Levels Of Cognitive Load Research Articles

Addressing mental health challenges in college students: A biomechanical approach to mitigating stress and cognitive load through physical intervention strategies

The main objective of this investigation is to find out how biomechanical actions, such as physical activity programs, balanced adjustments, and mindfulness-based posture correction, are successful at reducing Cognitive Load (CL) and levels of stress among college students. Mechanical adjustments decreased stress, physical activity reduced CL, and mindfulness-based posture correction improved Mental Health (MH), according to a long-term experiment with 28 students from four distinct educational fields. Investigators examined students’ stress, CL, and BH levels monthly. The outcome results dealt with all three predictions. A student’s Perceived Stress Scale (PSS) score dropped from 24.66 to 18.10 (mean difference = 6.56, t = 4.82, p = 0.0001) after experiencing practical changes, demonstrating a significant decrease in stress levels. Following the exercise support, CL, as determined by the NASA Task Load Index (NASA-TLX), dropped from 65.38 to 54.23 (mean difference = 11.15, t = 5.29, p = 0.00005). Ratings for BH increased significantly after exercising mindfulness-based posture correction (from 78.63 to 85.13; mean difference = −6.50, t = −4.92, p = 0.00007). The median variation in PF (VO2 Max) went from 40.46 to 45.11 (t = −3.78, p = 0.0012), and the difference in value was −4.65.

No Evidence for a Negative Effect of Realism When Learning About a Process Despite an Increase in Cognitive Load

ABSTRACTRealistic visualizations have been found to enhance the learning of object shapes, but regarding the learning of processes, the effects of realism are largely unclear. Previous findings indicate that realism can induce a higher level of extraneous cognitive load compared with schematics and that details are attention‐consuming. The present experiment was conducted to assess the effects of realism on learning about a process with combinations of texts and pictures. It was hypothesized that realism would be a hindrance in engaging with texts due to the potential for distraction exerted by details. Students either learned with a schematic or a realistic version of a life‐cycle diagram accompanied by texts. Realism increased participants' extraneous cognitive load, while there were no significant differences regarding retention performance. The results confirm previous results that realism can increase subjective extraneous cognitive load, but highlight that realism may not prevent learners from understanding textual information.

An innovative approach in middle school science courses: effects of collaborative augmented reality activities on motivation, cognitive load, and satisfaction

ABSTRACT This study examines the effects of collaborative augmented reality activities supported by the 5E teaching model on students’ motivation, cognitive load, and satisfaction levels in a science course. In this context, explanatory design, one of the mixed research methods, was used in this study. The research process was conducted with 18 sixth-grade (5 girls and 13 boys) students studying in a private school for four hours a week for four weeks in a science course. In the quantitative dimension of the study, the effects of collaborative augmented reality activities were examined using one group pretest-posttest design through questionnaires and scales, and student views were evaluated through semi-structured interviews as a result. The quantitative data analysis used the Wilcoxon signed-rank test and descriptive statistics methods. Qualitative data were analyzed through the content analysis method. As a result of the study, it was observed that collaborative augmented reality activities positively affected students’ motivation and satisfaction levels in the science learning process. In addition, collaborative augmented reality activities contributed to balancing the cognitive load. In this regard, it can be asserted that students’ learning processes can be made more effective using collaborative augmented reality activities, especially in teaching complex science subjects.

Digital eye strain symptoms worsen during prolonged digital tasks, associated with a reduction in productivity

PurposeIt is often stated that digital eye strain can impact productivity and/or work accuracy, but the relationship between symptoms, productivity, and work accuracy has been unclear. Hence this study tracked the development of visuo-ocular symptoms during prolonged digital tasks and to explore the impact of higher and lower cognitive load levels on visuo-ocular symptoms, productivity, and work accuracy. MethodsThirty-five participants (23.2 ± 4.0 years) who had digital eye strain (diagnosed with the Computer Vision Syndrome Questionnaire) undertook an uninterrupted laptop adapted Eriksen Flanker task for 60 min on two separate days, once with a high cognitive load in randomised sequence. Symptoms were assessed at baseline and every 10 min throughout the task. ResultsAll visuo-ocular symptom scores significantly worsened (F = 87.783, p < 0.001) with time, with the symptom severity increasing faster with the higher cognitive load (F = 7.110, p < 0.001). The mean total number of tasks completed was greater for the lower (1060.8 ± 296.5) than the higher (532.2 ± 123.4) cognitive load tasks (F = 138.830, p < 0.001), reducing with time (F = 7.339, p < 0.001), but in a similar way for both cognitive loads (F = 1.795, p = 0.154). Task accuracy was not affected by the cognitive load of the task (F = 1.729, p = 0.197) and was consistent over time (F = 1.885, p = 0.135). ConclusionsSymptoms increased with task duration in individuals with digital eye strain, with a faster rate for more demanding tasks. This was associated with a decrease in the task completion rate (productivity), but not the work accuracy.

