Assistant Platform Research Articles

Investigation and application of big data and AI-Powered provincial power grid accurate planning support platform

ABSTRACT This study presents an accurate planning assistance platform for big data and artificial intelligence-based provincial power grids. The platform greatly increases the efficiency of data integration and management by building a planning big database containing fundamental geographic information, thematic data, remote sensing image data, and planning business data. Technically speaking, this paper addresses the issues of accuracy and speed in the processing of remote sensing image data by using deep learning algorithms like convolutional neural networks (CNN) and full convolutional neural networks (FCN) to achieve automatic classification of high-resolution optical remote sensing images and residential area extraction. In the meantime, the intelligent routing approach, which is based on cost surface modelling and multi-criteria decision-making, optimises the routing of power lines by carefully taking into account a wide range of variables, including land use, accessibility for vehicles, and environmental impact. In addition to resolving the primary issues with present power grid planning, this study’s exact planning support platform for provincial power grids greatly enhances real-time, accurate, and scientific planning.

Online assessment in the age of artificial intelligence

Online education, while not a new phenomenon, underwent a monumental shift during the COVID-19 pandemic, pushing educators and students alike into the uncharted waters of full-time digital learning. With this shift came renewed concerns about the integrity of online assessments. Amidst a landscape rapidly being reshaped by online exam/homework assistance platforms, which witnessed soaring stocks as students availed its questionable exam assistance, and the emergence of sophisticated artificial intelligence tools like ChatGPT, the traditional methods of assessment faced unprecedented challenges. This paper presents the results of an observational study, using data from an introductory statistics course taught every semester by the author, and delves into the proliferation of cheating methods. Analyzing exam score results from the pre and post introduction of ChatGPT periods, the research unpacks the extent of cheating and provides strategies to counteract this trend. The findings starkly illustrate significant increases in exam scores from when exams of similar difficulty were administered in person (pre-Covid) versus online. The format, difficulty, and grading of the exams was the same throughout. Although randomized controlled experiments are generally more effective than observational studies, we will indicate when we present the data why experiments would not be feasible for this research. In addition to presenting experimental findings, the paper offers some insights, based on the author's extensive experience, to guide educators in crafting more secure online assessments in this new era, both for courses at the introductory level and more advances courses The results and findings are relevant to introductory courses that can use multiple choice exams in any subject but the recommendations for upper-level courses will be relevant primarily to STEM subjects. The research underscores the pressing need for reinventing assessment techniques to uphold the sanctity of online education.

Integrating international Chinese visualization teaching and vocational skills training: leveraging attention-connectionist temporal classification models.

The teaching of Chinese as a second language has become increasingly crucial for promoting cross-cultural exchange and mutual learning worldwide. However, traditional approaches to international Chinese language teaching have limitations that hinder their effectiveness, such as outdated teaching materials, lack of qualified instructors, and limited access to learning facilities. To overcome these challenges, it is imperative to develop intelligent and visually engaging methods for teaching international Chinese language learners. In this article, we propose leveraging speech recognition technology within artificial intelligence to create an oral assistance platform that provides visualized pinyin-formatted feedback to learners. Additionally, this system can identify accent errors and provide vocational skills training to improve learners' communication abilities. To achieve this, we propose the Attention-Connectionist Temporal Classification (CTC) model, which utilizes a specific temporal convolutional neural network to capture the location information necessary for accurate speech recognition. Our experimental results demonstrate that this model outperforms similar approaches, with significant reductions in error rates for both validation and test sets, compared with the original Attention model, Claim, Evidence, Reasoning (CER) is reduced by 0.67%. Overall, our proposed approach has significant potential for enhancing the efficiency and effectiveness of vocational skills training for international Chinese language learners.

SnowMotion: A Wearable Sensor-Based Mobile Platform for Alpine Skiing Technique Assistance.

Skiing technique and performance improvements are crucial for athletes and enthusiasts alike. This study presents SnowMotion, a digital human motion training assistance platform that addresses the key challenges of reliability, real-time analysis, usability, and cost in current motion monitoring techniques for skiing. SnowMotion utilizes wearable sensors fixed at five key positions on the skier's body to achieve high-precision kinematic data monitoring. The monitored data are processed and analyzed in real time through the SnowMotion app, generating a panoramic digital human image and reproducing the skiing motion. Validation tests demonstrated high motion capture accuracy (cc > 0.95) and reliability compared to the Vicon system, with a mean error of 5.033 and a root-mean-square error of less than 12.50 for typical skiing movements. SnowMotion provides new ideas for technical advancement and training innovation in alpine skiing, enabling coaches and athletes to analyze movement details, identify deficiencies, and develop targeted training plans. The system is expected to contribute to popularization, training, and competition in alpine skiing, injecting new vitality into this challenging sport.

