Teaching is ineffective when students are distracted: Cognitive load interference in medical education

Teaching is ineffective when students are distracted: Cognitive load interference in medical education | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Teaching is ineffective when students are distracted: Cognitive load interference in medical education Andrea Storck, Clemens Grahl Römer, Steffen Ansorge, Eva Schönefeld, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7433821/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Distraction impairs attention—a growing concern in medical training, where students face constant digital alerts and multitasking demands. While teaching strategies, such as cueing, have proven effective under optimal conditions, their performance under cognitive overload remains unexplored. This raises the question: Can effective instruction buffer against the detrimental effects of distraction? Methods In a 2x2 factorial experiment, 117 second-year medical students without prior knowledge watched a standardized instructional video on abdominal ultrasound. Distraction was induced via concurrent mental arithmetic task, and cues were provided via eye movement modeling examples of an expert’s gaze. Image interpretation performance as a primary outcome and cognitive load were measured before, and after training. Results As expected, cueing alone reduced extraneous cognitive load and improved learning. Distraction alone impaired learning. However, when both were combined, the performance benefits of cueing disappeared. Distracted learners receiving cues performed no better than uncued distracted learners, indicating no compensatory effect. Conclusions The disappearance of cueing benefits under distraction suggests a load interference mechanism : learners cannot benefit from helpful educational instructions when their cognitive capacity is already taxed by competing demands. This indicates that internal distraction effectively monopolizes limited working memory resources of students. We discuss the implications for medical education in increasingly distraction-rich learning environments characterized by social media, smartphone notifications, and electronic health record alerts. Special Education Medical education distraction cognitive load cognitive load interference learning performance eye tracking Figures Figure 1 Figure 2 Figure 3 Introduction In a hyper-connected world, medical students are increasingly exposed to task-irrelevant information, from technical notifications and multitasking demands to pervasive screen use. These distractions have been linked to declines in sustained attention, working memory, and learning efficiency (Liao & Wu, 2022 ), posing a growing challenge to instructional effectiveness in both classroom and clinical settings. Reflecting these concerns, there is a growing debate in higher education about banning smartphones in lectures and practical courses, with some institutions implementing strict device policies in an attempt to preserve students’ cognitive focus and learning outcomes (Böttger & Zierer, 2024 ). The goal of the current study was to examine how a well-established educational intervention—cueing—performs under distraction. Cueing refers to techniques that direct learners' attention to task-relevant information (Johnson et al., 2015; van Gog, 2021 ; Bellstedt et al., 2024 ). As such, it holds the potential to actively restore or refocus attentional resources in cognitively demanding situations. To investigate this interaction, we employed a factorial design that allowed us to examine both interventions independently and in combination. Theoretical background The current study builds on Cognitive Load Theory (CLT), which provides a framework for understanding how distractions might affect learning (Young et al., 2024; Sweller et al., 2019 ). According to CLT, learning is limited by the capacity of working memory, which serves as the bottleneck for information processing. Sweller distinguishes three types of cognitive load: intrinsic load, determined by the inherent complexity of the learning material; extraneous load, imposed by the instructional presentation method or unrelated activities; and germane load, representing the mental effort devoted to constructing meaningful cognitive schemas. While intrinsic load is largely unavoidable and depends on the material's complexity, extraneous load represents unnecessary cognitive processing that results from distractions or poor instructional design. Germane load, in contrast, constitutes desirable mental effort that directly supports learning and schema construction. Effective instructional design therefore aims to minimize extraneous load while optimizing conditions for germane processing. Cues, such as gaze cueing techniques or eye movement modeling examples, represent a well-established method for reducing extraneous load (Darici et al., 2023 ). By directing learners' attention to essential elements through demonstrated eye movement patterns, these cues help distinguish relevant from irrelevant information, thereby lowering extraneous cognitive demands and freeing resources for germane processing. In contrast, distractions impose additional extraneous load unrelated to the primary learning objective (Persoon et al., 2011 ; Zureick et al., 2018 ; Rice et al., 2020 , Sieg et al., 2025 ). Research demonstrates that when learners engage in internal distraction (“preoccupation with task-irrelevant information”), fewer cognitive resources remain available for processing external stimuli, resulting in slower response times (Madsen et al., 2021 ). Engaging in a concurrent task (“multitasking”) requires sustained executive control to hold intermediate results in working memory, and inhibit interference from competing task demands. As such, it consumes limited-capacity resources—particularly the central executive and phonological or visuospatial subsystems—that are essential for processing and integrating new instructional material (Allen et al., 2017 ). The present study In the context of increasingly distraction-rich medical education environments, can evidence-based instructional design maintain their effectiveness when students face concurrent cognitive demands? According on CLT, cueing and distraction would compete for learners' attention and limited working memory capacity. To simulate this condition, we use a dual-task paradigm (Pashler, 1994 ), which allow us to examine how performance on each task is affected by the concurrent demands of the other. The factorial design enables separate calculation of individual effects as well as their combined impact. We anticipate three possible outcomes: (A) Full compensation: Cueing remains fully effective despite distraction, such that distracted learners receiving cues perform just as well as undistracted learners with cues. (B) Full interference: Distraction entirely eliminates the benefits of cueing, leaving distracted learners with cues performing no better than those without cues. (C) Partial compensation: Cueing mitigates—but does not fully counteract—the detrimental effects of distraction, resulting in performance that lies between distracted learners without cues and undistracted learners with cues. Methods Study Design This randomized controlled double-blind trial employed a factorial pre-post design. The study was reviewed by the ethics committee of the university (“Ethik Kommission Westfalen-Lippe”) and deemed not to require formal medical ethics approval (reference: 2023-631-f-N). All procedures were carried out in accordance with the Declaration of Helsinki and its later amendments. Participants Second-year medical students from the University of Münster were recruited prior to a mandatory course for anatomy and imaging. Participation in the study was voluntary, and informed consent was received. Inclusion criteria were: (1) completion of regular anatomical curriculum, (2) no prior knowledge in medical imaging or sonography, and (3) voluntary participation with informed consent. One participant with extreme values (> 3 SD) was excluded after data inspection. Procedure The study was conducted in a controlled multimedia laboratory equipped with standardized computers and noise-cancelling headphones. Participants completed the study individually using an offline HTML5-based study environment to ensure consistent conditions and prevent external influences. Students remained blinded to their intervention conditions throughout the study duration. The study followed a structured timeline as illustrated in Fig. 1: Pre-intervention phase (15 minutes): Participants completed sociodemographic questionnaires, followed by a pre-test of sonographic image interpretation and initial cognitive load assessment. Randomization: The platform automatically allocated students to one of four experimental groups using simple random allocation based on their assigned experimental condition. Training intervention (5 minutes): Participants viewed an audio-commented routine sonography examination of the abdomen performed by a senior radiologist with 10 + years of experience. The examination followed standardized protocols as outlined in Darici et al. ( 2023 ). The sonographic video included pictograms indicating probe location and orientation. Post-intervention phase (20 minutes): Participants completed post-training cognitive load questionnaires, repeated the sonographic image interpretation test (identical items in randomized order), and provided final cognitive load measurements. Visual cueing Visual cueing was implemented through eye movement modeling examples (EMMEs) as validated in Darici et al. ( 2023 ). Eye movements of an expert radiologist were recorded during the original examination using screen-based eye-tracking technology. These gaze patterns were overlaid onto the sonographic video as a semi-transparent moving circle indicating the expert's visual attention focus in real-time. Previous research demonstrated that this intervention significantly improved participants' image interpretation performance by directing attention to task-relevant areas. Distraction Cognitive distraction was experimentally induced using a dual-task paradigm following Madsen et al. ( 2021 ) and