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Article

Exploring the Impact of Female Student’s Digital Intelligence on Sustainable Learning and Digital Mental Well-Being: A Case Study of Saudi Arabia

by
Norah Muflih Alruwaili
1,
Zaiba Ali
2,
Mohd Shuaib Siddiqui
3,
Asad Hassan Butt
4,*,
Hassan Ahmad
5,
Rahila Ali
6 and
Shaden Hamad Alsalem
1
1
Department of Social Work, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
2
Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
3
Department of Management, Faculty of Business Administration, University of Tabuk, Tabuk 71491, Saudi Arabia
4
Department of Marketing, Faculty of Business Administration, University of Tabuk, Tabuk 71491, Saudi Arabia
5
Business School, Liaoning University, Shenyang 110036, China
6
Faculty of Management, Jagran Lakecity University, Bhopal 462044, MP, India
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(14), 6632; https://doi.org/10.3390/su17146632
Submission received: 18 May 2025 / Revised: 26 June 2025 / Accepted: 14 July 2025 / Published: 21 July 2025
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Abstract

This study investigates the interplay between adaptive online learning, students’ digital intelligence, sustainable learning, and digital mental well-being among female university students in Saudi Arabia. In response to the growing reliance on digital platforms in higher education, a structured questionnaire was distributed via social media to capture student perceptions of their online learning experiences. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), the analysis revealed that while adaptive online learning is a critical enabler, its influence is most effective when mediated by students’ digital intelligence. The findings highlighted that students with higher digital intelligence are more likely to engage in sustainable learning practices and maintain better mental well-being in digital environments. Furthermore, innovative teaching practices were shown to strengthen these relationships, underscoring the importance of interactive and adaptive pedagogies. This research contributes to the growing discourse on digital education by emphasizing the importance of indirect pathways and learner-centred dynamics in shaping positive educational and psychological outcomes. This study offers practical and theoretical implications for educators, institutions, and policymakers aiming to create inclusive, resilient, and psychologically supportive digital learning environments. Future research is encouraged to examine these relationships across different cultural and institutional contexts and explore the longitudinal impacts of digital learning strategies.

1. Introduction

Technological advancements have changed our lives over the last decade and have been integrated into many industries, such as shopping, finance, gaming, and education [1,2,3]. Education, especially post-COVID, has changed and requires new technologies to be integrated to enhance student learning [4]. Studies have shown that personalized learning can cater to the diverse needs of students more effectively [5,6]. Therefore, at the institutional level, there is a demand to understand adaptive learning processes. The use of data-driven approaches with the help of AI technology for online instruction involving customized learning experiences is known as adaptive online learning (AOL) [7]. This approach occurs when faculty members modify their teaching methods to address the diverse needs of students, such as varying learning styles, skill levels, cultural backgrounds, and accessibility requirements. Adaptive online learning (AOL) is built upon student feedback, data assessments, course analytics, and algorithms that can help students achieve curriculum objectives and master the content [8]. Previous scholarly work predicts that adaptive learning can have a stronger impact on student learning experience [9,10].
The roles of women in Saudi Arabia have undergone profound changes over the last two decades, especially in education, employment, and civic engagement, driven by broader national reforms [11,12]. Insights generated by female student researchers can inform policies and design services that better support women’s academic, professional, and psychological well-being, aligning with Saudi Vision 2030 goals [13]. Adaptive online learning (AOL) can work as a transformative tool to provide a customized learning experience by leveraging real-time analytics and artificial intelligence [14]. Adaptive online learning (AOL) can create efficient and inclusive learning environments by tailoring feedback, pace, and content to meet individual student needs, thereby enhancing learner performance. AOL requires further exploration, but its broader implications for digital well-being, sustainable learning, and digital intelligence also require investigation. Digital intelligence refers to the ability to effectively use digital platforms by applying a combination of cognitive, emotional, and social skills [15]. Understanding the impact of adaptive online learning requires a clear explanation of how students develop digital intelligence, digital well-being, and sustainable learning. Digital intelligence is cultivated through structured opportunities that enhance critical thinking, the responsible use of technology, and effective digital communication skills. Here, the current study is focused on students’ digital intelligence and its impact on sustainable learning and digital well-being. Past studies have found that students’ digital intelligence is fostered by adaptive online learning (AOL), encouraging active engagement, responsible digital behaviour, and critical thinking [7,16]. AOL also has the function of guiding and supporting students to track their learning progress, and it helps in the development of metacognitive abilities [17].
Sustainable learning is an educational approach focused on ensuring that knowledge and skills are retained and effectively applied over the long term [18]. It fosters lifelong learning habits, encouraging learners to continuously adapt and grow in changing environments. Sustainable learning develops when students engage in reflective and self-regulated learning practices that promote long-term retention and the ability to adapt knowledge to new contexts [19]. Adaptive online learning (AOL) emphasizes learner autonomy, capabilities, and meaningful engagement with content [20]. With the use of AOL platforms, a learner’s ability can evolve through the retention of knowledge, growth, and development and result in lower dropout rates [21]. Strong, sustainable learning experiences can be developed with such adaptive systems, where students can customize their educational journeys [22]. Keeping a balance with mental and emotional health through digital engagement is known as digital well-being [23]. Digital well-being is fostered as learners acquire strategies to manage their technology use in ways that support their mental and emotional health. AOL platforms can produce less stress and time by combining gamified activities with visualizations that can help a student reduce cognitive fatigue and anxiety while enhancing self-efficacy and motivation [24]. The current study will try to bridge the gap between students’ ability to learn and an adaptive online learning experience. A few research questions can be addressed with the current framework provided in Figure 1. RQ1: How does adaptive online learning influence the development of students’ digital intelligence in higher education settings? RQ2: How does the adaptive online learning experience impact students’ digital well-being and sustainable learning? Moreover, intelligent alerts and timely feedback systems can assist in regulating screen time and encouraging positive digital habits, fostering a more balanced and mindful approach to digital learning.
Innovative teaching can be understood as the thoughtful use of modern educational practices that combine digital tools, flexible instructional methods, and student-centred strategies [25]. Its goal is to create dynamic learning environments that not only convey subject knowledge but also build students’ skills in critical thinking, problem-solving, and collaboration. This can involve the use of AI tools for feedback, digital storytelling, gamification, project-based learning, and flipped classrooms for the students’ learning experience [26,27]. Studies have shown that innovative teaching can support students’ inclusive learning while developing creativity and critical thinking [28,29]. The current framework provided in Figure 1 shows innovative teaching as a moderator. The current study of innovative teaching with AOL can help understand students’ needs for personalization, passive learning, and deeper engagement in learning experiences. The current framework will help in understanding AOL’s influence on students’ digital competencies, digital well-being, and sustainable learning. Female university students conducting academic inquiries on women’s issues offer invaluable, first-hand insights, thereby enriching the depth and authenticity of findings on evolving gender norms [30]. The following sections of this study will focus on the literature review, methodology, data analysis, discussion, and conclusion.

