AI Usage as a Mediator Between Study Habits, Academic Procrastination, and Academic Self-Esteem

AI Usage as a Mediator Between Study Habits, Academic Procrastination, and Academic Self-Esteem | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI Usage as a Mediator Between Study Habits, Academic Procrastination, and Academic Self-Esteem Debasmita Chakraborty, Deviga Subramani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9043305/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The growing use of Artificial Intelligence (AI) in higher education is reshaping how students approach learning, manage time, and evaluate their abilities. Generative AI systems are increasingly embedded within everyday study practices, raising questions about how these technologies shape learning behaviour and academic outcomes. Rooted in cyberpsychology and educational behaviour research, the present study examines whether academic AI usage mediates the relationship between students’ study approaches, academic procrastination, and academic self-esteem. A quantitative cross-sectional study was conducted with 402 Indian college students using standardised measures of study habits, academic AI usage, procrastination, and academic self-esteem. Results showed that students with surface study habits tended to procrastinate more and reported lower self-esteem, while those with deeper study approaches displayed greater confidence and academic consistency. AI usage partially mediated the relationship between surface study habits and procrastination, indicating that students adopting surface strategies were more likely to incorporate AI into last-minute academic work. AI usage also produced a small positive indirect effect on academic self-esteem, suggesting that AI may function as a temporary form of academic scaffolding that enhances perceived competence without necessarily strengthening deeper learning processes. These findings illustrate how AI-mediated learning practices interact with students’ study approaches, shaping patterns of delay, engagement, and academic self-perception. The study contributes to emerging research on generative AI in higher education by demonstrating that the educational impact of AI depends on how it becomes integrated into students’ learning behaviours. Keywords: Artificial Intelligence, Study Habits, Academic Procrastination, Academic Self-Esteem, Higher Education, AI-Mediated Learning, Cyberpsychology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Artificial Intelligence (AI) has become deeply woven into the routines of higher education, reshaping how students search for information, organise academic work, and complete assignments. Tools such as ChatGPT, Gemini, and Perplexity now function as everyday cognitive aids, providing instant explanations, personalised feedback, and real-time support for writing and problem-solving (Huang et al., 2021 ; Gond et al., 2024 ; Lee et al., 2024 ). As these systems grow more sophisticated, their influence on students’ thinking and behaviour has expanded. Emerging research in cyberpsychology argues that AI not only change what students do, but also how they regulate motivation, perceive academic challenges, and experience confidence (Fatima, 2025 ; Favero et al., 2025 ). This dual potential, enhancement versus overreliance, makes understanding AI’s psychological effects increasingly urgent. Study habits represent a crucial psychological foundation in this context. They reflect how students organise learning, regulate effort, and pursue mastery (Biggs, 1993 ; Aharony, 2006 ). Deep learning approaches, driven by intrinsic motivation and understanding, contrast with surface approaches focused on rote memorisation and task completion (Teoh et al., 2014 ; Biggs, 1993 ). Contemporary research emphasises that students are not permanently “deep” or “surface” learners; rather, they shift strategies depending on context and task demands (Biggs et al., 2001 ; Delgado et al., 2018 ). These study patterns carry significant implications for both learning outcomes and psychological well-being. Deep learning promotes persistence and academic confidence, whereas surface learning is closely associated with avoidance and procrastination (Howell & Watson, 2007 ). Procrastination, one of the most persistent challenges in higher education, is defined as the voluntary delay of academic tasks despite foreseeing negative consequences (Steel, 2007 ). According to Temporal Motivation Theory (Steel & König, 2006 ), procrastination occurs when short-term rewards outweigh long-term goals. The rise of AI-based tools may amplify this dynamic: while they can support organisation and time management (Kabudi et al., 2021 ; Seo et al., 2021 ), their instant outputs, such as summaries or ready-made drafts, can tempt students to delay independent work (Abbas et al., 2024 ; Wang et al., 2025 ). Thus, AI may simultaneously function as both a self-regulatory aid and a facilitator of avoidance. This paradox highlights the need to examine how AI shapes students’ self-control, discipline, and engagement with learning tasks. Another important outcome connected to study behaviour is academic self-esteem, defined as students’ confidence in their ability to meet academic challenges (Tiwari, 2014 ). Grounded in Bandura’s ( 1991 ) social-cognitive theory, self-esteem develops through mastery experiences and effective self-regulation (Zimmerman, 2002 ). When students experience success through sustained effort, their self-efficacy and self-worth strengthen. However, when learning becomes mediated by automation, this process may be disrupted. Some studies suggest that AI can enhance confidence when used as a scaffold or guide (Huang et al., 2023; Vieriu & Petrea, 2025 ), while others warn that overdependence on AI creates cognitive debt – a sense of accomplishment not matched by genuine competence (Kosmyna et al., 2025; Clark, 2025 ). In contrast, when AI use is framed as a learning partner rather than a substitute, students may gain authentic confidence and autonomy (Ward et al., 2024 ; Chong et al., 2021 ; Zhai et al., 2024 ). These psychological processes take on particular significance in Indian higher education, where academic competition is intense, rote learning remains prevalent, and access to technology is uneven (Thankachan, 2024 ; Singh, 2022 ). The expansion of EdTech platforms has accelerated AI adoption, but challenges persist around digital literacy, academic integrity, and the authenticity of AI-assisted work (Bhatia et al., 2024 ; Gupta et al., 2025 ). Within this context, understanding how AI influences the relationships between study habits, procrastination, and self-esteem is both timely and necessary. The present study addresses this gap by investigating whether AI usage mediates the relationship between students’ study habits (deep and surface) and two key psychological outcomes—academic procrastination and academic self-esteem. Integrating insights from self-regulated learning theory, social-cognitive theory, and cyberpsychology, this study explores how AI engagement affects not only students’ academic behaviours but also their psychological well-being. Its findings aim to inform both educational psychology and practice by clarifying whether AI supports effective learning and self-belief, or whether it reinforces procrastination and surface-level engagement. Literature review Study Habits, Procrastination, and Self-Esteem The psychological mechanisms driving academic behaviour are founded on Self-Regulated Learning (SRL) Theory, which views learning as a cyclical process of forethought, performance, and self-reflection (Zimmerman, 2002 ). In this framework, study habits represent the strategic or deficient execution of self-regulation, determining the outcomes. The causal link between study habits and academic outcomes is robustly established. Procrastination is recognised not as a simple time management issue but as a quintessential failure of self-regulation rooted in poor organisational skills and self-control, core components of a surface approach to learning (Steel, 2007 ). A meta-analytic review by Steel ( 2007 ) confirmed that self-regulation failure is the key predictor of procrastination. Conversely, students who adopt deep study habits (e.g., critical reflection, conceptual connection) are inherently more self-regulated and equipped to resist the impulse to delay tasks (Grunschel et al., 2013; Schwinger et al., 2021 ; Ragusa et al., 2023 ). These study approaches also fundamentally shape a student’s self-perception. Effective, self-regulated strategies lead to repeated experiences of mastery, which consistently strengthen academic self-efficacy and self-esteem (Islam, 2021 ; Nakhostin-Khayyat et al., 2024 ). In contrast, reliance on a surface approach, characterised by ineffective strategies like rote memorisation, often leads to poor performance, which is internalised as a failure of ability, thereby eroding their self-esteem (Duru & Balkis, 2017 ). While the direct correlation between study habits and psychological outcomes is clear, a gap remains in understanding how this relationship has been fundamentally altered by the pervasive integration of artificial intelligence (AI) into the learning process. AI usage as mediator The rapid integration of AI tools necessitates understanding them not just as technological aids, but also as psychological actors that mediate cognition and motivation. Within this cyberpsychological framework, the literature presents a consistent dual-edged impact of AI, which serves as the rationale for positioning AI Usage as the central mediator. The dual nature of AI can be categorised into two opposing pathways. Adaptive Scaffolding, that is, the active use, is when students use AI to strengthen metacognitive skills, such as generating discussion prompts, defining objectives, or analysing mistakes. AI functions as adaptive scaffolding (Strielkowski et al., 2024 ; Xu, 2024 ; Ward et al., 2024 ; Zhai et al., 2024 ; Fitria, 2021). Research suggests that when AI is used as a collaborative partner, it enhances performance, offloads routine cognitive tasks, and enables deeper learning (Dibek et al., 2024 ; Zhai et al., 2024 ; Grinschgl et al., 2021 ; Gerlich, 2025 ). Deep learners are observed to use AI for adaptive scaffolding, consistent with the proactive, process-oriented phases of the SRL cycle (Garzón et al., 2025 ; Jin et al., 2023 ; Syafiuddin et al., 2024 ; Namjoo et al., 2023 ). Maladaptive Offloading, that is, the passive use, occurs when students delegate complex cognitive tasks – idea generation, or summarising – to the AI tool, resulting in limited internalisation of the underlying skills (Gerlich, 2025 ). This reliance may reduce genuine cognitive effort and undermine independent problem-solving. Critically, this passive reliance can lead to cognitive debt, where a student’s perceived competence diverges significantly from their actual ability (Kosmyna et al., 2025). Surface learners are positively correlated with maladaptive offloading, reinforcing existing low-effort habits (Gezgin, 2024). The evidence strongly suggests that AI usage is a predictable behaviour driven by existing study habits. AI Usage, Procrastination and Self-Esteem The literature presents contradictory findings on AI and procrastination. On one hand, AI can function as a catalyst for procrastination. Studies link excessive AI dependence to reduced intrinsic motivation and increased procrastination, suggesting that AI provides an effortless "quick fix" that reinforces delaying behaviour (Abbas et al., 2024 ; Steel, 2007 ). Specifically, dependence on AI is positively correlated with and is predictive of higher levels of academic procrastination among university students (Morales-García et al., 2024 ; Zhang et al., 2024). This supports the pathway that Maladaptive Offloading (Passive Use) deepens pre-existing procrastination habits. On the other hand, a separate body of research presents AI as a tool to counteract procrastination. AI-driven support, such as personalised reminders or providing non-graded early feedback, can act as behavioural nudges that reduce the initial aversiveness of a task. This helps students start assignments and mitigate procrastination (Duan et al., 2024 ; Ibrahim et al., 2025 ). This supports the hypothesis that Adaptive Scaffolding (Active Use) reduces procrastination. A similar contradiction exists regarding self-esteem. Positive links are observed when AI provides personalised, encouragement-rich feedback, boosting self-esteem through perceived competence (Parsakia, 2023 ; Rodríguez-Ruiz et al., 2024 ). However, over-reliance on AI is linked to a fragile academic self-concept and lower self-esteem (Zhang et al., 2024; Batool et al., 2025) since the maladaptive offloading risks reinforcing an anxious self-concept, which can be magnified in high-stress environments. Research Gaps The synthesis of the literature confirms that existing knowledge is structurally fragmented and methodologically incomplete, revealing significant gaps that provide the rationale for the present study. Current research on these crucial variables is largely confined to isolated, bivariate associations of study habits and their outcomes or AI usage and its outcomes. What is systematically absent is a study that synthesises these pairs by integrating these core psychological and behavioural factors – study habits, AI usage, procrastination, and self-esteem – into a single, comprehensive model. The structural gap is compounded by a methodological deficiency in the extant research. Despite the high global interest in Generative AI, research shows “little robust evidence” regarding its actual impact on education (Department for Education, 2024 ), with many findings based on qualitative or small-scale samples (Wu et al., 2024 ), which limits the ability to establish a causal mechanism. To address these limitations, a quantitative research design is employed to facilitate data collection from a large, representative sample, which is essential for ensuring that the findings are statistically generalizable to the broader population of