Abstract
Background
The influence of technology on education is evident. Nonetheless, its impact on students’ well-being and functioning remains an essential research topic. This study examined how digital cognitive load mediated the relationship between healthcare students’ well-being and their use of multiple educational technology platforms.
Methods
A descriptive, correlational cross-sectional methodology was employed to conveniently choose a sample of 160 healthcare students from seven departments within the college. Self-administered questionnaires functioned as the principal instruments for data collection. The subsequent data analysis incorporated both descriptive and inferential methods. The Amos software was utilized to examine the mediation model.
Results
A statistically significant negative correlation was identified between students’ scores across multiple educational technology platforms and their digital cognitive load and well-being, respectively, at (r = − 0.546, p = 0.000) and (r = − 0.61, p = 0.000). Multiple educational technology platforms positively correlated with digital cognitive load scores (r = 0.635, p = 0.000). According to the regression analysis, students’ digital cognitive load could predict their well-being (F (1, 158) = 53.32, p <.001, adj. R2 = 0.404). The mediation analysis indicated a substantial direct effect of emotional intelligence on academic stress when the mediator was present (β = − 0.4, SE = 0.037, Z = − 10.81, p =.000).
Conclusion
Learning institutions must evaluate the number of platforms used. Excessive platform utilization can adversely affect well-being and elevate cognitive stress. Training courses on appropriate technology utilization can enhance students’ confidence and engagement with the platforms, alleviating the mental burden.
Supplementary information
The online version contains supplementary material available at 10.1186/s12912-025-03655-z.
Keywords: Cognitive psychology, Cognitive dissonance, Cognitive exhaustion, Cognitive orientation, Psychological well-being, Physical well-being, Medical education, Educational technology, Digital technology
Introduction
The increasing integration of digital tools has expanded access to educational resources [1]. However, the use of several digital platforms has introduced new concerns, which include the mental health and well-being of students [2]. As the platforms become available, the large number of platforms creates some add-on modes of learning, which in turn results in a significant increase in demand with the switching between multiple digital platforms and the processing of different sources or data at the same time [3]. The increasing use of digital platforms in research has led to several questions being raised about the amount of research that must be carried out on a fundamental understanding of whether the digital shift will have an impact on both academic performance and the mental health of the student population [4].
The complexities of digital learning in healthcare education have grown substantially in recent years [5, 6]. Numerous educational institutions swiftly transitioned to remote and digital platforms for clinical training [7]. This shift has been accompanied by a significant rise in both the availability of interactive learning tools and the cognitive demands they place on students [8]. Training in technology tools prepares students for real-world clinical environments. However, it has now become significant that there are concerns raised regarding the control of many platforms that students are using at the same time, which can lead to the development of stress, fatigue, as well as the reduction of attention to the task and a decrease in the overall learning and well-being of the students [9]. In the longer term, more research should be carried out to determine whether there will be any negative impact on the overall learning activities carried out by students and the well-being of these individuals [10].
John Sweller first introduced Cognitive Load Theory (CLT) in the 1980s, providing a foundational framework to understand the learning process. CLT breaks learning down into three key components: the learning process itself, the types of cognitive load influencing learning, and memory systems, specifically working memory and long-term memory [11]. According to CLT, learners have limited working memory capacity, which can be overwhelmed when too much information is presented or when tasks are overly complex, impairing learning outcomes. Despite its growing relevance, digital cognitive load has rarely been examined as a mediating factor that explains how platform use may affect student outcomes, especially in the context of well-being [12].
Building on this theory, the concept of digital cognitive load emerges as a modern extension, reflecting the heavy mental stress placed on students due to increasing workloads and the need to manage multiple digital tasks simultaneously [12]. In clinical and academic settings in healthcare education, this cognitive burden significantly affects how students perform and progress, especially in study-related activities [13, 14]. For example, a study conducted in 2025 involving 305 second-year medical students from a public university in Mexico found that Excessive screen time, particularly due to recreational electronic media use, negatively affects cognitive performance and academic outcomes in medical students [15].
The increase in cognitive demands from using multiple electronic devices and digital tools often exceeds the mental effort required by traditional learning methods, causing a division in subject areas and study outcomes [12]. Moreover, maladaptive coping strategies in response to these demands can further decrease learning efficiency and increase burnout among students [16]. Therefore, understanding students’ responses to digital cognitive load is essential for developing strategies that mitigate its negative effects.
DCL not only impacts academic performance but also affects students’ physical and mental well-being. When students attempt to process multiple streams of information simultaneously or frequently switch between various computing platforms, their cognitive system becomes overloaded [17]. Prolonged use of these digital platforms has been shown to elevate stress hormone levels and shorten attention spans [8], underscoring the need to reassess the broader implications of digital learning environments on students’ health [18].