AIED in English Preparatory Courses: A Comparative Study of UK and North American Universities and Its Impact on Chinese International Student Mobility

With the development of AI and other computer technologies, AIED has begun to cause new reforms in the international education. At present, most Higher education institutions in European and American countries adopt AIED to varying degrees to assist and improve the teaching environment and teaching experience of international students. This article will mainly compare the application of AIED in universities in the United States, the United Kingdom, and Canada, interview Chinese students and faculty at each university through questionnaires, and analyze the interviewers attitudes towards AIED. This article will also collect reviews from international students and international education practitioners on some commonly used learning assistance platforms based on AI technology, such as MOOCS and Grammarly. This article decoded the collected interview comments and coded and integrated similar information as well. The final conclusion of this article is that the vast majority of educational professionals and international students believe that the benefits of AIEDs application outweigh the disadvantages, but ethical consideration could be drastically significant.

РОЗРОБКА КІБЕРФІЗИЧНОЇ СИСТЕМИ ДЛЯ АВТОМАТИЗАЦІЇ ТА КЕРУВАННЯ ІНТЕРНЕТОМ РЕЧЕЙ З ВИКОРИСТАННЯМ ПЛАТФОРМИ HOME ASSISTANT

The paper presents the development of a cyber-physical system based on the HomeAssistant platform for efficient automation and control of Internet of Things (IoT) devices. The architectural features, technical implementation and prospects for the development of the system are considered, with an emphasis on the ability to integrate a wide range of sensors and IoT devices into a single network to create adaptive and intelligent solutions. The main focus is on the implementation of automation scenarios for indoor climate control, which optimise living conditions depending on the internal needs of users and external weather conditions, significantly increasing energy efficiency and overall comfort. Data storage and analysis strategies are described in detail, including the use of NAS servers for backup, the use of MariaDB for storage and InfluxDB and Grafana for analytics and visualisation, ensuring a high level of reliability and availability of information. Testing was conducted to assess the delay of notifications transmitted via Telegram and the internal network of the cyber-physical system. The obtained results confirm the high efficiency of the implemented cyber-physical system in ensuring instant delivery of notifications, which is a key aspect for rapid response to critical situations in a dynamic environment. The Home Assistant platform has been found to have extensive capabilities to support artificial intelligence services. It integrates with a variety of services such as Google Assistant, TensorFlow, DeepStack, Amazon Alexa, and allows you to easily expand its functionality with additional components and plug-ins. Using intelligent algorithms and data analysis, the system can independently make decisions on the optimal use of resources, monitor devices in accordance with changing conditions and user needs, and respond to dangerous or unpredictable situations.

Application of task processing system based on wireless sensor network in English writing intelligent assistance platform

With the widespread use of 32-bit embedded task processing systems, embedded systems are used in various fields. Traditional software development is gradually being replaced by development methods based on embedded operating systems. The built-in processing system inherits the entire operating system and retains its core components, including thread synchronization and exclusive access to individual resources. However, embedded system resources are relatively limited, and operating requirements are more complex and flexible, which makes the development of embedded operating systems more complicated and difficult than general PC systems. The corpus as the basic intelligent corpus is the research capital used by scientists to study language, and it is also an important part of English writing teaching. In fact, after European linguists represented by Jeffrey Ritchie proposed the application of smart corpus to the teaching of English writing, smart corpus entered the field of view of scientists. This means that smart corpus will become an indispensable part of linguistics. This research method expands the research method of English writing, covering two main aspects: speech output and language error analysis. The application of this research method to the teaching system of English writing is mainly reflected in “language error analysis”. This article discusses corpus-based English writing. In addition, in order to enrich the application influence of the English teaching management system, the system discussed in this article aims at the optimization of the teaching system and has designed more useful functions (such as English broadcasting, personal center users, system settings, etc.), so that students can learn more. It is easy to record their English learning status and enrich the content of English courses.

Towards Autonomous Retinal Microsurgery Using RGB-D Images.