Sieg et al. ( 2025 ). Participants in the distraction condition were instructed to perform continuous mental subtraction (serial sevens: 400-7 = 393, 393-7 = 386, etc.) while viewing the sonographic video. This arithmetic task creates substantial cognitive load and competes for working memory resources that simulates conditions where healthcare professionals must process multiple information streams simultaneously. While our mental subtraction paradigm may appear artificial compared to naturalistic clinical distractions, it specifically targets the same working memory subsystems that are compromised during internal distractions, making it a proxy for understanding load interference principles (see Hitch et al., 2024). Measurements Measurements included sociodemographic variables of age (free-text response) and gender (man/woman/non-binary), along 20 single-choice items (1 of 5) related to image interpretation performance in sonography (before training: Cronbach's α = .474, after training: α = .345). We measured the prior knowledge in anatomy with 10 single-choice items (1 of 5; α = .471). Cognitive load was measured using the scale by Klepsch et al. ( 2017 ) with 3 items for ICL (t0: α = .633, t1: α = .589, t2: α = .741), and 2 items for ECL (t0: α = .744, t1: α = .569, t2: α = .785) respectively. Statistical analysis Statistical analyses and visualizations were performed using R (R Core Team, 2020). Code in the Supplements. An ANOVA for repeated measures was conducted with time (pre-training vs. post-training) as within-subject, the two factors gaze Cueing (+/-) and Distraction (+/-) as between-subject, and image interpretation performance score as dependent. Secondary analyses examined cognitive load change across conditions and time points using similar mixed-design ANOVA. Effect sizes were reported as partial eta-squared ( η p ²), interpreted according to Cohen's conventions: η p ² = 0.01 (small effects), η p ² = 0.06 (medium effects), and η p ² = 0.14 (large effects). Results Participants The final sample ( N = 117) representing around 80% of the semester cohort was evenly distributed across the four experimental groups. Participants' mean age ranged from 20.7 to 21.8 years across groups, with age variability slightly higher in the cueing without distraction condition ( Standard deviation = 3.89, range: 19–40) compared to the other groups ( SDs 1–1.6, ranges: 19–26). Self-reported ultrasound-related pre-knowledge scores (scale: 1–5) were low and showed moderate variability, with means between 1.68 and 1.92 and standard deviations around 0.5–0.6. Preparatory engagement and anatomical knowledge scores were also generally balanced across groups, with no substantial group differences evident prior to the intervention. These baseline comparabilities support the interpretation that post-training effects are attributable to the experimental manipulations. Cueing benefits learning only when attentional resources are fully available Post-test performance was examined using a mixed ANOVA, which revealed significant interactions of time × cueing, F (1, 224) = 5.07, p = .025, ηp² = .022, time × distraction, F (1, 224) = 7.17, p = .008, ηp² = .031, and cueing × distraction, F (1, 224) = 6.46, p = .012, ηp² = .028. The three-way interaction did not reach significance, F (1, 224) = 1.99, p = .160. As shown in Fig. 2A, when learners were undistracted, cueing produced substantial learning gains, with mean scores rising from mean 7.7 to 11.5 out of 20. In contrast, undistracted learners without cues improved only from 8.2 to 9.8, demonstrating a clear benefit of cueing when attentional resources were fully available. However, this benefit compromised under distraction. Distracted learners receiving gaze cues improved marginally (8.5 to 9.6), performing almost identically to their distracted, no-cue counterparts (9.7 to 10.4). The near-parallel performance trajectories in the distracted groups (Fig. 2A, right) highlight the failure of cueing to enhance learning when cognitive capacity was taxed. Change-score analysis (Fig. 2B) corroborates this pattern: the no-distraction + cueing group exhibited the largest median gain (+ 4.5), whereas all other groups showed smaller, overlapping changes. Under distraction, cueing conferred no measurable advantage, with gains clustering near zero. Cueing reduces cognitive load during training, particularly under distraction During the training phase (t2), cueing demonstrated a selective beneficial effect on cognitive load management. Between-subjects analyses revealed that cueing significantly reduced extraneous cognitive load (ECL) compared to the no-cueing conditions (F(1,113) = 9.16, p = .003) (Fig. 3B). This reduction in ECL occurred without any corresponding effects on intrinsic cognitive load (ICL), which remained unaffected by cueing (F(1,113) = 1.78, p = .185) (Fig. 3A). The presence of distraction did not significantly influence either type of cognitive load during training, with no main effects observed for ECL (F(1,113) = 1.58, p = .211) or ICL (F(1,113) = 0.19, p = .668). Additionally, no significant interactions were found between cueing and distraction for either ECL (F(1,113) = 0.39, p = .532) or ICL (F(1,113) = 0.12, p = .727). These findings indicate that cueing specifically targets and reduces the extraneous processing demands during learning. Discussion Our findings show a complete interference pattern: gaze cueing, while effective under undistracted conditions, failed to produce learning benefits when learners’ attention was divided by a concurrent mental subtraction task. This suggests that even instructionally sound interventions cannot compensate when available cognitive resources are already monopolized by competing demands. We show that when attentional capacity was fully available, cueing leads to substantial performance gains and reduced extraneous cognitive load (ECL) during training. This replicates prior research (e.g., Darici et al., 2023 ) that visual cues such as eye movement modeling examples help learners filter relevant from irrelevant information, thereby freeing working memory for germane processing. The reduction in ECL occurred without changes in intrinsic cognitive load (ICL), consistent with Cognitive Load Theory (CLT), which posits that cueing enhances instructional efficiency without altering the inherent complexity of the material (Van Gog, 2021 ). However, we found that distraction nullified these benefits. Distracted learners—whether cued or not—showed only marginal performance improvements, with almost identical learning trajectories. This pattern aligns with a load interference hypothesis: when the working memory system is already taxed by extraneous demands, it cannot allocate enough resources to benefit from additional instructional guidance. In other words, cueing requires available capacity to be effective; under high extraneous load, it becomes instructional “noise” that cannot penetrate the bottleneck. Interestingly, cueing still lowered perceived extraneous load during training even under distraction—yet this did not translate into performance gains. This decoupling between subjective load ratings and objective performance suggests that learners may feel aided by cues even when they cannot process them effectively. This underscores the limitation of relying solely on self-reported load measures as proxies for cognitive resource availability (Hessler et al., 2018 ). Implications for medical education theory and practice Modern medical education environments present multiple competing demands on students' limited working memory: smartphone notifications and social media alerts during lectures; multitasking between electronic health records, clinical decision support systems, and patient care during clerkships; simultaneous processing of diagnostic imaging, laboratory results, and patient history in clinical reasoning tasks; interruptions from pagers, hospital communication systems, and urgent clinical alerts during bedside teaching; and cognitive switching between AI-assisted diagnostic tools, and automated documentation systems. The proliferation of digital technologies, AI integration, and interconnected healthcare systems means these distractors are steadily increasing in both number and complexity, creating an escalating challenge for cognitive resource management in medical education. These distractors create extraneous cognitive load that monopolizes the same working memory resources needed for effective learning, rendering even well-designed instructional interventions ineffective. In practice, this means pairing instructional enhancements with systematic distraction control at the curriculum, technology, and policy levels. Device management and distraction control A blanket “no phones” rule is attractive but evidence is mixed. Several quasi-experimental and review papers report improved focus and, in some contexts, better academic outcomes following restrictions, while others find little effect on grades or well-being when bans are implemented in isolation, without broader behavior change or after-class digital habits shifting (Campbell et al., 2024 ). Together, these findings support targeted, enforceable, context-specific restrictions (e.g., bell-to-bell or session-bounded device control) paired with pedagogy and habit training rather than bans alone (Böttger & Zierer, 2024 ). For large-group didactics and skills videos—settings most vulnerable to divided attention—default phone-off/away policies, lockboxes or signal-pouches, and enforced laptop-only use for task-relevant work are justified. Institutions should evaluate not only grades but proximal outcomes (on-task gaze, note quality, question rates) when assessing policy impact. Independent of outright bans, notifications themselves impair attention and performance—even when devices are not actively used through salience, expectancy, and task interruption costs (Skowronek et al., 2023 ). Evidence from lab and field studies shows that reducing notification-caused interruptions improves performance and lowers strain; related work suggests that the mere presence of a smartphone can sap working memory capacity, with partial replications and boundary conditions noted (Ohly & Bastin, 2023 ). For