2. Literature Review

2.1. Adaptive Online Learning (AOL) and Constructivist Learning Theory

The COVID-19 pandemic changed the way we think about life, education, and business [14,31]. With technological advancements, there has been more emphasis on digital innovation and smart education systems [32]. The Kingdom of Saudi Arabia, at the moment, is driven by Vision 2030, and it has had a positive impact on rapid educational transformation in recent years [33]. Therefore, the accelerated adoption of adaptive online learning platforms, particularly in higher education, has occurred as institutions have sought scalable, flexible, and student-centred alternatives to traditional instruction. The adaptive online learning systems used include Blackboard Ultra, Madrasati, and international AI-based tools such as CenturyTech and Alef Education [7,34]. These systems personalize learning paths, recommend resources, and provide real-time feedback based on individual student performance and learning behaviour. Despite the progress that has been made, challenges remain, such as faculty resistance, digital literacy gaps, and infrastructure disparities [35]. However, the policy environment is supportive, and the Saudi Digital Learning Initiative (SDL) aims to enhance national capacities in adaptive and AI-driven education.
Adaptive online learning (AOL) is rooted in Constructivist Learning Theory, shaped by Vygotsky’s Zone of Proximal Development (ZPD) and Piaget’s cognitive development principles in particular [36,37]. This theory emphasizes that learners actively construct knowledge through engagement, reflection, and guided support [38]. Adaptive technologies embody this by offering personalized learning pathways tailored to a student’s current level of understanding. AI-driven systems assess each learner’s abilities and provide content just beyond their comfort zone, encouraging growth in line with the ZPD. Features such as multimedia interactions, self-paced modules, and immediate feedback not only support knowledge construction but also create an engaging and motivating learning experience that fosters deeper understanding.
A key benefit of adaptive online learning is its role in enhancing student’s digital intelligence (SDI). Through real-time feedback and tailored digital tasks, students build core digital literacy skills such as online research, information evaluation, and responsible digital problem-solving [39]. Many adaptive platforms also embed elements of cyber responsibility, educating students about digital safety, privacy awareness, and ethical online behaviour [34]. Adaptive systems also significantly contribute to sustainable learning (SLR) by encouraging students to become self-regulated learners [40]. Personalized pacing, goal-setting features, and reflective prompts empower students to take ownership of their learning journey [22]. This fosters a lifelong learning mindset, where learners remain curious, motivated, and adaptable. In the context of Saudi Arabia’s Vision 2030, which aims to cultivate a digitally skilled and knowledge-based society, adaptive online learning helps reduce learning fatigue and dropout rates by tailoring educational experiences to individual needs and capacities. This sustainable approach ensures students build resilience and confidence for continuous personal and professional development. In Figure 1, we can see the variables involved in the current framework.
Most notably, adaptive learning platforms have a positive impact on students’ digital mental well-being (MWB). By aligning content delivery with each learner’s cognitive readiness, these systems help alleviate mental overload, thereby reducing stress and frustration during the learning process [41]. By organizing study time and fostering purposeful digital engagement, adaptive platforms support the development of healthier screen time habits. Additionally, elements such as gamified activities, constructive feedback, and progress monitoring help strengthen emotional resilience, boost motivation, and foster a sense of achievement [42]. In Saudi Arabia, where young learners are frequently exposed to extended screen time, adaptive online learning (AOL) systems that integrate well-being features can play a vital role in preventing digital burnout, encouraging balanced technology use, and supporting students’ mental health within digital learning environments. Therefore, we can assume the following:
H1. 
Adaptive online learning has a positive impact on digital mental well-being.
H2. 
Adaptive online learning has a positive impact on sustainable learning.
H3. 
Adaptive online learning has a positive impact on student digital intelligence.

