Research Alert: Motivational Self-Regulation Strategies and Their Relationship with Academic Adjustment Across Adolescent High-Ability, Learning-Disability, and Regular Students

Good article reinforcing the importance of self-regulation learning (SRL) strategies in learning and motivation.  It is open access and available for download here.

Abstract

Motivational self-regulation strategies (MRSs) play a key role in how students initiate, sustain, and direct effort during learning. However, little is known about their use among adolescents with different educational profiles. This study aimed to explore possible differences in the use of MRSs among adolescent students with varying learning potential (high-ability [MGS], learning-disability [SWLD] and regular students [RS]) and to examine whether the relationship between MRSs and academic adjustment differed across these educational profiles. We recruited a sample of 2191 students enrolled in compulsory secondary education, comprising regular students (81.8%, N = 1792), students with learning disabilities (15.8%, N = 347), and high-ability students (2.4%, N = 52). The participants completed self-report measures of MRSs and academic adjustment. Path models were used to examine cross-sectional direct and indirect associations among the study variables, and they revealed different patterns across the three groups. High-ability status was directly and positively associated with academic adjustment, while learning-difficulty status showed a direct negative relationship with adjustment. The three groups of students showed indirect associations with adjustment via various types of MRSs.

From Conclusions

Our findings support the role of motivational self-regulation in the academic adjustment of adolescent students with varying learning potential. These types of students seem to manifest different tendencies in the use of MRSs with unequal adaptive value. Whereas RS and MGS were associated with motivational regulation strategies positively related to academic adjustment, SWLD showed a pattern characterized by lower use of such strategies and greater use of work-avoidance self-talk.

Intervention programs, whether they are targeted at students with learning disabilities or high-ability students who are underachieving, typically include training on self-regulated learning skills (Peña-San José et al., 2026; Perry et al., 2020). However, motivational self-regulation is often overlooked, even though a failure to employ (meta)cognitive strategies is often accompanied by negative emotions and motivational beliefs, thereby undermining students' motivation (Al-Hoorie, 2024; Efklides, 2019). Moreover, low-achieving students tend to show lower levels of self-efficacy and task values (Fong et al., 2023; Raoof et al., 2024).

Blogmaster comment:  SRL is an important part of achievement competence motivation.  Humble brag—SRL is included as one of the critical domains in achievement competence motivation (along with motivational orientations and self-beliefs) in McGrew’s (2022) Cognitive-Affective-Motivation Model of Learning; CAMML; article available here.  Prior blog posts about the CAMML model and concepts can be found here.  

See images below for representation of CAMML model and concepts

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More “the CHC cognitive taxonomy is the consensus taxonomy of human cognitive ability” quotes

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Another AI evaluation framework informed by the human CHC cognitive abilities taxonomy

According to my tracking of the literature (of course, most likely not complete), here is a third article (open access from the Journal of Intelligence) that proposes using the CHC cognitive ability taxonomy (or parts of it) to evaluate an aspect of AI performance—in this case AI-assisted organizational decision-making.  The other two proposed AI evaluation framework articles using the CHC taxonomy (alone or as part of a much grander framework) can be found here and here. 

In a related development, Kameron Green—the developer behind an ambitious and grand AI evaluation framework (in second to last link above)—today posted a link to an AI generated video summary overview of his HCQM taxonomic model—which would help most readers understand his lengthy HCQM paper.

Cognitive Performance Under AI Advice: Development and Initial Validation of a CHC-Informed Assessment for Organizational Decision-Making

Abstract

Artificial intelligence (AI) is increasingly embedded in organizational decision-making, requiring employees not only to use AI-generated recommendations but also to evaluate their quality and determine when reliance is appropriate. Although established research examines behavioral reliance on algorithmic and AI advice, fewer studies have approached performance under AI advice as an individual-differences assessment problem integrating psychometric structure, cognitive correlates, process indicators, and criterion-related evidence. This study developed and initially validated a CHC-informed, performance-based assessment of cognitive performance under AI advice using 24 organizational decision scenarios. The assessment was designed around three closely related content/performance dimensions—AI error detection, evidence integration, and cognitive control and adaptive reliance—while also capturing confidence, response time, and reliance behavior. The validation sample comprised 780 employed adults in Türkiye. Psychometric analyses included confirmatory factor analysis, multidimensional item response theory, response-time analyses, scenario-level logistic regression, measurement invariance, differential item functioning, and internal cross-validation. Results indicated a dominant general cognitive-performance component together with additional structure corresponding to the three theoretically specified dimensions. Assessment performance was positively associated with established cognitive measures, including ICAR-16 reasoning performance, working memory, processing speed, and attentional control, whereas associations with AI-related self-reports were generally weaker. Dimension-aligned analyses supported the expected associations of ICAR-16 with AI error detection and working memory with evidence integration. Performance was also moderately associated with concurrently assessed organizational decision quality (r = 0.408) and explained additional variance in this criterion beyond demographic and work characteristics, AI experience, conventional cognitive-performance measures, and AI-related self-reports (ΔR2 = 0.103, p < .001). In contrast, several hypothesized scenario-specific associations involving AI confidence, time pressure, interruptions, and resistance to confidently inaccurate advice were not supported. Overall, the findings provide initial evidence for a performance-based approach to assessing how employees evaluate and respond to AI advice, while indicating that the proposed scenario-specific mechanisms and group-comparability findings require further replication before consequential applications are considered.