Passive social networking, peer group pressure and procrastination on academic tasks: Time management skill as a mediator and academic self-efficacy as a moderator.

This study examined the associations between different qualities of motivation and distinct forms of delay within the framework of Self-Determination Theory. A total of 155 effect sizes from 64 independent studies ( N = 28,019) were synthesized using meta-analytic techniques. Overall, the relationship between motivation and delay was multifaceted and depended on the quality of motivation as well as the specific form of delay examined. Intrinsic motivation was negatively associated with delay, whereas extrinsic motivation showed no significant association. Controlled motivation and amotivation were positively associated with delay, whereas autonomous motivation was negatively related to delay. Importantly, these associations varied across forms of delay. Academic procrastination and procrastination, maladaptive forms of delay shared similar patterns: negatively correlated with intrinsic and autonomous motivation, and positively correlated with controlled motivation. In contrast, strategic delay was positively associated with intrinsic and autonomous motivation and negatively associated with controlled motivation. General procrastination showed no significant relation to the extrinsic motivation or controlled motivation. Overall, the findings underscore the importance of motivational quality—particularly autonomy—and the distinction between forms of delay in understanding procrastination.

ABSTRACT The rapid proliferation of artificial intelligence (AI) tools in higher education has profoundly reshaped students’ learning behaviors, yet their associations with academic procrastination remain underexplored. Grounded in social cognitive theory, this study proposed and tested a chain mediation model to examine how college students’ generalized reliance on AI tools is associated with academic procrastination through academic self-efficacy and self-regulated learning. A total of 729 undergraduates completed a survey using validated scales. Regression-based mediation analysis revealed that generalized AI reliance was positively associated with academic procrastination. Both academic self-efficacy and self-regulated learning served as partial mediators between generalized AI reliance and procrastination. They also formed a significant chain mediation path, accounting for a relatively modest proportion of the total association (10.25%). These findings suggest that academic self-efficacy and self-regulated learning may represent one plausible set of psychological pathways linking generalized AI reliance with academic procrastination, without establishing causal or temporal ordering. The study advances understanding of the potential dual-edged associations of AI-supported learning environments and provides practical implications that should be interpreted with appropriate caution, including the need to help students use AI tools more reflectively and strengthen self-regulatory skills in higher education.

Academic procrastination has become a chronic phenomenon among university students, which is frequently normalized in digital spaces. This study aims to explore the psychological dynamics behind students' procrastinating behavior and formulate solutions from the perspective of Guidance and Counseling. The research method employed is qualitative, utilizing library research and digital content analysis. Data sources were gathered from accredited scientific journal articles (2021–2026) and student narrative trends on social media platforms such as Twitter, TikTok, and Instagram. The results indicate that academic procrastination is not merely a matter of poor time management, but rather a manifestation of self-regulation failure and emotional dysregulation. Students use procrastination as an immediate mood repair coping strategy to avoid negative emotions, anxiety, and fear of failure driven by cognitive distortions like irrational perfectionism. Social media further reinforces this behavior through Social Learning Theory, which normalizes cramming. In conclusion, addressing procrastination requires counseling interventions that target cognitive roots. The Cognitive Behavioral Therapy (CBT) approach through cognitive restructuring techniques is highly recommended for university counselors to reshape students' irrational thoughts and restore their self-regulation capacities.

Narrative perspective matters: Designing chatbot interventions for academic procrastination through motivational framing.

Academic procrastination is prevalent among Chinese college students, significantly impacting their academic performance and quality of life. As a stable personality, the underlying pathway linking trait anxiety to academic procrastination among college students requires further investigation. This study aims to explore the association between trait anxiety and academic procrastination while examining the chained mediating pathway formed by self-control and short video addiction. Using data from a two-wave longitudinal study of 727 Chinese college students, this study employs cross-lagged and mediation models to systematically examine the relationship between trait anxiety and academic procrastination, as well as the mediation role of self-control in short video addiction. Research findings indicate that trait anxiety is associated with a direct positive influence on academic procrastination. Additionally, trait anxiety is also associated with academic procrastination through chain mediation effects involving self-control and short video addiction. Furthermore, we discovered a potential reciprocal relationship between short video addiction and academic procrastination. This study offers a new perspective on the relationship between trait anxiety and academic procrastination, providing a theoretical basis for subsequent interventions.