2.2. Student Digital Intelligence (SDI)

Developing student digital intelligence (SDI) is becoming increasingly essential in modern educational environments, especially as learners navigate complex online ecosystems [43]. SDI encompasses more than technical skills; it includes digital literacy, ethical online behaviour, critical thinking, and the ability to manage digital tools effectively [44]. Adaptive online learning platforms play a vital role in cultivating these competencies by offering personalized tasks, feedback, and interactive learning environments that mirror real-world digital challenges. As students engage with these adaptive systems, they learn how to evaluate information credibility, protect their privacy, and interact responsibly in digital spaces [45]. This development is particularly relevant in higher education, where digital competence is a prerequisite for academic success and future employability [46].
Importantly, higher levels of digital intelligence directly support both sustainable learning and digital mental well-being. Students who possess strong digital skills are more likely to self-regulate their learning, manage their online time effectively, and cope with digital stressors [47,48]. Adaptive learning environments, aligning their content with individual readiness and providing timely feedback, reduce information overload and help learners maintain focus and motivation [49]. This promotes long-term engagement and minimizes burnout—key elements of digital well-being [50]. Furthermore, the ability to reflect on digital behaviours through dashboards and progress tracking enhances metacognition and fosters sustainable learning habits such as goal-setting and continuous improvement [51]. As such, enhancing SDI through adaptive technologies contributes to the holistic development of learners, ensuring they thrive both academically and emotionally in digital learning spaces. Therefore, we can assume the following:
H4. 
Student’s digital intelligence positively impacts sustainable learning.
H5. 
Student’s digital intelligence positively impacts digital mental well-being.

2.3. Sustainable Learning (SLR)

Sustainable learning (SLR) refers to the ability of learners to continuously build, retain, and apply knowledge over time through self-regulated, goal-driven, and reflective practices [52]. In digital environments, this form of learning is closely tied to the promotion of digital mental well-being, as it encourages balance, autonomy, and meaningful engagement with technology. Grounded in Self-Determination Theory [53]—which highlights autonomy, competence, and relatedness as essential psychological needs—sustainable learning supports learners by fostering personal choice, mastery, and meaningful connections within their educational journey [54]. When learners are supported to pace their studies, set personal goals, and reflect on their progress, they are less likely to experience digital fatigue, anxiety, or cognitive overload—challenges commonly associated with unstructured online learning [55]. Sustainable learning environments that integrate personalized feedback and adaptive tools satisfy the need for competence and autonomy, helping students maintain a sense of control and purpose, thereby enhancing both motivation and emotional stability [56]. This connection suggests that cultivating sustainable learning habits not only strengthens academic outcomes but also protects learners’ mental health in technology-rich settings, particularly when screen time is high and digital distractions are prevalent [57]. We can assume the following:
H6. 
Sustainable learning positively impacts digital mental well-being.

2.4. Moderating Role of Innovative Teaching

In the rapidly evolving digital learning environment, the integration of adaptive online learning platforms is transforming educational practices [9]. As these platforms become more sophisticated, students are expected to develop and utilize digital intelligence—an emergent concept that refers to the cognitive, emotional, and social skills required to effectively navigate digital environments [8,22]. However, the effectiveness of this development does not solely depend on the availability of adaptive learning systems; it also depends on how these technologies are implemented by educators. This is where innovative teaching emerges as a crucial moderator. These platforms promote personalized learning, encourage engagement, and support differentiated instruction. However, without the guidance of innovative teaching practices, the full potential of adaptive technologies may not be realized. Innovative teaching is characterized by the integration of technology [26]. Without innovative teaching, students may use adaptive tools passively, limiting opportunities to build deeper digital competencies. Thus, innovative teaching may strengthen the positive impact of adaptive learning on student’s digital intelligence by fostering curiosity, digital responsibility, and the ability to learn autonomously in virtual contexts.
Students with high digital intelligence are more likely to achieve sustainable learning outcomes because they can leverage digital tools for research, collaboration, and problem-solving [58]. However, the transition from digital intelligence to sustainable learning is not automatic. Innovative teaching plays a vital role in moderating this relationship. Instructors who use innovative methods can design learning experiences that encourage students to apply their digital competencies across real-life scenarios, reinforcing long-term retention and adaptability [59]. For example, when educators integrate project-based learning using real-world digital platforms or simulate industry-specific digital tasks, students begin to internalize how their digital skills translate into professional and personal contexts. This may foster deep learning and reinforce sustainability in knowledge and skill development [60]. On the other hand, traditional or rigid teaching approaches may fail to activate the full spectrum of students’ digital intelligence, resulting in surface learning or short-term knowledge gains. Therefore, innovative teaching amplifies the influence of digital intelligence on sustainable learning by ensuring that students can meaningfully connect, transfer, and apply what they know in diverse digital and non-digital settings. We can assume the following:
H7. 
Innovative teaching moderates the relationship between adaptive online learning and students’ digital intelligence.
H8. 
Innovative teaching moderates the relationship between adaptive online learning and sustainable learning.