teaching sessions that rely on attentional guidance (e.g., ultrasound or surgery training), we recommend institutional defaults: airplane mode or focus mode mandated at entry, silent-by-default campus apps, and “notification-blackout” windows aligned with core teaching blocks. Cognitive load optimization Our results imply that cueing is most effective when paired with slack in cognitive load. Educators should therefore make low-friction design choices that widen the margin for teaching to work: slower pacing, momentary micro-pauses after salient cues, tiered signalling (few, consistent visual channels), and batching of interactive prompts rather than continuous dual-task demands (Biard et al., 2018 ; Darici et al., 2024 ). Where devices are necessary (e.g., polling), schedule “attention checkpoints” that explicitly pause nonessential apps. Consider pre-commitment rituals (“phones away, focus mode on”) and visible timers that bound concentrated work periods. This extends to assessments: if image-interpretation is the target, minimize concurrent digital demands during practice and testing so measured performance reflects learning rather than interruption tolerance. Importantly, the integration of AI systems into medical education requires careful attention to cognitive load interference (Hudon et al., 2021 ). AI-powered diagnostic tools, intelligent tutoring systems, and clinical decision support must be designed with load interference principles in mind. When learners' cognitive resources are already taxed by AI interfaces and notifications, the primary learning tasks cannot penetrate the working memory bottleneck. Institutions should implement AI tools that batch notifications, provide silent background adaptation, and offer focused, single-task learning modes rather than continuous dual-task demands. Given the pervasiveness of screens and notifications in clinical education, students need explicit training in attention management as part of digital professionalism. Short, skills-based modules can cover: configuring focus modes, batching notifications, single-tasking protocols during critical learning, and reflective tracking of attention failures. This complements policy and avoids framing the issue as purely punitive. Programs could teach “clinical attention hygiene” alongside handoffs and situational awareness. Limitations and future directions Several limitations should be considered when interpreting these findings. First, our distraction paradigm employed a standardized mental arithmetic task (serial sevens) that may not fully capture the ecological complexity of real-world clinical distractions. While this approach ensured experimental control and replicability, actual clinical environments present more heterogeneous interruptions—varying in timing, modality, urgency, and cognitive demands. Future studies should examine load interference effects using more naturalistic distraction paradigms, such as simulated clinical communication interruptions, or authentic multitasking scenarios encountered during clerkships. Second, our study focused on a single instructional domain (abdominal ultrasound interpretation) with novice learners. The generalizability of load interference effects across different medical education contexts remains unclear. Different types of learning tasks may show varying vulnerability to distraction—procedural skills acquisition, clinical reasoning, or interpersonal communication training might exhibit distinct patterns of cognitive load interference. Additionally, the expertise reversal effect suggests that advanced learners may respond differently under distraction, as their developed schemas could provide some protection against cognitive overload. Finally, the measurement of cognitive load through self-report scales, while validated, may not capture the full complexity of resource allocation during dual-task performance. Our finding that cueing reduced perceived extraneous load even when performance benefits disappeared suggests potential dissociation between subjective experience and objective cognitive processing. Future investigations should incorporate physiological measures (eye-tracking, EEG, heart rate variability) or secondary task performance indicators to provide more objective indices of cognitive resource depletion. Several promising directions emerge for future research. First, investigating individual differences in distraction susceptibility could inform personalized approaches to attention management training. Factors such as working memory capacity, attentional control, and prior clinical experience may moderate load interference effects, suggesting that some learners might benefit more from distraction-control interventions than others. Second, research should examine the temporal dynamics of load interference—how quickly cognitive resources become unavailable under distraction, whether brief respites can restore cueing effectiveness, and what recovery periods are needed between competing cognitive demands. This could inform optimal scheduling of high-attention learning activities and the design of "cognitive break" protocols. Third, the development and testing of attention-aware educational technologies represents a critical frontier. Adaptive systems that monitor learner cognitive load in real-time and automatically adjust instructional complexity, reduce notifications, or suggest breaks could help maintain the cognitive conditions necessary for effective learning. However, such systems must be carefully designed to avoid creating additional cognitive burdens through their monitoring and intervention mechanisms. Conclusion Our findings demonstrate a clear distraction-wins pattern: even well-designed instructional interventions like cueing cannot compensate when learners' cognitive resources are monopolized by competing demands. This load interference mechanism has profound implications for medical education in an increasingly connected world, where digital distractions are proliferating rapidly. Rather than relying solely on improved instructional design, medical educators must engineer learning environments that protect cognitive resources through systematic distraction control, attention management training, and technology design principles that prioritize focused learning over constant connectivity. The future of medical education depends not just on what we teach, but on creating conditions where effective learning can unfold. Declarations Conflicts of interest: The authors declare no conflicts of interest. AI disclosure Claude v. 4 Sonnet and GPT-5 have been used for language editing. All content and ideas remain the original work of the authors, with AI assistance to improve linguistic clarity. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgements: We thank all the students for participating in this study. References Allen RJ, Baddeley AD, Hitch GJ (2017) Executive and perceptual distraction in visual working memory. J Exp Psychol Hum Percept Perform 43(9):1677–1693. https://doi.org/10.1037/xhp0000413 Barbian B, Shiozawa T, Gellisch M, Brunk I, Langer-Fischer K, Wagner N et al (2025) Anatomy learning profiles in relation to student motivation and academic success: A multi-center cross-sectional study. Anat Sci Educ 00:1–13. https://doi.org/10.1002/ase.70065 Bellstedt M, Holtrup A, Otto N, Berndt M, Scherff AD, Papan C, Darici D (2024) Gaze cueing improves pattern recognition of histology learners. Anat Sci Educ 00:1–12. https://doi.org/10.1002/ase.2498 Biard N, Cojean S, Jamet E (2018) Effects of segmentation and pacing on procedural learning by video. Comput Hum Behav 89:411–417. https://doi.org/10.1016/j.chb.2017.12.002 Böttger T, Zierer K (2024) To ban or not to ban? A rapid review on the impact of smartphone bans in schools on social well-being and academic performance. Educ Sci 14(8):906. https://doi.org/10.3390/educsci14080906 Campbell M, Edwards EJ, Pennell D, Poed S, Lister V, Gillett-Swan J, Kelly A, Zec D, Nguyen T-A (2024) Evidence for and against banning mobile phones in schools: A scoping review. J Psychologists Counsellors Schools 34(3):242–265. https://doi.org/10.1177/20556365241270394 Darici D, Masthoff M, Rischen R, Schmitz M, Ohlenburg H, Missler M (2023) Medical imaging training with eye movement modeling examples: A randomized controlled study. Medical Teacher. Advance online publication. https://doi.org/10.1080/0142159X.2023.2189538 Darici D, Flägel K, Sternecker K, Missler M (2024) Transfer of learning in histology: Insights from a longitudinal study. Anat Sci Educ 17(2):274–286. https://doi.org/10.1002/ase.2363 Hessler M, Pöpping DM, Hollstein H, Ohlenburg H, Arnemann PH, Massoth C, Seidel LM, Zarbock A, Wenk M (2018) Availability of cookies during an academic course session affects evaluation of teaching. Med Educ 52(10):1064–1072. https://doi.org/10.1111/medu.13627 Hitch GJ, Allen RJ, Baddeley AD (2025) The multicomponent model of working memory fifty years on. Q J Experimental Psychol 78(2):222–239. https://doi.org/10.1177/17470218241290909 Hudon A, Demazure T, Karran A, Léger PM, Sénécal S (2021) Explainable artificial intelligence (XAI): How the visualization of AI predictions affects user cognitive load and confidence. In NeuroIS Retreat (pp. 237–246). Springer. https://doi.org/10.1007/978-3-030-79987-9_17 Klepsch M, Schmitz F, Seufert T (2017) Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load. Frontiers in Psychology, 8 , 1997. https://doi.org/10.3389/fpsyg.2017.01997 Liao CH, Wu JY (2022) Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance. Comput Educ 190:104599. https://doi.org/10.1016/j.compedu.2022.104599 Madsen J, Júlio SU, Gucik PJ, Steinberg R, Parra LC (2021) Synchronized eye movements predict test scores in online video education. Proc Natl Acad Sci USA 118(5):e2016980118. https://doi.org/10.1073/pnas.2016980118 Mayer RE (2005) Cognitive theory of multimedia learning. In: Mayer RE (ed) The Cambridge handbook of multimedia learning. Cambridge University Press, pp 31–48 Mayer RE (2017) Using multimedia for e-learning. J Comput Assist Learn 33(5):403–423. https://doi.org/10.1111/jcal.12197 Ohly S, Bastin L (2023) Effects of task interruptions caused by notifications from communication applications on strain and performance. J