Retinal surgery is a challenging procedure requiring precise manipulation of the fragile retinal tissue, often at the scale of tens-of-micrometers. Its difficulty has motivated the development of robotic assistance platforms to enable precise motion, and more recently, novel sensors such as microscope integrated optical coherence tomography (OCT) for RGB-D view of the surgical workspace. The combination of these devices opens new possibilities for robotic automation of tasks such as subretinal injection (SI), a procedure that involves precise needle insertion into the retina for targeted drug delivery. Motivated by this opportunity, we develop a framework for autonomous needle navigation during SI. We develop a system which enables the surgeon to specify waypoint goals in the microscope and OCT views, and the system autonomously navigates the needle to the desired subretinal space in real-time. Our system integrates OCT and microscope images with convolutional neural networks (CNNs) to automatically segment the surgical tool and retinal tissue boundaries, and model predictive control that generates optimal trajectories that respect kinematic constraints to ensure patient safety. We validate our system by demonstrating 30 successful SI trials on pig eyes. Preliminary comparisons to a human operator in robot-assisted mode highlight the enhanced safety and performance of our system.

Chatbot for Social Need Screening and Resource Sharing With Vulnerable Families: Iterative Design and Evaluation Study.

Health outcomes are significantly influenced by unmet social needs. Although screening for social needs has become common in health care settings, there is often poor linkage to resources after needs are identified. The structural barriers (eg, staffing, time, and space) to helping address social needs could be overcome by a technology-based solution. This study aims to present the design and evaluation of a chatbot, DAPHNE (Dialog-Based Assistant Platform for Healthcare and Needs Ecosystem), which screens for social needs and links patients and families to resources. This research used a three-stage study approach: (1) an end-user survey to understand unmet needs and perception toward chatbots, (2) iterative design with interdisciplinary stakeholder groups, and (3) a feasibility and usability assessment. In study 1, a web-based survey was conducted with low-income US resident households (n=201). Following that, in study 2, web-based sessions were held with an interdisciplinary group of stakeholders (n=10) using thematic and content analysis to inform the chatbot's design and development. Finally, in study 3, the assessment on feasibility and usability was completed via a mix of a web-based survey and focus group interviews following scenario-based usability testing with community health workers (family advocates; n=4) and social workers (n=9). We reported descriptive statistics and chi-square test results for the household survey. Content analysis and thematic analysis were used to analyze qualitative data. Usability score was descriptively reported. Among the survey participants, employed and younger individuals reported a higher likelihood of using a chatbot to address social needs, in contrast to the oldest age group. Regarding designing the chatbot, the stakeholders emphasized the importance of provider-technology collaboration, inclusive conversational design, and user education. The participants found that the chatbot's capabilities met expectations and that the chatbot was easy to use (System Usability Scale score=72/100). However, there were common concerns about the accuracy of suggested resources, electronic health record integration, and trust with a chatbot. Chatbots can provide personalized feedback for families to identify and meet social needs. Our study highlights the importance of user-centered iterative design and development of chatbots for social needs. Future research should examine the efficacy, cost-effectiveness, and scalability of chatbot interventions to address social needs.

Financial Sustainability of IoT Platforms: The Role of Quality and Security

The economic significance of Internet of Things (IoT) platforms is evident from the proliferation of smart home assistant platforms like Amazon Alexa and Google Home that center themselves around providing unique and connected experiences to users. Customers do not perceive these platforms valuable if they do not experience adequate levels of quality and security from both the platform and applications (apps) they use. Therefore, the IoT platform provider as well as app developers need to go beyond collaboration and cocreate quality and security for each app on the platform. In this setting, we study the dynamics of the financial sustainability of the platform provider and app developers and find that the collaborative nature of the IoT platform has a significant role in the behavior and profitability of all parties. Specifically, a neutral entrant to the platform not only increases the effort levels of all existing parties, but also increases the profits due to network effects that are intertwined with economies of scale. We further find that a substantial number of apps are needed in the platform for an economically viable platform and that a less efficient platform leader needs even a larger application base. However, based on the number of apps on the platform and which party is more effective in the collaboration, the platform provider might need to provide additional incentives, or sometimes, extract additional rent from the app developers to financially sustain the platform. Furthermore, we verify our results in the presence of competition or complementarity among apps and further derive additional interesting results and managerial insights. For example, we find that the entry of a competing app may be beneficial for existing apps under some conditions.