2.5. Mediating Role of Student’s Digital Intelligence

Adaptive online learning systems are designed to respond dynamically to learners’ needs, offering personalized pathways, feedback, and pacing [8,40]. While these systems enhance engagement and comprehension, their long-term impact on sustainable learning—defined as the ability to retain and apply knowledge across contexts over time—may depend on how effectively students internalize and use digital tools. This is where student digital intelligence (SDI) becomes crucial. Digital intelligence involves not only technical proficiency but also metacognitive awareness, digital ethics, and the capacity for autonomous learning in digital environments [61]. Students who possess high levels of digital intelligence are better equipped to extract value from adaptive systems by managing their learning pace, curating resources, and critically engaging with content [62]. Therefore, digital intelligence acts as a bridge, transforming the personalized experience provided by adaptive online learning into meaningful, long-term educational gains. In other words, adaptive learning platforms set the stage, but student digital intelligence directs the performance toward sustainability.
As students increasingly interact with adaptive online platforms, their digital mental well-being (DWB) helps manage stress and screen time, producing better healthy digital habits [63]. However, adaptive platforms alone do not guarantee positive mental well-being. In fact, without the right skill set, students might experience decision fatigue, distractions, or anxiety due to complex digital interfaces and continuous feedback loops [64]. High digital intelligence allows students to critically assess the information presented, set digital boundaries, practice self-discipline, and avoid digital burnout [65]. It promotes not only effective learning but also digital resilience—the ability to thrive in online environments while maintaining psychological balance. As such, the presence of SDI may mediate the relationship between adaptive systems and mental well-being, ensuring that personalization leads to empowerment, not stress. We assume the following:
H9. 
Student’s digital intelligence mediates the relationship between adaptive online learning and sustainable learning.
H10. 
Student’s digital intelligence mediates the relationship between adaptive online learning and digital mental well-being.

3. Methodology

This research employed a quantitative research design with a survey-based approach to examine the hypothesized relationships between variables. The rationale for adopting a quantitative strategy lies in its capacity to provide statistical evidence, test theoretical constructs, and enhance the generalizability of findings [66]. A structured, self-administered questionnaire was developed, drawing upon validated instruments used in previous empirical studies to ensure content validity and theoretical alignment [67].

3.1. Questionnaire Design and Distribution

The questionnaire was designed using closed-ended questions anchored on a five-point Likert scale (ranging from “strongly disagree” to “strongly agree”) to capture participants’ perceptions and agreement levels regarding each construct. Measurement items for each variable were carefully adapted from previously validated scales to maintain consistency with the theoretical model. The questionnaire was first reviewed by two academic experts for clarity and content adequacy and then pilot-tested with 20 respondents to confirm reliability and comprehension before full-scale distribution.
In response to the increasing digital engagement of the target demographic, the questionnaire was hosted on an online survey platform (Google Forms) to ensure accessibility and ease of data collection. It was disseminated through various social media platforms, including WhatsApp, Facebook, and Instagram. These platforms were chosen due to their high usage rates among young adults and students in Saudi Arabia, thus enabling a broader reach and convenience for participation [68]. In order to empirically quantify the constructs of this research, a standardized survey instrument was constructed using well-established and previously published scales, and items were modified based on the context of this research. All items were rated using a 5-point Likert scale with responses 1 (“Strongly Disagree”) to 5 (“Strongly Agree”). Adaptive online learning was measured through 7 items adapted from [69,70]. Items included “I had a clear understanding of the aims and goals of the lessons”. Innovative teaching was assessed using 4 items and adapted from [71]. The scale focused on the ability of teachers to apply new teaching approaches and technology-enabled pedagogical practices to enhance learning results. Items such as digital mental well-being were captured using 7 items adapted from Li and Wang [71] and used to measure students’ psychological well-being and emotional stability in virtual learning spaces, such as their capacity to deal with stress, remain engaged, and achieve a digital–life balance. The student’s digital intelligence was assessed using 8 items from [72]. Items included “My ability to build a wholesome online and offline identity” and “My ability to understand, mitigate, and manage various cyber-risks through the safe, responsible, and ethical use of technology”. Sustainable learning was assessed using 6 items from Li and Wang [71] and Yassin et al. [73], and this scale assessed to what degree students adopt learning habits that are long-term, self-directed, and environmentally and socially sustainable. The adapted items were piloted by educationally qualified experts in education and electronic learning to ensure contextual suitability and understanding. Some minor adjustments in wording were made to ensure that the statements were suitable for the electronic learning paradigm of the study.

3.2. Target Population and Sampling Technique

The target population for this study comprised female students currently enrolled in bachelor’s and master’s degree programmes at universities in Saudi Arabia. This specific demographic was chosen due to its relevance to the research objectives and the increasing academic and social engagement of women in the Saudi educational landscape, in line with Vision 2030 reforms. Given the exploratory nature and practical constraints of this study, a non-probability convenience sampling technique was employed. While this method may limit generalizability, it offers practical advantages in terms of accessibility and rapid data collection, especially in online research contexts [74]. Participants were invited voluntarily and informed about the purpose, duration, and confidentiality measures associated with the survey.

