Competence is the strongest independent psychological predictor of AI literacy performance, followed by autonomy; relatedness has no significant independent effect¶

Claim · All claims
Evidence · 1 study · causal r3 · q3 · i2 medium
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q3 i2 In the PLS-SEM model, competence was the strongest independent predictor of performance (β = 0.358), followed by autonomy (β = 0.132); relatedness was not significant (β = 0.071, p = 0.153). → Yu S 2026

Evidence¶

Yu S 2026¶

Yu S, Lin Y and Chen C (2026) Designing for AI literacy: psychological mechanisms and self-determination. Front. Psychol. 17:1937845. https://doi.org/10.3389/fpsyg.2026.1937845

q3 · i2 · causal · r3

PLS-SEM structural model with the three psychological needs, measured after the task on self-report scales, predicting rubric-scored AI literacy performance among 320 university students in a randomized 2 × 2 experiment (the needs were measured, not manipulated, so these paths are associations). Competence (β = 0.358, p < 0.001) and autonomy (β = 0.132, p = 0.030) were significant; relatedness was not (β = 0.071, p = 0.153).

"Among the psychological needs, competence was the strongest independent predictor of AI literacy performance (β = 0.358, p < 0.001), followed by autonomy (β = 0.132, p = 0.030). Relatedness did not have a significant independent effect (β = 0.071, p = 0.153)."

Discussion¶

Learner Variables¶

• Belonging — predictor: learners who differ on it differ in outcomes

Related Claims¶

• Basic psychological need satisfaction is strongly associated with learning engagement among medical undergraduates — the general claim this finding bears on, with the studies behind it
• In agentic AI-supported online learning, perceived autonomy, competence, and relatedness are each positively associated with learning motivation — related
• Instructional approach operates indirectly through autonomy and competence, while task type operates primarily through autonomy — related
• Perceived Competence, Intrinsic Motivation, and Psychological Safety each significantly positively predict Deep Revision Engagement — related
• Engineering students perceive AI chatbot benefits as strongest for relief from competence frustration, moderate for autonomy, and weakest for relatedness — related
• Perceived competence predicts autonomous motivation for AI use but not autonomy support for AI use — related
• The comprehensive psychological need satisfaction configuration (competence core) has the highest coverage for high learning engagement, with GAI use unconstrained — related
• Satisfaction of basic psychological needs is positively associated with autonomous motivation, with competence the strongest predictor — related
• Autonomy shows a strong content signal: 53 of 64 outcomes satisfy the a priori autonomy rule, with exceptions for self-efficacy (competence) and collegial support (relatedness) — related
• Students with higher baseline relatedness satisfaction perceive less additional relatedness support from AI chatbot use — related
• CHAT analysis identified three Type 2 secondary contradictions whose operation through psychological need frustration explained motivational decline despite growing competence — related
• Competence need satisfaction is the strongest psychological-need predictor of students' self-determined motivation types — related
• In EERs, autonomy, competence, and relatedness are each supported by specific design features per self-determination theory — related
• Self-determination theory grew incrementally by adding mini-theories as evidence accumulated — related