Which ERP components are effective in measuring cognitive load in multimedia learning? A meta-analysis based on relevant studies.

The open and generative nature of multimedia learning environments tends to cause cognitive overload in learners, and cognitive load is difficult for researchers to observe objectively because of its implicit and complex nature. Event-related potentials (ERP), a method of studying potential changes associated with specific events or stimuli by recording the electroencephalogram (EEG), has become an important method of measuring cognitive load in cognitive psychology. Although many studies have relied on ERP output measurements to compare different levels of cognitive load in multimedia learning, the results of the effect of cognitive load on ERP have been inconsistent. In this study, we used a meta-analysis of evidence-based research to quantitatively analyze 17 experimental studies to quantitatively evaluate which ERP component (amplitude) is most sensitive to cognitive load. Forty five effect sizes from 26 studies involving 360 participants were calculated. (1) The results of the studies analyzed in subgroups indicated high level effect sizes for P300 and P200 (2) Analyses of moderating variables for signal acquisition did not find that different methods of signal acquisition had a significant effect on the measurement of cognitive load (3) Analyses of moderating variables for task design found that a task system with feedback was more convenient for the measurement of cognitive load, and that designing for 3 levels of cognitive load was more convenient for the measurement of cognitive load than for 2 levels of cognitive load. (4) Analyses of continuous moderating variables for subject characteristics did not find significant effects of age, gender, or sample size on the results.

A training and assessment system for human-computer interaction combining fNIRS and eye-tracking data

In the domain of engineering interactive product design, the optimization of Human-Computer Interaction (HCI) is central to enhancing user experience and system efficiency. Notably, the application of cognitive load theory holds significant prominence in this field. Efficient evaluation of cognitive load not only facilitates the optimization of resource management and task allocation, but is also the key to creating a fluent interactive experience. With the advancement of Artificial Intelligence (AI) and physiological measurement technologies, new methodologies have emerged to measure and understand cognitive load. This research introduces a system for HCI training and assessment. The system, through the development of digital memory paradigms, induces and verifies varying levels of cognitive load with precision, thereby providing data-driven insights for HCI design. Specifically, this study constructs an AI-assisted multimodal cognitive load assessment framework, integrating physiological data collected from Functional Near-Infrared Spectroscopy (fNIRS) and eye-tracking technologies. By extracting and analyzing 29 physiological features, including channel features, graph-theoretic features, and features pertaining to eyelid and iris movements, we propose an innovative multimodal recognition approach to classify cognitive load across individuals. The experimental results not only verified the validity of the method, but also revealed changes in physiological patterns at different levels of cognitive load. In particular, the significant potential of the fNIRS feature in band I. This discovery suggests that we can monitor and predict cognitive load with greater precision through physiological signals, hence optimizing the design of HCI systems to alleviate user strain, prevent information overload, and enhance the naturalness and intuitiveness of interaction.

Unlocking Potential: Assessing Motivation, Learning Strategies, and Cognitive Load in Flipped Learning during Online Teaching in Higher Education

Flipped learning has become the focus in many educational contexts especially with the integration of technological tools in learning space for the last decade. In flipped learning, learners perform surface learning at home, whereas deep learning such as problem-solving, critical thinking exercises etc. is covered in the classroom which potentially increases motivation and satisfaction of learners. Cognitive load, the assumed load of the working memory caused by the processing of tasks, has also attracted attention in online learning settings which pose different processing demands. This study took place in the extraordinary settings of education during the Covid-19 pandemic which required online education for all education levels. A fourth-year course of an ELT program was delivered through a flipped learning approach during online education. Learners’ motivation, use of learning strategies, satisfaction and cognitive load levels were calculated at the end of the intervention and their perceptions regarding the experience were investigated through an open-ended questionnaire. The results showed that their motivation and satisfaction levels were quite high as well as their strategy uses whereas cognitive load levels were comparatively low. Inferential statistics showed that relationships exist between and among the motivation, learning strategies and cognitive load at various levels. Motivation emerged to be a significant predictor of course satisfaction whereas time spent for the flipped lectures predicted course achievement. Learners’ perceptions of flipped learning process were generally positive while they also articulated some drawbacks of it like instructional tools-related and individual learner-related problems.