A Prioritization Method to Optimize School Operational Assistance Fund by Using AHP Approach

In Indonesia, education funding provided by the central government is made up of the components of personnel costs, investment costs, and operating costs. Since its implementation in 2005, the program has found several obstacles. It’s crucial to have an optimization tool to overcome the complications. The main problems that have arisen in the allocation of these funds so far have been lacking in planning and the lack of participation of the school ecosystem, in particular in determining the planning, planning documents and the priority scale of the projects. Program and activity plans based on the School Operating Assistance (BOS) platform. The Analytical Hierarchy Process (AHP) model is a prioritization approach based on determining the priority scale on the comparison of one option with another. The AHP model is a powerful but simple method that is usually used to make decisions. This approach has been implemented in various sectors, business, government, management, and education. In this study, the AHP model was used to determine the priority scale on the criteria of BOS allocated funds in the current year so that it could be absorbed optimally and can be utilized properly in its implementation for Vocational School activities. The results of the study show that the use of AHP makes it easier for school residents to prioritize the use of funds for school activities. Using the AHP model allows managers to record strategic goals using weighted criteria as the basis for prioritizing the use of funds. AHP can also work among different stakeholders in the school (principals, treasurers, teachers, and committee, parents/guardians) to participate in decisions about the use of BOS funds, to improve the quality and form of decision support taken to improve school performance.

Research on Evaluation of Online Education Effectiveness Based on AI Teaching Aid System

Abstract In the age of technology, the application of intelligent assistive systems in teaching is getting more and more attention. After systematically describing the framework of the AI teaching assistance system and the process of online education effect evaluation, the study selects the course of an AI teaching assistance platform as a research case, collects the students’ learning behavior data, and applies the K-means-based clustering algorithm to divide them into clusters to obtain the learner portrait. The association rule algorithm based on Apriori is used to initially mine the connection between online learning behavior and educational effects. Furthermore, the multivariate linear regression method is applied to determine specific associations between online learning behaviors and educational effects to complete the assessment of online educational effects. Through the analysis, four types of learners were summarized: general (37.89%), negative (6.84%), interactive (9.48%), and positive (45.79%). Significant correlations between students’ online learning behaviors and educational outcomes were obtained, with regression equations between them. The effectiveness of online education is greatly influenced by students’ time commitment and learning motivation. Subsequent teaching can start from these two aspects to promote the improvement of the quality of teaching in online education.

Development and Evaluation of a Passive Monitoring System for the Elderly Using the Matter Protocol

Developing a passive monitoring prototype for elderly care is vital for ensuring their safety while respecting their autonomy. However, existing approaches often compromise confidentiality or necessitate the elderly to wear intrusive devices. To address this, we focus on recognizing behavioral patterns through Activities of Daily Living (ADLs). To detect and classify an ADL, data from various sensors is necessary, with motion sensors being the most prevalent. Consequently, our research delves deeper into understanding the functionality of these sensors and exploring the latest advancements in IoT communication protocols. Matter emerges as a standout choice, together with the OpenThread Border Router, which offer seamless interoperability, ensuring compatibility between devices and network infrastructure while prioritizing security and reliability. Leveraging the capabilities of these technologies, we developed a prototype for collecting data about an individual’s activity based on an occupancy sensor, found in a home setting. User-friendly management of the smart home is done through the Home Assistant platform, together with its HomeKit addon, enabling seamless interaction with commissioned devices directly from mobile phones. Our project advances through multiple stages, from laying the technical groundwork to defining data acquisition methods, successfully achieving these initial milestones. Our focus now shifts to data pre-processing, model development/selection for accurate sensor data classification within Activities of Daily Living (ADLs), and exploring system deployment options (cloud, edge, or on-device). Through our prototype, we showcase the feasibility of an elderly care monitoring system, emphasizing privacy and dignity with advanced IoT technologies.

