Multiple exposure pathways and health risk assessment of PAHs in Lanzhou city, a semi-arid region in northwest China.

In the sparse studies for multiple pathway exposure, attention has predominantly been directed towards developed regions, thereby overlooking the exposure level and health outcome for the inhabitants of the semi-arid regions in northwest China. However, cities within these regions grapple with myriad challenges, encompassing insufficient sanitation infrastructure and outdated heating. In this study, we analyzed the characteristics and sources of polycyclic aromatic hydrocarbons (PAHs) pollution in PM2.5, water, diet, and dust during different periods in Lanzhou, and estimated corresponding carcinogenic health risk through inhalation, ingestion, and dermal absorption. Our observations revealed the concentrations of PAHs in PM2.5, food, soil, and water are 200.11 ng m-3, 8.67 mg kg-1, 3.91 mg kg-1, and 14.5 ng L-1, respectively, indicating that the Lanzhou area was seriously polluted. Lifetime incremental cancer risk (ILCR) showed a heightened cancer risk to men compared to women, to the younger than the elderly, and during heating period as opposed to non-heating period. Notably, the inhalation was the primary route of PAHs exposure and the risk of exposure by inhalation cannot be ignored. The total environmental exposure assessment of PAHs can achieve accurate prevention and control of PAHs environmental exposure according to local conditions and targets.

Understanding preferences for receiving health communications and information about clinical trials: a cross-sectional study among US adults.

OBJECTIVE: Effective health communication is critical for understanding and acting on health information. This cross-sectional study explored participants' understanding of their health condition, their preferences for receiving health communications, and their interest in receiving clinical trial results across several therapeutic areas. METHODS: The study recruited participants via social media, email newsletters, and advocacy organizations. An online screener captured demographic information (health conditions, age, race/ethnicity, gender, and education). Eligible participants were emailed an online survey assessing preferred sources and formats for receiving health information, interest in learning about topics related to the results of clinical trials, and health literacy levels. RESULTS: In total, 449 participants (median age, 35 years [range, 18─76]; White, 53%; higher education, 65%; mean (range) health literacy score, 1.9 [0.4 ─ 3.0]) from 45 US states completed the survey representing 12 disease indications (bipolar, blood and solid tumor cancers, irritable bowel syndrome, inflammatory bowel disease, major depressive disorder, migraine, Parkinson's, psoriasis/atopic dermatitis, retinal vein occlusion/macular degeneration, rheumatoid arthritis, and spasticity). Healthcare providers were the preferred source of health information (59%), followed by internet searches (11%). Least preferred sources were social media (5%), friends/family (3%), and email newsletters (2%). Participants preferred multiple formats and ranked reading materials online as most preferred (33%), along with videos (28%) and infographics (27%). Printed materials (14%) and audio podcasts (9%) were the least preferred formats. A majority of the participants reported that the health information they found was hard to understand (57%) and confusing (62%). Most participants (85%) were somewhat/very interested in learning about clinical trial results, with the highest interest in short summaries of safety (78%) and efficacy (74%) results. CONCLUSION: Effective health communication may be achieved via multiple formats shared directly by healthcare providers.

Researchers wanted to learn how people preferred to receive health-related communications, including information about the results of clinical trials. They surveyed 449 people from 45 US states with 12 different health conditions. The survey questions asked people about their preferred sources and ways of getting health information. It also asked about their interest in learning about clinical trials related to their health condition.The results showed that most people preferred to get health information from their healthcare providers (59%). The Internet was the second most popular choice (11%) for getting health information. People did not like getting health information from social media, friends or family, or email newsletters as much.When it came to how health information was shared, people liked reading materials online (33%), watching videos (28%), and looking at infographics (27%). They did not like printed materials and audio podcasts as much.Most people (85%) were interested in learning about the results of clinical trials in short summaries. They wanted to know about the safety (78%) and how well the treatments worked (74%) in the short summaries.In conclusion, people liked getting health information from healthcare providers like doctors, nurses, and others in different formats. Sharing information in different formats through healthcare providers may improve communication for patients with different health conditions.

Development and validation of a point-of-care nursing mobile tool to guide the diagnosis of malnutrition in hospitalized adult patients: a multicenter, prospective cohort study.