Indian college students (Creswell & Creswell, 2018 ). Furthermore, the application of mediation analysis is utilised to identify and explain the specific psychological mechanism, i.e. the nature of AI usage, through which study habits influence academic outcomes (Coutts & Hayes, 2022 ; O’Rourke & MacKinnon, 2018 ). Finally, this research fills a critical contextual void by focusing on Indian higher education. Given the country’s unique combination of increasing AI adoption (The Indian Express, 2025), yet uneven access to technology, and pervasive academic pressure where rote learning traditions persist (Thankachan, 2024 ; Singh, 2022 ; Bhatia et al., 2024 ), the findings from this specific population are vital. These cultural and structural factors create a unique setting for examining how AI shapes psychological outcomes. Without this context-specific, mechanism-based evidence, educational institutions may lack the necessary data to design effective policies that promote Adaptive Scaffolding while mitigating the risks of Maladaptive Offloading. Thus, the objective of this study is to examine whether academic AI usage facilitates the relationships between students’ study habits (deep and surface) and two psychological outcomes: academic procrastination and academic self-esteem. Hypotheses H1. AI usage mediates the relationship between a deep study approach and academic procrastination. H2. AI usage mediates the relationship between a deep study approach and academic self-esteem. H3. AI usage mediates the relationship between a surface study approach and academic procrastination. H4. AI usage mediates the relationship between a surface study approach and academic self-esteem. Methods Study Design A quantitative, cross-sectional design was used to explore how study approaches relate to academic procrastination and academic self-esteem, with AI usage as a potential mediator. The sample comprised 402 Indian college students (aged 18–25) enrolled in undergraduate, postgraduate, diploma, and doctoral programs across diverse disciplines. Inclusion criteria were: (a) currently enrolled in a higher education program in India; (b) minimum educational qualification of Higher Secondary Examination (10 + 2) or equivalent; and (c) fluency in English, as all tools were administered in English. Exclusion criteria were: (a) severe physical or cognitive impairments that prevented engagement with the questionnaires; and (b) non-resident Indian students, migrants, or foreign nationals. Participants were recruited through purposive sampling using online forms shared through institutional and social media networks. Ethical approval was granted by the Institutional Review Board of CHRIST (Deemed to be University), Bangalore, and all participants provided informed consent. Measures Study Approaches: The Revised Two-Factor Study Process Questionnaire (R-SPQ-2F; Biggs et al., 2001 ) assessed how students typically engage with academic material. It yields two ten-item subscales: deep approach (α = .73) and surface approach (α = .64). Responses are rated on a 5-point scale. AI Usage: The Academic AI Usage Scale (AAIUS; Chakraborty & Subramani, 2025 ) measured how students employ AI tools for learning. The 24-item scale showed strong reliability (α = .86; test–retest r = .83, p < .001). Academic Procrastination: The Procrastination Assessment Scale for Students (Solomon & Rothblum, 1984 ) evaluated how often students delay academic tasks. Eighteen items rated on a 5-point frequency scale provide a total procrastination score; α = .84. Academic Self-Esteem: The Academic Self-Esteem Scale (Tiwari, 2011) measured students’ confidence in their academic competence. Originally a 7-item scale for adolescents, this tool was adapted for the present study to measure self-esteem in higher education. The eight-item version used here demonstrated excellent content validity (S-CVI/Ave ≥ .92) and reliability (α = .85). Data Analysis Data were analysed using Jamovi (v 2.6.17). Screening confirmed normality, linearity, and absence of multicollinearity. Descriptive statistics and Pearson’s correlations described relationships among variables. Multiple regression analyses tested the predictive roles of study approaches and AI usage on procrastination and self-esteem. Mediation analysis (generalised linear model) examined whether AI usage mediated these associations. Only the surface-study model met statistical assumptions for mediation. Qualitative responses to three open-ended questions on AI use were subjected to content analysis to identify common themes. Results Demographics A total of 402 students participated in the study, with an age range from 18 to 25 years. Of these, 292 (72.6%) were female, 102 (25.3%) were male, and 8 identified themselves as other genders. The descriptives (Fig. 1) indicate that nearly half of the sample were from East India, followed by South India. The sample was almost equally split between undergraduate and postgraduate students, with a small proportion pursuing diplomas, doctorates, or other qualifications. The most common academic disciplines were Social Sciences, Science, and Arts and Humanities, with smaller representation from Medicine and Allied Health Sciences, Engineering & Technology and other fields. A majority of participants (68.4%) reported first using AI tools in 2023–2024, while about 27% had begun between 2020 and 2022. In terms of daily AI usage, 36.6% used it for less than 30 minutes, 32.8% for 30 minutes to 1 hour, and 18.2% for 1–2 hours, with 10.7% reporting more than 2 hours of daily use. Most students (77.1%) believed that AI tools improved their academic efficiency. Descriptive Statistics of Main Variables Descriptive analyses were conducted for all continuous study variables, including Deep Approach Study, Surface Approach Study, Academic AI Usage, Academic Procrastination, and Academic Self-Esteem. Table 1 presents the sample size, mean scores, standard deviations, and observed score ranges for each measure. Table 1 Descriptive statistics for the main study variables (N = 402) Deep Study N Mean Median Mode SD 402 32.2 32.0 29.0 6.91 Surface Study 402 23.6 23.0 22.0 7.32 AI Usage 402 79.0 80.0 78.0 13.85 Procrastination 402 35.3 36.0 36.0 8.76 Self-esteem 402 29.0 30.0 32.0 5.84 All variables approximated normal distributions; the mean, median, and mode were closely aligned, and the coefficients of variation (CV) were below 50%, indicating acceptable dispersion (Mishra et al., 2019 ; Paramasivam et al., 2024 ). Given the sample size (N = 300), the Central Limit Theorem further supports the use of parametric tests (Kwak & Kim, 2017 ; Ghasemi & Zahediasl, 2012 ). Visual inspection of histograms with superimposed density curves (Fig. 2) further confirmed that each variable approximated a normal distribution. Correlation analyses Pearson’s product-moment correlations were computed to examine the relationships among the main study variables: Deep Study Habits (M = 32.2, SD = 6.91), Surface Study Habits, Academic AI Usage, Academic Procrastination, and Academic Self-Esteem. Table 2 presents the correlation coefficients, degrees of freedom, and significance levels. Table 2 Pearson’s correlations among variables (N = 402) Variables r p Surface Study vs AI usage 0.38** < 0.001 Surface Study vs Academic Procrastination 0.22** < 0.001 Surface Study vs Academic Self-esteem -0.10* 0.046 AI usage vs Academic Procrastination 0.23** < 0.001 Deep Study vs Academic Self-esteem 0.38** < 0.001 AI usage vs Academic Self-esteem 0.08 0.098 Deep Study vs AI Usage 0.001 0.982 Deep Study vs Academic Procrastination -0.09 0.067 Note. **p < 0.001, *p < 0.05. The results indicated that a deep approach to study was significantly and positively associated with academic self-esteem (r = .38, p < .001). However, it was not significantly related to academic AI usage (r = .001, p = .982) or academic procrastination (r = .09, p = .067). In contrast, the surface approach was positively correlated with AI usage (r = .38, p < .001) and procrastination (r = .22, p < .001), and negatively correlated with self-esteem (r = − .10, p = .046). AI usage was also significantly and positively related to procrastination (r = .23, p < .001) but showed no significant association with self-esteem (r = .08, p = .098). These results suggest distinct patterns for deep and surface study approaches: deep approaches appear beneficial for self-esteem, while surface approaches relate to higher AI usage and procrastination, with decreasing self-esteem. Regression analyses Regression analyses were conducted to test the predictive effects of study approaches and AI usage on academic procrastination and academic self-esteem. This preliminary step is considered essential for testing mediation, as mediation analysis builds on sequential regression models (Hair et al., 2021 ; Koirala, 2025 ). All assumptions of linearity, homoscedasticity, independence, and multicollinearity were met (Field, 2017 ; Osbourne & Waters, 2002 ; Cook, 1977 ). VIF values were below 2.0, and Cook’s Distance < 1.0. For academic procrastination, both surface study approach (B = .19, p = .002) and AI usage (B = .11, p = .002) emerged as significant positive predictors, while deep study approach showed a weak, nonsignificant trend (B = − .12, p = .06). For academic self-esteem, the deep study approach significantly predicted higher self-esteem (B = .32, p < .001) even after accounting for AI usage, whereas the surface study approach predicted lower self-esteem (B = − .12, p = .004). AI usage had a small but significant positive effect on self-esteem (B = .06, p = .009). Together, these models accounted for modest but meaningful proportions of variance (R² ≈ .06 − .15). The results indicated that AI usage was a consistent predictor of procrastination and a modest enhancer of self-esteem, whereas study approaches exerted differentiated effects: deep learning supported academic self-esteem, while surface learning related to both greater procrastination and reduced self-esteem. Detailed regression coefficients and model diagnostics are provided in the Additional File 1 . Mediation analysis Based on the preliminary regression findings, only the Surface Study models met the criteria for mediation testing. In these models, the independent variable (surface study approach) significantly predicted the proposed mediator (academic AI usage) and the dependent variables (academic procrastination and academic self-esteem). AI usage also significantly predicted both outcomes. Together, these significant paths satisfy the conditions recommended by Baron and Kenny ( 1986 ) and Hayes ( 2018 ) for conducting mediation analysis. In contrast, the Deep Study models were not carried forward because deep study habits did not predict AI usage ( p = .982), and in the self-esteem model, AI usage did not significantly predict the outcome. With these non-significant paths, the necessary conditions for mediation are not supported theoretically or empirically. Figure 3 shows the model examining the mediating role of Academic AI Usage in the relationship between Surface Study Approach and Academic Procrastination. Table 3 Indirect and Total Effects (Surface Study ⇒ AI Usage ⇒ Procrastination) Type Effect Estimate SE β z p Indirect Surface Study ⇒ AI Usage ⇒ Procrastination 0.07 0.03 0.06 2.97 0.003 Component Surface Study ⇒ AI Usage 0.72 0.09 0.38 8.20 < .001 AI Usage ⇒ Procrastination 0.10 0.03 0.17 3.19 0.001 Direct Surface Study ⇒ Procrastination 0.19 0.06 0.16 3.06 0.002 Total Surface Study ⇒ Procrastination 0.26 0.06 0.22 4.54 < .001 Note. Betas are completely standardised effect sizes. The mediation analysis showed that AI usage significantly mediated the relationship between surface study habits and academic procrastination. Surface study positively predicted AI usage (β = .38, p < .001), and AI usage in turn predicted higher procrastination (β = .17, p = .001). The indirect effect was significant (Estimate = .07, SE = .03, β = .06, z = 2.97, p = .003), representing 28.3% of the total effect. The direct effect of surface study on procrastination remained significant (β = .16, p = .002), and the total effect was also significant (β = .22, p < .001). These results indicate partial mediation, suggesting that AI usage partly explains the link between surface study habits and higher procrastination. Figure 4 shows the model examining the mediating role of Academic AI Usage in the relationship between Surface Study Approach and Academic Self-esteem. Table 4 Indirect and Total Effects (Surface Study ⇒ AI Usage ⇒ Self-esteem) Type Effect Estimate SE β z p Indirect Surface Study ⇒ AI Usage ⇒ Self-esteem 0.04 0.02 0.05 2.52 0.012 Component Surface Study ⇒ AI Usage 0.72 0.09 0.38 8.20 < .001 AI Usage ⇒ Self-esteem 0.06 0.02 0.14 2.64 0.008 Direct Surface Study ⇒ Self-esteem -0.12 0.04 -0.15 -2.88 0.004 Total Surface Study ⇒ Self-esteem -0.08 0.04 -0.10 -2.01 0.045 Note. Betas are completely standardised effect sizes. The mediation analysis revealed that AI usage significantly mediated the relationship between surface study habits and academic self-esteem. Surface study positively predicted AI usage (β = .38, p < .001), and AI usage in turn positively predicted self-esteem (β = .14, p = .008). The indirect effect was significant (Estimate = .04, SE = .02, β = .05, z = 2.52, p = .012), representing 25.8% of the total effect, indicating that greater reliance on surface learning was associated with increased AI usage, which in turn was linked to slightly higher self-esteem. However, the direct effect of surface study remained negative and significant (β = − .15, p = .004), suggesting that, even after accounting for AI usage, surface study independently predicted lower self-esteem. The total effect of surface study on self-esteem was also significant (β = − .10, p = .045). See Table 4 . Overall, these findings indicate partial mediation, suggesting that AI usage somewhat buffers, but does not remove, the negative association between surface study habits and academic self-esteem. To summarise the findings: a) deep study approaches predicted higher self-esteem but showed no relationship with