Occup Health 65(1):e12408. https://doi.org/10.1002/1348-9585.12408 Otto N, Böckers A, Shiozawa T, Brunk I, Schumann S, Kugelmann D, Missler M, Darici D (2024) Profiling learning strategies of medical students: A person-centered approach. Med Educ. Advance online publication https://doi.org/10.1111/medu.15388 Pashler H (1994) Dual-task interference in simple tasks: data and theory. Psychol Bull 116(2):220–244. https://doi.org/10.1037/0033-2909.116.2.220 Persoon MC, van Putten K, Muijtjens AM, Witjes JA, Hendrikx AJ, Scherpbier AJ (2011) Effect of distraction on the performance of endourological tasks: A randomized controlled trial. BJU Int 107(10):1653–1657. https://doi.org/10.1111/j.1464-410X.2010.09685.x R Core Team (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org/ Rice SC, Slaughter JC, Smalley W, Obstein KL (2020) The impact of distraction minimization on endoscopic mentoring and performance. Endoscopy Int Open 8(12):E1804–E1810. https://doi.org/10.1055/a-1251-5618 Schweppe J, Rummer R (2014) Attention, working memory, and long-term memory in multimedia learning: An integrated perspective based on process models of working memory. Educational Psychol Rev 26(2):285–306. https://doi.org/10.1007/s10648-013-9246-y Sieg L, Eismann H, Schneider B, Karsten J, Darici D (2025) How distractions undermine attentional synchronisation in work-based learning: A randomised controlled study using dual mobile eye-tracking. J Comput Assist Learn 41(3):e70059. https://doi.org/10.1111/jcal.70059 Skowronek J, Seifert A, Lindberg S (2023) The mere presence of a smartphone reduces basal attentional performance. Sci Rep 13(1):9363. https://doi.org/10.1038/s41598-023-36256-4 Sweller J, van Merriënboer JJG, Paas F (2019) Cognitive architecture and instructional design: 20 years later. Educational Psychol Rev 31(2):261–292. https://doi.org/10.1007/s10648-019-09465-5 Van Gog T (2021) The signaling (or cueing) principle in multimedia learning. In: Mayer RE (ed) The Cambridge handbook of multimedia learning. Cambridge University Press, pp 221–230 Young JQ, Van Merriënboer J, Durning S, Ten Cate O (2014) Cognitive load theory: Implications for medical education: AMEE Guide 86. Med Teach 36(5):371–384. https://doi.org/10.3109/0142159X.2014.889290 Zureick AH, Burk-Rafel J, Purkiss JA, Hortsch M (2018) The interrupted learner: How distractions during live and video lectures influence learning outcomes. Anat Sci Educ 11(4):366–376. https://doi.org/10.1002/ase.1754 Additional Declarations The authors declare no competing interests. Supplementary Files SUPPLEMENTS.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7433821","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504169731,"identity":"5cb473ce-9ab1-47d0-8009-3d7ef7d868ec","order_by":0,"name":"Andrea Storck","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Storck","suffix":""},{"id":504169732,"identity":"b3149eae-c729-450d-9bad-a40679d491a5","order_by":1,"name":"Clemens Grahl Römer","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Clemens","middleName":"Grahl","lastName":"Römer","suffix":""},{"id":504169733,"identity":"e56df01b-3fde-4abb-bb7f-a7eead8f957b","order_by":2,"name":"Steffen Ansorge","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Steffen","middleName":"","lastName":"Ansorge","suffix":""},{"id":504169734,"identity":"2182e5e5-8b0c-410a-bb96-b0147021b674","order_by":3,"name":"Eva Schönefeld","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Schönefeld","suffix":""},{"id":504169735,"identity":"00fc4b70-a6a0-4de4-a0e6-b7831eea542d","order_by":4,"name":"Michelle Bellstedt","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"","lastName":"Bellstedt","suffix":""},{"id":504169736,"identity":"72342d22-b8cd-4482-bff6-2192c4cd865a","order_by":5,"name":"Birte Barbian","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Birte","middleName":"","lastName":"Barbian","suffix":""},{"id":504169737,"identity":"5844a23e-fe75-4c2e-a4ef-9148cee0e1ce","order_by":6,"name":"Martin Janssen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Janssen","suffix":""},{"id":504169738,"identity":"f956ee02-e719-4b90-bfc4-dd7cb5c6b8ae","order_by":7,"name":"Konstantin E. Seifert","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Konstantin","middleName":"E.","lastName":"Seifert","suffix":""},{"id":504169739,"identity":"0bf9a0e7-59e8-46b1-9665-aedd4d6bc2f4","order_by":8,"name":"Dogus Darici","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDCCAyBkYMPAwIwsykNYSxpCCw8xWoDgMJoF+LTwHT/78MCPgvOJ29kZWDfzVNyxt2fvfcDwpgK3Fskz6QYHewxuJ+5sZmC7zXPmWWIPz3EDxjlncGsxOJAGdBVQy4bD/N9u87YdTuCRSGNg5m3Do+X8M5CWc0AtQFt4/x2255F/BtTyD4+WG2BbDkC1NBxm7JFgA2ppwOOXG88YgH5JNgZpuTnn2OHEnjNpDAfnHMOthe98GvOHH3/sZDecP8B2403NYXv29mOMD97U4NaCHRwgVcMoGAWjYBSMAlQAANKGVNr0v77OAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Dogus","middleName":"","lastName":"Darici","suffix":""}],"badges":[],"createdAt":"2025-08-22 10:57:09","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7433821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7433821/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89990270,"identity":"9c49c4ae-ac89-4053-9b7b-360747143b70","added_by":"auto","created_at":"2025-08-27 07:10:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1707926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExperimental conditions and study design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e (A) Example still frames from the abdominal sonography training video showing the two cueing conditions. In the \u003cstrong\u003eCueing -\u003c/strong\u003e condition (top), learners viewed the unmodified sonographic video. In the \u003cstrong\u003eCueing +\u003c/strong\u003econdition (bottom), gaze-based visual cues were overlaid as a semi-transparent moving circle (red) indicating the expert’s point of visual focus in real time.\u003cem\u003e \u003c/em\u003e(B)\u003cem\u003e \u003c/em\u003eFlowchart of the study procedure. One hundred eighteen second-year medical students completed baseline assessments including a structure identification task using 20 sonography images and cognitive load questionnaires (ICL, ECL). Participants were then randomly allocated using simple randomization to one of four training conditions in a factorial design. All groups received structured sonography training of the abdomen under their assigned experimental conditions. Post-training assessments included the identical structure identification task and cognitive load measurements administered at three time points (t1: pre-training, t2: during training, t3: post-training).\u003cbr\u003e\n\u003cem\u003e* Numbers do not add up due to participant exclusion in this group. ICL = Intrinsic Cognitive Load; ECL = Extraneous Cognitive Load.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7433821/v1/85f8398aa69489455f5fb411.png"},{"id":89990196,"identity":"98750108-5270-474c-a004-10f76dcf521b","added_by":"auto","created_at":"2025-08-27 07:10:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":846699,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCueing benefits learning only when attentional resources are fully available\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e (A) \u003cem\u003eMean\u003c/em\u003e pre- and post-training performance (maximum 20 points) in the structure identification task, separated by distraction (columns) and cueing condition (colors). Error bars indicate ±1 \u003cem\u003estandard error\u003c/em\u003e. Without distraction, cueing produced substantial learning gains, whereas under distraction, cueing benefits disappeared. (B) Distribution of change scores (post-test minus pre-test performance) for each condition. The largest gains occurred in the no-distraction + cueing group, while all other groups showed smaller or negligible improvements. The dashed red line marks zero change.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7433821/v1/e231fb3acda2074ae890b751.png"},{"id":89990197,"identity":"396742c7-d27a-46b2-8eff-56d87d0d33d9","added_by":"auto","created_at":"2025-08-27 07:10:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1785240,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCueing reduces perceived extraneous cognitive load during training, particularly under distraction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eMean\u003c/em\u003eintrinsic cognitive load (ICL; panel A) and extraneous cognitive load (ECL; panel B) at pre-test (t1), during training (t2), and post-test (t3) for cued (red) and uncued (blue) learners under no-distraction and distraction conditions. Error bars represent ±1 \u003cem\u003estandard error\u003c/em\u003e of the \u003cem\u003emean\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7433821/v1/ca412eaf04f8621e4e9d8494.png"},{"id":89992305,"identity":"9babe50a-00c1-4561-8583-ddba7fccb3b5","added_by":"auto","created_at":"2025-08-27 07:34:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3338261,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7433821/v1/3af30b47-c052-4df6-ad5b-e8851002e81b.pdf"},{"id":89990260,"identity":"080d5dc1-40a9-47f1-a072-9f0664a99dd6","added_by":"auto","created_at":"2025-08-27 07:10:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":804259,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTS.docx","url":"https://assets-eu.researchsquare.com/files/rs-7433821/v1/57bd72908bab9fe8c0df72a4.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTeaching is ineffective when students are distracted:\u003cbr\u003e\nCognitive load interference in medical education\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn a hyper-connected world, medical students are increasingly exposed to task-irrelevant information, from technical notifications and multitasking demands to pervasive screen use. These distractions have been linked to declines in sustained attention, working memory, and learning efficiency (Liao \u0026amp; Wu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), posing a growing challenge to instructional effectiveness in both classroom and clinical settings. Reflecting these concerns, there is a growing debate in higher education about banning smartphones in lectures and practical courses, with some institutions implementing strict device policies in an attempt to preserve students\u0026rsquo; cognitive focus and learning outcomes (B\u0026ouml;ttger \u0026amp; Zierer, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe goal of the current study was to examine how a well-established educational intervention\u0026mdash;cueing\u0026mdash;performs under distraction. Cueing refers to techniques that direct learners' attention to task-relevant information (Johnson et al., 2015; van Gog, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bellstedt et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As such, it holds the potential to actively restore or refocus attentional resources in cognitively demanding situations. To investigate this interaction, we employed a factorial design that allowed us to examine both interventions independently and in combination.