predictor: outcomes differ by group: a gap reflecting circumstances and treatment, not a cause

Related Claims¶

• Growing Achievers used StatBot significantly more than Declining Performers, driven mainly by initiating new inquiries — related
• Higher-achieving graduate students were less likely to abstain entirely from StatBot use — related
• Voluntary StatBot use was highly uneven and deadline-driven, with about 27% of students never using the chatbot — related
• Greater prior AI learning experience and female gender predicted upward motivational profile transitions — related
• Chatbot design carries a documented female-by-default gender bias — related
• LLM learners are younger, more likely male, and score higher on technology affinity than non-learners — reports the opposite
• Male and female students used ChatGPT differently: females used it more frequently and for questions, males more sporadically and to check answers — related
• Male participants were almost twice as likely as females to be engaged in gaming — related
• Gender related to students' emotions but showed no significant effects on math grades, while intelligence and SES related positively to grades — related
• Male gender predicted lower grades, moderated by cohort — related