3.3. Data Collection and Cleaning

A total of 307 responses were collected during the data collection period. After careful screening for completeness, consistency, and data quality—such as the removal of straight-lining responses, logical inconsistencies, and incomplete submissions—a final dataset comprising 284 valid responses was retained for statistical analysis. These valid responses were processed using data-cleaning procedures in Excel and SPSS 29.0.10 to ensure accuracy and reliability. The following Table 1 represents the demographic profile.

3.4. Data Analysis Method

To evaluate the hypothesized relationships between constructs, this study employed Partial Least Squares Structural Equation Modelling (PLS-SEM) using SmartPLS 4.0 software. PLS-SEM was selected as the preferred analytical technique due to its robustness in handling complex models, its suitability for exploratory research, and its ability to work effectively with moderate sample sizes and non-normal data distributions [67]. The method is particularly valuable when the research objective includes both prediction and theory development. The analytical process involved two major stages: assessments of the measurement model and the structural model. In the measurement model, this study tested construct reliability (using Cronbach’s alpha and Composite Reliability), convergent validity (through Average Variance Extracted—AVE), and discriminant validity (using the Fornell–Larcker criterion and HTMT ratios). In the structural model, the path coefficients, coefficient of determination (R2), and effect sizes (f2) were used to evaluate the strength and significance of hypothesized relationships. In addition, a bootstrapping procedure with 5000 resamples was conducted to determine the significance levels of the path coefficients and ensure the robustness of the results.

4. Data Analysis

4.1. Measurement Model

Three general measures were used to establish the measurement model: internal consistency reliability, convergent validity, and discrimination validity. As can be seen in this table, all constructs met the requirements, indicating the adequacy of the measurement model. Cronbach’s alpha and Composite Reliability (CR) were used to assess internal consistency reliability. According to Sarstedt et al. [75], a Cronbach’s alpha of 0.7 or more indicated acceptable reliability, and a value of more than 0.8 was considered highly reliable. Furthermore, with a Composite Reliability (CR) score above 0.7, the construct can be considered internally consistent. All the Cronbach’s alpha and CR values that we obtained from this study were higher than the recommended value, indicating that the measurement items meaningfully reflected the respective constructs. Average Variance Extracted (AVE), which shows the common variance of items of a particular latent construct, was used to analyze this method’s convergent validity.
Fornell and Larcker [76] suggested that the AVE value should be greater than or equal to 0.5, which means that 50% or more of the variance in the construct should be accounted for by its indicators. Based on the results of all constructs, these values were above the threshold of 0.7, which means that the items shared a strong correlation with each of the constructs, and we were able to measure the underlying theoretical purpose well. Table 2 contains the reliability and validity results.
Discriminant validity was examined through the Fornell–Larcker criterion and Heterotrait–Monotrait (HTMT) ratio (See Table 3). The square root of AVE for each construct should always be greater than correlations with other constructs according to the Fornell–Larcker criterion, confirming that each construct is distinguishable. All constructs met this criterion, confirming discriminant validity. Furthermore, the Heterotrait–Monotrait (HTMT) ratio, which was suggested by Henseler et al. [77], was also examined to further assess discriminant validity. A common cut-off value that is considered acceptable is HTMT < 0.85, but in more exploratory research, a conservative cut-off value of HTMT < 0.90 is often used for conceptually distinct constructs. All HTMT values in this study were below the recommended threshold, demonstrating the discriminant validity of constructs [77]. The measurement model was found to have high reliability, strong convergent validity, and well-established discriminant validity. The results confirmed that the constructs were statistically sound and could, therefore, be subjected to structural model analysis.