In healthcare education specifically, the use of diverse educational technology platforms is critical for learning [19]. These platforms range from course management systems to complex simulation tools [20]. Notably, healthcare students now use an average of 6.3 digital platforms daily, compared to just two or three five years ago [21]. The interactions between digital cognitive load and the use of multiple educational technology platforms are, therefore, crucial to understand, as they can inform approaches to protect students’ health in academic settings [22]. Such understanding enables optimization of educational design to reduce cognitive fatigue and supports the lifelong development of healthcare educators [2, 3]
Despite the growing reliance on digital technologies, there is insufficient research on how digital cognitive load and educational technology use affect healthcare students’ well-being [23]. Current gaps include a lack of empirical data on the overall cognitive effects of digital platforms [24], limited investigation into the long-term psychological impacts on student success and mental health, and a shortage of appropriate tools to measure cognitive load related to digital use in healthcare education [25]. Addressing these gaps can improve educational design and support systems, enhancing both cognitive experiences and psychosocial resilience among healthcare students.
Although prior research has examined the individual effects of digital technology use or cognitive overload on student performance or stress, to our knowledge, this is the first study to explore this mediation model among healthcare students, addressing a significant gap in the literature. Therefore, this study aims to fill this critical knowledge gap by examining the relationship between digital cognitive load, the use of multiple educational technology platforms, and the well-being of healthcare students. As digital technologies become increasingly integrated into learning environments, the resulting cognitive load from managing multiple platforms may lead to stress, fatigue, and diminished academic performance. Understanding how digital cognitive load mediates the association between educational technology use and student well-being is essential for creating healthier, more effective learning environments for future healthcare professionals.
Research hypotheses
Hypothesis 1
Multiple educational technology platforms can directly affect the well-being of healthcare students.
Hypothesis 2
Digital cognitive load mediates the relationship between multiple educational technology platforms and the well-being of healthcare students.
The conceptual framework (Fig. 1) illustrates the interrelationship between the use of educational technology platforms, digital cognitive load, and student well-being. It posits that various aspects of platform usage-namely, platform usage frequency, simultaneous platform usage and usage patterns, and the level of platform integration-collectively influence students’ interaction with educational technology. These interactions contribute to the development of digital cognitive load, which is further shaped by mental, physical, and temporal demands. In turn, digital cognitive load affects critical outcomes such as frustration, effort, and performance. Ultimately, both the nature of educational technology use and the resulting cognitive load impact overall student well-being. This framework underscores the importance of managing digital tools and cognitive demands in educational settings to promote positive student outcomes (see Fig. 1).
Fig. 1.
Conceptual framework
Materials and methods
Research design, setting, and sample
A cross-sectional, correlational quantitative survey was executed from February to March 2025 throughout the 2024–2025 academic year. The research was conducted at Fatima College of Health Sciences in the United Arab Emirates. The inclusion criteria were undergraduate students from eight departments: General Requirement, Nursing, Psychology, Physiotherapy, Emergency Health, Midwifery, Medical Imaging, and Pharmacy, aged 18 years or older. The exclusion criteria were students in the last semester taking clinical courses only, as we considered them graduates. Before administering the survey, the participants were comprehensively briefed on the study’s objectives, methodologies, risks, and benefits to secure informed consent. Participants for the study were chosen by convenience sampling. The study participants were full-time undergraduate healthcare students in levels 1 through 4 who consented to participate and understood the study’s goal.
The Epi Info application, version 7, which was developed by the U.S. Centers for Disease Control and Prevention, is a statistical software that is widely used in public health and epidemiological research. It allows researchers to design surveys, perform statistical analysis, and calculate sample sizes based on predefined parameters such as confidence level, power, and margin of error. It was utilized to ascertain the number of study participants. The estimate accounted for a 5% margin of error, a 95% confidence level, and a power of 0.80 at a significance level of 0.05. A nonresponse rate of 5% was also considered. The final sample comprised 160 students who were accessible and eager to participate.
Sample
Study instruments
Demographic data
Gender, age, and student level are among the demographic information.
Educational technology platforms questionnaire
The researchers developed this tool (see Supplementary File 1) to measure the technology platform’s frequency and usage patterns among healthcare students. It was based on a comprehensive review of literature relevant to the subject [18, 19, 21, 22]. This questionnaire is divided into three subsections: platform usage frequency (10 items), simultaneous platform usage and using pattern (4 items), and platform integration (5 items). The overall score is 9 to 95, so an average platform usage is found for the person who used it. The scores are categorized as low, moderate, or high.
