Effort regulation negatively predicts, and general academic help seeking positively predicts, reported AI dependency; time management is non-significant¶

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

Subclaims¶

q2 i? Stronger effort regulation is associated with lower AI dependency, more frequent general academic help seeking with higher dependency, and time/study environment management shows no significant association. → Hilit Maizel 2026

Evidence¶

Hilit Maizel 2026¶

Hilit Maizel, Maya Kalman Halevi, Miri Sarid, and Rony Tutian. (2026). Understanding Student Dependency on AI: The Role of AI Literacy, Academic Self-Efficacy, and Resource Management Strategies. Educ. Sci. 2026, 16, 1123. https://doi.org/10.3390/educsci16071123

q2 · i? · associational · r2

Same multiple regression (N = 478). The MSLQ help-seeking subscale measures general academic help seeking from human sources, not help seeking from AI tools, so the positive association cannot indicate substitution of AI for human helpers.

"effort regulation was negatively associated (β = −0.134, p = 0.013), help seeking was positively associated (β = 0.124, p = 0.003), and time and study environment management was not significant (β = −0.046, p = 0.374)"

Discussion¶

Related Claims¶

• Evidence suggests over-reliance on generative AI may negatively affect students' self-regulated learning behaviors, potentially harming retention and transfer to novel contexts — the general claim this finding bears on, with the studies behind it
• The four profiles differ in academic self-efficacy, time/study management, and effort regulation, with HL highest, but not in help seeking — related
• Peer-related help-seeking and learner-learner engagement strategies appear less often in profiles of students with lower digital distraction — related
• In association-rule mining of survey data from 530 college students in online courses, goal setting, environment structuring and time management were the strategies most often associated with lower digital distraction — related
• Cognitive offloading to generative AI is associated with reduced learner engagement, lower critical thinking, and lower-quality reasoning — related
• The eight-predictor model explains 27.3% of variance in AI dependency, and multidimensional AI literacy adds explanatory value over a single aggregate score
• Both academic self-efficacy and AI self-efficacy are negatively associated with reported AI dependency, supporting a unified compensatory mechanism — related
• Help seeking negatively predicts goal attainment in MOOCs — related
• Students report peer support and distraction as effective coping, rarely self-regulation or professional help-seeking — related