Generative artificial intelligence is increasingly used in higher education, but researchers still need transparent procedures for collecting, scoring, and organizing learner-level survey data in this rapidly changing context. This article presents a reproducible survey-scoring and C5.0 decision-tree workflow for classifying self-reported higher-order thinking among college students who have used generative artificial intelligence tools for academic learning. The protocol covers participant recruitment, questionnaire administration, response-quality screening, composite-score calculation, binary coding, training-testing partitioning, C5.0 tree construction, pruning, model-output export, and interpretation of node-based classification paths. The workflow is demonstrated using a single-university survey dataset of 776 undergraduate students collected in China from March 7 to March 15, 2023. Higher-order thinking is operationalized as a self-reported questionnaire score rather than as directly observed cognitive performance. In the demonstration dataset, the pruned tree retained eight learner-related variables: generative artificial intelligence anxiety, trust in generative artificial intelligence, problematic smartphone use, academic procrastination, academic performance, parental upbringing, negative emotions, and attitudes toward generative artificial intelligence. The retained model achieved 89.52% accuracy in the training subset and 86.21% accuracy in the testing subset. However, class-specific evaluation showed uneven performance, with substantially weaker recall for the Low-HOT class than for the High-HOT class. Therefore, the tree should be interpreted as an auxiliary, interpretable workflow demonstration rather than as a validated screening tool. This protocol may support researchers who require a documented and repeatable procedure for survey-based learner profiling in generative artificial intelligence-supported learning environments.

This study examined the relationships among teaching presence (TP), social presence (SP), and cognitive presence (CP) within the community of inquiry framework, academic procrastination (AP), and achievement among nontraditional students. An online survey was administered to 801 participants to measure the three presences and AP through a learning management system at the end of the semester. Achievement was measured using GPA. Multiple regression analysis and the Johnson–Neyman technique indicated that the mediation and moderating effects were statistically significant. The results of the mediation effect revealed that CP fully mediated the influence of TP and SP on GPA. SP partially mediated the influence of TP on CP. The relationship between CP and GPA was moderated by the AP level. When AP was low, GPA decreased as CP increased, whereas when AP was high, GPA increased as CP increased. Furthermore, a one-way MANOVA revealed that learners with low AP demonstrated significantly higher CP across all sub-constructs (trigger event, exploration, integration, and resolution) than those with high AP. The practical implications and directions for future research are also discussed.

Within China's rural educational landscape, students in residential primary boarding institutions face distinctive psychosocial difficulties, with mathematics-related anxiety demonstrating heightened prevalence in these settings. As primary sources of social interaction within school settings, teachers exert significant influence on students' academic outcomes and psychological well-being through shaping their perceptions of educator support. In this study, we recruited 400 fifth-grade students from rural boarding primary schools in Northwestern China and employed validated scales to measure perceived teacher support, self-efficacy, academic procrastination, and mathematics anxiety. A structural equation modelling (SEM) framework was utilized to explore the pathways through which perceived teacher support impacts mathematics anxiety, with particular attention to the independent and chain mediating roles of self-efficacy and academic procrastination. Key findings indicated that: (i) Perceived teacher support exhibited significant negative correlations with academic procrastination and mathematics anxiety, while showing a significant positive correlation with self-efficacy; (ii) Both self-efficacy and academic procrastination independently mediated the relationship between perceived teacher support and mathematics anxiety; (iii) A sequential chain mediation pathway was identified, whereby perceived teacher support enhanced self-efficacy, which subsequently reduced academic procrastination, ultimately alleviating mathematics anxiety. These findings emphasize the critical role of perceived teacher support in mitigating mathematics anxiety among rural boarding students through cognitive and behavioural mechanisms, providing empirical foundations for targeted educational interventions.

Relationship between time management disposition and academic procrastination among Chinese students: Systematic review and meta analysis.

This study aimed to identify the role of artificial intelligence applications in enhancing future-foreseeing skills among early childhood teachers.The study adopted the descriptive analytical method, and data was collected using a questionnaire administered to a sample of 120 early childhood teachers in Al-Qurayyat Governorate.The results of the "first axis" showed that teachers have a positive vision regarding future foresight for the axis, with a high mean of (4.2).The phrase "I constantly develop my skills to keep pace with future changes" ranked first, followed by "I can adapt to technological changes in the field of education."In the "second axis," the results showed that teachers use technology to enhance their skills for the axis, with a high mean of (4.3).The phrase "Artificial intelligence helps save the teacher's time" ranked first, followed by "I use artificial intelligence applications to develop innovative study plans."In the "third axis," the results for the axis showed that teachers face challenges in using artificial intelligence applications with a high mean of (4.3).The phrase "There is a lack of educational resources related to artificial intelligence" ranked first, followed by "The technical infrastructure is insufficient to support the use of artificial intelligence."In the "fourth axis," the results showed a high approval rate for the proposals for the axis, with a high mean of (4.4).The phrase "Providing continuous training programs on the use of artificial intelligence" ranked first, followed by "Enhancing cooperation between educational and technical institutions will contribute to the development of integrated educational curricula that include artificial intelligence applications."Based on these results, a proposed vision was developed aimed at improving the quality of education by developing teachers' skills and empowering them to effectively face future challenges.

