Aug 2026· Frontiers in Psychology· Vol 17· 0 citations· 40 references
Medicine
TL;DR
The findings reveal that AI-based technology use was positively associated with critical thinking and self-regulated learning, while cognitive load showed a negative association with both variables and moderated mediation analysis indicated that cognitive load partially mediated the relationship between AI use and critical thinking.
Abstract
Background The increasing integration of artificial intelligence (AI) in medical education has raised important questions regarding its impact on higher-order cognitive processes, particularly critical thinking. Aim and Objectives This study examines the interplay of cognitive load and self-regulated learning in explaining how AI-based educational technology influences critical thinking among medical university students. Methods Using a cross-sectional research design, data were collected from 480 undergraduate medical students with prior experience using AI tools. Data were collected using standardized measures assessing AI usage, cognitive load, self-regulated learning, and critical thinking. Hayes' PROCESS Model 14 was employed to test mediation and moderated mediation effects. Results The findings revealed that AI-based technology use was positively associated with critical thinking and self-regulated learning, while cognitive load showed a negative association with both variables. Mediation analysis indicated that cognitive load partially mediated the relationship between AI use and critical thinking. Furthermore, the moderated mediation analysis demonstrated that self- regulated learning significantly moderated the indirect effect, such that the negative impact of cognitive load on critical thinking weakened at higher levels of self-regulation. Conclusion These findings highlight that the effectiveness of AI in enhancing critical thinking depends not only on its cognitive support but also on learners' ability to regulate their engagement. The study provides theoretical and practical implications for integrating AI in educational contexts.
Critical Thinking and Decision Fatigue moderated the relationship between Critical Thinking and passive acceptance in several academic activities, with the strength of this evidence varying according to the applied correction for multiple comparisons.
Marco Zuin, Vanessa Donadel· European Journal of Investig...· 0 citations
The advancement of artificial intelligence in higher education has transformed academic practices and has increased the need to understand how students’ critical thinking competencies shape their interaction with technology-mediated learning environments. In this context, the present study aimed to examine the predictive associations between critical thinking dimensions, task functionality, and the frequency of artificial intelligence use among university students at the Pontifical Catholic University of Ecuador, Santo Domingo Campus, using a PLS-SEM model.
The study followed a quantitative approach with a non-experimental, cross-sectional, and explanatory-predictive design. The sample consisted of 401 university students selected through intentional and voluntary non-probability sampling. Data were collected using a structured Likert-type questionnaire. SPSS was used for data screening, descriptive statistics, and preliminary reliability analysis. AMOS was used only for a preliminary covariance-based confirmatory factor analysis to examine the factorial structure of the instrument. The final measurement and structural models were estimated using PLS-SEM, with bootstrapping applied to assess the statistical stability of the structural paths.
The findings indicated partial empirical support for the proposed model. Epistemic awareness was positively associated with task functionality, whereas task functionality showed the strongest statistically supported association with the frequency of artificial intelligence use. The remaining direct associations between critical thinking dimensions and task functionality or AI use frequency were not statistically supported.
Critical thinking dimensions, task functionality, and artificial intelligence use constitute an interrelated system within the proposed PLS-SEM model. The findings suggest that epistemic awareness is associated with students’ perceptions of task functionality, and that task functionality is associated with the reported frequency of artificial intelligence use in higher education.
Ángel Ramón Sabando-García, Darwin Maccoll Primero Llacsaguache-Calle, W. R. Villota-Oyarvide et al.· Frontiers in Education· 0 citations
Background As artificial intelligence (AI) becomes increasingly integrated into education, fostering student creativity is a critical priority. While prior research suggests a positive link between AI literacy and creative self-efficacy, the underlying psychological mechanisms and key contextual factors remain largely unexplored. This study aimed to address this gap by examining the mediating roles of learning adaptability (cognitive, behavioral, and emotional) and the moderating role of teacher support in this relationship. Methods A total of 509 university engineering students (68.6% male; M age = 19.36 years) participated in a survey. They completed validated measures of AI literacy, learning adaptability, teacher support, and creative self-efficacy. A moderated mediation model was tested using statistical analysis. Results The findings indicated a significant positive association between AI literacy and creative self-efficacy. This relationship was significantly mediated by behavioral and emotional adaptability, but not by cognitive adaptability. Furthermore, teacher support moderated the positive effect of AI literacy on emotional adaptability; this effect was stronger for students who perceived higher levels of teacher support. Conclusion The study advances a nuanced model demonstrating that AI literacy enhances creative confidence primarily by fostering a willingness to act (behavioral adaptability) and bolstering emotional resilience (emotional adaptability). Teacher support is identified as a crucial contextual resource that amplifies this positive process. These findings offer valuable insights for educators and policymakers aiming to design interventions that maximize the creative potential of AI in learning environments.