procrastination or AI usage, b) surface study approaches predicted higher AI usage, and this greater use was associated with higher procrastination, c) surface study habits were linked to lower self-esteem overall; however, AI usage slightly buffered this effect, showing a small positive indirect influence, d) AI usage emerged as a partial mediator, reinforcing procrastination and moderating the self-esteem impact of surface study behaviours. Based on these results, H1 and H2 were rejected, as deep study habits did not predict AI usage and therefore did not produce any mediated effects. In contrast, H3 and H4 were supported, with AI usage demonstrating significant partial mediation in the relationships between surface study habits and both procrastination and self-esteem. Content analysis To get a clearer picture of how students actually use AI in their academic routines, a content analysis was carried out on three questions about their usage patterns. The participants reported the types of AI tools they use (Table 5 ), the tasks for which they use AI (Table 6 ), and the times at which they typically turn to these tools (Table 7 ). Each question included fixed options and an open-ended “Other” choice, which allowed students to describe uses beyond the predefined categories. Frequencies were calculated for the fixed responses, and the open-ended comments were coded to identify recurring themes. Table 5 Types of AI tools used for academic work Category Frequency (mentions) AI-based writing assistants (e.g., Grammarly, Quillbot, ChatGPT) 383 AI-based research tools (e.g., Elicit, Scite) 109 AI tutors/learning platforms (e.g., Khan Academy AI, Google Socratic) 92 AI for coding/programming (e.g., GitHub Copilot) 62 Others 11 Most students reported using writing-related tools such as Grammarly, ChatGPT, etc. These were followed by research tools, and learning platforms. AI tools as programming assistants were also relevant among students in technical disciplines. In the “Other” category, students listed tools like Gemini, Claude, Perplexity, and DeepSeek, which naturally fit into the writing or research categories. A few responses mentioned tools embedded in messaging or social-media platforms (e.g., WhatsApp Meta AI, Snapchat AI Bot), which shows that some students use AI in less formal academic spaces too. Table 6 Academic tasks for which AI is typically used Category Frequency (mentions) Writing assignments & reports 290 Researching & summarising content 330 Exam preparation & self-study 343 Coding & programming tasks 64 Solving numerical/statistical problems 116 Others 12 Students primarily used AI for writing, searching and summarising content, and preparing for exams. These three tasks accounted for the largest share of responses. Some also used AI to solve numerical or statistical questions or for coding. The open-ended comments revealed two clear themes. The first related to skill-based support: students mentioned using AI to create practice questions, check citations, interpret visuals, organise their study schedule, and seek feedback on their writing. The second theme involved academic enrichment beyond the curriculum. Several students used AI to explore topics out of curiosity, learn new skills, or think through broader questions in a reflective way. These comments show that AI is not only used for task completion but also for developing skills and pursuing personal learning goals. Table 7 Specific times when AI is used Category Frequency (mentions) During class lectures 48 While studying at home 316 Just before assignment deadlines 242 During exams/revision 301 Others 6 The timing of AI use followed a similar pattern. Most students turned to AI when studying at home or during exam preparation, and many used it just before assignment deadlines. Using AI during lectures was relatively uncommon. The open-ended responses here yielded two themes. Firstly, supplementary use, i.e. some students described using AI after finishing a chapter to clarify doubts, prepare notes, or generate short quizzes for practice. Another subtheme was supportive context, as others mentioned using AI when they felt mentally fatigued, or when helping others. These responses indicate that AI sometimes functions as a supportive tool when students want quick clarification or need help sustaining their focus. Taken together, the findings show that students use AI most often for writing, understanding content, revising, and solving problems. The open-ended responses illustrate additional roles that AI plays in skill-building, organising work, reinforcing learning after study sessions, and providing small pockets of support when students feel stuck or distracted. These patterns offer useful context for interpreting the quantitative results by showing how AI fits into the day-to-day realities of students’ academic routines. Discussion The present study examined how deep and surface study approaches relate to academic procrastination and academic self-esteem, and whether these relationships are shaped by students’ engagement with academic AI tools. While deep and surface approaches are commonly treated as separate learner “types,” research shows that they operate as flexible tendencies within individuals, shifting with task demands, discipline, and confidence levels (Biggs, 1993 ; Biggs et al., 2001 ; Delgado et al., 2018 ). This flexibility is reflected in the present findings, where the two approaches produced sharply diverging psychological patterns. A consistent pattern emerged for the deep approach: deep study habits were strongly and reliably associated with higher academic self-esteem. This appeared at both the correlational level (r = .38, p < .001) and in regression, where deep study remained a significant predictor of self-esteem even after controlling for AI usage (B = 0.32, p < .001). These results align with Self-Regulated Learning theory, in which planning, monitoring, and deliberate cognitive engagement create mastery experiences that enhance students’ confidence in their academic abilities (Zimmerman, 2002 ; Howell & Watson, 2007 ). They also reflect Social Cognitive Theory, which emphasises that repeated successful effort builds self-efficacy (Bandura, 1991 ). Recent research similarly shows that deep engagement predicts stronger academic self-belief and reduced academic anxiety, even in digitally saturated learning environments (Delgado et al., 2018 ; Chan & Hu, 2023 ; Nakhostin-Khayyat et al., 2024 ). Importantly, deep learning showed no significant association with procrastination (r = − .09, p = .067) and no relationship with AI usage (r = .001, p = .982). This suggests that students who adopt deep strategies regulate their work internally rather than depending on external tools. Contemporary studies confirm this pattern: students with higher intrinsic motivation rely less on generative AI for core cognitive tasks and instead use it selectively for planning or feedback support (Jin et al., 2023 ; Zhai et al., 2024 ). These findings together indicate that deep learning is largely self-sustaining and operates independently of the affordances of AI. The disciplinary distribution of the sample also contextualises this pattern. In humanities and social sciences, deep approaches often manifest as critical engagement with texts and synthesis of complex arguments (Qu et al., 2024 ), which are strongly tied to self-efficacy and identity development (Raposas –Rabut, 2024 ). In science fields, deep approaches are linked with problem-solving, hypothesis testing, and conceptual understanding (Qu et al., 2024 ), and these activities similarly build mastery and reinforce confidence (Sandrone, 2022 ). That both clusters of students reported higher self-esteem when adopting deep strategies suggests that, across domains, the benefits of deep engagement accrue through intrinsic processes of meaning-making and mastery rather than through technological shortcuts. The surface approach revealed a markedly different psychological profile compared to the deep approach. Surface study habits were positively related to academic procrastination at the correlational level (r = .22, p < .001) and significantly predicted procrastination in regression, even after accounting for AI usage (B = .19, p = .002). These results are consistent with longstanding evidence that surface strategies are linked with avoidance, poor time management, and weaker self-regulation (Diseth, 2011 ; Liu et al., 2022 ). AI usage further clarified this pattern: it partially mediated the relationship between surface study and procrastination, accounting for about 28.3% of the total effect (β_indirect = .06, p = .003). Temporal Motivation Theory (Steel & König, 2006 ) helps explain that when tasks feel aversive and rewards are distant, procrastination becomes more likely. For surface-oriented students, generative AI tools may heighten this imbalance by offering immediate, low-effort outputs, making delay an even more attractive choice. This interpretation aligns with recent empirical work showing that higher reliance on generative AI is associated with increased academic delay, especially when used for shortcuts such as paraphrasing, summarising, or drafting under deadline pressure (Abbas et al., 2024 ; Morales-García et al., 2024 ; Wang et al., 2025 ). The content-analysis results further reinforce the mechanism that students most frequently reported using AI for writing assistance, paraphrasing, summarising, and last-minute academic tasks: patterns captured in Tables 5 and 6 . AI was used heavily during independent study (316 mentions) and immediately before assignment deadlines (242 mentions), and during exam-focused revision (301 mentions). These usage patterns align precisely with surface-oriented tendencies and help explain how AI becomes a tool that supports postponement rather than sustained engagement (Gerlich, 2025 ; Kosmyna et al., 2025). The Technology Acceptance Model (Davis, 1989) also helps clarify the reason AI is being embedded in these surface-oriented pathways. From this perspective, students adopt academic technologies when they perceive them as both useful, easy to use and requiring low effort. This perceived usefulness was especially salient under conditions of time pressure, aligning with content-analysis. Recent studies have similarly reported that students gravitate toward generative AI when it minimises cognitive load and offers quick solutions, particularly in writing-heavy coursework or problem-solving tasks (Jin et al., 2023 ; Qu et al., 2024 ). Surface habits were also negatively related to self-esteem (r = − .10, p = .046) and predicted lower self-esteem directly (β_direct = − .15, p = .004). However, a partial mediation emerged for self-esteem as AI usage produced a small positive indirect effect (β_indirect = .05, p = .012), amounting to about 25.8% of the total effect. The self-esteem findings can also be interpreted through a Vygotskian lens (Vygotsky, 1980 ). AI appears to act as a form of scaffolding, offering immediate support that helps students complete tasks and momentarily feel more capable. Yet, because surface learners typically engage with AI at the level of performance rather than understanding, this scaffolding does not lead to internalisation. Instead, it produces a short-lived boost, which is consistent with the well-documented phenomenon of “illusion of competence,” where students feel temporarily more confident after receiving polished AI-generated text or instant explanations, even without genuine skill development (Nazaretsky et al., 2025 ; Bai & Wang, 2025 ). Qualitative work shows that such boosts arise from the immediacy and fluency of AI feedback, which can create a perception of competence detached from actual understanding (Ma’amor et al., 2024; Rodríguez-Ruiz et al., 2024 ). This pattern fits the present data: surface strategies erode authentic self-esteem, but AI provides a short-lived lift that does not address underlying weaknesses. The absence of any mediation effect in the deep pathway reinforces this interpretation that deep learners do not offload in ways that would produce artificial confidence. Together, these findings suggest that surface learners not only delay tasks and feel less confident but also use AI in ways that compound procrastination while temporarily lifting their sense of competence. Overall, the results point to a clear psychological divide in how students engage with their work and with AI. Deep strategies seem to carry their own momentum, building confidence through effort and understanding, and leaving little need for AI to alter that pattern. Surface-level study habits, in contrast, make AI especially tempting. When the work feels too much, the deadline is too close, or confidence drops, students turn to AI because it gives quick relief. In those moments, AI doesn’t change how they study; rather, it simply strengthens the habits they already have. It helps them get through the immediate stress, but it also quietly encourages more delay and shallow work in the long run. Within the pressures of Indian higher education, where students often face heavy writing loads, exam-oriented curricula, and limited academic support (Thankachan, 2024 ; Singh, 2022 ), it becomes easier to see why AI slots into these pathways the way it does. Rather than functioning as a blanket-solution, AI mirrors the study habits students bring to it – supporting meaningful engagement when motivation and self-regulation are present, and offering short-term relief when those are lacking. The study, however, looked mainly at how frequently and when students used AI, not at how they engaged with it. Future research may explore the depth of AI interaction, a factor that emerging work identifies as critical for understanding AI’s educational impact (Kasneci et al., 2023 ). Understanding this distinction may be crucial to seeing how AI shapes learning and self-regulation over time. Practical Implications Institutions can guide students toward more deliberate and process-focused use of AI by embedding structure into how these tools are used. Approaches such as AI-assisted study planning, supervised