The scoring system has three categories. First, the platform usage frequency, the range of all responses calculated, was between (0–50), categorized as 0–10: low diversity, 11–30: moderate diversity, and 31–50: high diversity. Second, simultaneous platform usage and using pattern sums questions 11–14 and ranges between (4–20), categorized as: 4–9: low simultaneity, 10–15: moderate simultaneity, and 16–20: high simultaneity and intensity. Third, the platform integration score consists of 15–19 summed questions ranging from 5 to 25. The categorizations are 5–11: low integration, 12–18: moderate integration, and 19–25: high integration. The total score equals the sum of all scored sections that range between 9–95. Overall categorization is 9–37: low platform usage, 38–66: moderate platform usage, and 67–95: high platform usage.
To ensure content validity, the initial pool of items was developed based on themes and domains that are most frequently reported in the literature. The tool was designed to align with theoretical and empirical findings on students’ interaction with educational technology, particularly in the context of nursing education. Expert reviews were also solicited from faculty members with experience in educational technology and questionnaire design to assess the relevance, clarity, and coverage of the items. The first draft of the tool was reviewed by an expert panel of nursing education and instructional design specialists to determine its applicability, clarity, and completeness. Input from this expert panel led to several revisions of the wording and structure of items, all aimed at facilitating alignment with real-world classroom experiences.
An Exploratory Factor Analysis (EFA) was conducted on the 19 items of the Educational Technology Platforms Questionnaire to assess construct validity. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.84, indicating that the sample was suitable for factor analysis. Bartlett’s Test of Sphericity was significant (χ² = 1456.32, df = 171, p < 0.001), confirming that correlations between items were sufficient to proceed with factor extraction; see Supplementary 1.
Using principal component analysis with Varimax rotation, three factors with eigenvalues greater than 1 were extracted, aligning with the theoretical structure of the tool. These three factors explained 68.4% of the total variance. The first factor (Platform Usage Frequency) accounted for 35.6%, the second factor (Simultaneous Platform Usage and Pattern) for 19.2%, and the third factor (Platform Integration) for 13.6% of the variance; see Supplementary 1.
All items loaded strongly on their respective factors, with factor loadings ranging from 0.48 to 0.82, and no significant cross-loadings were observed. Internal consistency was confirmed, with Cronbach’s alpha values of 0.89 for Platform Usage Frequency, 0.81 for Simultaneous Usage, and 0.85 for Platform Integration, indicating good to excellent reliability across all subscales; see Supplementary 1.
Student well-being questionnaire
Student well-being was evaluated using the WHO-5 Well-being Index, which concentrates on the student’s mental, emotional, and physical health [26]. The scale has a rating system from 0 to 5, with 0 signifying no time and 5 denoting all the time. The raw score is determined by summing the points from each of the five questions. The score varies from zero to 25, with zero indicating the lowest well-being and 25 denoting the highest. The total score was divided into three categories: less than 13: poor, 13–16: moderate, and 17–25: high. The highest score, signifying optimal well-being, suggests superior well-being. The previous study reported the Cronbach’s alpha of 0.88, indicating a robust internal consistency [27]. The Cronbach alpha in the current study is α = 0.855.
Digital cognitive load questionnaire
The Task Load Index (TLX) of the National Aeronautics and Space Administration (NASA) is a subjective workload assessment tool. The NASA-TLX was selected as the cognitive load questionnaire due to its thorough and multidimensional approach for evaluating workload. It consists of six dimensions: mental demand, physical demand, temporal demand, frustration, effort, and performance to evaluate the perceived workload associated with performing a task [28]. Mental demand evaluates the extent of cognitive and perceptual engagement required, while physical demand measures the level of physical exertion necessary. Temporal demand evaluates the extent of time pressure experienced by the participant because of the task’s pace or rate. Performance evaluates the participant’s perception of their accomplishment in achieving the task’s objectives. Effort examines the intensity of labor required by the person to attain their performance level, whereas Frustration Level measures what extent to which the participant experienced insecurity, discouragement, irritation, stress, and annoyance during the task.
In the raw rating section, participants rate each dimension between 0 and 100. The raw score is determined by multiplying the domain’s rating by the domain weight. The domain weight is determined by ranking the number of times a particular dimension was selected by students in ascending order, from the lowest supplied zero to the greatest, which equals five. The weighted scores for each domain are then combined and divided by 15.The categorized interpretation of workload is very low (0–20), low (21–40), moderate (41–60), high (61–80), and very high (81–100) [29]. The instrument’s reliability has been determined in past research studies, particularly in online learning settings, with a good internal consistency of 0.83 to 0.89 for Cronbach’s alpha [30, 31]. The Cronbach’s Alpha in this study is (α = 0.901). The Kaiser–Meyer–Olkin (KMO) value is equal to 0.831. Bartlett’s Test of Sphericity was significant (χ² = 1229.17, df = 154, p < 0.001), indicating solid interitem correlations.
