Aug 2026· Journal of Global Social Transformation· Vol 2, pp. 254-266· 0 citations· 37 references
TL;DR
GenAI's impact on academic achievement is significantly channeled through enhanced student engagement rather than occurring solely through direct cognitive offloading, providing empirical evidence for shifting pedagogical strategies from passive AI consumption to structured, engagement-driven AI integration that explicitly targets higher-order cognitive processes.
Abstract
The rapid integration of generative artificial intelligence (GenAI) in higher education has recontextualized cognitive development, particularly regarding Bloom’s higher-order thinking skills (HOTS). While GenAI's potential to enhance learning is widely recognized, the mechanisms through which GenAI-assisted HOTS influence academic achievement remain underexplored. This study addresses this gap by examining the mediating role of multidimensional student engagement (behavioral, emotional, and cognitive) in the relationship between GenAI-supported HOTS and academic achievement. Employing a quantitative, cross-sectional design, data were collected from 504 higher education students in Islamabad. Structural equation modeling (SEM) and bootstrap mediation analyses were utilized to test the hypothesized framework. Results indicated that GenAI-assisted HOTS significantly predicted both student engagement (β = .62, p < .001) and academic achievement (β = .28, p < .001). Crucially, student engagement partially mediated the relationship between GenAI-HOTS and academic achievement, accounting for 50.9% of the total effect (β_indirect = .29, p < .001), with cognitive engagement emerging as the strongest mediating pathway. Multi-group analysis confirmed the structural invariance of this model across academic levels and disciplines. These findings demonstrate that GenAI's impact on academic achievement is significantly channeled through enhanced student engagement rather than occurring solely through direct cognitive offloading. The study provides empirical evidence for shifting pedagogical strategies from passive AI consumption to structured, engagement-driven AI integration that explicitly targets higher-order cognitive processes.
The rapid integration of generative artificial intelligence (GenAI) into higher education is reshaping how university students engage in learning, reasoning, and problem solving; however, the cognitive mechanisms underlying the effective use of these technologies remain insufficiently understood. This study examined the relationships among cognitive flexibility, GenAI adoption, metacognitive awareness, and problem-solving performance among university students, with particular emphasis on the mediating role of metacognitive awareness. A quantitative, cross-sectional research design was employed with a sample of 477 university students recruited from higher education institutions in Punjab and Sindh, Pakistan. Data were collected through an adopted and contextually adapted structured questionnaire administered online using Google Forms. The collected responses were systematically organized and analyzed using appropriate statistical procedures, including Pearson correlation analysis, multiple linear regression, one-way analysis of variance, and mediation analysis. The findings demonstrated significant positive relationships among cognitive flexibility, GenAI adoption, metacognitive awareness, and problem-solving performance. Cognitive flexibility, GenAI adoption, and metacognitive awareness also emerged as significant predictors of students’ problem-solving performance. Furthermore, students with higher levels of GenAI adoption demonstrated comparatively stronger problem-solving performance than those with lower levels of adoption. Mediation analysis further revealed that metacognitive awareness significantly mediated the relationships between cognitive flexibility and problem-solving performance and between GenAI adoption and problem-solving performance. These findings highlight the importance of students’ capacity to monitor, regulate, and evaluate their own cognitive processes when engaging with AI-generated information. The study contributes to the emerging literature on GenAI in higher education by demonstrating that the educational value of these technologies extends beyond technological adoption and is closely associated with students’ cognitive flexibility and metacognitive capabilities. The findings provide implications for educators, policymakers, and university administrators seeking to develop learner-centered and AI-integrated educational environments that strengthen independent thinking, reflective learning, adaptive cognition, and problem-solving capabilities while minimizing passive dependence on AI technologies.