Exploring cognitive load through neuropsychological features: an analysis using fNIRS-eye tracking.

Cognition is crucial to brain function, and accurately classifying cognitive load is essential for understanding the psychological processes across tasks. This paper innovatively combines functional near-infrared spectroscopy (fNIRS) with eye tracking technology to delve into the classification of cognitive load at the neurocognitive level. This integration overcomes the limitations of a single modality, addressing challenges such as feature selection, high dimensionality, and insufficient sample capacity. We employ fNIRS-eye tracking technology to collect neural activity and eye tracking data during various cognitive tasks, followed by preprocessing. Using the maximum relevance minimum redundancy algorithm, we extract the most relevant features and evaluate their impact on the classification task. We evaluate the classification performance by building models (naive Bayes, support vector machine, K-nearest neighbors, and random forest) and employing cross-validation. The results demonstrate the effectiveness of fNIRS-eye tracking, the maximum relevance minimum redundancy algorithm, and machine learning techniques in discriminating cognitive load levels. This study emphasizes the impact of the number of features on performance, highlighting the need for an optimal feature set to improve accuracy. These findings advance our understanding of neuroscientific features related to cognitive load, propelling neural psychology research to deeper levels and holding significant implications for future cognitive science.

Effect of sequential tasks in the case studies of business English textbooks on fostering critical thinking based on cognitive load theory

There is a consensus that case studies in Business English class are helpful to foster critical thinking, but little research has been done to explore how case studies facilitate it. To bridge the gap, the present study uses the revised Bloom’s taxonomy as a tool to qualitatively and quantitatively investigate the effect of case studies in Market Leader textbooks on fostering critical thinking based on cognitive load theory. First, the presence of six cognitive levels targeted in the tasks is examined to see how case studies sequence their tasks according to the increase of cognitive complexity. Secondly, the scaffoldings distributed across the sequential tasks are explored to investigate how well they impose appropriate amount of cognitive load on students. Although six cognitive levels are found, there is an inappropriate administration of task scaffolding in a single task or task chain, leading to insufficient or superficial scaffolds and thus inducing high level of cognitive load for students to develop critical thinking. Accordingly, case study writers and teachers are recommended, when designing and adapting sequential tasks, to consider systematic inculcation of cognitive levels among tasks and proper administration of scaffolding with regard to the aspects of task input, conditions, progress and outcome so that a balanced level of cognitive load aligned with the state of working memory capacity of students can be achieved for fostering critical thinking.

Learning with videos: Do task instructions and the availability of a pause button matter?

AbstractBackgroundWhen watching educational online videos, learners need to determine whether the videos' contents are suitable for learning. Whereas this may induce metacognitive monitoring processes, it may also distract learners from the learning materials.ObjectivesIn the current set of experiments, we investigated whether asking participants to rate the suitability of three educational videos to impart knowledge (both experiments) and enabling participants to pause the video (Experiment 2) affects learning.MethodsExperiment 1 was conducted in the laboratory with a sample of university students. Participants were randomly assigned to one of two experimental conditions (rating task vs. viewing task). Experiment 2 was conducted online. Participants were randomly assigned to one of four conditions resulting from the two factors task (rating task vs. viewing task) and pause button (available vs. not available).Results and ConclusionIn Experiment 1, we observed detrimental effects of the rating task on learning outcomes, but more accurate judgements of learning. In Experiment 2, we did not find any effects of the rating task on learning outcomes and the accuracy of the participants' judgements of learning. However, the inclusion of a pause button did result in more accurate judgements of learning. Further, exploratory analyses revealed that older participants reported higher levels of intrinsic and germane cognitive load than the younger participants. This increased effort only translated into better learning outcomes in the condition in that participants could pause the video. Thus, age is introduced as a potential boundary condition that should be investigated in instructional design research.