Sample size and sampling technique
Data collection
The survey’s front page included an explanation of the study’s goals, methods, and results, and a guarantee that participant names would remain anonymous. Before the study started, prospective participants were asked to select whether they wanted to participate by clicking on the agreement or refusal buttons. Participants will be given the researchers’ contact details, and after the study, they will need to click the “submit” button to return the survey. Data was collected through an online survey distributed via university email lists and student groups on Google Forms. Participants could complete the study conveniently using personal devices, ensuring flexibility and accessibility. The responses were collected anonymously to maintain confidentiality.
Statistical analysis
The statistical analysis for this study was carried out using IBM SPSS Statistics, version 26.0. Mean and frequency distributions were calculated to evaluate perceptions across various dimensions. The reliability and internal consistency of the questionnaire were assessed using Cronbach’s alpha. Pearson’s correlation was used to determine the correlation between the studied variables. Multiple linear regression to predict student well-being using multiple education technology platforms and digital cognitive load as predictors. A significance threshold of p < 0.05 was used to determine statistical significance. Mediation analysis to examine whether digital cognitive load mediated the effect of multiple educational technology platforms on student well-being through IBM SPSS Amos. Model fit was assessed using several indices: χ²(48) = 73.45, p =.012; CMIN/df = 1.53; CFI = 0.96; TLI = 0.94; RMSEA = 0.058; and SRMR = 0.042. These values collectively indicate an acceptable to good model fit, supporting the proposed mediation framework.
Ethical considerations
The Research Ethics Committee of Fatima College of Health Sciences granted ethical approval for the study [IRB approval number: FECE-2-24-25-R. IBRAHIM3]. Each participant received information on data protection and their rights. All responses are securely saved for research because the survey does not gather personal information. Every participant in the study provided informed consent. Every procedure complied with the rules.
Results
The demographic profile comprehensively analyses participants’ attributes, including age, educational level, time of day for concurrent platform usage, and departmental affiliation. Most participants fall within the 18 - < 21 group (60.0%) and with 44.37% from level 4. The survey results indicate that the afternoon (12 PM - 6 PM) is the peak time when most respondents (40.6%) engage with multiple platforms simultaneously, while late-night/early morning usage (10%) is the least common. Nursing students comprised more than a third of the study sample (42.5%), Table 1.
Table 1.
Distribution of the studied cases according to demographic data (n = 160)
| Sociodemographic characteristics | No. | % |
|---|---|---|
| Age (years) | ||
| 18 - < 21 | 96 | 60.0% |
| 21 or more | 64 | 40.0% |
| Student Level | ||
| Level 1 | 22 | 13.75% |
| Level 2 | 22 | 13.75% |
| Level 3 | 45 | 28.13% |
| Level 4 | 71 | 44.37% |
| What time of day do you most frequently use multiple platforms simultaneously? | ||
| Afternoon (12 PM - 6 PM) | 65 | 40.6% |
| Evening (6 PM - 12 AM) | 53 | 33.13% |
| Morning (6 AM - 12 PM) | 26 | 16.25% |
| Late night/early morning (12 AM - 6 AM) | 16 | 10.0% |
| Department | ||
| Nursing | 68 | 42.5% |
| Psychology | 31 | 19.37% |
| General Requirement Department | 26 | 16.25% |
| Emergency Health | 12 | 7.5% |
| Pharmacy | 9 | 5.6% |
| Physiotherapy | 7 | 4.4% |
| Midwifery | 7 | 4.4% |
SD: Standard deviation
The mean score for digital cognitive load was 36.73 ± 24.79, with values spanning from 13 to 72, signifying a moderate cognitive capacity. The total student well-being mean was 14.53 ± 7.39, ranging from 2 to 37, indicating moderate well-being. The multiple educational technology platforms exhibited the most significant variability, with a mean of 46.76 ± 17.53 and a range from 13 to 89, indicating a moderate usage level (Table 2).
Table 2.