Ali Yousuf Khan, Maryam Aslam, Amber Baig et al.· Journal of Global Social Tra...· 0 citations
Academic underachievement in Indonesian vocational high schools (SMK) is often attributed to cognitive ability alone, yet non-cognitive factors may matter. Drawing on Bandura's social cognitive theory and Astin's involvement theory, this study tested whether student engagement mediates the associations of self-efficacy and learning motivation with examination performance. A cross-sectional survey was administered to 282 students in four state vocational high schools in Pesisir Selatan Regency, West Sumatra, sampled proportionately by school. Constructs were modelled as reflective-reflective higher-order constructs and estimated with the disjoint two-stage approach in PLS-SEM; performance was a single summative examination score drawn from school records and not equated across the four schools. After item purification the engagement construct retained only its behavioural and emotional dimensions, so H5 to H7 test a narrower construct than the introduction describes. Discriminant validity was not established for the self-efficacy and engagement pair (HTMT = .908, above the pre-specified .90). Self-efficacy (β = .640, 95% CI [.537, .743]) and learning motivation (β = .141, 95% CI [.027, .255]) were associated with engagement, explaining 55.7% of its variance. No path reached examination performance: the model explained essentially none of its variance (R² = .005, adjusted R² = -.006), and all three paths were statistically equivalent to zero within ±.20. Five hypotheses were not supported. The null result is consistent with criterion misalignment, but the design did not test that explanation.
Curiosity, creative self-efficacy, and critical thinking are increasingly recognized as critical psychological capacities that drive science learning and achievement. Curiosity motivates exploration and inquiry, creative self-efficacy enables innovative problem-solving, and critical thinking supports evidence-based reasoning, all essential for success in STEM education. This study examines how these capacities relate to science achievement across six Asian education systems selected to represent diverse performance levels in PISA 2022: high-performing (Singapore, Macao-China), medium-performing (Vietnam, Mongolia), and low-performing (Philippines, Indonesia) systems. Drawing on 44,689 students nested within 1,181 schools, multi-item indices were constructed from PISA questionnaire data, showing acceptable-to-strong reliability (curiosity α = 0.65; creative self-efficacy α = 0.81; critical thinking α = 0.69). Weighted mixed-effects models accounting for school-level clustering (ICC = 0.301–0.351) were estimated separately for each of ten plausible values and combined using Rubin’s rules. Across the pooled analytic sample, curiosity showed a significant negative within-school association with science achievement (β = −13.225, 95% CI [− 17.634, − 8.816]), while creative self-efficacy demonstrated a positive within-school relationship (β = 6.916, 95% CI [2.543, 11.290]) and critical thinking a modest non-significant positive association (β = 4.110, 95% CI [− 0.672, 8.892]) after adjusting for socioeconomic background and demographics. Country-level analyses revealed curiosity was consistently negative across all six systems (β range: −8.771 to − 19.306), creative self-efficacy was context-dependent, and critical thinking showed variable patterns. Findings reveal that the within-school influence of STEM-related dispositions on science achievement is shaped by school-level contextual factors and highlight the importance of institutional conditions in moderating how psychological capacities translate into academic performance across diverse educational systems.
Emmanuel Atiatorme, F. C. Onwunyili, John Amoako Mensah et al.· Discover Education· 0 citations
The rapid integration of Generative Artificial Intelligence (GenAI) into higher education is transforming how university students access information, engage with learning tasks, and approach complex academic problems, making it increasingly important to understand the cognitive mechanisms that support effective AI-assisted learning. The effects of cognitive agility, Generative AI-assisted learning, metacognitive competence, and problem solving with an emphasis on the mediating role of metacognitive competence on university students in Punjab and Sindh, Pakistan were analyzed. A quantitative, cross-sectional research design was utilized. 397 students were selected from universities using an adopted structured questionnaire filled in using the online Google Forms. Data was collected and analyzed in SPSS after being organized and screened in Microsoft Excel. Among the study variables, cognitive agility and Generative AI learning had a positive correlation which was statistically significant and explained metacognitive competence and problem solving. In terms of predictors, cognitive agility, Generative AI learning, and metacognitive competence had a positive correlation which was statistically significant and explained problem solving, with metacognitive competence having a more significant contribution. According to the mediation analysis, metacognitive competence was a significant mediator of the relation between cognitive agility and problem solving and between Generative AI learning and problem solving. This means that in terms of flexible thinking and learning enhanced by AI, planning, monitoring, evaluating and controlling one’s cognitive processes is a crucial factor in the effective resolution of problems. This study indicates that describing Generative AI as a teaching tool would not be enough. Teaching flexible thinking and planning, monitoring and controlling through metacognition is of equal importance. These findings can help higher education professionals, curriculum developers, and policy creators build responsible, reflective, and cognitively empowering AI-enabled learning environments in universities.