writing feedback, and reflective checkpoints can help students organise their workload and stay engaged with the material (Mandhare et al., 2025 ; Youn et al., 2025 ). Learning designs that break complex tasks into stages and include opportunities for feedback can complement these AI-supported practices by encouraging students to monitor their thinking and stay accountable (Howell & Watson, 2007 ; Panadero, 2017 ; Zimmerman, 2002 ) and can strengthen confidence without promoting shortcuts (Vieriu & Petrea, 2025 ). Instructors can draw on these practices by asking students to show how AI contributed to their drafts, explain the revisions they made, or compare AI-generated suggestions with their own thinking. These steps align with broader recommendations for integrating AI in ways that emphasise learning processes rather than rapid output (Kasneci et al., 2023 ). Finally, the disciplinary differences in AI usage observed in the sample suggest that AI literacy should not be taught as a one-size-fits-all skill. Writing-intensive fields may need guidance on integrating AI into drafting and revision without undermining originality or critical thinking, whereas scientific and technical programs may need to focus on balancing AI-based problem-solving with conceptual understanding. Tailoring AI literacy to the demands of each field can help students use AI in ways that support, rather than replace, the cognitive processes central to their discipline (Kasneci et al., 2023 ). Conclusion The study shows that deep and surface study approaches co-exist within students and shape academic outcomes in distinct ways. Deep strategies were linked to stronger self-esteem and minimal dependence on AI, while surface strategies predicted greater procrastination and lower academic self-esteem, partly explained by opportunistic AI use. These findings show that AI’s educational impact depends less on its presence and more on how it is integrated into learning. When used reflectively, AI can support mastery and self-regulation; when used as a shortcut, it may amplify avoidance of tasks. For educators, the priority is to design learning environments that encourage deep engagement: through goal-setting, feedback, and reflective tasks, while guiding students towards constructive, discipline-specific AI use. Future research may build on these insights using longitudinal and multi-method approaches to test whether structured, reflective AI use can transform surface learning into meaningful academic growth. Abbreviations AAIUS Academic AI Usage Scale AI Artificial Intelligence B Unstandardized Regression Coefficient CV Coefficient of Variation M Mean R² Coefficient of Determination R-SPQ-2F Revised Two-Factor Study Process Questionnaire S-CVI/Ave Scale Content Validity Index/Average SD Standard Deviation SE Standard Error SRL Self-Regulated Learning VIF Variance Inflation Factor Declarations Consent to participate Informed consent was obtained electronically from all participants prior to participation. Data were collected anonymously, and participants were informed about the voluntary nature of their participation and their right to withdraw at any time without penalty. Consent to publish Not applicable, as no identifying information or images of participants are included in this manuscript. Funding The authors did not receive support from any organisation for the submitted work. Author Contribution D.C.: Conceptualisation, data collection, data analysis and manuscript draft.D.S.: Supervision, conceptual guidance, and manuscript revision. Acknowledgement The authors gratefully acknowledge Dr. Priyesh C for his guidance on statistical analyses, Ms. Anandita Datta for her input during the early stages of the study, and Ms. Saptadwipa Paul for assistance with proofreading the final manuscript. Data Availability The datasets generated and analysed during the current study contain confidential student responses and are therefore not publicly available. However, the corresponding author can provide de-identified data supporting the main findings upon reasonable request. The manuscript and supplementary materials include all essential statistical outputs. References Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. 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Digital divide in Indian higher Education: A National survey of Access and Equity. Journal of Advanced Zoology . https://doi.org/10.53555/jaz.v43i1.4910 Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology , 31 (4), 503–509. https://doi.org/10.1037/0022-0167.31.4.503 Steel, P. (2007). The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological Bulletin , 133 (1), 65–94. https://doi.org/10.1037/0033-2909.133.1.65 Steel, P., & König, C. J. (2006). Integrating theories of motivation. Academy of Management Review , 31 (4), 889–913. https://doi.org/10.5465/amr.2006.22527462 Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2024). AI-driven adaptive learning for sustainable educational transformation. Sustainable Development , 33 (2), 1921–1947. https://doi.org/10.1002/sd.3221 Syafiuddin, N., Unde, A. A., & Akbar, M. (2024). The influence of Technology literacy and the use of Artificial intelligence (AI) by Hasanuddin University students on the change of habits in completing academic tasks. International Journal of Religion , 5 (10), 4595–4610. https://doi.org/10.61707/vs303751 Teoh, H. C., Abdullah, M. C., Roslan, S., & Daud, S. M. (2014). Assessing students approaches to learning using a matrix framework in a Malaysian public university. SpringerPlus , 3 (1). https://doi.org/10.1186/2193-1801-3-54 Thankachan, K. J. (2024). Paradigm shift from rote learning to critical thinking, experiential learning, and holistic development in the indian education system. Journal of Management Research and Analysis , 11 (3), 140–141. https://doi.org/10.18231/j.jmra.2024.023 The Indian Express (2025, December 12). India accelerates ahead in GenAI learning with 3.6 mn enrolments in 2025: Coursera report. The Indian Express. https://indianexpress.com/article/technology/artificial-intelligence/india-ahead-in-genai-learning-with-3-6-mn-enrolments-in-2025-10415027/ Tiwari, G. K. (2014). Academic self-esteem, feedback and adolescents’ academic achievement. Dhsgsu. https://www.academia.edu/7020772/Academic_Self_esteem_Feedback_and_Adolescents_Academic_Achievement Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences , 15 (3), 343. https://doi.org/10.3390/educsci15030343 Vygotsky, L. S. (1980). Mind in society. In Harvard University Press eBooks. https://doi.org/10.2307/j.ctvjf9vz4 Wang, F., Li, N., Cheung, A. C. K., & Wong, G. K. W. (2025). In GenAI we trust: An investigation of university students’ reliance on and resistance to generative AI in language learning. International Journal of Educational Technology in Higher Education , 22 (1). https://doi.org/10.1186/s41239-025-00547-9 Ward, B., Bhati, D., Neha, F., & Guercio, A. (2024). Analyzing the impact of AI tools on student study habits and academic performance. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2412.02166 Wu, D., Chen, M., Chen, X., & Liu, X. (2024). Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy. Computers and Education Artificial Intelligence , 7 , 100295. https://doi.org/10.1016/j.caeai.2024.100295 Xu, Z. (2024). AI in education: Enhancing learning experiences and student outcomes. Applied and Computational Engineering , 51 (1), 104–111. https://doi.org/10.54254/2755-2721/51/20241187 Youn, C. H., Salam, A. R., & Rahman, A. A. (2025). AI-Driven tools in providing feedback on students’ writing. International Journal of Research and Innovation in Social Science IX(IIIS , 58–67. https://doi.org/10.47772/ijriss.2025.903sedu0006 Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments , 11 (1). https://doi.org/10.1186/s40561-024-00316-7 Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology , 81 (3), 329–339. https://doi.org/10.1037/0022-0663.81.3.329 Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory Into Practice , 41 (2), 64–70. https://doi.org/10.1207/s15430421tip4102_2 Additional Declarations No competing interests reported. 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Tools such as ChatGPT, Gemini, and Perplexity now function as everyday cognitive aids, providing instant explanations, personalised feedback, and real-time support for writing and problem-solving (Huang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gond et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As these systems grow more sophisticated, their influence on students\u0026rsquo; thinking and behaviour has expanded. Emerging research in cyberpsychology argues that AI not only change what students do, but also how they regulate motivation, perceive academic challenges, and experience confidence (Fatima, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Favero et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This dual potential, enhancement versus overreliance, makes understanding AI\u0026rsquo;s psychological effects increasingly urgent.\u003c/p\u003e \u003cp\u003eStudy habits represent a crucial psychological foundation in this context. They reflect how students organise learning, regulate effort, and pursue mastery (Biggs, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Aharony, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Deep learning approaches, driven by intrinsic motivation and understanding, contrast with surface approaches focused on rote memorisation and task completion (Teoh et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Biggs, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Contemporary research emphasises that students are not permanently \u0026ldquo;deep\u0026rdquo; or \u0026ldquo;surface\u0026rdquo; learners; rather, they shift strategies depending on context and task demands (Biggs et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Delgado et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These study patterns carry significant implications for both learning outcomes and psychological well-being. Deep learning promotes persistence and academic confidence, whereas surface learning is closely associated with avoidance and procrastination (Howell \u0026amp; Watson, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProcrastination, one of the most persistent challenges in higher education, is defined as the voluntary delay of academic tasks despite foreseeing negative consequences (Steel, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). According to Temporal Motivation Theory (Steel \u0026amp; K\u0026ouml;nig, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), procrastination occurs when short-term rewards outweigh long-term goals. The rise of AI-based tools may amplify this dynamic: while they can support organisation and time management (Kabudi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Seo et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), their instant outputs, such as summaries or ready-made drafts, can tempt students to delay independent work (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Thus, AI may simultaneously function as both a self-regulatory aid and a facilitator of avoidance. This paradox highlights the need to examine how AI shapes students\u0026rsquo; self-control, discipline, and engagement with learning tasks.\u003c/p\u003e \u003cp\u003eAnother important outcome connected to study behaviour is academic self-esteem, defined as students\u0026rsquo; confidence in their ability to meet academic challenges (Tiwari, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Grounded in Bandura\u0026rsquo;s (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) social-cognitive theory, self-esteem develops through mastery experiences and effective self-regulation (Zimmerman, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). When students experience success through sustained effort, their self-efficacy and self-worth strengthen. However, when learning becomes mediated by automation, this process may be disrupted. Some studies suggest that AI can enhance confidence when used as a scaffold or guide (Huang et al., 2023; Vieriu \u0026amp; Petrea, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), while others warn that overdependence on AI creates cognitive debt \u0026ndash; a sense of accomplishment not matched by genuine competence (Kosmyna et al., 2025; Clark, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In contrast, when AI use is framed as a learning partner rather than a substitute, students may gain authentic confidence and autonomy (Ward et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chong et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhai et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese psychological processes take on particular significance in Indian higher education, where academic competition is intense, rote learning remains prevalent, and access to technology is uneven (Thankachan, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Singh, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The expansion of EdTech platforms has accelerated AI adoption, but challenges persist around digital literacy, academic integrity, and the authenticity of AI-assisted work (Bhatia et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Within this context, understanding how AI influences the relationships between study habits, procrastination, and self-esteem is both timely and necessary.\u003c/p\u003e \u003cp\u003eThe present study addresses this gap by investigating whether AI usage mediates the relationship between students\u0026rsquo; study habits (deep and surface) and two key psychological outcomes\u0026mdash;academic procrastination and academic self-esteem. Integrating insights from self-regulated learning theory, social-cognitive theory, and cyberpsychology, this study explores how AI engagement affects not only students\u0026rsquo; academic behaviours but also their psychological well-being. Its findings aim to inform both educational psychology and practice by clarifying whether AI supports effective learning and self-belief, or whether it reinforces procrastination and surface-level engagement.