Exploring the effect of cognitive load in scenarios of daily driving

Although driving to and from work is an unavoidable part of the working life of many people, little is known about the relationship between work-related and drive-related cognitive load in daily commuting. To explore those relationships, Study 1 examined the effect of a demanding driving period on the performance of a subsequent cognitive task (mimicking a home-to-work commuting scenario). That driving-related higher cognitive load than the control condition was associated with an increased accuracy in the following cognitive task. Study 2 examined the effects of a period of demanding cognitive tasks on the performance of a subsequent driving task (mimicking a work-to-home commuting scenario). Although no reliable effect on speed or lane keeping ability in a virtual motorway scenario was observed, the completion of tasks under the higher cognitive load condition before driving led to a modest increase in the distance kept from the car ahead. The two sets of findings suggest that moderate levels of cognitive load could modulate the performance in timely contiguous tasks. The process underpinning possible spillover effects with such timestamp is unknow but might be linked to the activation of long-lasting attentional processes involving alertness. Hence, this exploratory study can be a catalyst for future studies investigating the interplay between cognitive load and driving in scenarios of daily commuting.

The association between physiological and eye-tracking metrics and cognitive load in drivers: A meta-analysis

Driving performance can be impaired by a high cognitive load of drivers. Thus, it is important to estimate drivers’ cognitive load. Although physiological and eye-tracking metrics have been widely used in many studies to assess cognitive load while driving, conflicts still exist regarding the association between physiological and eye-tracking metrics and different levels of cognitive load. Through a meta-analysis, our study aims to quantify the association between physiological, eye-tracking metrics and cognitive load induced by n-back tasks. A total of 18 articles met the inclusion criteria for the meta-analysis. The results indicate four types of metrics, including the sensitive-to-low ones that can only differentiate the low to medium level of cognitive load (i.e., the power spectrum of θ wave of electroencephalogram at Fp1 channel); high-resolution ones that can differentiate all levels of cognitive load (including pupil size, heart rate, and skin conductance); and low-resolution ones that can only differentiate low and high cognitive load (including the total power spectrum of electrocardiogram, eye blink rate, and respiration rate) and others (the power spectrum of θ wave of electroencephalogram at Fp2 channel). Furthermore, the association between metrics and cognitive load can be modulated by the n-back version, modality of n-back task, automation level, and percentage of male participants. In summary, this study contributes to the literature by quantifying associations between physiological and eye-tracking metrics and different cognitive load levels. Practically, we provide evidence for the selection of physiological and eye-tracking metrics for future driving cognitive load monitoring system design.

Skillful and strategic navigation in soccer - a motor-cognitive dual-task approach for the evaluation of a dribbling task under different cognitive load conditions.

Soccer is a competitive sport that relies on distinct motor skills and cognitive processes. However, cognitive aspects are often overlooked, with a focus mainly on motor skills. Limited research has explored screening tests within motor-cognitive navigation dual-task (DT) paradigms. This study aims to validate a sensitive approach for assessing soccer-specific dribbling by evaluating the Trail-Dribbling Test (TDT) as a method to differentiate high-performance (HP) from low-performance (LP) players. Two hundred and seventy-five participants (41 females) aged between 12 and 34 completed the Trail-Making Test (TMT), the Trail-Walking Test (TWT), and the soccer-specific TDT under three levels of cognitive load. Results indicated shorter TDT durations for HP compared to LP players, with increased cognitive load accentuating differences (TDT-M: p = 0.044, d = 0.260; TDT-A: p < 0.001, d = 0.449; TDT-B: p < 0.001, d = 0.653). The TDT effectively discriminated between HP and LP players in the 14-15 (AUC = 0.712-0.820) and 16-17 age groups (AUC = 0.634-0.839). In conclusion, the ecologically valid TDT demonstrates the potential for quantifying soccer-specific dribbling, offering insights into motor and cognitive aspects of dribbling performance, especially among soccer players aged 14-17.

Advancing maritime technology: Evaluating situation awareness support systems for engine room monitoring at the future shore control centre

ABSTRACT Remote operation offers promising advantages as a viable transitional phase toward autonomous ships, but at the same time, it introduces new challenges in maintaining situation awareness. However, situation awareness is misunderstood as an essential construct for only navigational tasks, albeit situation awareness issues are recurrently reported during system monitoring, e.g. power plants. Hence, this within-subjects study developed the two situation awareness support systems for engine room monitoring based on information requirements and schema instantiation. Sixteen marine engineers participated in the four simulation experiments, and their objective and subjective situation awareness, fault detection rate, response speed and change blindness were evaluated. The findings describe that presenting a few critical perception elements for comprehension is promising to mitigate interface complexity and enable efficient pattern matching. However, caution is necessary for selecting the means of access to elements of low importance, as it can induce attentional tunnelling and additional cognitive load, which can devastate the situation awareness and fault-detecting performance when compounded by high cognitive load from primary monitoring tasks. This study recommends entailing the current level of situation awareness, cognitive load and stress into the support system design to determine the extent and timing of assistance for varying operational demands.