An empirical study on students’ personalized teaching mode and educational effect of combining artificial intelligence technology in college civics education

Abstract With the rapid popularization of the new generation of information technology, education informatization has become a major trend in today’s education development, and more and more intelligent educational assistance platforms have emerged. The study proposes an empirical Q-matrix neurocognitive diagnostic model by introducing a neurocognitive diagnostic model, which improves the diagnostic effect of the NCD-Q model by learning the complex interaction function between students and exercises through neural networks. CFR-CD, an enhanced personalized exercise recommendation technique, is proposed to create a learner model of similar exercise expertise using a collaborative filtering algorithm. The CFR-CD algorithm is tested experimentally against other recommendation algorithms by suggesting exercises that are based on students’ knowledge mastery and similar learners’ answer data. Among them, the recommended effect of the CFR-CD model on simple test questions is better than the NMF model, the precision rate of the CFR-CD model is improved by about 3% on 50% and 60% of test question test sets, and the recall rate and F1 value of this model are the best among the three models. Finally, the experimental pre and post-tests were conducted to explore the teaching effect of the CFR-CD model in Civics education, and three dimensions, as well as sub-dimensions, were divided to analyze the influence effect of the CFR-CD model, which concluded that the Civics character, cultural literacy, and value concepts were all improved, among which the improvement effect of Civics character was the most obvious.

Digital assistance to support integrated healthcare.

This abstract is devoted to digital assistance as a supportive tool of integrated healthcare. According to the World Health Organization, developing more integrated people-centred care systems has the potential to generate significant benefits to health and healthcare of all people, including improved access to care, improved health and clinical outcomes, better health literacy and self-care, increased satisfaction with care, improved job satisfaction, improved efficiency of services, and reduced overall costs [1]. At the same time, the concept of Digital Health supports the implementation of a person-centred integrative approach in healthcare. The European Commission published a Communication on the digital transformation of health and care, which identified the priorities, including personalised medicine through shared European data infrastructure, allowing researchers and other professionals to pool resources, and citizen empowerment with digital tools for user feedback and person-centred care using digital tools to empower people to look after their health, stimulate prevention and enable feedback and interaction between users and healthcare providers [2].
 A project to develop a digital assistance platform for long-COVID and associated myalgic encephalomyelitis/ chronic fatigue syndrome (ME/CFS) patient healthcare is planned in Latvia. The current main challenge is diagnosis, stratification and monitoring of long-COVID and associated ME/CFS at the point of care, as well as patients’ self-awareness and proper practical navigation within the health system. A technology-grounded solution for m-Health based screening and self-management of ME/CFS will be developed within the patient-centred App model. The objectives of this model are: 1) Patient-centred, societally-oriented, real-time decision-support using an innovative App; 2) Using artificial intelligence to support decision-making on patients and treatment options; 3) Learning opportunities for patients, caregivers and health professionals about the illness. Sustainability of project activities will be supported by integrating of the new databases with the national e-Health and Electronic Health Records (EHRs) systems and national research institutions’ IT systems with the future option to become a base for further developing a common long-COVID and ME/CFS Patients’ Registry.
 The project intends to actively involve patients and clinicians in the development of a fully-fledged digital assistance platform that will support the development of digital health literacy and the implementation of integrated healthcare principles.
 
 
 

Utility of Albert health assistant platform for the patient support program provided in the setting of enteral nutrition

Utility of Albert health assistant platform for the patient support program provided in the setting of enteral nutrition

Study on Professor Diangui Li's Medication Experience in Treating Atrophic Gastritis Concomitant with Helicobacter Pylori Infection Based on Data Mining

