Journal of Intelligence

Developing flexible thinking, generating alternative solution strategies, and producing original ideas are increasingly emphasized in mathematics education. This study examines the relationships among preservice elementary mathematics teachers’ problem-solving self-efficacy, critical thinking disposition, and mathematical creativity self-efficacy. Drawing on a theoretically informed framework, it specifically investigates whether critical thinking disposition statistically mediates the association between problem-solving self-efficacy and mathematical creativity self-efficacy. Data were collected from 554 preservice elementary mathematics teachers. Analyses included descriptive statistics, Pearson correlations, and bootstrap-based observed-variable path analysis to obtain robust estimates of direct and indirect associations. Findings indicated that positive problem-solving self-efficacy was strongly associated with critical thinking disposition, which was, in turn, positively associated with mathematical creativity self-efficacy. Critical thinking disposition also statistically mediated the association between problem-solving self-efficacy and mathematical creativity self-efficacy. A reverse-direction model showed comparably good overall fit, indicating that the observed data were also compatible with an alternative directional ordering. These findings suggest that problem-solving self-efficacy and critical thinking disposition represent complementary cognitive-affective constructs that are empirically associated with perceptions of mathematical creativity. Given the cross-sectional design, the findings should not be interpreted as evidence of causal direction. The results instead provide empirical support for a theoretically motivated model of the associations among problem-solving self-efficacy, critical thinking disposition, and mathematical creativity self-efficacy.

The rapid advancement of generative artificial intelligence (AI) is transforming human cognitive activities and creating a growing need to preserve cognitive autonomy in human–AI interactions. Although recent research has examined metacognition in AI-mediated environments, existing work has primarily focused on improving AI use and human–AI collaboration, with less attention to how individuals regulate the role and extent of AI involvement in their own cognitive activities. To address this gap, the present study introduced Human-Centered AI Metacognition (HC-AIM), defined as the capacity to recognize AI capabilities and limitations, monitor AI-generated information, and regulate the extent to which AI participates in one’s own cognitive activities while maintaining cognitive autonomy. Across three studies, we developed the Chinese version of the Human-Centered AI Metacognition Scale (HC-AIMS) and conducted an initial psychometric evaluation. Study 1 examined the scale structure using exploratory factor analysis and supported a predominantly unidimensional structure with good internal consistency. Study 2 examined this structure using confirmatory factor analysis and provided evidence of its convergent validity through its association with general metacognition. Study 3 further replicated the scale structure and internal consistency and examined its relationships with AI literacy, collaborative AI metacognition, and autonomous cognitive offloading. HC-AIM was positively associated with all three constructs. Comparisons of theoretically specified factor models provided preliminary evidence of empirical distinguishability between HC-AIM and both AI literacy and collaborative AI metacognition, despite substantial correlations with these constructs. HC-AIM was also positively associated with autonomous cognitive offloading, providing complementary self-reported criterion-related evidence for its validity. Taken together, the findings suggest that HC-AIM represents a contextualized, human-centered form of metacognitive regulation that is closely related to broader metacognitive and AI-related competencies while specifically emphasizing the regulation of AI participation in human cognition. The Chinese version of the HC-AIMS provides a theoretically grounded starting point for investigating human cognition and cognitive autonomy in AI-mediated environments.

Although acute aerobic exercise selectively influences specific subcomponents of working memory, whether such benefits transfer to a subsequent cognitive task engaging a different domain remains unclear. Forty-seven participants completed, in randomized order, an exercise condition (10 min of moderate-intensity aerobic exercise) and a control condition, each immediately followed by a visuospatial construction task (the Equilibrio game) and a 2-Back working memory task, with heart rate variability (HRV) recorded throughout. Completion time on the Equilibrio task was significantly shorter following exercise than following control, W = 292.5, z = −2.873, p = .004, r_B = −0.481. In contrast, no significant between-condition differences emerged on any behavioral parameter of the 2-Back task, including completion time, accuracy, hit rate, discrimination sensitivity (d′), and response bias (c; all ps > 0.05). During the 2-Back task, however, sympathetic indices (SNS Index, Stress Index, LF/HF ratio) were significantly elevated and parasympathetic/vagal indices (RMSSD, SDNN, PNS Index, HF n.u.) were significantly suppressed following exercise, despite unchanged behavior. These findings indicate that exercise-induced cognitive benefit is task-specific and does not extend to a subsequent task engaging a different working memory subcomponent, even as autonomic activation persists. This dissociation challenges general-arousal accounts of exercise–cognition effects and supports a domain-specific, timing-sensitive framework.