4.2. Structural Model and Discussion

Path coefficients (β), t-values, and p-values were used to assess the structural model results (the significance of the hypothesized relationships) 38. The results indicate support for most of the direct effects, except for two non-significant relationships. Moreover, the mediation analysis shows that student’s digital intelligence fully mediates the relationship between adaptive online learning and sustainable learning and adaptive online learning and digital mental well-being. The following contains detailed explanations for each hypothesis.
H1 Adaptive online learning → digital mental well-being (β = 0.010, t = 0.205, p = 0.837) is not supported by the results illustrated in Table 4. Adaptive online learning would have a direct positive effect on sustainable learning. However, as the relationship is statistically insignificant, it is evident that adaptive online learning does not have any direct positive effect on sustainable learning. This result seems to point to the fact that the presence of adaptive learning technologies alone does not ensure sustainable learning. One possible explanation is that while adaptive learning enhances engagement and knowledge acquisition, it does not inherently ensure that students develop sustainable learning habits unless other cognitive and behavioural factors, such as digital intelligence, mediate the relationship. This is consistent with earlier research that highlights the role of critical thinking, self-regulation, and digital literacy in sustainable learning [78].
Adaptive online learning also has no direct effect on sustainable learning (H2), as shown by the results. Although adaptive learning systems can tailor their content and pace, these features might not contribute to reducing digital stress and enhancing mental well-being. Adaptive learning can also add to cognitive overload or anxiety under some circumstances, particularly if students feel they need to hit personalized performance benchmarks [79]. Digital intelligence can mediate the effects of dynamic learning and well-being: the results show that this effect is mediated by digital intelligence, which can lead to cognitive overload and poor coping with online learning or the maintenance of student well-being.
The results illustrate a significant positive relationship between adaptive online learning and students’ digital intelligence (H3). Adaptive learning systems typically improve students’ capacity to navigate digital space, build critical reading skills, and behave ethically in their digital lives [80], leading to higher digital intelligence. These results underscore the need for effective adaptive learning environments that promote both content knowledge and student’s ability to make informed digital decisions and develop technological adaptability. The results also show that individuals who score as more digitally intelligent exhibit more sustainable learning behaviours (H4). Now, this aligns with studies that posit that students with better digital skills, information literacy, and self-regulation are more inclined to form long-term learning strategies [81]. A higher level of abstract learning, which is referred to as digital intelligence, makes learners better at framing things according to their relevance, engaging in critical negotiation, and being adaptable enough to adopt changes in technology that would allow them to keep up with learning even after formal learning ends.
The results indicated a positive and strong relationship between students’ digital intelligence and digital mental well-being (H5). This means that as stronger digital awareness, self-regulation, and online safety skills develop, lower digital stress is experienced (along with better mental health outcomes) [82,83]. When students learn how to avoid online distractions, prevent digital fatigue, and keep their learning environment balanced, their mental well-being increases, thereby helping to solidify digital intelligence as a vital component of adaptive learning environments. The findings also validate the fact that sustainable learning significantly improves digital mental well-being (H6). Such forms of learning ultimately lead to stress-free and self-calibrated learners, who have performed substantial learning due to deep study, critical reflection, and self-pacing [71,84]. Notably, it indicates that students’ psychological well-being may benefit from creating self-regulated and meaningful learning experiences that ease anxiety about performance pressure and digital overload.
The interaction effect states that innovative teaching boosts the effect of adaptive online learning on students’ digital intelligence (H7). Educators contribute to the development of students’ digital intelligence by combining adaptive learning platforms with interactive forms of teaching, gamification, and problem-based learning [85]. This is where pedagogical innovation is relevant to improving adaptive learning. The findings also reveal that innovative teaching strengthens the relationship between student’s digital intelligence and sustainable learning (H8). This indicates that students apply their digital intelligence in a long learning experience when they are exposed to creative and interesting teaching formats [86]. This finding highlights the role of teachers as facilitators of digital intelligence and sustainable learning [87].
The results in Table 5 indicate that H9 is significant and supports the contention that students’ digital intelligence fully mediates the relationship between adaptive online learning and sustainable learning. This means that adaptive learning alone is not enough to promote sustainable learning; instead, it fosters digital intelligence, which, in turn, enables students to engage in long-term learning behaviours. Likewise, the findings demonstrate that students’ digital intelligence completely mediates the impact of adaptive online learning on digital mental well-being. This means that adaptive learning can only have a positive effect on mental well-being if it indeed supports students in becoming more digitally intelligent regarding screen time, distraction, and healthy digital habits. This study highlights the importance of student’s digital intelligence as a mediator between adaptive online learning, sustainable learning, and digital mental well-being, as evidenced by the reported statistics. These findings suggest that technology alone does not guarantee success; students need to strengthen their digital intelligence skills to take full advantage of adaptive learning environments. Furthermore, innovative teaching methods are essential in maximizing these impacts and information is explained in the (Figure 2).

5. Discussion

This study aimed to explore how adaptive online learning (AOL), student’s digital intelligence (SDI), sustainable learning (SLR), and innovative teaching (INT) practices influence digital mental well-being (DWB) in female students enrolled in higher education programmes in Saudi Arabia. The findings revealed that while adaptive online learning alone does not have a significant direct effect on students’ mental well-being or sustainable learning, its indirect impact—especially through the enhancement of digital intelligence—is highly influential. Digital intelligence emerged as a central factor in this model. Students who demonstrated higher digital intelligence were more likely to engage in sustainable learning and maintain positive digital mental well-being. This suggests that having the technical and cognitive capacity to navigate digital environments can help students better manage online learning demands, supporting earlier research emphasizing the importance of digital literacy and self-regulated learning [23,24].
Moreover, sustainable learning was found to be a strong predictor of digital mental well-being, implying that when students are involved in meaningful, self-directed, and future-oriented learning, they are more likely to experience reduced stress and a sense of fulfilment. This supports theories from positive education and lifelong learning, which link personal growth in learning with psychological resilience [20,88]. The role of innovative teaching was also found to be significant. When educators applied modern, engaging teaching practices, the positive effects of AOL on digital intelligence and, consequently, on sustainable learning were strengthened. This aligns with the idea that pedagogy matters just as much as technology. Effective digital education requires not only the right tools but also the right instructional strategies [89]. Finally, mediation analysis showed that digital intelligence serves as a bridge between adaptive online learning and the desired learning and mental well-being outcomes. This underscores the idea that technologies are not inherently transformative; their value depends on how well students are equipped to use them. These results echo the growing consensus that developing digital competence is essential for success in modern learning environments [46,90].