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Habits, Procrastination, and Self-Esteem\u003c/h2\u003e \u003cp\u003eThe psychological mechanisms driving academic behaviour are founded on Self-Regulated Learning (SRL) Theory, which views learning as a cyclical process of forethought, performance, and self-reflection (Zimmerman, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). In this framework, study habits represent the strategic or deficient execution of self-regulation, determining the outcomes.\u003c/p\u003e \u003cp\u003eThe causal link between study habits and academic outcomes is robustly established. Procrastination is recognised not as a simple time management issue but as a quintessential failure of self-regulation rooted in poor organisational skills and self-control, core components of a surface approach to learning (Steel, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). A meta-analytic review by Steel (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) confirmed that self-regulation failure is the key predictor of procrastination. Conversely, students who adopt deep study habits (e.g., critical reflection, conceptual connection) are inherently more self-regulated and equipped to resist the impulse to delay tasks (Grunschel et al., 2013; Schwinger et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ragusa et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese study approaches also fundamentally shape a student’s self-perception. Effective, self-regulated strategies lead to repeated experiences of mastery, which consistently strengthen academic self-efficacy and self-esteem (Islam, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nakhostin-Khayyat et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In contrast, reliance on a surface approach, characterised by ineffective strategies like rote memorisation, often leads to poor performance, which is internalised as a failure of ability, thereby eroding their self-esteem (Duru \u0026amp; Balkis, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the direct correlation between study habits and psychological outcomes is clear, a gap remains in understanding how this relationship has been fundamentally altered by the pervasive integration of artificial intelligence (AI) into the learning process.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAI usage as mediator\u003c/h3\u003e\n\u003cp\u003eThe rapid integration of AI tools necessitates understanding them not just as technological aids, but also as psychological actors that mediate cognition and motivation. Within this cyberpsychological framework, the literature presents a consistent dual-edged impact of AI, which serves as the rationale for positioning AI Usage as the central mediator.\u003c/p\u003e \u003cp\u003eThe dual nature of AI can be categorised into two opposing pathways. Adaptive Scaffolding, that is, the active use, is when students use AI to strengthen metacognitive skills, such as generating discussion prompts, defining objectives, or analysing mistakes. AI functions as adaptive scaffolding (Strielkowski et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ward et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhai et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fitria, 2021). Research suggests that when AI is used as a collaborative partner, it enhances performance, offloads routine cognitive tasks, and enables deeper learning (Dibek et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhai et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Grinschgl et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gerlich, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Deep learners are observed to use AI for adaptive scaffolding, consistent with the proactive, process-oriented phases of the SRL cycle (Garzón et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jin et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Syafiuddin et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Namjoo et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMaladaptive Offloading, that is, the passive use, occurs when students delegate complex cognitive tasks – idea generation, or summarising – to the AI tool, resulting in limited internalisation of the underlying skills (Gerlich, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). This reliance may reduce genuine cognitive effort and undermine independent problem-solving. Critically, this passive reliance can lead to cognitive debt, where a student’s perceived competence diverges significantly from their actual ability (Kosmyna et al., 2025). Surface learners are positively correlated with maladaptive offloading, reinforcing existing low-effort habits (Gezgin, 2024). The evidence strongly suggests that AI usage is a predictable behaviour driven by existing study habits.\u003c/p\u003e\n\u003ch3\u003eAI Usage, Procrastination and Self-Esteem\u003c/h3\u003e\n\u003cp\u003eThe literature presents contradictory findings on AI and procrastination. On one hand, AI can function as a catalyst for procrastination. Studies link excessive AI dependence to reduced intrinsic motivation and increased procrastination, suggesting that AI provides an effortless \"quick fix\" that reinforces delaying behaviour (Abbas et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Steel, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Specifically, dependence on AI is positively correlated with and is predictive of higher levels of academic procrastination among university students (Morales-García et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., 2024). This supports the pathway that Maladaptive Offloading (Passive Use) deepens pre-existing procrastination habits.\u003c/p\u003e \u003cp\u003eOn the other hand, a separate body of research presents AI as a tool to counteract procrastination. AI-driven support, such as personalised reminders or providing non-graded early feedback, can act as behavioural nudges that reduce the initial aversiveness of a task. This helps students start assignments and mitigate procrastination (Duan et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ibrahim et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). This supports the hypothesis that Adaptive Scaffolding (Active Use) reduces procrastination.\u003c/p\u003e \u003cp\u003eA similar contradiction exists regarding self-esteem. Positive links are observed when AI provides personalised, encouragement-rich feedback, boosting self-esteem through perceived competence (Parsakia, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rodríguez-Ruiz et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, over-reliance on AI is linked to a fragile academic self-concept and lower self-esteem (Zhang et al., 2024; Batool et al., 2025) since the maladaptive offloading risks reinforcing an anxious self-concept, which can be magnified in high-stress environments.\u003c/p\u003e\n\u003ch3\u003eResearch Gaps\u003c/h3\u003e\n\u003cp\u003eThe synthesis of the literature confirms that existing knowledge is structurally fragmented and methodologically incomplete, revealing significant gaps that provide the rationale for the present study. Current research on these crucial variables is largely confined to isolated, bivariate associations of study habits and their outcomes or AI usage and its outcomes. What is systematically absent is a study that synthesises these pairs by integrating these core psychological and behavioural factors – study habits, AI usage, procrastination, and self-esteem – into a single, comprehensive model. The structural gap is compounded by a methodological deficiency in the extant research. Despite the high global interest in Generative AI, research shows “little robust evidence” regarding its actual impact on education (Department for Education, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), with many findings based on qualitative or small-scale samples (Wu et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), which limits the ability to establish a causal mechanism. To address these limitations, a quantitative research design is employed to facilitate data collection from a large, representative sample, which is essential for ensuring that the findings are statistically generalizable to the broader population of Indian college students (Creswell \u0026amp; Creswell, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, the application of mediation analysis is utilised to identify and explain the specific psychological mechanism, i.e. the nature of AI usage, through which study habits influence academic outcomes (Coutts \u0026amp; Hayes, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; O’Rourke \u0026amp; MacKinnon, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, this research fills a critical contextual void by focusing on Indian higher education. Given the country’s unique combination of increasing AI adoption (The Indian Express, 2025), yet uneven access to technology, and pervasive academic pressure where rote learning traditions persist (Thankachan, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Singh, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bhatia et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), the findings from this specific population are vital. These cultural and structural factors create a unique setting for examining how AI shapes psychological outcomes. Without this context-specific, mechanism-based evidence, educational institutions may lack the necessary data to design effective policies that promote Adaptive Scaffolding while mitigating the risks of Maladaptive Offloading.\u003c/p\u003e \u003cp\u003eThus, the objective of this study is to examine whether academic AI usage facilitates the relationships between students’ study habits (deep and surface) and two psychological outcomes: academic procrastination and academic self-esteem.\u003c/p\u003e\n\u003ch3\u003eHypotheses\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eH1.\u003c/b\u003e AI usage mediates the relationship between a deep study approach and academic procrastination.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2.\u003c/b\u003e AI usage mediates the relationship between a deep study approach and academic self-esteem.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3.\u003c/b\u003e AI usage mediates the relationship between a surface study approach and academic procrastination.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4.\u003c/b\u003e AI usage mediates the relationship between a surface study approach and academic self-esteem.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n "},{"header":"Methods","content":"\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eA quantitative, cross-sectional design was used to explore how study approaches relate to academic procrastination and academic self-esteem, with AI usage as a potential mediator. The sample comprised 402 Indian college students (aged 18–25) enrolled in undergraduate, postgraduate, diploma, and doctoral programs across diverse disciplines. Inclusion criteria were: (a) currently enrolled in a higher education program in India; (b) minimum educational qualification of Higher Secondary Examination (10 + 2) or equivalent; and (c) fluency in English, as all tools were administered in English. Exclusion criteria were: (a) severe physical or cognitive impairments that prevented engagement with the questionnaires; and (b) non-resident Indian students, migrants, or foreign nationals. Participants were recruited through purposive sampling using online forms shared through institutional and social media networks.\u003c/p\u003e\u003cp\u003e Ethical approval was granted by the Institutional Review Board of CHRIST (Deemed to be University), Bangalore, and all participants provided informed consent.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch3\u003eMeasures\u003c/h3\u003e\u003cp\u003eStudy Approaches: The Revised Two-Factor Study Process Questionnaire (R-SPQ-2F; Biggs et al., \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e) assessed how students typically engage with academic material. It yields two ten-item subscales: deep approach (α = .73) and surface approach (α = .64). Responses are rated on a 5-point scale.\u003c/p\u003e\u003cp\u003eAI Usage: The Academic AI Usage Scale (AAIUS; Chakraborty \u0026amp; Subramani, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e) measured how students employ AI tools for learning. The 24-item scale showed strong reliability (α = .86; test–retest r = .83, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001).\u003c/p\u003e\u003cp\u003eAcademic Procrastination: The Procrastination Assessment Scale for Students (Solomon \u0026amp; Rothblum, \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e) evaluated how often students delay academic tasks. Eighteen items rated on a 5-point frequency scale provide a total procrastination score; α = .84.\u003c/p\u003e\u003cp\u003eAcademic Self-Esteem: The Academic Self-Esteem Scale (Tiwari, 2011) measured students’ confidence in their academic competence. Originally a 7-item scale for adolescents, this tool was adapted for the present study to measure self-esteem in higher education. The eight-item version used here demonstrated excellent content validity (S-CVI/Ave ≥ .92) and reliability (α = .85).\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eData were analysed using Jamovi (v 2.6.17). Screening confirmed normality, linearity, and absence of multicollinearity. Descriptive statistics and Pearson’s correlations described relationships among variables. Multiple regression analyses tested the predictive roles of study approaches and AI usage on procrastination and self-esteem. Mediation analysis (generalised linear model) examined whether AI usage mediated these associations. Only the surface-study model met statistical assumptions for mediation. Qualitative responses to three open-ended questions on AI use were subjected to content analysis to identify common themes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eDemographics\u003c/h2\u003e\n\u003cp\u003eA total of 402 students participated in the study, with an age range from 18 to 25 years. Of these, 292 (72.6%) were female, 102 (25.3%) were male, and 8 identified themselves as other genders. The descriptives (Fig.