\u003c/p\u003e\n\u003ch3\u003eTheoretical background\u003c/h3\u003e\n\u003cp\u003eThe current study builds on \u003cem\u003eCognitive Load Theory\u003c/em\u003e (CLT), which provides a framework for understanding \u003cem\u003ehow\u003c/em\u003e distractions might affect learning (Young et al., 2024; Sweller et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to CLT, learning is limited by the capacity of working memory, which serves as the bottleneck for information processing. Sweller distinguishes three types of cognitive load: intrinsic load, determined by the inherent complexity of the learning material; extraneous load, imposed by the instructional presentation method or unrelated activities; and germane load, representing the mental effort devoted to constructing meaningful cognitive schemas.\u003c/p\u003e\u003cp\u003eWhile intrinsic load is largely unavoidable and depends on the material's complexity, extraneous load represents unnecessary cognitive processing that results from distractions or poor instructional design. Germane load, in contrast, constitutes desirable mental effort that directly supports learning and schema construction. Effective instructional design therefore aims to minimize extraneous load while optimizing conditions for germane processing.\u003c/p\u003e\u003cp\u003eCues, such as gaze cueing techniques or eye movement modeling examples, represent a well-established method for reducing extraneous load (Darici et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By directing learners' attention to essential elements through demonstrated eye movement patterns, these cues help distinguish relevant from irrelevant information, thereby lowering extraneous cognitive demands and freeing resources for germane processing.\u003c/p\u003e\u003cp\u003eIn contrast, distractions impose additional extraneous load unrelated to the primary learning objective (Persoon et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zureick et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rice et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Sieg et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Research demonstrates that when learners engage in internal distraction (\u0026ldquo;preoccupation with task-irrelevant information\u0026rdquo;), fewer cognitive resources remain available for processing external stimuli, resulting in slower response times (Madsen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Engaging in a concurrent task (\u0026ldquo;multitasking\u0026rdquo;) requires sustained executive control to hold intermediate results in working memory, and inhibit interference from competing task demands. As such, it consumes limited-capacity resources\u0026mdash;particularly the central executive and phonological or visuospatial subsystems\u0026mdash;that are essential for processing and integrating new instructional material (Allen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eThe present study\u003c/h2\u003e\u003cp\u003eIn the context of increasingly distraction-rich medical education environments, can evidence-based instructional design maintain their effectiveness when students face concurrent cognitive demands? According on CLT, cueing and distraction would compete for learners' attention and limited working memory capacity. To simulate this condition, we use a dual-task paradigm (Pashler, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), which allow us to examine how performance on each task is affected by the concurrent demands of the other. The factorial design enables separate calculation of individual effects as well as their combined impact.\u003c/p\u003e\u003cp\u003eWe anticipate three possible outcomes:\u003c/p\u003e\u003cp\u003e(A) Full compensation: Cueing remains fully effective despite distraction, such that distracted learners receiving cues perform just as well as undistracted learners with cues.\u003c/p\u003e\u003cp\u003e(B) Full interference: Distraction entirely eliminates the benefits of cueing, leaving distracted learners with cues performing no better than those without cues.\u003c/p\u003e\u003cp\u003e(C) Partial compensation: Cueing mitigates\u0026mdash;but does not fully counteract\u0026mdash;the detrimental effects of distraction, resulting in performance that lies between distracted learners without cues and undistracted learners with cues.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design\u003c/h2\u003e\n \u003cp\u003eThis randomized controlled double-blind trial employed a factorial pre-post design. The study was reviewed by the ethics committee of the university (\u0026ldquo;Ethik Kommission Westfalen-Lippe\u0026rdquo;) and deemed not to require formal medical ethics approval (reference: 2023-631-f-N). All procedures were carried out in accordance with the Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eSecond-year medical students from the University of M\u0026uuml;nster were recruited prior to a mandatory course for anatomy and imaging. Participation in the study was voluntary, and informed consent was received. Inclusion criteria were: (1) completion of regular anatomical curriculum, (2) no prior knowledge in medical imaging or sonography, and (3) voluntary participation with informed consent. One participant with extreme values (\u0026gt;\u0026thinsp;3 SD) was excluded after data inspection.\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in a controlled multimedia laboratory equipped with standardized computers and noise-cancelling headphones. Participants completed the study individually using an offline HTML5-based study environment to ensure consistent conditions and prevent external influences. Students remained blinded to their intervention conditions throughout the study duration.\u003c/p\u003e\n\u003cp\u003eThe study followed a structured timeline as illustrated in Fig. 1:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003ePre-intervention phase (15 minutes): Participants completed sociodemographic questionnaires, followed by a pre-test of sonographic image interpretation and initial cognitive load assessment.\u003c/li\u003e\n \u003cli\u003eRandomization: The platform automatically allocated students to one of four experimental groups using simple random allocation based on their assigned experimental condition.\u003c/li\u003e\n \u003cli\u003eTraining intervention (5 minutes): Participants viewed an audio-commented routine sonography examination of the abdomen performed by a senior radiologist with 10\u0026thinsp;+\u0026thinsp;years of experience. The examination followed standardized protocols as outlined in Darici et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The sonographic video included pictograms indicating probe location and orientation.\u003c/li\u003e\n \u003cli\u003ePost-intervention phase (20 minutes): Participants completed post-training cognitive load questionnaires, repeated the sonographic image interpretation test (identical items in randomized order), and provided final cognitive load measurements.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch3\u003eVisual cueing\u003c/h3\u003e\n\u003cp\u003eVisual cueing was implemented through eye movement modeling examples (EMMEs) as validated in Darici et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Eye movements of an expert radiologist were recorded during the original examination using screen-based eye-tracking technology. These gaze patterns were overlaid onto the sonographic video as a semi-transparent moving circle indicating the expert\u0026apos;s visual attention focus in real-time. Previous research demonstrated that this intervention significantly improved participants\u0026apos; image interpretation performance by directing attention to task-relevant areas.\u003c/p\u003e\n\u003ch3\u003eDistraction\u003c/h3\u003e\n\u003cp\u003eCognitive distraction was experimentally induced using a dual-task paradigm following Madsen et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Sieg et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Participants in the distraction condition were instructed to perform continuous mental subtraction (serial sevens: 400-7\u0026thinsp;=\u0026thinsp;393, 393-7\u0026thinsp;=\u0026thinsp;386, etc.) while viewing the sonographic video. This arithmetic task creates substantial cognitive load and competes for working memory resources that simulates conditions where healthcare professionals must process multiple information streams simultaneously. While our mental subtraction paradigm may appear artificial compared to naturalistic clinical distractions, it specifically targets the same working memory subsystems that are compromised during internal distractions, making it a proxy for understanding load interference principles (see Hitch et al., 2024).