Malnutrition is a prevalent and severe issue in hospitalized patients with chronic diseases. However, malnutrition screening is often overlooked or inaccurate due to lack of awareness and experience among health care providers. This study aimed to develop and validate a novel digital smartphone-based self-administered tool that uses facial features, especially the ocular area, as indicators of malnutrition in inpatient patients with chronic diseases. Facial photographs and malnutrition screening scales were collected from 619 patients in four different hospitals. A machine learning model based on back propagation neural network was trained, validated, and tested using these data. The model showed a significant correlation (p < 0.05) and a high accuracy (area under the curve 0.834-0.927) in different patient groups. The point-of-care mobile tool can be used to screen malnutrition with good accuracy and accessibility, showing its potential for screening malnutrition in patients with chronic diseases.

Occurrence and spatiotemporal distribution of natural and synthetic steroid hormones in soil, water, and sediment systems in suburban agricultural area of Guangzhou City, China.

Steroid hormones are highly potent compounds that can disrupt the endocrine systems of aquatic organisms. This study explored the spatiotemporal distribution of 49 steroid hormones in agricultural soils, ditch water, and sediment from suburban areas of Guangzhou City, China. The average concentrations of Σsteroid hormones in the water, soils, and sediment were 97.7 ng/L, 4460 ng/kg, and 9140 ng/kg, respectively. Elevated hormone concentrations were notable in water during the flood season compared to the dry season, whereas an inverse trend was observed in soils and sediment. These observations were attributed to illegal wastewater discharge during the flood season, and sediment partitioning of hormones and manure fertilization during the dry season. Correlation analysis further showed that population, precipitation, and number of slaughtered animals significantly influenced the spatial distribution of steroid hormones across various districts. Moreover, there was substantial mass transfer among the three media, with steroid hormones predominantly distributed in the sediment (60.8 %) and soils (34.4 %). Risk quotients, calculated as the measured concentration and predicted no-effect concentration, exceeded 1 at certain sites for some hormones, indicating high risks. This study reveals that the risk assessment of steroid hormones requires consideration of their spatiotemporal variability and inter-media mass transfer dynamics in agroecosystems.

Comparative study of AR infographic posters vs. offline videos for micro-video delivery in cardiology education.

Augmented reality has promised a new paradigm in medical education. Multimedia videos are the most preferred assent for augmentation. So, this study aimed to assess the effect of using an augmented reality infographic poster for delivering micro-videos on the knowledge and satisfaction of medical students in cardiology rotation. Sixty students participated in this quasi-experimental study and were allocated to three study groups; namely routine method, routine method plus offline micro-video delivery, and routine method plus micro-video delivery in an augmented reality infographic poster. The students' knowledge and satisfaction were evaluated through a multiple-choice question pre and post-test and a satisfaction questionnaire respectively. Within-group comparison of pre and post-test scores showed a significant increase in each study group (all p-values = 0.000). The highest post-test score was for the offline micro-video delivery group and pairwise comparisons of post-test scores showed a significant difference between this group and the control one (p-value = 0.013). Additionally, the augmented reality infographic poster group had the highest satisfaction score (p-value = 0.000). This experience showed the positive effect of micro-videos in clinical education. Although students were satisfied with accessing these videos through an augmented reality infographic poster, their knowledge acquisition was better when they received them offline.

Effects and prediction of cognitive load on encoding model of brain response to auditory and linguistic stimuli in educational multimedia.

Multimedia is extensively used for educational purposes. However, certain types of multimedia lack proper design, which could impose a cognitive load on the user. Therefore, it is essential to predict cognitive load and understand how it impairs brain functioning. Participants watched a version of educational multimedia that applied Mayer's principles, followed by a version that did not. Meanwhile, their electroencephalography (EEG) was recorded. Subsequently, they participated in a post-test and completed a self-reported cognitive load questionnaire. The audio envelope and word frequency were extracted from the multimedia, and the temporal response functions (TRFs) were obtained using a linear encoding model. We observed that the behavioral data are different between the two groups and the TRFs of the two multimedia versions were different. We saw changes in the amplitude and latencies of both early and late components. In addition, correlations were found between behavioral data and the amplitude and latencies of TRF components. Cognitive load decreased participants' attention to the multimedia, and semantic processing of words also occurred with a delay and smaller amplitude. Hence, encoding models provide insights into the temporal and spatial mapping of the cognitive load activity, which could help us detect and reduce cognitive load in potential environments such as educational multimedia or simulators for different purposes.