\u0026nbsp;1) indicate that nearly half of the sample were from East India, followed by South India. The sample was almost equally split between undergraduate and postgraduate students, with a small proportion pursuing diplomas, doctorates, or other qualifications. The most common academic disciplines were Social Sciences, Science, and Arts and Humanities, with smaller representation from Medicine and Allied Health Sciences, Engineering \u0026amp; Technology and other fields.\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003cp\u003eA majority of participants (68.4%) reported first using AI tools in 2023\u0026ndash;2024, while about 27% had begun between 2020 and 2022. In terms of daily AI usage, 36.6% used it for less than 30 minutes, 32.8% for 30 minutes to 1 hour, and 18.2% for 1\u0026ndash;2 hours, with 10.7% reporting more than 2 hours of daily use. Most students (77.1%) believed that AI tools improved their academic efficiency.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003eDescriptive Statistics of Main Variables\u003c/h2\u003e\n\u003cp\u003eDescriptive analyses were conducted for all continuous study variables, including Deep Approach Study, Surface Approach Study, Academic AI Usage, Academic Procrastination, and Academic Self-Esteem. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the sample size, mean scores, standard deviations, and observed score ranges for each measure.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003eDescriptive statistics for the main study variables (N\u0026thinsp;=\u0026thinsp;402)\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDeep Study\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMode\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e402\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e32.2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e32.0\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e29.0\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e6.91\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurface Study\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e402\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e23.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAI Usage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e402\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e78.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eProcrastination\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e402\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.76\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-esteem\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e402\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e32.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.84\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAll variables approximated normal distributions; the mean, median, and mode were closely aligned, and the coefficients of variation (CV) were below 50%, indicating acceptable dispersion (Mishra et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Paramasivam et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given the sample size (N\u0026thinsp;=\u0026thinsp;300), the Central Limit Theorem further supports the use of parametric tests (Kwak \u0026amp; Kim, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ghasemi \u0026amp; Zahediasl, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Visual inspection of histograms with superimposed density curves (Fig.\u0026nbsp;2) further confirmed that each variable approximated a normal distribution.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003eCorrelation analyses\u003c/h2\u003e\n\u003cp\u003ePearson\u0026rsquo;s product-moment correlations were computed to examine the relationships among the main study variables: Deep Study Habits (M\u0026thinsp;=\u0026thinsp;32.2, SD\u0026thinsp;=\u0026thinsp;6.91), Surface Study Habits, Academic AI Usage, Academic Procrastination, and Academic Self-Esteem. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the correlation coefficients, degrees of freedom, and significance levels.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003ePearson\u0026rsquo;s correlations among variables (N\u0026thinsp;=\u0026thinsp;402)\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003er\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurface Study vs AI usage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.38**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurface Study vs Academic Procrastination\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.22**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSurface Study vs Academic Self-esteem\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.10*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAI usage vs Academic Procrastination\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.23**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDeep Study vs Academic Self-esteem\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.38**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAI usage vs Academic Self-esteem\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDeep Study vs AI Usage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.982\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDeep Study vs Academic Procrastination\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote.\u003c/em\u003e **p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results indicated that a deep approach to study was significantly and positively associated with academic self-esteem (r = .38, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). However, it was not significantly related to academic AI usage (r = .001, \u003cem\u003ep\u003c/em\u003e = .982) or academic procrastination (r = .09, \u003cem\u003ep\u003c/em\u003e = .067). In contrast, the surface approach was positively correlated with AI usage (r = .38, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and procrastination (r = .22, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and negatively correlated with self-esteem (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.10, \u003cem\u003ep\u003c/em\u003e = .046).\u003c/p\u003e\n\u003cp\u003eAI usage was also significantly and positively related to procrastination (r = .23, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) but showed no significant association with self-esteem (r = .08, \u003cem\u003ep\u003c/em\u003e = .098).\u003c/p\u003e\n\u003cp\u003eThese results suggest distinct patterns for deep and surface study approaches: deep approaches appear beneficial for self-esteem, while surface approaches relate to higher AI usage and procrastination, with decreasing self-esteem.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003eRegression analyses\u003c/h2\u003e\n\u003cp\u003eRegression analyses were conducted to test the predictive effects of study approaches and AI usage on academic procrastination and academic self-esteem. This preliminary step is considered essential for testing mediation, as mediation analysis builds on sequential regression models (Hair et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Koirala, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). All assumptions of linearity, homoscedasticity, independence, and multicollinearity were met (Field, \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Osbourne \u0026amp; Waters, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Cook, \u003cspan class=\"CitationRef\"\u003e1977\u003c/span\u003e). VIF values were below 2.0, and Cook\u0026rsquo;s Distance\u0026thinsp;\u0026lt;\u0026thinsp;1.0.\u003c/p\u003e\n\u003cp\u003eFor academic procrastination, both surface study approach (B = .19, \u003cem\u003ep\u003c/em\u003e = .002) and AI usage (B = .11, \u003cem\u003ep\u003c/em\u003e = .002) emerged as significant positive predictors, while deep study approach showed a weak, nonsignificant trend (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.12, \u003cem\u003ep\u003c/em\u003e = .06).\u003c/p\u003e\n\u003cp\u003eFor academic self-esteem, the deep study approach significantly predicted higher self-esteem (B = .32, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) even after accounting for AI usage, whereas the surface study approach predicted lower self-esteem (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.12, \u003cem\u003ep\u003c/em\u003e = .004). AI usage had a small but significant positive effect on self-esteem (B = .06, \u003cem\u003ep\u003c/em\u003e = .009).\u003c/p\u003e\n\u003cp\u003eTogether, these models accounted for modest but meaningful proportions of variance (R\u0026sup2; \u0026asymp; .06 \u0026minus;\u0026thinsp;.15). The results indicated that AI usage was a consistent predictor of procrastination and a modest enhancer of self-esteem, whereas study approaches exerted differentiated effects: deep learning supported academic self-esteem, while surface learning related to both greater procrastination and reduced self-esteem.\u003c/p\u003e\n\u003cp\u003eDetailed regression coefficients and model diagnostics are provided in the \u003cstrong\u003eAdditional File 1\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003eMediation analysis\u003c/h2\u003e\n\u003cp\u003eBased on the preliminary regression findings, only the Surface Study models met the criteria for mediation testing. In these models, the independent variable (surface study approach) significantly predicted the proposed mediator (academic AI usage) and the dependent variables (academic procrastination and academic self-esteem). AI usage also significantly predicted both outcomes. Together, these significant paths satisfy the conditions recommended by Baron and Kenny (\u003cspan class=\"CitationRef\"\u003e1986\u003c/span\u003e) and Hayes (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) for conducting mediation analysis.\u003c/p\u003e\n\u003cp\u003eIn contrast, the Deep Study models were not carried forward because deep study habits did not predict AI usage (\u003cem\u003ep\u003c/em\u003e = .982), and in the self-esteem model, AI usage did not significantly predict the outcome. With these non-significant paths, the necessary conditions for mediation are not supported theoretically or empirically.\u003c/p\u003e\n\u003cp\u003eFigure 3 shows the model examining the mediating role of Academic AI Usage in the relationship between Surface Study Approach and Academic Procrastination.\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003eIndirect and Total Effects (Surface Study \u0026rArr; AI Usage \u0026rArr; Procrastination)\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eType\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEffect\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ez\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIndirect\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurface Study \u0026rArr; AI Usage \u0026rArr; Procrastination\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurface Study \u0026rArr; AI Usage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAI Usage \u0026rArr; Procrastination\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDirect\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurface Study \u0026rArr; Procrastination\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurface Study \u0026rArr; Procrastination\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote.\u003c/em\u003e Betas are completely standardised effect sizes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe mediation analysis showed that AI usage significantly mediated the relationship between surface study habits and academic procrastination. Surface study positively predicted AI usage (\u0026beta;\u0026thinsp;=\u0026thinsp;.38, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and AI usage in turn predicted higher procrastination (\u0026beta;\u0026thinsp;=\u0026thinsp;.17, \u003cem\u003ep\u003c/em\u003e = .001). The indirect effect was significant (Estimate = .07, SE = .03, \u0026beta;\u0026thinsp;=\u0026thinsp;.06, z\u0026thinsp;=\u0026thinsp;2.97, \u003cem\u003ep\u003c/em\u003e = .003), representing 28.3% of the total effect. The direct effect of surface study on procrastination remained significant (\u0026beta;\u0026thinsp;=\u0026thinsp;.16, \u003cem\u003ep\u003c/em\u003e = .002), and the total effect was also significant (\u0026beta;\u0026thinsp;=\u0026thinsp;.22, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). These results indicate partial mediation, suggesting that AI usage partly explains the link between surface study habits and higher procrastination.\u003c/p\u003e\n\u003cp\u003eFigure 4 shows the model examining the mediating role of Academic AI Usage in the relationship between Surface Study Approach and Academic Self-esteem.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003eIndirect and Total Effects (Surface Study \u0026rArr; AI Usage \u0026rArr; Self-esteem)\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eType\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEffect\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEstimate\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026beta;\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ez\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eIndirect\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurface Study \u0026rArr; AI Usage \u0026rArr; Self-esteem\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eComponent\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurface Study \u0026rArr; AI Usage\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAI Usage \u0026rArr; Self-esteem\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eDirect\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurface Study \u0026rArr; Self-esteem\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSurface Study \u0026rArr; Self-esteem\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-2.