The role of artificial intelligence (AI) tools in education has transformed the way students learn and manage their academic tasks. However, health professions education has not sufficiently studied the role of AI tools in procrastination, time management, and academic achievement. To investigate the associations of AI-based educational tools usage, academic procrastination, academic achievement, and time management among undergraduate nursing students. A cross-sectional study was conducted with a sample of 167 Jordanian undergraduate nursing students. Data was collected through validated scales measuring procrastination (General Procrastination Scale), time management skills (Time Management Questionnaire), and a researcher-developed scale measuring AI-based educational tool usage. Participants demonstrated moderate levels of academic procrastination (mean = 58.28 ± 13.00) and time management (mean = 56.86 ± 11.34), alongside a very good academic achievement (GPA mean = 81.25 ± 7.80). Multiple linear regression analysis indicated that AI-based educational tool usage (B = -0.22, p = 0.007) and time management (B = -0.38, p < 0.001) were significant predictors of academic procrastination. AI-based educational tool usage was also a significant predictor of time management (B = 0.24, p = 0.003). In addition, academic achievement was significantly predicted by AI-based educational tools usage (B = 0.24, p = 0.003), time management (B = 0.21, p = 0.007), and study hours (B = 2.85, p = 0.012). The use of AI-based educational tools was significantly associated with better academic performance among nursing students, alongside lower academic procrastination and better time management skills. The incorporation of AI into the learning process for nursing students may support the development of efficient learning habits and positive academic outcomes.

ABSTRACT Background Academic procrastination is a common problem among university students and can negatively impact individuals’ futures. This study examined the relationships among cognitive flexibility, career decision efficacy, and academic procrastination, and also tested the buffering roles of self-esteem and socioeconomic status in these relationships. Methods A cross-sectional study was designed, and online data were collected from 911 university students in Türkiye using the Cognitive Flexibility, Career Decision Efficacy, Academic Procrastination, and Self-Esteem scales, along with demographic questions. Results Cognitive flexibility was positively related to career decision efficacy, while career decision efficacy was negatively related to academic procrastination. In addition, cognitive flexibility was indirectly related to academic procrastination via career decision efficacy, indicating a fully mediated pattern. Self-esteem significantly buffered the relationships between cognitive flexibility and both career decision efficacy and academic procrastination. The relationship between cognitive flexibility and career decision efficacy was stronger among individuals with low self-esteem. Furthermore, the positive relationship between cognitive flexibility and career decision efficacy was more pronounced among individuals with higher socioeconomic status (SES). Conclusions The findings suggest that cognitive flexibility and career decision efficacy may play important roles in understanding academic procrastination among university students. The study also highlights the potential protective roles of self-esteem and socioeconomic status in these relationships and offers important implications for academics and career professionals.

BackgroundSocial media platforms (SMPs) have become an integral part of students' lives, influencing learning behaviors, time management, and mental well-being. However, there is limited evidence regarding their impact among medical students in Aruba. This study aimed to evaluate social media usage patterns and examine their perceived effects on mental health, academic performance, and academic procrastination.MethodsA cross-sectional online survey was conducted between April and July 2025 among 25 medical students at Xavier University School of Medicine using a structured 29-item questionnaire. Participants were recruited through convenience sampling via voluntary online participation. Descriptive statistics were reported as frequencies and percentages. Due to the small sample size, Fisher's exact test was used to assess associations between social media usage patterns (platform and duration) and outcomes including mental health, academic performance, and procrastination. Pearson correlation analysis was performed to evaluate relationships between daily usage (hours/day) and academic score, perceived academic performance, and procrastination. A p-value of <0.05 was considered statistically significant. The small sample size is an important limitation and may limit the generalizability of the findings.ResultsParticipants classified as heavy social media users (>3 hours/day; n=16/25 (64%)) reported higher levels of anxiety (66.7% vs. 22.2%; p=0.041) and greater mental or physical health impacts (55.6% vs. 11.1%; p=0.040) compared to light users (≤3 hours/day). They also demonstrated significantly higher academic procrastination, including increased distraction while studying (66.7% vs. 22.2%; p=0.041) and a greater likelihood of missing academic deadlines (55.6% vs. 11.1%; p=0.040). Although heavy users reported a stronger urge to use social media (61.1% vs. 22.2%), this difference was not statistically significant (p=0.097). Light users were significantly more likely to use social media for academic purposes (77.8% vs. 22.2%; p=0.017). Correlation analysis showed that increased time spent on social media was significantly negatively associated with academic score (r=-0.47; p=0.015) and perceived academic performance (r=-0.44; p=0.019). A weak positive correlation was observed between social media use and academic procrastination (r=0.17), which was not statistically significant (p=0.41).ConclusionModerate social media use may support academic engagement, particularly when used for educational purposes; however, excessive use is associated with increased anxiety, poorer well-being, greater academic procrastination, and lower academic performance. These findings suggest that the impact of social media depends not only on duration but also on the purpose and pattern of use. Promoting mindful, structured, and balanced social media habits may help optimize students' academic outcomes and well-being.