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMeasurements\u003c/h2\u003e\n \u003cp\u003eMeasurements included sociodemographic variables of age (free-text response) and gender (man/woman/non-binary), along 20 single-choice items (1 of 5) related to image interpretation performance in sonography (before training: Cronbach\u0026apos;s \u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.474, after training: \u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.345). We measured the prior knowledge in anatomy with 10 single-choice items (1 of 5; \u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.471). Cognitive load was measured using the scale by Klepsch et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) with 3 items for ICL (t0: \u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.633, t1: \u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.589, t2: \u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.741), and 2 items for ECL (t0: \u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.744, t1: \u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.569, t2: \u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.785) respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analyses and visualizations were performed using R (R Core Team, 2020). Code in the Supplements. An ANOVA for repeated measures was conducted with time (pre-training vs. post-training) as within-subject, the two factors gaze Cueing (+/-) and Distraction (+/-) as between-subject, and image interpretation performance score as dependent. Secondary analyses examined cognitive load change across conditions and time points using similar mixed-design ANOVA. Effect sizes were reported as partial eta-squared (\u003cem\u003e\u0026eta;\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u0026sup2;), interpreted according to Cohen\u0026apos;s conventions: \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u0026sup2; = 0.01 (small effects), \u0026eta;\u003csub\u003ep\u003c/sub\u003e\u0026sup2; = 0.06 (medium effects), and \u003cem\u003e\u0026eta;\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e\u0026sup2; = 0.14 (large effects).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u003c/h2\u003e\n \u003cp\u003eThe final sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;117) representing around 80% of the semester cohort was evenly distributed across the four experimental groups. Participants\u0026apos; mean age ranged from 20.7 to 21.8 years across groups, with age variability slightly higher in the \u003cem\u003ecueing without distraction\u003c/em\u003e condition (\u003cem\u003eStandard deviation\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.89, range: 19\u0026ndash;40) compared to the other groups (\u003cem\u003eSDs\u003c/em\u003e 1\u0026ndash;1.6, ranges: 19\u0026ndash;26). Self-reported ultrasound-related pre-knowledge scores (scale: 1\u0026ndash;5) were low and showed moderate variability, with means between 1.68 and 1.92 and standard deviations around 0.5\u0026ndash;0.6. Preparatory engagement and anatomical knowledge scores were also generally balanced across groups, with no substantial group differences evident prior to the intervention. These baseline comparabilities support the interpretation that post-training effects are attributable to the experimental manipulations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eCueing benefits learning only when attentional resources are fully available\u003c/h2\u003e\n \u003cp\u003ePost-test performance was examined using a mixed ANOVA, which revealed significant interactions of time \u0026times; cueing, \u003cem\u003eF\u003c/em\u003e(1, 224)\u0026thinsp;=\u0026thinsp;5.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.025, \u0026eta;p\u0026sup2; = .022, time \u0026times; distraction, \u003cem\u003eF\u003c/em\u003e(1, 224)\u0026thinsp;=\u0026thinsp;7.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.008, \u0026eta;p\u0026sup2; = .031, and cueing \u0026times; distraction, \u003cem\u003eF\u003c/em\u003e(1, 224)\u0026thinsp;=\u0026thinsp;6.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.012, \u0026eta;p\u0026sup2; = .028. The three-way interaction did not reach significance, \u003cem\u003eF\u003c/em\u003e(1, 224)\u0026thinsp;=\u0026thinsp;1.99, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.160.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. 2A, when learners were undistracted, cueing produced substantial learning gains, with mean scores rising from \u003cem\u003emean\u003c/em\u003e 7.7 to 11.5 out of 20. In contrast, undistracted learners without cues improved only from 8.2 to 9.8, demonstrating a clear benefit of cueing when attentional resources were fully available.\u003c/p\u003e\n \u003cp\u003eHowever, this benefit compromised under distraction. Distracted learners receiving gaze cues improved marginally (8.5 to 9.6), performing almost identically to their distracted, no-cue counterparts (9.7 to 10.4). The near-parallel performance trajectories in the distracted groups (Fig.\u0026nbsp;2A, right) highlight the failure of cueing to enhance learning when cognitive capacity was taxed.\u003c/p\u003e\n \u003cp\u003eChange-score analysis (Fig. 2B) corroborates this pattern: the no-distraction\u0026thinsp;+\u0026thinsp;cueing group exhibited the largest median gain (+\u0026thinsp;4.5), whereas all other groups showed smaller, overlapping changes. Under distraction, cueing conferred no measurable advantage, with gains clustering near zero.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eCueing reduces cognitive load during training, particularly under distraction\u003c/h2\u003e\n \u003cp\u003eDuring the training phase (t2), cueing demonstrated a selective beneficial effect on cognitive load management. Between-subjects analyses revealed that cueing significantly reduced extraneous cognitive load (ECL) compared to the no-cueing conditions (F(1,113)\u0026thinsp;=\u0026thinsp;9.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003) (Fig. 3B). This reduction in ECL occurred without any corresponding effects on intrinsic cognitive load (ICL), which remained unaffected by cueing (F(1,113)\u0026thinsp;=\u0026thinsp;1.78, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.185) (Fig.\u0026nbsp;3A).\u003c/p\u003e\n \u003cp\u003eThe presence of distraction did not significantly influence either type of cognitive load during training, with no main effects observed for ECL (F(1,113)\u0026thinsp;=\u0026thinsp;1.58, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.211) or ICL (F(1,113)\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.668). Additionally, no significant interactions were found between cueing and distraction for either ECL (F(1,113)\u0026thinsp;=\u0026thinsp;0.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.532) or ICL (F(1,113)\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.727).\u003c/p\u003e\n \u003cp\u003eThese findings indicate that cueing specifically targets and reduces the extraneous processing demands during learning.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings show a complete interference pattern: gaze cueing, while effective under undistracted conditions, failed to produce learning benefits when learners\u0026rsquo; attention was divided by a concurrent mental subtraction task. This suggests that even instructionally sound interventions cannot compensate when available cognitive resources are already monopolized by competing demands.\u003c/p\u003e\u003cp\u003eWe show that when attentional capacity was fully available, cueing leads to substantial performance gains and reduced extraneous cognitive load (ECL) during training. This replicates prior research (e.g., Darici et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) that visual cues such as eye movement modeling examples help learners filter relevant from irrelevant information, thereby freeing working memory for germane processing. The reduction in ECL occurred without changes in intrinsic cognitive load (ICL), consistent with Cognitive Load Theory (CLT), which posits that cueing enhances instructional efficiency without altering the inherent complexity of the material (Van Gog, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, we found that distraction nullified these benefits. Distracted learners\u0026mdash;whether cued or not\u0026mdash;showed only marginal performance improvements, with almost identical learning trajectories. This pattern aligns with a load interference hypothesis: when the working memory system is already taxed by extraneous demands, it cannot allocate enough resources to benefit from additional instructional guidance. In other words, cueing requires available capacity to be effective; under high extraneous load, it becomes instructional \u0026ldquo;noise\u0026rdquo; that cannot penetrate the bottleneck.\u003c/p\u003e\u003cp\u003eInterestingly, cueing still lowered perceived extraneous load during training even under distraction\u0026mdash;yet this did not translate into performance gains. This decoupling between subjective load ratings and objective performance suggests that learners may feel aided by cues even when they cannot process them effectively. This underscores the limitation of relying solely on self-reported load measures as proxies for cognitive resource availability (Hessler et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eImplications for medical education theory and practice\u003c/h2\u003e\u003cp\u003eModern medical education environments present multiple competing demands on students' limited working memory: smartphone notifications and social media alerts during lectures; multitasking between electronic health records, clinical decision support systems, and patient care during clerkships; simultaneous processing of diagnostic imaging, laboratory results, and patient history in clinical reasoning tasks; interruptions from pagers, hospital communication systems, and urgent clinical alerts during bedside teaching; and cognitive switching between AI-assisted diagnostic tools, and automated documentation systems. The proliferation of digital technologies, AI integration, and interconnected healthcare systems means these distractors are steadily increasing in both number and complexity, creating an escalating challenge for cognitive resource management in medical education. These distractors create extraneous cognitive load that monopolizes the same working memory resources needed for effective learning, rendering even well-designed instructional interventions ineffective. In practice, this means pairing instructional enhancements with systematic distraction control at the curriculum, technology, and policy levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eDevice management and distraction control\u003c/h2\u003e\u003cp\u003eA blanket \u0026ldquo;no phones\u0026rdquo; rule is attractive but evidence is mixed. Several quasi-experimental and review papers report improved focus and, in some contexts, better academic outcomes following restrictions, while others find little effect on grades or well-being when bans are implemented in isolation, without broader behavior change or after-class digital habits shifting (Campbell et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Together, these findings support targeted, enforceable, context-specific restrictions (e.g., bell-to-bell or session-bounded device control) paired with pedagogy and habit training rather than bans alone (B\u0026ouml;ttger \u0026amp; Zierer, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For large-group didactics and skills videos\u0026mdash;settings most vulnerable to divided attention\u0026mdash;default phone-off/away policies, lockboxes or signal-pouches, and enforced laptop-only use for task-relevant work are justified. Institutions should evaluate not only grades but proximal outcomes (on-task gaze, note quality, question rates) when assessing policy impact.