Research on the prediction of English topic richness in the context of multimedia data.

With the evolution of the Internet and multimedia technologies, delving deep into multimedia data for predicting topic richness holds significant practical implications in public opinion monitoring and data discourse power competition. This study introduces an algorithm for predicting English topic richness based on the Transformer model, applied specifically to the Twitter platform. Initially, relevant data is organized and extracted following an analysis of Twitter's characteristics. Subsequently, a feature fusion approach is employed to mine, extract, and construct features from Twitter blogs and users, encompassing blog features, topic features, and user features, which are amalgamated into multimodal features. Lastly, the combined features undergo training and learning using the Transformer model. Through experimentation on the Twitter topic richness dataset, our algorithm achieves an accuracy of 82.3%, affirming the efficacy and superior performance of the proposed approach.

"The Design and Acceptability of a Hip Hop Themed Integrated Nutrition Math Curriculum for Minoritized 5th Grade Students Using the Multisensory Multilevel Health Education Model".

Digital technology creates new opportunities to design multisensory learning experiences. Evidence suggests that digital innovation can greatly benefit health education, including nutrition programs. The COVID-19 pandemic disrupted the education sector, forcing schools to modify standard practices from exclusively in-person delivery to online or blended learning. Digitalized curriculums became particularly useful as an Emergency Remote Teaching tool. This article focuses on developing and implementing a multimedia, multisensory, and scalable Hip-Hop Healthy Eating and Living in Schools (H.E.A.L.S.) Nutrition-Math Curriculum (NMC). NMC comprises 20 lessons-music-based multimedia resources used in the classroom or at home. Fourteen lessons represent self-directed online modules (asynchronous learning) hosted on a Learning Management System (LMS) called "Gooru." The remaining six lessons are teacher-facilitated (in person or using Zoom) review sessions (synchronous learning). The article discusses (1) the development of NMC through the lens of the Multisensory Multilevel Health Education Model (MMHEM), (2) the high acceptability of NMC evaluated using a mixed-methods design among minoritized fifth-grade students attending an after-school program, and (3) the students' completion and mastery rates of the NMC modules based on LMS data. Multimedia nutrition education programs integrated with common core curriculum content, such as NMC, may be a promising avenue for disseminating health education to minoritized children living in New York City and similar high fast-food density cities.

Increasing the realism of on-screen embodied instructors creates more looking but less learning.

BACKGROUND: Although adding embodied instructors on the screen is considered an effective way to improve online multimedia learning, its effectiveness is still controversial. The level of realism of embodied on-screen instructors may be an influencing factor, but it is unclear how it affects multimedia learning. AIMS: We explored whether and how embodied on-screen instructors rendered with different levels of realism in multimedia lessons affect learning process and learning outcomes. SAMPLES: We recruited 125 college students as participants. METHODS: Students learned about neural transmission in an online multimedia lesson that included a real human, cartoon human, cartoon animal or no instructor. RESULTS: Students learning with cartoon human or cartoon animal instructors tended to fixate more on the relevant portions of the screen and performed better on retention and transfer tests than no instructor group. The real human group fixated more on the instructor, fixated less on the relevant portion of the screen and performed worse on a retention test in comparison to the cartoon human group. Fixation time on the instructor fully mediated the relationship between instructor realism and retention score. CONCLUSIONS: The addition of embodied on-screen instructors can promote multimedia learning, but the promotion effect would be better if the embodied instructor was a cartoon animal or cartoon human rather than a real human. This suggests an important boundary condition in which less realism of on-screen embodied instructors produces better learning processes and outcomes.

Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography.

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images. Additionally, they often require third-party authorization to verify the image integrity. On the other hand, visual cryptography offers a streamlined approach. It divides images into shares, where each pixel represented uniquely, thus allowing visual decryption without complex computations. The optimized multi-tiered authentication protocol (OMTAP), which is integrated with the visual sharing scheme (VSS), takes secure image sharing to the next level. It reduces share count, prioritizes image fidelity and transmission security, and introduces the self-verification of decrypted image integrity through asymmetric key matrix generators, thus eliminating external validation. Rigorous testing has confirmed OMTAP's robustness and broad applicability, thereby ensuring that decrypted images maintain their quality with a peak signal-to-noise ratio (PSNR) of 40 dB and full integrity at the receiver's end.