Lu Pan, Xue Shuang, Yiping Liu· Frontiers in Psychology· 0 citations
The rapid proliferation of generative artificial intelligence (AI) has raised concerns about its impact on creativity, yet mechanisms and boundary conditions remain underexplored. Using one survey and two experiments, we investigated how AI dependence affects university students’ creativity. Study 1 (cross-sectional) found AI dependence was negatively related to creativity, fully mediated by critical thinking. Study 2 (between-subjects experiment) experimentally demonstrated that high AI dependence was associated with lower creativity than low dependence, with critical thinking again mediating. Study 3 employed a 2 × 2 design with thinking orientation as a moderator in a first-stage moderated mediation model. Thinking orientation moderated the “AI dependence → critical thinking → creativity” pathway: the indirect effect was significant under an outcome-oriented condition but non-significant under a process-oriented condition; the index of moderated mediation was 0.22. These findings indicate that critical thinking is a central pathway through which AI dependence undermines creativity, and process-oriented thinking buffers this negative impact by safeguarding critical thinking. The study provides theoretical foundations and intervention directions for the judicious use of AI in education.
Adaptive AI-generated feedback (AIF) is increasingly integrated into higher education; however, its cognitive and psychological implications remain insufficiently understood, particularly in engineering education. Grounded in feedback intervention theory and emotion regulation theory, this study developed and tested an integrative model examining the perceived relationships between AIF, cognitive flexibility (CF), and academic psychological safety (APS), including mediating and moderating patterns.
A cross-sectional survey was conducted among 471 engineering students. Data were analyzed using partial least squares structural equation modeling. The measurement model demonstrated satisfactory reliability and validity, with factor loadings above 0.70, AVE values exceeding 0.50, composite reliability values ranging from 0.908 to 0.936, Cronbach’s alpha values ranging from 0.887 to 0.921, and HTMT values below 0.85. The structural model showed moderate explanatory power for CF (
R
2
= 0.443) and APS (
R
2
= 0.626), with adequate predictive relevance and no multicollinearity concerns.
AIF was significantly associated with CF (
β
= 0.580,
p
< 0.001) and APS (
β
= 0.352,
p
< 0.001). CF was significantly associated with APS and showed a significant indirect association between AIF and APS (indirect effect
β
= 0.257,
p
< 0.001). Significant interaction effects indicated that ESAF was associated with a stronger relationship between AIF and CF, whereas MSAF was associated with a stronger relationship between CF and APS.
Perceived AIF was associated with APS directly and indirectly through CF, while emotional and mindfulness-supportive features were associated with a greater strength in these relationships. Given the study’s cross-sectional design, the findings indicate associations rather than causal effects. This study advances the current understanding of cognitive and affective patterns in AI-supported engineering education and highlights the importance of integrating adaptive, emotional, and reflective features into AI feedback systems.
Amani BinJwair· Frontiers in Psychology· 0 citations
Objective: The adoption of Artificial Intelligence (AI) tools, including platforms like ChatGPT, in educational environments has garnered growing attention, especially among students in healthcare-related programs. While AI presents opportunities to enhance learning efficiency, concerns remain about its influence on students' academic performance and critical thinking capabilities. This study seeks to evaluate the level of dependence on AI among young healthcare professional students and explore its impact on both their academic achievements and critical thinking skills.
Materials and Methods: A cross-sectional survey was administered to undergraduate healthcare students. Total 395 students participated in the study. AI- Health Professionals & Students Dependency Index, academic performance scale and critical thinking evaluation scale was used. The questionnaire covered the frequency and manner of AI tool usage, self-reported academic outcomes, and measures of critical thinking. Data were analyzed using descriptive statistics and correlation techniques to explore the associations between AI use and academic metrics.
Results: Initial findings reveal that many students frequently use AI tools for academic purposes. Which may impair critical thinking, problem-solving abilities, academic performance and professional skills with the p- value of 0.05 of Pearson correlation coefficient.
Conclusion: This study highlights the importance of balanced AI integration in academic settings. It advocates for the establishment of structured guidelines to ensure AI tools complement, rather than replace, traditional educational practices—thereby maintaining and promoting critical thinking among healthcare students.
Dharmita Yogeshwar (pt), Janvhi Singh (pt), Ajeet Kumar Saharan (pt) et al.· Adolescência e Saúde· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