Mean scores of digital cognitive load, students’ well-being, and multiple educational technology platforms (n = 160)
| Study Variables | Mean | SD | Min | Max | Interpretation |
|---|---|---|---|---|---|
| Overall Digital Cognitive Load | 36.73 | 24.79 | 3.33 | 100 | Moderate |
| Overall Students wellbeing | 14.53 | 7.39 | 2 | 37 | Moderate |
| Overall Multiple Educational Technology Platforms | 46.76 | 17.53 | 13 | 89 | Moderate |
SD: Standard Deviation
The correlation analysis presents a robust, negative, and statistically significant correlation between using multiple educational technology platforms and the students’ well-being scale, r (158) = − 0.546, p = 0.000. The previously identified relationship between digital cognitive load and student wellbeing, r (158) = − 0.61, p = 0.000, was detected. A substantial positive correlation was observed between the utilization of multiple educational technology platforms and digital cognitive load, r (158) = 0.635, p = 0.000; see more in Table 3 and Fig. 2.
Table 3.
Correlation between study parameters (n = 160)
| Multiple Educational Technology Platforms | Student well-being | Digital Cognitive Load | |
|---|---|---|---|
| Multiple Educational Technology Platforms | |||
| r | 1.000 | −0.546 | 0.635 |
| p | 0.000** | 0.000** | |
| Student well-being | |||
| r | 1.000 | −0.61 | |
| p | 0.000** | ||
| Digital Cognitive Load | |||
| r | 1.000 | ||
| P |
r: Pearson coefficient
*: Statistically significant at p ≤ 0.05
Fig. 2.
Scatter plot between study variables
Model 1 in Table 4 showed that multiple educational technology platforms (B = − 0.228, p = 0.000) had a significant negative effect on student well-being, explaining 29.4% of the variation in student well-being (F = 65.67, p < p < 0.001). In model 2, the digital cognitive load was entered (B = − 0.128, P = 0.000), and multiple educational technology platforms (B = − 0.113, P = 0.000). The model explained 40.4% of the variation in student well-being (F = 53.32, p < 0.001).
Table 4.
Linear regression for student wellbeing (n = 160)
| Model 1 | Model 2 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Variable | B | SE | Beta | t | p | B | SE | Beta | t | p |
| Intercept | 25.22 | 1.4 | 17.9 | 0.000 | 24.55 | 1.3 | 18.8 | 0.000 | ||
| Multiple Educational Technology Platforms | −0.228 | 0.028 | −0.54 | −8.10 | 0.000 | −0.113 | 0.033 | −0.268 | −3.36 | 0.000 |
| Digital Cognitive Load | −0.128 | 0.023 | −0.431 | −5.4 | 0.000 | |||||
| R2 = 0.294, F = 65.67, p 0.000 | R2 0.404, F = 53.32, p 0.000 | |||||||||
F, p: f and p values for the model
R2: Coefficient of determination
B: Unstandardized Coefficients
Beta: Standardized Coefficients
t: t-test of significance
SE: standard error
*: Statistically significant at p ≤ 0.05
The mediation analysis reveals several significant relationships among the variables. The direct effect of multiple educational technology platforms on digital cognitive load is positive and significant (β = 0.898, SE = 0.087, Z = 10.33, p =.000*), suggesting that using multiple educational technology platforms increases digital cognitive load. Similarly, the direct effect of digital cognitive load on student well-being is negative and significant (β = − 0.185, SE = 0.019, Z = − 9.67, p = 0.000), suggesting that higher levels of digital cognitive load reduce student well-being. The direct effect of multiple educational technology platforms on student well-being is negative and highly significant (β = − 0.234, SE = 0.029, Z = − 8.19, p =.000), showing that multiple educational technology platforms enormously diminish student well-being; see Table 5.
Table 5.
Mediation analysis results for the effect of digital cognitive load on the relationship between multiple educational technology platforms and healthcare students’ well-being
| β | SE | Z value | p-value | |
|---|---|---|---|---|
| Direct (Multiple Educational Technology Platforms → Digital Cognitive Load) | 0.898 | 0.087 | 10.33 | 0.000** |
| Direct (Digital Cognitive Load → Students’ Well-being) | −0.185 | 0.019 | −9.67 | 0.000** |
| Direct (Multiple Educational Technology Platforms → Students’ Well-being) | −0.234 | 0.029 | −8.19 | 0.000** |
| Path a: Relationship between Multiple Educational Technology Platforms → Digital Cognitive Load | 0.898 | 0.087 | 10.33 | 0.000** |
| Path b: Digital Cognitive Load → Students’ Well-being | −0.185 | 0.019 | −9.67 | 0.000** |
| Indirect Effect (Multiple Educational Technology Platforms Students’ Well-being via Digital Cognitive Load) | −0.166 | 0.024 | −7.06 | 0.000** |
| Total Effect (Multiple Educational Technology Platforms Students’ Well-being) | −0.4 | 0.037 | −10.81 | 0.000** |
Figure 3 displays the path diagram from the mediation model tested using AMOS. It shows both the direct and indirect effects of multiple educational platforms on student well-being, mediated by digital cognitive load. The diagram highlights the significant direct paths from platform use to cognitive load (β = 0.898), from cognitive load to well-being (β = − 0.185), and the total indirect effect (β = − 0.166). The total effect (β = − 0.4) illustrates the full mediating role digital cognitive load plays in this relationship.