5.1. Theoretical Implications

This study provides empirical support for the growing body of literature that places digital intelligence at the heart of effective online learning. It reinforces digital education frameworks, suggesting that learners’ digital competencies directly influence learning outcomes and personal development in digital settings [46]. The findings affirm [91] the constructivist view that students build knowledge through interactions with their environment, mediated, in this context, through adaptive technologies and intelligent learning practices. Digital intelligence acts as the key enabler for this process in the modern educational context. The role of innovative teaching in moderating AOL’s impact suggests that different technological education frameworks remain highly relevant. It emphasizes that teachers not only need subject expertise but also the ability to integrate pedagogy with digital tools effectively [92].
Thirdly, the findings of this study emphasize a meaningful connection between sustainable learning and digital mental well-being. When students engage in learning that is purposeful, continuous, and personally relevant, they are more likely to experience psychological satisfaction and reduced feelings of digital fatigue or burnout. This suggests that sustainable learning environments—where learners are encouraged to take ownership of their education, reflect critically, and connect learning to practical or future applications—can nurture a sense of agency, confidence, and emotional balance in digitally mediated settings [18,40]. Rather than being overwhelmed by the constant demands of technology, students who find meaning and value in their learning are better positioned to cope with the pressures of online environments.
Finally, this research highlights the value of exploring indirect pathways when studying the effectiveness of digital learning tools. The findings indicate that adaptive online learning alone may not be sufficient enough to improve student outcomes unless it also fosters key learner attributes, such as digital intelligence—the capacity to navigate, evaluate, and apply digital information effectively. This intermediary role reinforces the idea that technology’s influence on learning is not always direct; instead, it often works through the development of cognitive and emotional capacities that shape how learners interact with content [15,43]. By modelling these mediating relationships, researchers can move beyond surface-level cause-and-effect assumptions and instead build a richer, more learner-centred understanding of how educational technologies operate in real-world contexts. This shift in perspective is essential for designing interventions that are responsive to students’ developmental needs and psychological well-being rather than merely delivering content more efficiently.

5.2. Practical Implications

Educational institutions should provide targeted workshops and instructional modules aimed at strengthening students’ digital intelligence, with a particular emphasis on critical thinking, effective digital communication, information management, and maintaining well-being in online environments. They should be offering structured training programmes and workshops focused on enhancing students’ digital competencies, particularly in self-regulation, digital communication, cyber safety, and ethical usage. These capabilities are essential to maximize the effectiveness of adaptive learning environments. Teachers should be equipped with training that enables them to implement innovative, student-centred pedagogical strategies aimed at increasing student engagement and improving the effectiveness of adaptive online learning. Curriculums should be redesigned and reimagined to foster lifelong learning, emphasize project-based approaches, and incorporate sustainability-related themes into academic content. Such reforms have the potential to enhance academic performance while also supporting the development of psychological resilience among students.
University digital strategies should incorporate mental health resources—such as virtual counselling, digital wellness content, and peer support systems—embedded into learning management systems. This can ensure holistic student development in online and hybrid learning models. Faculty development programmes should prioritize innovative pedagogical strategies, including gamification, flipped classroom models, and personalized feedback. These approaches have been shown to significantly enhance the effectiveness of adaptive online learning (AOL) and contribute to the development of students’ digital intelligence, resulting in a more impactful and engaging learning experience. Policymakers in higher education should address digital equity by ensuring that all students have access to the necessary infrastructure and support services needed to develop digital intelligence.

6. Conclusions

This study provides meaningful insights into the impact of adaptive online learning environments on student development, with a particular focus on enhancing digital intelligence and encouraging sustainable learning practices that support digital mental well-being. It underscores the importance of designing digital learning experiences that are not only flexible and engaging but also promote both cognitive and emotional growth. Digital intelligence has emerged as a key factor, highlighting the need for educational institutions to integrate digital literacy, critical thinking, and responsible online behaviour into contemporary curricula. Furthermore, the link between emotionally engaging learning experiences and student well-being reinforces the significance of learner-centred approaches that address both academic success and psychological resilience. As digital learning becomes increasingly integrated into higher education, future research should examine how these relationships evolve, how institutional frameworks can enhance these outcomes, and how students from diverse backgrounds experience and benefit from such innovations. An ongoing exploration into these areas is vital for building inclusive, comprehensive, and future-oriented digital education systems.

Author Contributions

Conceptualization, A.H.B.; Methodology, N.M.A. and H.A.; Software, H.A.; Validation, N.M.A. and M.S.S.; Formal analysis, M.S.S., H.A. and S.H.A.; Investigation, Z.A. and S.H.A.; Resources, Z.A. and R.A.; Data curation, R.A.; Writing—original draft, A.H.B.; Writing—review & editing, Z.A. and M.S.S.; Visualization, A.H.B.; Supervision, N.M.A.; Funding acquisition, N.M.A. All authors have read and agreed to the published version of the manuscript.