Abstract Objective Our objective was to explore the national traditional Chinese medicine by studying master Diangui Li's medication experience in treating atrophic gastritis (AG) concomitant with Helicobacter pylori (Hp) infection based on data mining, and to provide the reference for the clinical treatment of relevant diseases. Methods The Chinese medicine prescriptions of the patients' first visits to the Famous Doctors' Consultation Room in Hebei Provincial Hospital of Chinese Medicine from May 2019 to December 2022 were collected and frequency analysis of the Chinese herbs, association rules analysis of the Chinese herbs, and cluster analysis were conducted using the Famous Doctor Inheritance Assistance Platform. Results A total of 188 prescriptions were collected, with 136 kinds of Chinese herbs. The highest use frequency of Chinese herbs was 185 and the lowest was 1. There were 28 kinds of Chinese herbs with high use frequency. The top 10 herbs with the high use frequency include Baihuasheshecao (Hedyotis Diffusae Herba), Huanglian (Coptidis Rhizoma), Yinchen (Artemisiae Scopariae Herba), Jigucao (Abriherba), Baishao (Paeoniae Radix Alba), Danggui (Angelicae Sinensis Radix), Banzhilian (Scutellariae Barbatae Herba), Baihe (Lilii Bulbus), Zhishi (Aurantii Immaturus Fructus), Chuanxiong (Chuanxiong Rhizoma). Chinese herbs with high frequency use are mostly heat-clearing herbs and qi-regulating herbs; the flavors of the herbs are mainly pungent, bitter, and sweet; the properties are mainly warm, cold, and neutral; the meridian tropism is mainly the spleen, liver, and stomach meridians. The core herbal medicines are Huanglian (Coptidis Rhizoma), Yinchen (Artemisiae Scopariae Herba), Baishao (Paeoniae Radix Alba), Danggui (Angelicae Sinensis Radix), etc.; the results of association rules: Jigucao (Abriherba)-Kushen (Sophorae Flavescentis Radix), Jiaogulan (Gynostemmatis Pentaphylli Rhizoma seu Herba)-Banlangen (Isatidis Radix), Huangqin (Scutellariae Radix)-Banlangen (Isatidis Radix), etc.; the results of cluster analysis showed that there were 10 clusters: (1) Yinchen (Artemisiae Scopariae Herba), Huanglian (Coptidis Rhizoma), Baihuasheshecao (Hedyotis Diffusae Herba), Banzhilian (Scutellariae Barbatae Herba), Jigucao (Abriherba); (2) Baishao (Paeoniae Radix Alba), Danggui (Angelicae Sinensis Radix), Chuanxiong (Chuanxiong Rhizoma), Wuyao (Linderae Radix); (3) Yuanhu (Corydalis Rhizoma), Danshen (Salviae Miltiorrhizae Radix et Rhizoma), Baizhi (Angelicae Dahuricae Radix). Conclusions For the treatment of AG concomitant with Hp infection, Professor Diangui Li mainly adopts the method of clearing heat and removing the toxin, regulating qi and resolving phlegm, combining disease differentiation with syndrome differentiation, performing reinforcing method, and reducing method simultaneously to regulate the mechanism of the human body.

Perbedaan Efektivitas Kuliah Metode Daring dan Metode Luring Pada Mata Kuliah Klasifikasi dan Kodefikasi Penyakit

Online/distance learning is learning that is carried out without face-to-face meetings, or using assistive platforms. Offline or face-to-face learning is a learning system that does not use an internet connection or use a learning assistant platform, in other words students and lecturers meet face to face. This study aims to find out the differences and effectiveness of learning online and offline methods in disease classification and coding courses conducted by Medical Record Students at Esa Unggul University. This study uses a quantitative approach to the research method, namely inferential analysis using the t dependent test instrument. The effectiveness of online learning methods is 33 or 44.0%, and the effectiveness of offline learning methods is 40 or 53.3%. based on the results of the dependent t-test, a significant score of 0.000 was obtained between the effectiveness of online learning and the effectiveness of offline learning. So it can be concluded that there are differences between online learning methods and offline learning methods. So it is advisable to combine learning methods or use hybrid learning methods.

Modern Design Intelligent Training Assistance Platform based on Lingnan Architectural Image Generation Algorithm

Modern Design Intelligent Training Assistance Platform based on Lingnan Architectural Image Generation Algorithm

Developing an AI-Assisted Educational Chatbot for Radiotherapy Using the IBM Watson Assistant Platform.

Objectives: This study aims to make radiotherapy knowledge regarding healthcare accessible to the general public by developing an AI-powered chatbot. The interactive nature of the chatbot is expected to facilitate better understanding of information on radiotherapy through communication with users. Methods: Using the IBM Watson Assistant platform on IBM Cloud, the chatbot was constructed following a pre-designed flowchart that outlines the conversation flow. This approach ensured the development of the chatbot with a clear mindset and allowed for effective tracking of the conversation. The chatbot is equipped to furnish users with information and quizzes on radiotherapy to assess their understanding of the subject. Results: By adopting a question-and-answer approach, the chatbot can engage in human-like communication with users seeking information about radiotherapy. As some users may feel anxious and struggle to articulate their queries, the chatbot is designed to be user-friendly and reassuring, providing a list of questions for the user to choose from. Feedback on the chatbot's content was mostly positive, despite a few limitations. The chatbot performed well and successfully conveyed knowledge as intended. Conclusions: There is a need to enhance the chatbot's conversation approach to improve user interaction. Including translation capabilities to cater to individuals with different first languages would also be advantageous. Lastly, the newly launched ChatGPT could potentially be developed into a medical chatbot to facilitate knowledge transfer.