Structure-aware contrastive hashing for unsupervised cross-modal retrieval.

Cross-modal hashing has attracted a lot of attention and achieved remarkable success in large-scale cross-media similarity retrieval applications because of its superior computational efficiency and low storage overhead. However, constructing similarity relationship among samples in cross-modal unsupervised hashing is challenging because of the lack of manual annotation. Most existing unsupervised methods directly use the representations extracted from the backbone of their respective modality to construct instance similarity matrices, leading to inaccurate similarity matrices and resulting in suboptimal hash codes. To address this issue, a novel unsupervised hashing model, named Structure-aware Contrastive Hashing for Unsupervised Cross-modal Retrieval (SACH), is proposed in this paper. Specifically, we concurrently employ both high-dimensional representations and discriminative representations learned by the network to construct a more informative semantic correlative matrix across modalities. Moreover, we design a multimodal structure-aware alignment network to minimize heterogeneous gap in the high-order semantic space of each modality, effectively reducing disparities within heterogeneous data sources and enhancing the consistency of semantic information across modalities. Extensive experimental results on two widely utilized datasets demonstrate the superiority of our proposed SACH method in cross-modal retrieval tasks over existing state-of-the-art methods.

Evaluating the impact of using storytelling and visual multimedia music intervention to improve knowledge of the Child Rights Act and reduce the propensity to engage in child marriage.

Child marriage has continued to rear its ugly head in Nigerian society. This study aimed to evaluate the impact of storytelling and multimedia music interventions in improving knowledge of the Child Rights Act and reducing the propensity to engage in child marriage. The researchers applied a quasi-experimental design and collected data using a structured questionnaire. The children were assigned into three groups (control, storytelling and multimedia music) of 173 participants. It was found that the interventions were effective. In particular, while storytelling contributed more to reducing the propensity to engage in child marriage, multimedia music contributed more to improving knowledge of the Child Rights Act. These results suggest that storytelling and multimedia music interventions can be effective approaches for addressing the lingering problem of child marriage in Nigeria.

Onscreen presence of instructors in video lectures affects learners' neural synchrony and visual attention during multimedia learning.

COVID-19 forced students to rely on online learning using multimedia tools, and multimedia learning continues to impact education beyond the pandemic. In this study, we combined behavioral, eye-tracking, and neuroimaging paradigms to identify multimedia learning processes and outcomes. College students viewed four video lectures including slides with either an onscreen human instructor, an animated instructor, or no onscreen instructor. Brain activity was recorded via fMRI, visual attention was recorded via eye-tracking, and learning outcome was assessed via post-tests. Onscreen presence of instructor, compared with no instructor presence, resulted in superior post-test performance, less visual attention on the slide, more synchronized eye movements during learning, and higher neural synchronization in cortical networks associated with socio-emotional processing and working memory. Individual variation in cognitive and socio-emotional abilities and intersubject neural synchronization revealed different levels of cognitive and socio-emotional processing in different learning conditions. The instructor-present condition evoked increased synchronization, likely reflecting extra processing demands in attentional control, working memory engagement, and socio-emotional processing. Although human instructors and animated instructors led to comparable learning outcomes, the effects were due to the dynamic interplay of information processing vs. attentional distraction. These findings reflect a benefit-cost trade-off where multimedia learning outcome is enhanced only when the cognitive benefits motivated by the social presence of onscreen instructor outweigh the cognitive costs brought about by concurrent attentional distraction unrelated to learning.

Comprehensive Assessment of Environmental Emissions, Fate, and Risks of Veterinary Antibiotics in China: An Environmental Fate Modeling Approach.

China is one of the major global consumers of veterinary antibiotics. Insufficient recognition of emissions and environmental contamination hamper global efforts to prevent antibiotic resistance development. This pioneering study combined empirical data and modeling approaches to predict total 2010-2020 emissions of 80 veterinary antibiotics ranging from 23,110 to 40,850 tonnes/year, after 36-50% antibiotic removal by manure treatment. Following an initial increase of 10% from 2010 to 2015, emissions declined thereafter by 43%. While 85% of emissions discharged into soils, approximately 56%, 23%, and 18% of environmental residue were ultimately distributed in soils, freshwaters, and seawaters under steady-state conditions. In 2020, 657 (319-1470) tonnes entered the ocean from inland freshwaters. Median ∑antibiotics concentrations were estimated at 4.7 × 103 ng/L in freshwaters and 2.9 ng/g in soils, with tetracyclines and sulfonamides as the predominant components. We identified 44 veterinary antibiotics potentially posing high risks of resistance development in freshwaters, with seven exhibiting high risks in >10% of Chinese freshwater areas. Tetracyclines were the category with the most antibiotics exhibiting elevated risks; however, sulfamethylthiazole demonstrated the highest individual compound risk. The Haihe River Basin displayed the highest susceptibility overall. The findings offer valuable support for control of veterinary antibiotic contamination in China.