\u003c/p\u003e\u003cp\u003eIndependent of outright bans, notifications themselves impair attention and performance\u0026mdash;even when devices are not actively used through salience, expectancy, and task interruption costs (Skowronek et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Evidence from lab and field studies shows that reducing notification-caused interruptions improves performance and lowers strain; related work suggests that the mere presence of a smartphone can sap working memory capacity, with partial replications and boundary conditions noted (Ohly \u0026amp; Bastin, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For teaching sessions that rely on attentional guidance (e.g., ultrasound or surgery training), we recommend institutional defaults: airplane mode or focus mode mandated at entry, silent-by-default campus apps, and \u0026ldquo;notification-blackout\u0026rdquo; windows aligned with core teaching blocks.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eCognitive load optimization\u003c/h2\u003e\u003cp\u003eOur results imply that cueing is most effective when paired with slack in cognitive load. Educators should therefore make low-friction design choices that widen the margin for teaching to work: slower pacing, momentary micro-pauses after salient cues, tiered signalling (few, consistent visual channels), and batching of interactive prompts rather than continuous dual-task demands (Biard et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Darici et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Where devices are necessary (e.g., polling), schedule \u0026ldquo;attention checkpoints\u0026rdquo; that explicitly pause nonessential apps. Consider pre-commitment rituals (\u0026ldquo;phones away, focus mode on\u0026rdquo;) and visible timers that bound concentrated work periods. This extends to assessments: if image-interpretation is the target, minimize concurrent digital demands during practice and testing so measured performance reflects learning rather than interruption tolerance.\u003c/p\u003e\u003cp\u003eImportantly, the integration of AI systems into medical education requires careful attention to cognitive load interference (Hudon et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AI-powered diagnostic tools, intelligent tutoring systems, and clinical decision support must be designed with load interference principles in mind. When learners' cognitive resources are already taxed by AI interfaces and notifications, the primary learning tasks cannot penetrate the working memory bottleneck. Institutions should implement AI tools that batch notifications, provide silent background adaptation, and offer focused, single-task learning modes rather than continuous dual-task demands.\u003c/p\u003e\u003cp\u003eGiven the pervasiveness of screens and notifications in clinical education, students need explicit training in attention management as part of digital professionalism. Short, skills-based modules can cover: configuring focus modes, batching notifications, single-tasking protocols during critical learning, and reflective tracking of attention failures. This complements policy and avoids framing the issue as purely punitive. Programs could teach \u0026ldquo;clinical attention hygiene\u0026rdquo; alongside handoffs and situational awareness.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eLimitations and future directions\u003c/h2\u003e\u003cp\u003eSeveral limitations should be considered when interpreting these findings. First, our distraction paradigm employed a standardized mental arithmetic task (serial sevens) that may not fully capture the ecological complexity of real-world clinical distractions. While this approach ensured experimental control and replicability, actual clinical environments present more heterogeneous interruptions\u0026mdash;varying in timing, modality, urgency, and cognitive demands. Future studies should examine load interference effects using more naturalistic distraction paradigms, such as simulated clinical communication interruptions, or authentic multitasking scenarios encountered during clerkships.\u003c/p\u003e\u003cp\u003eSecond, our study focused on a single instructional domain (abdominal ultrasound interpretation) with novice learners. The generalizability of load interference effects across different medical education contexts remains unclear. Different types of learning tasks may show varying vulnerability to distraction\u0026mdash;procedural skills acquisition, clinical reasoning, or interpersonal communication training might exhibit distinct patterns of cognitive load interference. Additionally, the expertise reversal effect suggests that advanced learners may respond differently under distraction, as their developed schemas could provide some protection against cognitive overload.\u003c/p\u003e\u003cp\u003eFinally, the measurement of cognitive load through self-report scales, while validated, may not capture the full complexity of resource allocation during dual-task performance. Our finding that cueing reduced perceived extraneous load even when performance benefits disappeared suggests potential dissociation between subjective experience and objective cognitive processing. Future investigations should incorporate physiological measures (eye-tracking, EEG, heart rate variability) or secondary task performance indicators to provide more objective indices of cognitive resource depletion.\u003c/p\u003e\u003cp\u003eSeveral promising directions emerge for future research. First, investigating individual differences in distraction susceptibility could inform personalized approaches to attention management training. Factors such as working memory capacity, attentional control, and prior clinical experience may moderate load interference effects, suggesting that some learners might benefit more from distraction-control interventions than others.\u003c/p\u003e\u003cp\u003eSecond, research should examine the temporal dynamics of load interference\u0026mdash;how quickly cognitive resources become unavailable under distraction, whether brief respites can restore cueing effectiveness, and what recovery periods are needed between competing cognitive demands. This could inform optimal scheduling of high-attention learning activities and the design of \"cognitive break\" protocols.\u003c/p\u003e\u003cp\u003eThird, the development and testing of attention-aware educational technologies represents a critical frontier. Adaptive systems that monitor learner cognitive load in real-time and automatically adjust instructional complexity, reduce notifications, or suggest breaks could help maintain the cognitive conditions necessary for effective learning. However, such systems must be carefully designed to avoid creating additional cognitive burdens through their monitoring and intervention mechanisms.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings demonstrate a clear distraction-wins pattern: even well-designed instructional interventions like cueing cannot compensate when learners' cognitive resources are monopolized by competing demands. This load interference mechanism has profound implications for medical education in an increasingly connected world, where digital distractions are proliferating rapidly. Rather than relying solely on improved instructional design, medical educators must engineer learning environments that protect cognitive resources through systematic distraction control, attention management training, and technology design principles that prioritize focused learning over constant connectivity. The future of medical education depends not just on what we teach, but on creating conditions where effective learning can unfold.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAI disclosure\u003c/strong\u003e\u003cp\u003eClaude v. 4 Sonnet and GPT-5 have been used for language editing. All content and ideas remain the original work of the authors, with AI assistance to improve linguistic clarity.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\u003cp\u003eWe thank all the students for participating in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eAllen RJ, Baddeley AD, Hitch GJ (2017) Executive and perceptual distraction in visual working memory. J Exp Psychol Hum Percept Perform 43(9):1677\u0026ndash;1693. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/xhp0000413\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBarbian B, Shiozawa T, Gellisch M, Brunk I, Langer-Fischer K, Wagner N et al (2025) Anatomy learning profiles in relation to student motivation and academic success: A multi-center cross-sectional study. Anat Sci Educ 00:1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ase.70065\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBellstedt M, Holtrup A, Otto N, Berndt M, Scherff AD, Papan C, Darici D (2024) Gaze cueing improves pattern recognition of histology learners. Anat Sci Educ 00:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ase.2498\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBiard N, Cojean S, Jamet E (2018) Effects of segmentation and pacing on procedural learning by video. Comput Hum Behav 89:411\u0026ndash;417. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2017.12.002\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eB\u0026ouml;ttger T, Zierer K (2024) To ban or not to ban? A rapid review on the impact of smartphone bans in schools on social well-being and academic performance. Educ Sci 14(8):906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/educsci14080906\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCampbell M, Edwards EJ, Pennell D, Poed S, Lister V, Gillett-Swan J, Kelly A, Zec D, Nguyen T-A (2024) Evidence for and against banning mobile phones in schools: A scoping review. J Psychologists Counsellors Schools 34(3):242\u0026ndash;265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20556365241270394\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDarici D, Masthoff M, Rischen R, Schmitz M, Ohlenburg H, Missler M (2023) Medical imaging training with eye movement modeling examples: A randomized controlled study. \u003cem\u003eMedical Teacher.\u003c/em\u003e Advance online publication. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/0142159X.2023.2189538\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDarici D, Fl\u0026auml;gel K, Sternecker K, Missler M (2024) Transfer of learning in histology: Insights from a longitudinal study. Anat Sci Educ 17(2):274\u0026ndash;286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ase.2363\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHessler M, P\u0026ouml;pping DM, Hollstein H, Ohlenburg H, Arnemann PH, Massoth C, Seidel LM, Zarbock A, Wenk M (2018) Availability of cookies during an academic course session affects evaluation of teaching. Med Educ 52(10):1064\u0026ndash;1072. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/medu.13627\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHitch GJ, Allen RJ, Baddeley AD (2025) The multicomponent model of working memory fifty years on. Q J Experimental Psychol 78(2):222\u0026ndash;239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/17470218241290909\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHudon A, Demazure T, Karran A, L\u0026eacute;ger PM, S\u0026eacute;n\u0026eacute;cal S (2021) Explainable artificial intelligence (XAI): How the visualization of AI predictions affects user cognitive load and confidence. In \u003cem\u003eNeuroIS Retreat\u003c/em\u003e (pp. 237\u0026ndash;246). Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-79987-9_17\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKlepsch M, Schmitz F, Seufert T (2017) Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load. \u003cem\u003eFrontiers in Psychology, 8\u003c/em\u003e, 1997. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2017.01997\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLiao CH, Wu JY (2022) Deploying multimodal learning analytics models to explore the impact of digital distraction and peer learning on student performance. Comput Educ 190:104599. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compedu.2022.104599\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMadsen J, J\u0026uacute;lio SU, Gucik PJ, Steinberg R, Parra LC (2021) Synchronized eye movements predict test scores in online video education. Proc Natl Acad Sci USA 118(5):e2016980118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2016980118\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMayer RE (2005) Cognitive theory of multimedia learning. In: Mayer RE (ed) The Cambridge handbook of multimedia learning. Cambridge University Press, pp 31\u0026ndash;48\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMayer RE (2017) Using multimedia for e-learning. J Comput Assist Learn 33(5):403\u0026ndash;423. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jcal.12197\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eOhly S, Bastin L (2023) Effects of task interruptions caused by notifications from communication applications on strain and performance. J Occup Health 65(1):e12408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/1348-9585.12408\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eOtto N, B\u0026ouml;ckers A, Shiozawa T, Brunk I, Schumann S, Kugelmann D, Missler M, Darici D (2024) Profiling learning strategies of medical students: A person-centered approach. Med Educ. Advance online publication \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/medu.15388\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePashler H (1994) Dual-task interference in simple tasks: data and theory. Psychol Bull 116(2):220\u0026ndash;244. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0033-2909.116.2.220\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePersoon MC, van Putten K, Muijtjens AM, Witjes JA, Hendrikx AJ, Scherpbier AJ (2011) Effect of distraction on the performance of endourological tasks: A randomized controlled trial. BJU Int 107(10):1653\u0026ndash;1657. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1464-410X.2010.09685.x\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eR Core Team (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eRice SC, Slaughter JC, Smalley W, Obstein KL (2020) The impact of distraction minimization on endoscopic mentoring and performance. Endoscopy Int Open 8(12):E1804\u0026ndash;E1810. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1055/a-1251-5618\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSchweppe J, Rummer R (2014) Attention, working memory, and long-term memory in multimedia learning: An integrated perspective based on process models of working memory. Educational Psychol Rev 26(2):285\u0026ndash;306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10648-013-9246-y\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSieg L, Eismann H, Schneider B, Karsten J, Darici D (2025) How distractions undermine attentional synchronisation in work-based learning: A randomised controlled study using dual mobile eye-tracking. J Comput Assist Learn 41(3):e70059. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jcal.70059\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSkowronek J, Seifert A, Lindberg S (2023) The mere presence of a smartphone reduces basal attentional performance. Sci Rep 13(1):9363. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-36256-4\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSweller J, van Merri\u0026euml;nboer JJG, Paas F (2019) Cognitive architecture and instructional design: 20 years later. Educational Psychol Rev 31(2):261\u0026ndash;292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10648-019-09465-5\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eVan Gog T (2021) The signaling (or cueing) principle in multimedia learning. In: Mayer RE (ed) The Cambridge handbook of multimedia learning. Cambridge University Press, pp 221\u0026ndash;230\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eYoung JQ, Van Merri\u0026euml;nboer J, Durning S, Ten Cate O (2014) Cognitive load theory: Implications for medical education: AMEE Guide 86. Med Teach 36(5):371\u0026ndash;384. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3109/0142159X.2014.889290\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eZureick AH, Burk-Rafel J, Purkiss JA, Hortsch M (2018) The interrupted learner: How distractions during live and video lectures influence learning outcomes. Anat Sci Educ 11(4):366\u0026ndash;376. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ase.1754\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Münster","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Medical education, distraction, cognitive load, cognitive load interference, learning performance, eye tracking","lastPublishedDoi":"10.21203/rs.3.rs-7433821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7433821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eDistraction impairs attention\u0026mdash;a growing concern in medical training, where students face constant digital alerts and multitasking demands. While teaching strategies, such as cueing, have proven effective under optimal conditions, their performance under cognitive overload remains unexplored. This raises the question: Can effective instruction buffer against the detrimental effects of distraction?\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn a 2x2 factorial experiment, 117 second-year medical students without prior knowledge watched a standardized instructional video on abdominal ultrasound. Distraction was induced via concurrent mental arithmetic task, and cues were provided via eye movement modeling examples of an expert\u0026rsquo;s gaze. Image interpretation performance as a primary outcome and cognitive load were measured before, and after training.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAs expected, cueing alone reduced extraneous cognitive load and improved learning. Distraction alone impaired learning. However, when both were combined, the performance benefits of cueing disappeared. Distracted learners receiving cues performed no better than uncued distracted learners, indicating no compensatory effect.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe disappearance of cueing benefits under distraction suggests a \u003cem\u003eload interference mechanism\u003c/em\u003e: learners cannot benefit from helpful educational instructions when their cognitive capacity is already taxed by competing demands. This indicates that internal distraction effectively monopolizes limited working memory resources of students. We discuss the implications for medical education in increasingly distraction-rich learning environments characterized by social media, smartphone notifications, and electronic health record alerts.\u003c/p\u003e","manuscriptTitle":"Teaching is ineffective when students are distracted:\nCognitive load interference in medical education","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 07:09:58","doi":"10.21203/rs.3.rs-7433821/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f63678c7-6c7a-4435-8678-a398e6658d36","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53566974,"name":"Special Education"}],"tags":[],"updatedAt":"2025-08-27T07:09:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 07:09:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7433821","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7433821","identity":"rs-7433821","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}