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.045\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote.\u003c/em\u003e Betas are completely standardised effect sizes.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe mediation analysis revealed that AI usage significantly mediated the relationship between surface study habits and academic self-esteem. Surface study positively predicted AI usage (\u0026beta;\u0026thinsp;=\u0026thinsp;.38, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and AI usage in turn positively predicted self-esteem (\u0026beta;\u0026thinsp;=\u0026thinsp;.14, \u003cem\u003ep\u003c/em\u003e = .008). The indirect effect was significant (Estimate = .04, SE = .02, \u0026beta;\u0026thinsp;=\u0026thinsp;.05, z\u0026thinsp;=\u0026thinsp;2.52, \u003cem\u003ep\u003c/em\u003e = .012), representing 25.8% of the total effect, indicating that greater reliance on surface learning was associated with increased AI usage, which in turn was linked to slightly higher self-esteem.\u003c/p\u003e\n\u003cp\u003eHowever, the direct effect of surface study remained negative and significant (\u0026beta; = \u0026minus;\u0026thinsp;.15, \u003cem\u003ep\u003c/em\u003e = .004), suggesting that, even after accounting for AI usage, surface study independently predicted lower self-esteem. The total effect of surface study on self-esteem was also significant (\u0026beta; = \u0026minus;\u0026thinsp;.10, \u003cem\u003ep\u003c/em\u003e = .045). See Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Overall, these findings indicate partial mediation, suggesting that AI usage somewhat buffers, but does not remove, the negative association between surface study habits and academic self-esteem.\u003c/p\u003e\n\u003cp\u003eTo summarise the findings: a) deep study approaches predicted higher self-esteem but showed no relationship with procrastination or AI usage, b) surface study approaches predicted higher AI usage, and this greater use was associated with higher procrastination, c) surface study habits were linked to lower self-esteem overall; however, AI usage slightly buffered this effect, showing a small positive indirect influence, d) AI usage emerged as a partial mediator, reinforcing procrastination and moderating the self-esteem impact of surface study behaviours. Based on these results, H1 and H2 were rejected, as deep study habits did not predict AI usage and therefore did not produce any mediated effects. In contrast, H3 and H4 were supported, with AI usage demonstrating significant partial mediation in the relationships between surface study habits and both procrastination and self-esteem.\u003c/p\u003e\n\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n\u003ch2\u003eContent analysis\u003c/h2\u003e\n\u003cp\u003eTo get a clearer picture of how students actually use AI in their academic routines, a content analysis was carried out on three questions about their usage patterns. The participants reported the types of AI tools they use (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e), the tasks for which they use AI (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), and the times at which they typically turn to these tools (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Each question included fixed options and an open-ended \u0026ldquo;Other\u0026rdquo; choice, which allowed students to describe uses beyond the predefined categories. Frequencies were calculated for the fixed responses, and the open-ended comments were coded to identify recurring themes.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003eTypes of AI tools used for academic work\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrequency (mentions)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAI-based writing assistants (e.g., Grammarly, Quillbot, ChatGPT)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e383\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAI-based research tools (e.g., Elicit, Scite)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e109\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAI tutors/learning platforms (e.g., Khan Academy AI, Google Socratic)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e92\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAI for coding/programming (e.g., GitHub Copilot)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMost students reported using writing-related tools such as Grammarly, ChatGPT, etc. These were followed by research tools, and learning platforms. AI tools as programming assistants were also relevant among students in technical disciplines. In the \u0026ldquo;Other\u0026rdquo; category, students listed tools like Gemini, Claude, Perplexity, and DeepSeek, which naturally fit into the writing or research categories. A few responses mentioned tools embedded in messaging or social-media platforms (e.g., WhatsApp Meta AI, Snapchat AI Bot), which shows that some students use AI in less formal academic spaces too.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003eAcademic tasks for which AI is typically used\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrequency (mentions)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWriting assignments \u0026amp; reports\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e290\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResearching \u0026amp; summarising content\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e330\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExam preparation \u0026amp; self-study\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e343\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoding \u0026amp; programming tasks\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSolving numerical/statistical problems\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e116\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eStudents primarily used AI for writing, searching and summarising content, and preparing for exams. These three tasks accounted for the largest share of responses. Some also used AI to solve numerical or statistical questions or for coding. The open-ended comments revealed two clear themes. The first related to skill-based support: students mentioned using AI to create practice questions, check citations, interpret visuals, organise their study schedule, and seek feedback on their writing. The second theme involved academic enrichment beyond the curriculum. Several students used AI to explore topics out of curiosity, learn new skills, or think through broader questions in a reflective way. These comments show that AI is not only used for task completion but also for developing skills and pursuing personal learning goals.\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e\u003cem\u003eSpecific times when AI is used\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrequency (mentions)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuring class lectures\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWhile studying at home\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e316\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJust before assignment deadlines\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e242\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuring exams/revision\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e301\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe timing of AI use followed a similar pattern. Most students turned to AI when studying at home or during exam preparation, and many used it just before assignment deadlines. Using AI during lectures was relatively uncommon. The open-ended responses here yielded two themes. Firstly, supplementary use, i.e. some students described using AI after finishing a chapter to clarify doubts, prepare notes, or generate short quizzes for practice. Another subtheme was supportive context, as others mentioned using AI when they felt mentally fatigued, or when helping others. These responses indicate that AI sometimes functions as a supportive tool when students want quick clarification or need help sustaining their focus.\u003c/p\u003e\n\u003cp\u003eTaken together, the findings show that students use AI most often for writing, understanding content, revising, and solving problems. The open-ended responses illustrate additional roles that AI plays in skill-building, organising work, reinforcing learning after study sessions, and providing small pockets of support when students feel stuck or distracted. These patterns offer useful context for interpreting the quantitative results by showing how AI fits into the day-to-day realities of students\u0026rsquo; academic routines.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study examined how deep and surface study approaches relate to academic procrastination and academic self-esteem, and whether these relationships are shaped by students\u0026rsquo; engagement with academic AI tools. While deep and surface approaches are commonly treated as separate learner \u0026ldquo;types,\u0026rdquo; research shows that they operate as flexible tendencies within individuals, shifting with task demands, discipline, and confidence levels (Biggs, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Biggs et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Delgado et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This flexibility is reflected in the present findings, where the two approaches produced sharply diverging psychological patterns.\u003c/p\u003e \u003cp\u003eA consistent pattern emerged for the deep approach: deep study habits were strongly and reliably associated with higher academic self-esteem. This appeared at both the correlational level (r = .38, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and in regression, where deep study remained a significant predictor of self-esteem even after controlling for AI usage (B\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). These results align with Self-Regulated Learning theory, in which planning, monitoring, and deliberate cognitive engagement create mastery experiences that enhance students\u0026rsquo; confidence in their academic abilities (Zimmerman, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Howell \u0026amp; Watson, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). They also reflect Social Cognitive Theory, which emphasises that repeated successful effort builds self-efficacy (Bandura, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Recent research similarly shows that deep engagement predicts stronger academic self-belief and reduced academic anxiety, even in digitally saturated learning environments (Delgado et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chan \u0026amp; Hu, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nakhostin-Khayyat et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Importantly, deep learning showed no significant association with procrastination (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.09, \u003cem\u003ep\u003c/em\u003e = .067) and no relationship with AI usage (r = .001, \u003cem\u003ep\u003c/em\u003e = .982). This suggests that students who adopt deep strategies regulate their work internally rather than depending on external tools. Contemporary studies confirm this pattern: students with higher intrinsic motivation rely less on generative AI for core cognitive tasks and instead use it selectively for planning or feedback support (Jin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhai et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These findings together indicate that deep learning is largely self-sustaining and operates independently of the affordances of AI.\u003c/p\u003e \u003cp\u003eThe disciplinary distribution of the sample also contextualises this pattern. In humanities and social sciences, deep approaches often manifest as critical engagement with texts and synthesis of complex arguments (Qu et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which are strongly tied to self-efficacy and identity development (Raposas \u0026ndash;Rabut, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In science fields, deep approaches are linked with problem-solving, hypothesis testing, and conceptual understanding (Qu et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and these activities similarly build mastery and reinforce confidence (Sandrone, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). That both clusters of students reported higher self-esteem when adopting deep strategies suggests that, across domains, the benefits of deep engagement accrue through intrinsic processes of meaning-making and mastery rather than through technological shortcuts.\u003c/p\u003e \u003cp\u003eThe surface approach revealed a markedly different psychological profile compared to the deep approach. Surface study habits were positively related to academic procrastination at the correlational level (r = .22, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and significantly predicted procrastination in regression, even after accounting for AI usage (B = .19, \u003cem\u003ep\u003c/em\u003e = .002). These results are consistent with longstanding evidence that surface strategies are linked with avoidance, poor time management, and weaker self-regulation (Diseth, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). AI usage further clarified this pattern: it partially mediated the relationship between surface study and procrastination, accounting for about 28.3% of the total effect (β_indirect = .06, \u003cem\u003ep\u003c/em\u003e = .003).