Procedures for updating and revising a learning platform

Abstract

The invention relates to a method for updating and revising a digitally stored and used learning platform. The method includes reading in initial platform structure data using a platform reading device, as well as reading in further platform structure data using a platform reading device, which depends on the authentication of a learning platform user. Based on the initial platform structure data and the further platform structure data, a learning platform is constructed such that a digital, interactive, and individualized visualization of the learning platform is created, through which the authenticated learning platform user can interact to digitally use and / or provide learning content. The method also includes the analysis of user interactions and / or learning progress of a learning platform user in order to identify patterns, needs, and / or potential for improvement.An artificial intelligence (AI) system analyzes the platform's architecture data and suggests improvements and / or adjustments to the learning content, structure, and / or user interface of the learning platform. These suggestions are prioritized based on the analyzed user interactions and / or learning progress on the platform.

Description

The present invention relates to a method for updating and revising a learning platform. A learning platform is a digital platform designed for providing, managing, and conducting learning processes. It allows users to access, interact with, and edit learning content, as well as track their learning progress. Learning platforms offer a variety of features, such as course management, communication tools (e.g., forums, chats), assessment and feedback systems, and collaboration tools. They support both synchronous and asynchronous learning methods and can be used by various target groups, including schools, universities, businesses, and self-learners. A key component of learning platforms is user management, which enables the authentication, categorization, and individualized communication of users. Conventional learning platforms have several disadvantages: The content becomes outdated and loses relevance, which impairs the quality of learning. They often convey outdated information, which affects the relevance and accuracy of the learning. This can not only lead to a loss of motivation among learners but also diminish the platform's attractiveness. Furthermore, they jeopardize the competitiveness of providers because they do not keep pace with modern standards and technologies such as AI or interactive methods. As a result, learners do not receive up-to-date training and are less prepared for the job market. These platforms also often lack the flexibility to accommodate individual needs or new curricula. Consequently, economic disadvantages arise because additional resources are needed for alternative knowledge sources, and users migrate to more modern platforms.Overall, such platforms do not meet the requirements of a dynamic educational landscape, making continuous updates essential. The object of the invention is to propose a possibility which avoids or at least reduces some of the disadvantages known in the prior art. The problem is solved according to the invention by means of a method according to the main claim, as well as a further method according to a dependent claim. The subject matter of the main claim relates to a method for updating and revising a learning platform. The learning platform is stored and used digitally. The method includes reading initial platform structure data using a platform reading tool. Furthermore, the method includes reading additional platform structure data using a platform reading tool. The initial platform structure data includes general data for the structure of the learning platform, while the additional platform structure data includes individual data for the customized structure of the learning platform. This additional platform structure data is dependent on the authentication of a learning platform user.Furthermore, the process involves building the learning platform based on initial and subsequent platform setup data, creating a digital, interactive, and personalized visualization of the platform. Authenticated users can then interact with this visualization to access and / or provide digital learning content. The process also includes analyzing user interactions and / or learning progress on the platform. Based on these analyses, patterns, needs, and / or areas for improvement are identified. Finally, the process incorporates the implementation of an artificial intelligence (AI) system that analyzes the platform setup data.Furthermore, the process involves generating suggestions for improving and / or adapting the learning content, structure, and / or user interface of the learning platform, based on the AI ​​system's analysis of the collected platform architecture data. The process also includes prioritizing these suggestions based on the analyzed user interactions and / or learning progress on the learning platform. The process steps can essentially be automated. However, the process steps can also be automated. A learning platform, as defined in the invention, can be a digital platform that provides learners and teachers with an environment for exchanging knowledge, materials, and information. It enables users to consume learning content, complete interactive exercises, interact with other learners, and track their learning progress. Learning platforms are frequently used in education and training and offer features such as course management (creation and organization of learning materials and exams), communication tools (forums, chats, video conferences), assessment and feedback tools, as well as opportunities to track the progress of participants, for example through quizzes or completed modules. Platform architecture data forms the basis for the operation of a learning platform. It encompasses everything from the technical aspects of the database and infrastructure to user-generated data and learning content. This data must be structured and organized to ensure that the platform functions efficiently and user-friendly while simultaneously meeting the necessary security and data protection requirements. Platform architecture data for a learning platform as defined in the invention can include all structured information and technical details necessary to create, operate, and manage the platform. This data forms the basis for the platform's design, functionality, and user experience. It is important for both technical implementation and organizational use. The categories of platform architecture data for a learning platform encompass various data necessary for structuring and ensuring the platform functions smoothly. This includes user data, which contains information about learners, instructors, and administrators, such as login credentials, roles, permissions, and learning