Fig. 3.
Structural equation modeling
Discussion
This study addressed the role of digital cognitive load in mediating the relationship between healthcare students’ well-being and their active use of multiple educational technology platforms. At the outset, the correlation results showed that the well-being of health sciences students was negatively correlated with the usage of multiple educational technology platforms and digital cognitive load. The mediation analysis’s results explained these findings with the direct effect of multiple educational technology platforms’ usage and digital cognitive load on healthcare students’ well-being (−0.234) and (β = − 0.185), respectively. According to the linear regression model, the addition of digital cognitive load and multiple educational platforms explained 40.4% of the variation in student well-being.
Several studies support this finding. A literature review reported a negative correlation between educational technology use and well-being, notably when insufficient training exacerbates user stress [32]. Thus, unfulfilled technological potential may increase anxiety and lower student satisfaction. This is further supported by a study among 174 healthcare students, which showed that while educational technologies may enhance academic outcomes, they do not automatically lead to improved emotional or psychological states. Poor technology integration can even contribute to stress among students [33]. Tokuç and Varol emphasize that the rapid evolution of medical education technology can lead to increased anxiety among learners who feel unprepared to utilize these tools effectively, thus impacting their educational experience and overall well-being [34]. A cross-sectional study conducted among 192 undergraduate medical and nursing students found that excessive screen time for academic purposes increases fatigue and anxiety and reduces performance [34]. Furthermore, a qualitative descriptive study in Indonesia indicates that online learning affects social and emotional behavior, resulting in decreased cooperation among students due to diminished tolerance, limited social interaction with peers, and children’s emotions often manifesting as boredom and sadness [35].
This can be attributed to several underlying factors, including technostress, excessive screen time, inadequate support systems [36], a lack of empathy from teaching staff, and insufficient training for staff on educational tools [37, 38]. Additionally, varying levels of digital literacy among healthcare students can make it difficult for some to use these platforms efficiently [39, 40]. These factors lead to student frustration and unmet expectations during online learning, which collectively contribute to mental and physical health challenges among students. This necessitates a more holistic approach wherein medical education institutions invest in technology and provide adequate support and training for students and educators to mitigate stress and enhance emotional well-being. The forthcoming studies on digital platforms designed to improve student well-being illustrate a need for user-centered designs that prioritize and integrate students’ mental health within the educational technology framework [41].
Conversely, a Johnson & Hall (2020) study argues that well-structured e-learning platforms can enhance knowledge retention and engagement. Digital tools also provide flexibility, allowing students to learn at their own pace, which can benefit self-directed learning. Some platforms even encourage collaboration through discussion forums and real-time group work, which can foster peer support and help alleviate stress. These opposing views suggest that the issue may not be technology but how it is implemented.
The research indicated that digital cognitive load adversely affects student well-being, especially among healthcare students, who frequently encounter high-pressure, rapid learning settings necessitating concurrent mastery of intricate theoretical concepts and actual clinical competencies. The responsibilities of overseeing many instructional platforms, such as simulations, patient care modules, and virtual laboratories, may exacerbate cognitive strain, rendering this demographic particularly susceptible to the adverse effects of digital overload. This study corresponds with a survey on cognitive load and mental health, which revealed that elevated cognitive load during academic work heightens stress and diminishes general well-being, especially in challenging learning contexts [42]. A study aimed at examining the correlation between the online learning environment and the mental health of university students revealed that elevated cognitive load during virtual academic assignments exacerbates stress and diminishes overall well-being among these students [43]. A paper examining the interplay between cognitive load theory, intrinsic motivation, and emotions in health professions education revealed that elevated cognitive load adversely affects student well-being and emotional health [44]. A randomized trial published results that challenge the idea that immersion is always beneficial. In their study on surgical training, immersion and virtual reality training methods could be identified as contributors to excessive extraneous load [45].
A study on cognitive load theory supports this assertion, indicating that excessive information processing via electronic devices might overload working memory, resulting in stress and diminished comprehension [46]. Research suggests that extended screen use and multitasking in educational environments lead to increased anxiety and decreased academic motivation among students [47]. These findings indicate that digital cognitive load must be meticulously regulated to enhance student well-being. A study among undergraduate medical and nursing students in Morocco indicated a strong association between healthcare students’ state anxiety and cognitive load [48].