Reducing Loneliness and Improving Social Support among Older Adults through Different Modalities of Personal Voice Assistants.

This study examines the potential of AI-powered personal voice assistants (PVAs) in reducing loneliness and increasing social support among older adults. With the aging population rapidly expanding, innovative solutions are essential. Prior research has indicated the effectiveness of various interactive communication technologies (ICTs) in mitigating loneliness, but studies focusing on PVAs, particularly considering their modality (audio vs. video), are limited. This research aims to fill this gap by evaluating how voice assistants, in both audio and video formats, influence perceived loneliness and social support. This study examined the impact of voice assistant technology (VAT) interventions, both audio-based (A-VAT) and video-based (V-VAT), on perceived loneliness and social support among 34 older adults living alone. Over three months, participants engaged with Amazon Alexa™ PVA through daily routines for at least 30 min. Using a hybrid natural language processing framework, interactions were analyzed. The results showed reductions in loneliness (Z = -2.99, p < 0.01; pre-study loneliness mean = 1.85, SD = 0.61; post-study loneliness mean = 1.65, SD = 0.57), increases in social support post intervention (Z = -2.23, p < 0.05; pre-study social support mean = 5.44, SD = 1.05; post-study loneliness mean = 5.65, SD = 1.20), and a correlation between increased social support and loneliness reduction when the two conditions are combined (ρ = -0.39, p < 0.05). In addition, V-VAT was more effective than A-VAT in reducing loneliness (U = 85.50, p < 0.05) and increasing social support (U = 95, p < 0.05). However, no significant correlation between changes in perceived social support and changes in perceived loneliness was observed in either intervention condition (V-VAT condition: ρ = -0.24, p = 0.37; A-VAT condition: ρ = -0.46, p = 0.06). This study's findings could significantly contribute to developing targeted interventions for improving the well-being of aging adults, addressing a critical global issue.

The Eschucha (Listen) Podcast Project: Psychosocial Innovation for Marginalized Mexican Youth and Young Adults.

The COVID-19 pandemic was a challenging period for young people in Mexico, particularly those already contending with social and structural inequality. In March 2021, the Colectivo Frontera, a research collective based in Mexico City, Mexico, which works on advancing equity and psychosocial wellbeing among marginalized communities, carried out an 8-week, online project to provide psychosocial support and promote resilience for marginalized young people from different locations in Mexico. The project entailed weekly journaling with the Pandemic Journaling Project (PJP), as well as weekly phone sessions with a mental health specialist who provided emotional support (acompañamiento emocional) through practices of active listening. The project culminated in the Escucha (Listen) Podcast for which each youth participant created an episode about their experiences during the pandemic. Many also submitted a photo to accompany their recording; one produced a song. Participant episodes were compiled into a series of five chapters. Each chapter of the podcast centers on a common theme, including reflections on loved ones lost to COVID-19, social fragmentation, gender-based constraints on expressing emotions, and the experiences and perspectives of children. The project provides a compelling example of a low-cost approach to providing support for the mental health and wellbeing of marginalized young people. It also demonstrates the importance of creating projects that help young people make meaningful connections and that leverage their creativity to foster resilience, improve social cohesion, and elevate their perspectives and voices.

An efficient deepfake video detection using robust deep learning.