Classification of Driver Cognitive Load in Conditionally Automated Driving: Utilizing Electrocardiogram-Based Spectrogram with Lightweight Neural Network

With the development of conditionally automated driving, drivers will be allowed to perform non-driving-related tasks. Under such circumstances, continuous monitoring of driver cognitive load will play an increasingly important role in ensuring that drivers have sufficient mental resources to take over control of the vehicle should the driving automation fail. However, estimation of cognitive load is challenging because of the difficulties in identifying high-level feature representation and accounting for interindividual differences. Physiological measures are believed to be promising candidates for cognitive load estimation in partially automated vehicles. However, current estimation methods are mainly based on the manual feature extraction of time- or frequency-domain indicators from physiological signals, which may not adapt to dynamic driving conditions. With the development of deep learning, the neural network has shown good performance in automatically capturing high-level features from input data. Inspired by this, we adopted a novel approach to classify driver cognitive load based on electrocardiogram (ECG) spectrograms, in which the driver’s ECG signal was collected and transformed into a 2D spectrogram by a short-time Fourier transform. A squeeze-and-excitation network-based deep-learning framework that can capture high-level features and pays more attention to the cognition-related features of the spectrogram was proposed for classification. Experiments on a publicly available dataset demonstrated that our model achieved an accuracy of 96.76% in differentiating two levels of cognitive load for a within-subject evaluation and 71.50% accuracy with an across-subjects evaluation. The results demonstrated the feasibility of detecting drivers’ cognitive load through deep learning using ECG spectrogram alone.

The Effect of Icon Color Combinations in Information Interfaces on Task Performance under Varying Levels of Cognitive Load

In human–computer interaction interfaces, icons serve as highly symbolic elements that convey information and significantly influence the performance of visual search and other tasks. Thus, the selection of appropriate colors plays a crucial role in the design of human–computer interfaces. This study aimed to investigate the effects of icon color combinations on human visual search task performance across various cognitive load conditions. The experiment was divided into two parts, involving a cognitive load test experiment and an icon search task, wherein the former required participants to select the target icon from 16 de-colored icons, and the latter involved selecting the target icon from 16 color combinations of 8 colors (background colors: black, red, blue, and purple; foreground colors: white, yellow, green, and turquoise). A total of 20 participants (11 females and 9 males, all aged between 18 and 24 years) were recruited for this experiment to perform both tasks sequentially. Through a comprehensive consideration of factors such as the NASA-TLX scale, retrieval time, etc., the experimental results revealed that cognitive load increased with time pressure, resulting in varying optimal color combinations for different cognitive load levels. Additionally, background colors did not affect task response time under different cognitive loads, whereas white foreground color was superior to turquoise foreground color in a medium cognitive load environment. Meanwhile, our results recommend prioritizing white-on-black as the preferred color combination, given that it demonstrated excellent cognitive performance in all three cognitive load environments. Conversely, white-on-blue is not recommended as a color combination for the design of high cognitive load environments. Concerning medium cognitive load environments, yellow-on-red or white-on-purple color combinations were preferred. Finally, in low cognitive load environments, white-on-purple is recommended as the primary color combination. Overall, this study provides a theoretical reference for the future design of interactive interface icons across various contexts.

The Differential Effects of Multisensory Attentional Cues on Task Performance in VR Depending on the Level of Cognitive Load and Cognitive Capacity.

As the utilization of VR is expanding across diverse fields, research on devising attentional cues that could optimize users' task performance in VR has become crucial. Since the cognitive load imposed by the context and the individual's cognitive capacity are representative factors that are known to determine task performance, we aimed to examine how the effects of multisensory attentional cues on task performance are modulated by the two factors. For this purpose, we designed a new experimental paradigm in which participants engaged in dual (N-back, visual search) tasks under different levels of cognitive load while an attentional cue (visual, tactile, or visuotactile) was presented to facilitate search performance. The results showed that multi-sensory attentional cues are generally more effective than uni-sensory cues in enhancing task performance, but the benefit of multi-sensory cues changes according to the level of cognitive load and the individual's cognitive capacity; the amount of benefit increases as the cognitive load is higher and the cognitive capacity is lower. The findings of this study provide practical implications for designing attentional cues to enhance VR task performance, considering both the complexity of the VR context and users' internal characteristics.