progress. Course data includes information about the learning content offered, course structures, assignments, assessments, and materials provided to participants. Communication data relates to the interaction options on the platform, such as messages, forum posts, chats, or video conferencing links. Other important data categories include assessment data, which contains learner performance data such as grades, feedback, and assessments of assignments or assessments.Finally, system and process data relating to the platform's technical infrastructure are relevant, such as server logs, error logs, and system update data, which ensure the stable and efficient operation of the learning platform. These data categories work together to create a user-friendly, efficient, and secure learning environment. Platform learning resources are crucial for making a learning platform accessible to new users and providing them with the necessary support to use the platform effectively. They can be offered in various formats (manuals, videos, interactive tours) and aim to provide learners and instructors with a smooth and enjoyable user experience. A platform introductory tool for a learning platform as defined in the invention can refer to the information, resources, and tools that serve to introduce users (especially learners and teachers) to the use of the platform and help them become familiar with the functions offered. This introductory tool provides guidance and support to ensure that users can use the platform effectively and efficiently. The categories of platform data sources for a learning platform encompass various data sources and mechanisms used to read and process information for the platform. These include user data sources, such as login credentials or authentication services, which enable user identification and authentication. Course content represents another important source, encompassing learning materials, assignments, assessments, and course structures from various formats, such as text, image, or video files. Data from communication tools like forums, chats, or messages provides valuable insights into interactions between instructors and learners and contributes to platform communication. Assessment data sources allow for the import of performance data such as grades, feedback, and progress information.System and process data, such as logs, server data, and system protocols, are also essential input sources for maintaining and optimizing the learning platform. These input sources ensure that the learning platform is always supplied with current and relevant data to guarantee smooth operation and a positive user experience. The purpose of platform data import tools is to efficiently integrate various data sources and ensure that the learning platform is always supplied with current, relevant, and consistent information. This includes importing user data, course content, communication data, assessment and performance information, as well as system and process data. The primary goal is to create a seamless and user-friendly learning environment that provides users with the right information at the right time. Furthermore, platform data import tools help promote interactivity and exchange between learners and instructors, monitor learning progress, and facilitate the management of learning content. They are also crucial for the platform's technical stability and scalability, as they ensure that all system processes run smoothly and the platform operates efficiently.Overall, the use of platform-based reading tools aims to promote a dynamic, flexible and user-centered learning environment that supports both learners and teachers. A digital, interactive, and personalized visualization of the learning platform, as defined in the invention, can refer to a visual representation of the learning environment that not only offers a static view of the platform but also integrates interactive and personalized elements to enhance and personalize the user experience. This visualization serves to provide learners and instructors with a clear, intuitive, and user-friendly representation of their learning activities and content, and to actively involve them in the learning process. Examples of digital interactive and individualized visualizations include interactive learning plans, such as a graphical schedule that shows learners which tasks they need to complete by when, with the option to postpone tasks or set new goals. 360° learning environments can, for example, include virtual, interactive 3D spaces in which learners can move around to discover course materials or go through simulations (e.g., in a virtual laboratory environment). Heatmaps of activities can provide a visual representation showing in which areas of the course learners are particularly active or where they have difficulties (e.g., in the form of colored markers). A digital, interactive, and personalized visualization of the learning platform combines visual, interactive, and personalized elements to make learning more engaging and effective. It offers users a clearly structured overview of their learning processes, enables direct interaction with the platform, and adapts to the individual needs and progress of the learners. This promotes a deeper engagement with the learning content and increases motivation through visual feedback and tailored content. User interactions and learning progress are central elements of a learning platform that structure and support learning. User interactions encompass all activities that learners and instructors perform on the platform, such as navigating course content, completing assignments, participating in forums or chats, using interactive elements like simulations, or customizing learning plans. These interactions actively shape the learning process and provide data that can be used to personalize the platform. Learning progress, on the other hand, describes the development of skills and the achievement of goals, measured by completed tasks, grades earned, time invested, milestones reached, and feedback. It shows how far learners have progressed and what advancements they have made.By combining both aspects, learning platforms can offer personalized learning experiences, promote motivation, and provide targeted support to increase individual learning success. Implementing an AI system for the automated updating of platform architecture data on a learning platform enables a dynamic, user-centric, and scalable environment. First, the platform's existing data and structures are analyzed to identify areas that should be dynamically updated by AI, such as course content, learning plans, or recommendation systems. Machine learning models are then used to analyze usage data and trends in order to suggest or automatically implement platform changes. This allows content to be optimized based on current curricula, user interests, or new developments. Natural Language Processing (NLP) can be used to extract relevant