Differential effects of student and parental mobile phone use on academic procrastination trajectories: Machine learning evidence

Background. High engagement in short video viewing among students can negatively impact academic activities, leading to increased academic procrastination. This tendency is exacerbated when students have low self-awareness. Purpose. This study aimed to analyze the effect of short video addiction on academic procrastination among high school students in Yogyakarta, with self-awareness serving as a moderating variable. Understanding this relationship is important for developing strategies to reduce academic procrastination and improve students' academic performance. Method. This quantitative study employed a cross-sectional design. Data were collected using validated psychological scales measuring short video addiction, academic procrastination, and self-awareness. The sample consisted of 309 high school students. Data analysis was conducted using factor analysis via SmartPLS 3.0. Results. The hypothesis test revealed that short video addiction positively affected academic procrastination, explaining 53.7% of the variance. Self-awareness negatively affected academic procrastination, accounting for 32.1% of the variance. Meanwhile, self-awareness was not statistically proven to moderate the effect of short video addiction on academic procrastination, because the p-values ??were 0.066 &gt; 0.05. Conclusion. Higher short video addiction was associated with higher levels of academic procrastination, while higher self-awareness was associated with lower levels of academic procrastination. However, self-awareness does not play an effective role in moderating the effect of short video addiction on academic procrastination.

Self-control and academic procrastination among chinese university students: a latent profile and moderated mediation analysis

Background: The rapid integration of Generative Artificial Intelligence (GenAI) in higher education has reshaped how students engage with academic work. While GenAI improves efficiency and accessibility, concerns arise regarding its effects on cognitive engagement and self regulation.Purpose: This study examined how Ease of Internet Access (EIA) and Frequency of GenAI Use (FGAI) influence Learning Motivation (LM), with Academic Procrastination (AP) as a mediating variable.Method: A total of 205 undergraduate students from Politeknik AKA Bogor completed standardized questionnaires adapted to GenAI-related learning. Data were analyzed using multiple regression, mediation analysis based on Baron and Kenny’s framework, and multi-group confirmatory factor analysis (MGCFA).Findings: The results showed EIA and FGAI did not significantly predict LM (R² = .006; p &gt; .05). EIA significantly predicted AP (β = .146, p = .039), and AP negatively predicted LM (β = −.603, p &lt; .001). Mediation analysis confirmed a significant indirect effect of EIA on LM through AP (Sobel = −2.075, p = .038). MGCFA supported configural and metric invariance across GenAI-use groups (ΔCFI = .003), with partial scalar invariance achieved.Implication: These findings indicate that digital accessibility may indirectly reduce motivation by increasing procrastination, emphasizing the importance of self-regulation and guided AI integration in higher education.

Major Identity and Academic Procrastination among Undergraduates at Private Universities in Shaanxi, China: The Mediating Role of Self-Control

The development of information and communication technology has brought significant changes to society, particularly among university students as active smartphone users. This study aims to analyze the effect of smartphone usage intensity on students’ academic achievement in higher education. The research employed a quantitative method using a survey approach through the distribution of questionnaires to 94 student respondents. The data were analyzed to determine the relationship between the frequency of smartphone use and students’ academic performance. The findings indicate that smartphone usage has both positive and negative impacts. On the positive side, smartphones facilitate students in accessing learning resources, scientific journals, online learning media, and academic communication with lecturers and fellow students. This contributes to improving the effectiveness of the learning process. However, excessive use for non-academic activities such as social media, entertainment, and digital games has the potential to cause academic procrastination, reduce sleep quality, and disrupt learning concentration. Nevertheless, the results show that most students are still able to maintain good academic performance, as reflected in stable GPA achievement. Therefore, the impact of smartphone use on academic achievement largely depends on usage patterns, self-control, and students’ ability to utilize technology productively.