Funding

The authors extend their appreciation to the Deanship of Scientific Research and Libraries in Princess Nourah bint Abdulrahman University for funding this research work through the Research Group project, Grant No. (RG-1445-0072).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of KACST, KSA (HAP-01-R-059, approval date 20 March 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Conceptual framework.
Figure 1. Conceptual framework.
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Figure 2. Structural model. Source: SmartPLS software-generated output.
Figure 2. Structural model. Source: SmartPLS software-generated output.
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Table 1. Demographics of respondents.
Table 1. Demographics of respondents.
Demographic VariableCategoryFrequency (N = 284)
Age Group18–23 years198
24–28 years66
29+ years20
Education LevelBachelor’s201
Master’s83
Family Income (Monthly)Below 50,000 SAR63
50,000–100,000 SAR167
100,001–150,000 SAR46
Above 150,000 SAR8
Table 2. Reliability and validity results.
Table 2. Reliability and validity results.
ConstructsItem CodeFactor LoadingsCronbach’s AlphaComposite Reliability Average Variance Extracted (AVE)
Adaptive Online LearningAOL10.7910.8860.9100.591
AOL20.726
AOL30.742
AOL40.815
AOL50.815
AOL60.750
AOL70.738
Innovative TeachingINT10.6500.8120.8760.641
INT20.872
INT30.776
INT40.883
Digital Mental Well-BeingMWB10.8680.9190.9330.666
MWB20.857
MWB30.868
MWB40.790
MWB50.790
MWB60.763
MWB70.767
Student’s Digital IntelligenceSDI10.6870.8730.8990.527
SDI20.713
SDI30.688
SDI40.773
SDI50.692
SDI60.766
SDI70.752
SDI80.729
Sustainable LearningSLR10.8640.9500.9600.799
SLR20.898
SLR30.897
SLR40.912
SLR50.887
SLR60.903
Table 3. Discriminant validity.
Table 3. Discriminant validity.
HTMT Ratio12345
1. Adaptive Online Learning
2. Innovative Teaching0.162
3. Digital Mental Well-Being0.3560.326
4. Student’s Digital Intelligence0.5940.4260.619
5. Sustainable Learning0.4730.3830.6960.714
Fornell-Larker Criterion12345
1. Adaptive Online Learning0.769
2. Innovative Teaching0.1490.800
3. Digital Mental Well-Being0.3740.2900.816
4. Student’s Digital Intelligence0.5360.3800.5960.726
5. Sustainable Learning0.4540.3460.6870.6820.894
Table 4. Hypothesis-testing bootstrapping @5000 subsamples.
Table 4. Hypothesis-testing bootstrapping @5000 subsamples.
RelationshipsβSDtp ValuesDecision
H1Adaptive Online Learning → Digital Mental Well-Being0.0100.0510.2050.837Not Supported
H2Adaptive Online Learning → Sustainable Learning0.1060.0571.8760.061Not Supported
H3Adaptive Online Learning → Student’s Digital Intelligence0.4880.0697.0400.000Supported
H4Student’s Digital Intelligence → Sustainable Learning0.5950.05710.3790.000Supported
H5Student’s Digital Intelligence → Digital Mental Well-Being0.2360.0763.1050.002Supported
H6Sustainable Learning → Digital Mental Well-Being0.5210.0677.7380.000Supported
H7Innovative Teaching × Adaptive Online Learning → Student’s Digital Intelligence0.1830.0762.3970.017Supported
H8Innovative Teaching × Student’s Digital Intelligence → Sustainable Learning0.0840.0422.0200.043Supported
SD = standard deviation; t = t-statistics.
Table 5. Mediation analysis.
Table 5. Mediation analysis.
RelationshipsβSDtp ValuesDecision
H9Adaptive Online Learning → Student’s Digital Intelligence → Sustainable Learning0.2900.0495.9580.000Full Mediation
H10Adaptive Online Learning → Student’s Digital Intelligence → Digital Mental Well-Being0.1150.0422.7200.007Full Mediation
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Alruwaili, N.M.; Ali, Z.; Siddiqui, M.S.; Butt, A.H.; Ahmad, H.; Ali, R.; Alsalem, S.H. Exploring the Impact of Female Student’s Digital Intelligence on Sustainable Learning and Digital Mental Well-Being: A Case Study of Saudi Arabia. Sustainability 2025, 17, 6632. https://doi.org/10.3390/su17146632

AMA Style

Alruwaili NM, Ali Z, Siddiqui MS, Butt AH, Ahmad H, Ali R, Alsalem SH. Exploring the Impact of Female Student’s Digital Intelligence on Sustainable Learning and Digital Mental Well-Being: A Case Study of Saudi Arabia. Sustainability. 2025; 17(14):6632. https://doi.org/10.3390/su17146632

Chicago/Turabian Style

Alruwaili, Norah Muflih, Zaiba Ali, Mohd Shuaib Siddiqui, Asad Hassan Butt, Hassan Ahmad, Rahila Ali, and Shaden Hamad Alsalem. 2025. "Exploring the Impact of Female Student’s Digital Intelligence on Sustainable Learning and Digital Mental Well-Being: A Case Study of Saudi Arabia" Sustainability 17, no. 14: 6632. https://doi.org/10.3390/su17146632

APA Style

Alruwaili, N. M., Ali, Z., Siddiqui, M. S., Butt, A. H., Ahmad, H., Ali, R., & Alsalem, S. H. (2025). Exploring the Impact of Female Student’s Digital Intelligence on Sustainable Learning and Digital Mental Well-Being: A Case Study of Saudi Arabia. Sustainability, 17(14), 6632. https://doi.org/10.3390/su17146632

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