Imran Mughal, Mehtab Panhwar, Elishba Khalil Akhtar· Journal of Global Social Tra...· 0 citations
Although generative AI is increasingly integrated into higher education, its impact on student’s learning experience remains unclear. This study examined factors predicting learning interactions in EFL (English as a Foreign Language) contexts, focusing on student’s AI competency, attitudes, and experience. Grounded in constructivist theory and the WEST model (Will, Experience, Skill, and Tools), a questionnaire was administered to 884 students at a Chinese higher vocational college. Structural equation modeling shows that AI integration and involvement in creative tasks directly predict learning interaction, while competency, attitudes, and experience exert indirect effects via these variables. Theoretically, this study provides quantitative evidence that student’s AI-related characteristics may contribute to learning interaction through AI-supported learning practices and creative task involvement. In practice, students can be more active in classroom interactions by adopting AI tools and participating in appropriate creative tasks designed by their teachers. Consequently, teachers play an important role in determining how AI tools are integrated and what types of tasks are assigned to students in the English classroom to support a better interactive learning environment.
The integration of generative AI (GenAI) into education, specifically into teaching and learning, offers potential for personalised learning, but at the same time, it poses a challenge to whether it fosters student agency. Moreover, it also threatens to exacerbate entrenched inequalities, which have the potential to create new barriers for most of the learners. A gap exists in understanding how GenAI can be rethought as a dynamic scaffold that promotes cognitive struggle for conceptual understanding while actively advancing equitable participation.
The study aims to critically analyse how secondary school mathematics teachers implement GenAI for adaptive scaffolding. Moreover, this study aims to investigate the pedagogical and ethical tensions, particularly concerning equity and power dynamics, that emerge from using GenAI during classroom interactions.
This study employed a qualitative research design. Data were collected over an eight‐week intervention, which included in‐depth teacher interviews (
n
= 10), student focus groups (
n
= 24), and classroom observations in two public secondary schools purposively selected for their contrasting socioeconomic and technology integration profiles.
Based on the results and findings, five distinct teacher‐mediated GenAI scaffolding practices that support equitable knowledge construction were identified. Findings indicate that strategies such as prompting for problem formulation through AI‐generated inquiry and facilitating critical comparison of AI‐generated outputs were associated with deeper student engagement and reasoning within the conditions of this study, particularly when teachers exercised intentional critical mediation. Conversely, unmediated use of GenAI amplified significant challenges, such as a redistribution of epistemic authority, a tangible “algorithmic divide” in AI performance between well‐resourced and under‐resourced schools, and risks due to the absence of how data should be handled. The technology consistently amplified existing pedagogical conditions, which are both effective and inequitable.
The promise of GenAI is strictly contingent on a commitment to equity. This means that its outcomes are determined not by the GenAI itself but by pedagogical intentionality and systemic support. Unexamined adoption poses risks that worsen disparities for marginalised students and reinforces passive learning. Thus, by proposing a framework for equitable integration centred on the teacher's role as a critical mediator, this expertise serves as a model for sustainable pedagogical practice in the digital age.
A. Canonigo· Journal of Computer Assisted...· 0 citations