Digital gamification and knowledge consolidation: revealing changes in the psychoemotional state of university students taking into account cognitive load

Introduction. Currently, there is conflicting evidence regarding the effects of digital gamification in education. The purpose of this study is to identify changes in the psycho-emotional state of university students in the conditions of using digital gamified resources in comparison with traditional training (face-to-face training), taking into account the cognitive load and if the resource itself is considered as a means of consolidating the studied material. Materials and methods. The study involved 80 students, as well as 17 experts. The study was accompanied by an initial diagnosis using the same test that was used in the final tests, and an analysis of the students' well-being, activity, and mood, according to the SAN Yu questionnaire. Doskin, self-assessment of the psycho-emotional state of students from pre- to post-test. The level of cognitive load was assessed by the expert group according to its three types: extraneous (external), basic (internal) and generative (relevant), according to the methodology of the Delphi method. The concordance coefficient was used to determine the consistency of expert opinions. Results. Digital gamification is effective in consolidating knowledge previously acquired by students if it has an optimal level of cognitive load, at the same time as the traditional teaching method: significant differences from pre- to post-test were observed in the control and experimental groups: the control group (t = -8.619, p &lt; 0.0001) and the experimental group (t = -11.108, p &lt; 0.0001). Activity (t = -3.021, p = 0.04) and selfassessment indicators of psychoemotional state (t = -2.908, p = 0.06) increased in the experimental group after using the digital corporate game, while the same changes were not observed in the control group. Conclusion. Forecasts regarding changes in the psycho-emotional state of students when using digital gamification, taking into account the optimal cognitive load, can be presented as cautiously optimistic. Keywords: digital gamification, psychoemotional state of university students, cognitive load, Delphi method, consolidation of students' knowledge

Creating an AI-powered platform for neurosurgery alongside a usability examination: Progressing towards minimally invasive robotics

Recent advancements in artificial intelligence have paved the way for promising applications in neurosurgery, aiming to improve patient outcomes while minimizing risks. This paper introduces a novel AI-driven system designed to assist neurosurgeons in accurately identifying and localizing brain tumors. Leveraging deep learning algorithms, the system was trained on a comprehensive dataset of brain MRI scans for segmentation and classification tasks. Evaluation of the system on an independent set of brain MRI scans revealed an average Dice similarity coefficient of 0.87, indicating high performance. Moreover, a user experience assessment conducted at the Department of Neurosurgery, University Hospital Ulm, demonstrated notable enhancements in accuracy, efficiency, and reduced cognitive load and stress levels among users. Notably, the system showcased adaptability across various surgical scenarios and provided personalized guidance to users. These findings underscore the potential of AI to augment the quality of neurosurgical interventions and ultimately enhance patient outcomes. Future endeavors will focus on integrating this system with robotic surgical tools to facilitate minimally invasive surgeries.

Creating an AI-powered platform for neurosurgery alongside a usability examination: Progressing towards minimally invasive robotics

Recent advancements in artificial intelligence have paved the way for promising applications in neurosurgery, aiming to improve patient outcomes while minimizing risks. This paper introduces a novel AI-driven system designed to assist neurosurgeons in accurately identifying and localizing brain tumors. Leveraging deep learning algorithms, the system was trained on a comprehensive dataset of brain MRI scans for segmentation and classification tasks. Evaluation of the system on an independent set of brain MRI scans revealed an average Dice similarity coefficient of 0.87, indicating high performance. Moreover, a user experience assessment conducted at the Department of Neurosurgery, University Hospital Ulm, demonstrated notable enhancements in accuracy, efficiency, and reduced cognitive load and stress levels among users. Notably, the system showcased adaptability across various surgical scenarios and provided personalized guidance to users. These findings underscore the potential of AI to augment the quality of neurosurgical interventions and ultimately enhance patient outcomes. Future endeavors will focus on integrating this system with robotic surgical tools to facilitate minimally invasive surgeries.