information from external sources, such as scientific articles or new curricula, and integrate it into the platform. The AI ​​system personalizes learning paths by analyzing user interactions and progress data to suggest customized content or structural changes. Users receive recommendations and dynamic platform adjustments tailored to their preferences and needs. External data sources and APIs enable the integration of content and updates in real time or at regular intervals. An administrator dashboard allows them to review, adjust, or manually approve changes, while trigger-based updates can automatically implement platform changes based on specific events. Security and data protection play a central role, with data being validated, anonymized, and processed in compliance with GDPR. A rollback function allows changes to be reversed in case of errors. Simultaneously, monitoring systems and feedback mechanisms ensure the continuous improvement of the AI ​​system. Regular updates and training of the AI ​​models guarantee that the system remains up-to-date and operates accurately. Such an AI system sustainably increases the relevance, efficiency, and user-friendliness of the learning platform, thereby improving both the quality of the content and the learning success of the users. A learning platform user, as defined in the invention, can be a person who has access to a learning platform for the purpose of learning, teaching, or otherwise using the platform. The user can have different roles and permissions that govern their behavior and interactions on the platform. The role of a learning platform user is typically tied to the specific functions they can perform on the platform, such as retrieving learning materials, creating and evaluating assignments, or managing courses. A user of a learning platform can assume different roles, each with specific tasks and permissions. Learners have access to courses and learning materials, can submit assignments, take tests, and monitor their learning progress. They actively participate in the learning process by contributing to discussions and group chats. Instructors, on the other hand, create and manage courses, upload materials, design assignments and tests, and assess learner performance. They also provide individual feedback and interact with learners through various communication channels. Administrators have the most extensive rights and are responsible for the technical administration of the platform. They manage user accounts, ensure the platform's security and configuration, and control user roles and access rights.Finally, there is also the guest user, who has limited access to certain public content but not the same full rights as the other roles. Each of these roles contributes to the smooth functioning of the learning platform and ensures that the needs of the different user groups are met. The invention offers the advantage of providing a method for updating and revising a digitally stored and used learning platform that incorporates an AI system. Such a learning platform offers numerous benefits that significantly support both learners and platform operators. Through the use of AI, content can be kept up-to-date because new information or developments from relevant sources are automatically detected and integrated. This ensures that learners always stay current, which is particularly important in dynamic fields such as technology, science, or law. Personalization is also significantly improved, as the AI ​​analyzes learners' progress and preferences and creates individualized content and learning paths based on this information. This increases motivation, as learners receive content precisely tailored to their needs. Furthermore, the AI ​​ensures the platform remains flexible by responding to changes in user behavior or new curriculum requirements and adapting the content accordingly. Another advantage lies in relieving the burden on teachers and administrators. Automated updates eliminate the time-consuming manual revision of content, freeing up resources for other tasks. At the same time, the platform enables greater efficiency, as it uses analysis and recommendations to identify learning barriers early on and offers appropriate solutions, such as providing additional support materials. The platform's interactive and dynamic design also enhances the user experience. Features such as visual feedback, personalized learning plans, and real-time updates encourage greater learner engagement. Furthermore, AI's ability to identify trends and patterns in large datasets supports the platform's continuous development, thereby increasing its long-term competitiveness. Overall, an AI-powered learning platform contributes to a sustainably higher quality of learning by maintaining both high-quality content and a contemporary user experience. The subject matter of a further dependent claim relates to a computer program product with program code which, when executed on a programmable processor, causes the execution of the method according to an exemplary embodiment of the invention. Before describing the embodiments of the invention in more detail, it should first be noted that the invention is not limited to the components or process steps described. Furthermore, the terminology used does not represent a limitation but is merely exemplary. Where the singular is used in the description and claims, the plural is always included unless the context explicitly excludes this. Any process steps can be automated unless the context explicitly excludes this. Further exemplary embodiments of the method according to the invention are explained below. In an initial exemplary configuration, the AI ​​system includes a data processing unit configured to analyze large datasets and identify patterns and relationships within that data. It also includes a machine learning algorithm to learn from the processed data, make predictions, and render decisions without requiring explicit programming for each individual decision. Furthermore, the AI ​​system incorporates a neural network and / or another machine learning model configured to recognize complex, non-linear relationships in the processed data and generate solutions or suggestions based on these. Finally, the AI ​​system includes a continuous improvement processing unit configured to learn from new data and results and adapt over time to optimize performance.As well as an interaction interface, set up to communicate with other systems, devices and / or users, for results presentation, receiving feedback and / or decision-making. A data processing unit within the meaning of the present invention is a central component that receives, analyzes, processes, and transforms incoming data into a form usable by the system. It acts as an interface between data acquisition and the algorithms that constitute the actual intelligence of the system. It encompasses various processes such as data preprocessing, including cleaning, normalization, and