The creation and manipulation of synthetic images have evolved rapidly, causing serious concerns about their effects on society. Although there have been various attempts to identify deep fake videos, these approaches are not universal. Identifying these misleading deepfakes is the first step in preventing them from spreading on social media sites. We introduce a unique deep-learning technique to identify fraudulent clips. Most deepfake identifiers currently focus on identifying face exchange, lip synchronous, expression modification, puppeteers, and other factors. However, exploring a consistent basis for all forms of fake videos and images in real-time forensics is challenging. We propose a hybrid technique that takes input from videos of successive targeted frames, then feeds these frames to the ResNet-Swish-BiLSTM, an optimized convolutional BiLSTM-based residual network for training and classification. This proposed method helps identify artifacts in deepfake images that do not seem real. To assess the robustness of our proposed model, we used the open deepfake detection challenge dataset (DFDC) and Face Forensics deepfake collections (FF++). We achieved 96.23% accuracy when using the FF++ digital record. In contrast, we attained 78.33% accuracy using the aggregated records from FF++ and DFDC. We performed extensive experiments and believe that our proposed method provides more significant results than existing techniques.

Transfer learning-based English translation text classification in a multimedia network environment.

In recent years, with the rapid development of the Internet and multimedia technology, English translation text classification has played an important role in various industries. However, English translation remains a complex and difficult problem. Seeking an efficient and accurate English translation method has become an urgent problem to be solved. The study first elucidated the possibility of the development of transfer learning technology in multimedia environments, which was recognized. Then, previous research on this issue, as well as the Bidirectional Encoder Representations from Transformers (BERT) model, the attention mechanism and bidirectional long short-term memory (Att-BILSTM) model, and the transfer learning based cross domain model (TLCM) and their theoretical foundations, were comprehensively explained. Through the application of transfer learning in multimedia network technology, we deconstructed and integrated these methods. A new text classification technology fusion model, the BATCL transfer learning model, has been established. We analyzed its requirements and label classification methods, proposed a data preprocessing method, and completed experiments to analyze different influencing factors. The research results indicate that the classification system obtained from the study has a similar trend to the BERT model at the macro level, and the classification method proposed in this study can surpass the BERT model by up to 28%. The classification accuracy of the Att-BILSTM model improves over time, but it does not exceed the classification accuracy of the method proposed in this study. This study not only helps to improve the accuracy of English translation, but also enhances the efficiency of machine learning algorithms, providing a new approach for solving English translation problems.

Chalk Talks for the Clinical Setting: Evaluation of a Medical Education Workshop for Fellows.

Introduction: Chalk talks are effective teaching tools in the clinical setting. However, data on optimal strategies for teaching medical educators how to develop and deliver them are limited. We designed and implemented two 50-minute workshops to help subspecialty fellows across GME create and deliver a chalk talk. Methods: The first workshop comprised a demonstration of an effective chalk talk and a discussion of best practices for creating chalk talks; the second was a practice session where fellows presented their chalk talks and received feedback from faculty and peers. We evaluated pre- and postworkshop confidence in the ability to create and deliver a chalk talk and develop learning objectives. Secondary outcomes were faculty and peer evaluations of the chalk talks. Results: Eighteen of 33 participants (54% response rate) completed both pre- and postsession surveys. Fellows reported improved confidence in their ability to create a chalk talk (22% vs. 83%, p < .001), deliver a chalk talk (17% vs. 83%, p < .001), and develop well-written learning objectives (11% vs. 83%, p < .001). After the workshop, participants were more likely to correctly identify a chalk talk that made use of an advanced organizer (67% vs. 89%, p < .05). Thirty-eight faculty and peers completed feedback evaluations of participants' chalk talks; most rated fellows' chalk talks highly in domains of content, delivery, design, learning objectives, and engagement. Discussion: The incorporation of these workshop within a course on medical education can effectively develop clinical teaching skills among subspecialty fellows in GME.

Examination of medical students' opinions on multimedia learning materials according to social cues: focusing on sound principles.

PURPOSE: Although interest in various forms of learning media is increasing due to the coronavirus disease 2019 (COVID-19) pandemic there is relatively little research on influencing student motivation by intervening in cognitive processing. The purpose of this study was to present the optimal form of learning materials provided to medical students. METHODS: This study provided learning materials in class at a level according to social cues (script, video [artificial intelligence (AI) voice], video [professor voice]) based on the principle of voices among the principles of personalization, voices, image, and embodiment of social cues in multimedia learning, and surveyed students' opinions. RESULTS: There was no statistically significant difference according to social clues in satisfaction and learning help, but both appeared in the order of silent videos containing the professor's voice, followed by videos containing the AI voice. CONCLUSION: This study is significant in that there is no research on the impact of student motivation on the provision of learning materials for medical school education in Korea, and we hope that it will help provide learning materials for self-directed learning of medical students in the post-COVID-19.