Multiple factors influence the correlation between digital cognitive load and student well-being. The intricacy of digital resources, the caliber of online education, personal learning styles, and time management abilities all significantly influence outcomes [49]. The availability of digital resources and the level of self-regulation in students’ study habits can affect their capacity to manage digital cognitive demands. Our suggestion is backed by the findings of a study in the United Arab Emirates that indicated that college students with robust digital skills may more effectively navigate online courses, interact with digital learning platforms, and participate in virtual classroom activities, whereas those deficient in digital literacy may encounter increased cognitive load, resulting in stress and diminished academic satisfaction [39].
Conversely, several research studies counter this correlation, asserting that digital tools can augment learning and alleviate cognitive strain when applied appropriately. Mayer and Moreno (2017) established that structured digital learning environments incorporating multimedia enhance information retention without necessarily elevating cognitive burden [50]. Furthermore, a study conducted by Zheng et al. (2021) emphasizes that adaptive e-learning systems can customize information delivery, alleviating the cognitive load on students [51]. These results indicate that the adverse effects of digital cognitive load may not be ubiquitous and can be alleviated by well-designed digital interfaces. Educators and institutions must implement proactive initiatives to resolve this issue. Establishing organized digital learning platforms, enhancing digital literacy, and employing cognitive load management techniques such as information segmentation, minimizing unnecessary content, and encouraging active learning can alleviate negative impacts. Promoting regular digital-free activities, enhancing mental health awareness, and improving the balance between digital and traditional learning techniques can also encourage student well-being [52].
The study reported that digital cognitive load positively correlates with adopting various educational technology platforms in learning environments. Moreover, it mediated the relationship between using multiple educational platforms and students’ well-being. Digital cognitive load increases among students who reported increased usage of different platforms. This aligns with the results of many researchers [23].
This result could be backed by previous studies’ conclusions that university students must navigate numerous workflows, interface with various technologies, and maintain concentration due to the proliferation of resources [16, 23, 35, 37, 49]. These platforms enhance accessibility, engagement, and personalized learning. Yet, they also elevate cognitive load due to the necessity of navigating several interfaces, recalling multiple credentials, and adjusting to diverse pedagogical approaches. Focusing on content instead of platform logistics is impeded by working memory deficits, decision fatigue, and fragmented attention resulting from platform changes [53, 54]. The utilization of various technologies increases cognitive load, causing learners to concentrate on technology rather than the subject matter [55]. This overload may induce mental fatigue, diminished recall, and reduced motivation, particularly in less digitally literate students [56].
No previous studies assessed digital cognitive load as a mediator between study variables with which to compare our results. However, a study conducted among undergraduate healthcare students reported that healthcare students had many similar experiences of cognitive load in online learning compared to their neurotypical peers, such as difficulties in asking questions, stress caused by assessments, confusion in navigating the content, and having to deal with technical issues [57]. Another cross-sectional study from seven medical colleges in Islamabad, Pakistan, among 633 medical students showed that the total cognitive load score partially mediated the relationship between digital literacy and academic self-efficacy [58].
Therefore, it is prudent to integrate digital technologies in a structured manner to enhance students’ performance rather than to exacerbate assignment workloads. It is recommended that the appropriate use of multiple types of technology be integrated with various teaching and learning methods that necessitate interaction and communication between students and their instructors. It is imperative to devise a strategy that enables the selection of appropriate platforms for achieving the course requirements from a wide range of alternatives.
This study not only validates previously noted correlations between educational technology utilization and student well-being but also enhances comprehension by recognizing digital cognitive load as a mediating factor. Previous research has demonstrated that excessive or inadequately integrated digital tools can result in worry, weariness, or diminished academic performance, although they frequently fail to elucidate the mechanisms by which these impacts manifest. Our findings experimentally validate that the mediating function of digital cognitive load indicates it is not alone the utilization of numerous platforms affecting students’ well-being, but rather the mental work necessary to navigate these tools that is crucial. Our findings corroborate and enhance prior observations within the field of medical and healthcare education, highlighting the importance of structured digital integration and cognitive support systems, specifically in these academic settings. Our work not only coincides with but also significantly extends current studies in instructional technology and student mental health.
Future research must prioritize longitudinal and comparative studies, including healthcare students, to more effectively elucidate the profession-specific cognitive and emotional obstacles inherent in digital learning environments.
Implications and limitations
To prevent overburdening students, medical education institutions must assess the number of platforms utilized. Overuse of platforms can harm well-being and raise cognitive stress. Students’ confidence and engagement with the platforms can be increased by offering training courses on using technology effectively, which can assist in lowering cognitive load. Since online learning can make anxiety and stress worse, universities should incorporate mental health facilities to help students who are dealing with an increased cognitive load. The detrimental impacts of digital cognitive load on well-being can be lessened by taking a comprehensive approach that incorporates socioemotional involvement.