\u003c/p\u003e \u003cp\u003eTemporal Motivation Theory (Steel \u0026amp; K\u0026ouml;nig, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) helps explain that when tasks feel aversive and rewards are distant, procrastination becomes more likely. For surface-oriented students, generative AI tools may heighten this imbalance by offering immediate, low-effort outputs, making delay an even more attractive choice. This interpretation aligns with recent empirical work showing that higher reliance on generative AI is associated with increased academic delay, especially when used for shortcuts such as paraphrasing, summarising, or drafting under deadline pressure (Abbas et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Morales-Garc\u0026iacute;a et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The content-analysis results further reinforce the mechanism that students most frequently reported using AI for writing assistance, paraphrasing, summarising, and last-minute academic tasks: patterns captured in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. AI was used heavily during independent study (316 mentions) and immediately before assignment deadlines (242 mentions), and during exam-focused revision (301 mentions). These usage patterns align precisely with surface-oriented tendencies and help explain how AI becomes a tool that supports postponement rather than sustained engagement (Gerlich, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kosmyna et al., 2025). The Technology Acceptance Model (Davis, 1989) also helps clarify the reason AI is being embedded in these surface-oriented pathways. From this perspective, students adopt academic technologies when they perceive them as both useful, easy to use and requiring low effort. This perceived usefulness was especially salient under conditions of time pressure, aligning with content-analysis. Recent studies have similarly reported that students gravitate toward generative AI when it minimises cognitive load and offers quick solutions, particularly in writing-heavy coursework or problem-solving tasks (Jin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Qu et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSurface habits were also negatively related to self-esteem (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.10, \u003cem\u003ep\u003c/em\u003e = .046) and predicted lower self-esteem directly (β_direct\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.15, \u003cem\u003ep\u003c/em\u003e = .004). However, a partial mediation emerged for self-esteem as AI usage produced a small positive indirect effect (β_indirect = .05, \u003cem\u003ep\u003c/em\u003e = .012), amounting to about 25.8% of the total effect. The self-esteem findings can also be interpreted through a Vygotskian lens (Vygotsky, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). AI appears to act as a form of scaffolding, offering immediate support that helps students complete tasks and momentarily feel more capable. Yet, because surface learners typically engage with AI at the level of performance rather than understanding, this scaffolding does not lead to internalisation. Instead, it produces a short-lived boost, which is consistent with the well-documented phenomenon of \u0026ldquo;illusion of competence,\u0026rdquo; where students feel temporarily more confident after receiving polished AI-generated text or instant explanations, even without genuine skill development (Nazaretsky et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bai \u0026amp; Wang, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Qualitative work shows that such boosts arise from the immediacy and fluency of AI feedback, which can create a perception of competence detached from actual understanding (Ma\u0026rsquo;amor et al., 2024; Rodr\u0026iacute;guez-Ruiz et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This pattern fits the present data: surface strategies erode authentic self-esteem, but AI provides a short-lived lift that does not address underlying weaknesses. The absence of any mediation effect in the deep pathway reinforces this interpretation that deep learners do not offload in ways that would produce artificial confidence. Together, these findings suggest that surface learners not only delay tasks and feel less confident but also use AI in ways that compound procrastination while temporarily lifting their sense of competence.\u003c/p\u003e \u003cp\u003eOverall, the results point to a clear psychological divide in how students engage with their work and with AI. Deep strategies seem to carry their own momentum, building confidence through effort and understanding, and leaving little need for AI to alter that pattern. Surface-level study habits, in contrast, make AI especially tempting. When the work feels too much, the deadline is too close, or confidence drops, students turn to AI because it gives quick relief. In those moments, AI doesn\u0026rsquo;t change how they study; rather, it simply strengthens the habits they already have. It helps them get through the immediate stress, but it also quietly encourages more delay and shallow work in the long run. Within the pressures of Indian higher education, where students often face heavy writing loads, exam-oriented curricula, and limited academic support (Thankachan, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Singh, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), it becomes easier to see why AI slots into these pathways the way it does. Rather than functioning as a blanket-solution, AI mirrors the study habits students bring to it \u0026ndash; supporting meaningful engagement when motivation and self-regulation are present, and offering short-term relief when those are lacking.\u003c/p\u003e \u003cp\u003eThe study, however, looked mainly at how frequently and when students used AI, not at how they engaged with it. Future research may explore the depth of AI interaction, a factor that emerging work identifies as critical for understanding AI\u0026rsquo;s educational impact (Kasneci et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Understanding this distinction may be crucial to seeing how AI shapes learning and self-regulation over time.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePractical Implications\u003c/h2\u003e \u003cp\u003eInstitutions can guide students toward more deliberate and process-focused use of AI by embedding structure into how these tools are used. Approaches such as AI-assisted study planning, supervised writing feedback, and reflective checkpoints can help students organise their workload and stay engaged with the material (Mandhare et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Youn et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Learning designs that break complex tasks into stages and include opportunities for feedback can complement these AI-supported practices by encouraging students to monitor their thinking and stay accountable (Howell \u0026amp; Watson, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Panadero, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zimmerman, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and can strengthen confidence without promoting shortcuts (Vieriu \u0026amp; Petrea, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Instructors can draw on these practices by asking students to show how AI contributed to their drafts, explain the revisions they made, or compare AI-generated suggestions with their own thinking. These steps align with broader recommendations for integrating AI in ways that emphasise learning processes rather than rapid output (Kasneci et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, the disciplinary differences in AI usage observed in the sample suggest that AI literacy should not be taught as a one-size-fits-all skill. Writing-intensive fields may need guidance on integrating AI into drafting and revision without undermining originality or critical thinking, whereas scientific and technical programs may need to focus on balancing AI-based problem-solving with conceptual understanding. Tailoring AI literacy to the demands of each field can help students use AI in ways that support, rather than replace, the cognitive processes central to their discipline (Kasneci et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study shows that deep and surface study approaches co-exist within students and shape academic outcomes in distinct ways. Deep strategies were linked to stronger self-esteem and minimal dependence on AI, while surface strategies predicted greater procrastination and lower academic self-esteem, partly explained by opportunistic AI use. These findings show that AI\u0026rsquo;s educational impact depends less on its presence and more on how it is integrated into learning. When used reflectively, AI can support mastery and self-regulation; when used as a shortcut, it may amplify avoidance of tasks. For educators, the priority is to design learning environments that encourage deep engagement: through goal-setting, feedback, and reflective tasks, while guiding students towards constructive, discipline-specific AI use.\u003c/p\u003e \u003cp\u003eFuture research may build on these insights using longitudinal and multi-method approaches to test whether structured, reflective AI use can transform surface learning into meaningful academic growth.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAAIUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcademic AI Usage Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArtificial Intelligence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnstandardized Regression Coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoefficient of Variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eR\u0026sup2;\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoefficient of Determination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eR-SPQ-2F\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRevised Two-Factor Study Process Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eS-CVI/Ave\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eScale Content Validity Index/Average\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-Regulated Learning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariance Inflation Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConsent to participate\u003c/h2\u003e \u003cp\u003e Informed consent was obtained electronically from all participants prior to participation. Data were collected anonymously, and participants were informed about the voluntary nature of their participation and their right to withdraw at any time without penalty.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publish\u003c/strong\u003e \u003cp\u003eNot applicable, as no identifying information or images of participants are included in this manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors did not receive support from any organisation for the submitted work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.C.: Conceptualisation, data collection, data analysis and manuscript draft.D.S.: Supervision, conceptual guidance, and manuscript revision.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge Dr. Priyesh C for his guidance on statistical analyses, Ms. Anandita Datta for her input during the early stages of the study, and Ms. Saptadwipa Paul for assistance with proofreading the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed during the current study contain confidential student responses and are therefore not publicly available. 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Becoming a Self-Regulated Learner: An Overview. \u003cem\u003eTheory Into Practice\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(2), 64\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1207/s15430421tip4102_2\u003c/span\u003e\u003cspan address=\"10.1207/s15430421tip4102_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9043305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9043305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The growing use of Artificial Intelligence (AI) in higher education is reshaping how students approach learning, manage time, and evaluate their abilities. Generative AI systems are increasingly embedded within everyday study practices, raising questions about how these technologies shape learning behaviour and academic outcomes. Rooted in cyberpsychology and educational behaviour research, the present study examines whether academic AI usage mediates the relationship between students’ study approaches, academic procrastination, and academic self-esteem. A quantitative cross-sectional study was conducted with 402 Indian college students using standardised measures of study habits, academic AI usage, procrastination, and academic self-esteem. Results showed that students with surface study habits tended to procrastinate more and reported lower self-esteem, while those with deeper study approaches displayed greater confidence and academic consistency. AI usage partially mediated the relationship between surface study habits and procrastination, indicating that students adopting surface strategies were more likely to incorporate AI into last-minute academic work. AI usage also produced a small positive indirect effect on academic self-esteem, suggesting that AI may function as a temporary form of academic scaffolding that enhances perceived competence without necessarily strengthening deeper learning processes. These findings illustrate how AI-mediated learning practices interact with students’ study approaches, shaping patterns of delay, engagement, and academic self-perception. The study contributes to emerging research on generative AI in higher education by demonstrating that the educational impact of AI depends on how it becomes integrated into students’ learning behaviours.\nKeywords: Artificial Intelligence, Study Habits, Academic Procrastination, Academic Self-Esteem, Higher Education, AI-Mediated Learning, Cyberpsychology","manuscriptTitle":"AI Usage as a Mediator Between Study Habits, Academic Procrastination, and Academic Self-Esteem","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 05:07:40","doi":"10.21203/rs.3.rs-9043305/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5488b1b-18e2-4d42-9bb2-af1931405ffe","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T11:26:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 05:07:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9043305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9043305","identity":"rs-9043305","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}