The study has limitations, even if it offers new perspectives for theory and further research. The study employed an online survey of undergraduate students. This could limit the findings’ generalizability in different contexts. The cross-sectional design limits the capacity to determine causation between academic accomplishment and the utilization of artificial intelligence learning platforms. The study relied on participants’ self-reports, which may be affected by social desirability or response bias, despite efforts to ensure anonymity and objectivity in the survey design. This limits the data’s applicability to larger cohorts of medical students. Furthermore, the fact that this study only assessed female students presents a substantial constraint in interpreting the findings. Female students may have considerably different levels of cognitive load than male students, and gender may have varied effects on these differences. Additionally, this study used convenience sampling, which may limit the generalizability of the findings due to potential sampling bias.
Conclusion
This study addressed the role of digital cognitive load in mediating the relationship between the usage of multiple educational technology platforms and the well-being of healthcare students. The primary findings indicate that digital cognitive load, multiple educational technology platforms, and student well-being received moderate ratings. The correlation study demonstrated a robust, negative, and statistically significant association between students’ scores across multiple educational technology platforms and their digital cognitive load and well-being. A notable positive correlation appears between results from multiple educational technology platforms and digital cognitive load scores. The mediation study revealed that the effect of various educational technology platforms on student well-being remained considerable with the inclusion of mediators. Consequently, digital cognitive load impacts the integration of multiple educational technology platforms and affects student well-being.
Institutions should carefully analyze and limit the number of digital platforms utilized in their curricula to reduce cognitive overload and protect children. Needs assessments help choose and manage educational technology by aligning platforms with learning objectives and avoiding redundancy. User-friendly platforms with intuitive interfaces and technology training courses can reduce cognitive effort and boost students’ confidence and engagement with platforms. Educational technology selection and maintenance require assessments to ensure platforms meet instructional objectives and avoid redundancy. System integration improves operations and reduces task-switching. Students need a clear platform and time management instructions to balance their digital lives. These methods maximize technology use, reduce cognitive strain, and boost student engagement and performance.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors extend their appreciation to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R720), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Abbreviations
- AI
Artificial intelligence
- CLT
Cognitive Load Theory
- CDC
Centers for Disease Control and Prevention
- NASA-TLX
NASA Task Load Index
- OPD
Outpatient Department
- WHO
World Health Organization
- TLX
Task Load Index
Author contributions
“Conceptualization”, Rasha Kadri Ibrahim, Yusraa Ahmed Al Marar, Modhi Salman; Methodology, Abdelaziz Hendy, Rasha Kadri, Sally Farghaly, validation, Shorok Hamed Alahmedi and Sally Farghaly; formal analysis, Abdelaziz Hendy, Rasha Kadri; writing—original draft preparation, Hossam Ali Ismail, Mohamed Hashem Kotp, Hasan Ahmed Awad, Aliaa Ezz Eldin Abd Elmoaty, Mohamed Ahmed Aly, Abdelaziz Hendy; editing and Ahmed Hendy; statistical analysis. Ahmed Hendy; writing—review and editing, Rasha Kadri Ibrahim, Yusraa Ahmed Al Marar, Modhi Salman, Shahed Jehad, Mariam Gaber Hamza, Ahmed Samir Abouelnasr, Sally Mohammed Farghaly, Shorok Hamed Alahmedi, Abdelaziz Hendy; project administration, Rasha Kadri Ibrahim; All the authors have read and agreed to the published version of the manuscript.”
Funding
Princess Nourah bint Abdulrahman University, Project number (PNURSP2025R720), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Data availability
The data are provided within the manuscript or supplementary information files.
Declarations
Ethics approval and consent to participate
The Research Ethics Committee of Fatima College of Health Sciences granted ethical approval for the study [IRB approval number: FECE-2-24-25-R. IBRAHIM3]. The study adhered to the principles of the Declaration of Helsinki. Every participant was provided with information regarding their rights and data protection requirements. All information will be safeguarded as the survey does not gather personal data, and all responses are securely preserved for research purposes exclusively. All subjects participating in the study provided informed consent. All procedures were conducted in compliance with pertinent rules and regulations.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Rasha Kadri Ibrahim, Email: Rasha.Ibrahim@actvet.gov.ae.
Abdelaziz Hendy, Email: abdelaziz.hendy@nursing.asu.edu.eg.
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Data Availability Statement
The data are provided within the manuscript or supplementary information files.