transformation to ensure that the data is consistent and suitable for the AI ​​models. Furthermore, it performs data aggregation, combining information from different sources, and enables the extraction of relevant features that are essential for the AI ​​system's decision-making. The data processing unit is designed to operate in real time or batch processing, depending on the system's requirements, and ensures that the processed data is in formats usable by machine learning algorithms or other AI components. Its central role in the data pipeline forms the basis for the efficiency and accuracy of the entire AI system and is crucial for its ability to make precise predictions or support decision-making. Machine learning algorithms, as defined in the present invention, are computer-based methods that enable computers to learn from data and recognize patterns or relationships without being explicitly programmed. They are based on mathematical and statistical models designed to self-optimize through repeated data processing. These algorithms analyze large datasets, extract relevant features, and create models that can be used for predictions, classifications, or decision-making. Depending on their objective, machine learning algorithms can be divided into different categories, such as supervised methods, which are based on predefined input and output data; unsupervised methods, which recognize patterns in unlabeled data; and reinforcement learning, which relies on reward systems. They are versatile and form the basis for many AI applications, from speech recognition and image processing to personalized recommendations and complex analyses. Their ability to adapt to new data and continuously improve makes machine learning algorithms essential for the development of intelligent systems. A neural network as defined in the present invention is a computer-based model inspired by the functioning of biological nervous systems and used to recognize and process complex patterns and relationships in data. It consists of a multitude of interconnected artificial neurons organized in layers: the input layer, one or more hidden layers, and the output layer. Each artificial neuron receives signals, processes them according to weights and activation functions, and passes them on to the next neurons. Neural networks are particularly powerful at analyzing and processing large, complex, and unstructured datasets, such as images, speech, or text. They learn by optimizing the connections between neurons through repeated data processing and adjustments to their weights—a process known as training. Their ability to model nonlinear relationships makes neural networks the foundation of many modern AI applications, such as facial recognition, machine translation, and autonomous driving. They are a central component of deep learning methodologies and contribute significantly to the performance of intelligent systems. A continuous improvement processing unit as defined in the present invention is a specialized component that analyzes data and results from past processes to continuously optimize the system. It is designed to identify weaknesses, recognize new patterns or trends in the data, and adjust the underlying algorithms or models accordingly. This iterative process is often based on methods such as feedback analysis, automatic learning from errors, or the integration of new data sources. The processing unit monitors the system's performance in real time or through periodic analyses, using metrics such as accuracy, efficiency, and user feedback to make adjustments. This allows the AI ​​system not only to respond to changing requirements but also to become more robust and precise in the long term. Such a unit is particularly advantageous in dynamic environments, as it ensures that the system remains up-to-date and continuously learns, significantly increasing effectiveness and user satisfaction. An interaction interface within the meaning of the present invention is the component that enables exchange between users and the system. It serves as a central platform through which users can enter information, make requests, or execute commands, while the AI ​​system provides feedback, results, or suggested actions. This interface can take various forms, including graphical user interfaces (GUIs), voice interfaces, chatbots, or application programming interfaces (APIs) specifically designed for developers. The interaction interface is designed to be intuitive and user-friendly to maximize accessibility and efficiency. It often utilizes AI technologies such as natural language processing (NLP) or visual recognition to understand input and deliver precise responses. Through its central role, the interaction interface connects the complex processes of the AI ​​system with user needs, enabling seamless communication that is crucial for the system's success and acceptance. According to another exemplary implementation, the method additionally integrates feedback loops in which the AI ​​system continuously considers new user data and dynamically adjusts platform update suggestions to ensure continuous optimization of the learning platform. Furthermore, the method provides personalized suggestions for learning platform users based on their learning progress, preferences, and previous interactions to recommend targeted, individually tailored learning paths and materials. This design offers numerous advantages that significantly increase the efficiency and flexibility of a system. Integrating feedback loops and providing personalized suggestions through an AI system offers numerous advantages for a learning platform. Feedback loops allow the system to continuously incorporate new user data and make dynamic adjustments. This ensures continuous optimization of the platform, as the AI ​​system can not only respond to new requirements and trends but also specifically identify and address weaknesses. This dynamic approach leads to a steady improvement in the user experience, making the platform increasingly relevant and effective. Furthermore, the provision of personalized suggestions offers targeted support for learners. By analyzing individual learning progress, preferences, and previous interactions, the platform suggests tailored learning paths and materials that meet the specific needs of the users. This increases motivation, as the content is directly aligned with the learners' interests, and improves the efficiency of the learning process by avoiding superfluous or overly challenging content. Overall, these mechanisms promote a personalized, optimized learning experience, strengthen user engagement with the platform, and contribute to maximizing learning success in the long term. According to another exemplary design, the AI ​​system involves revising the learning platform by updating course content, exams, tasks and / or interactive elements, preferably in line with the latest educational standards and trends. This offers numerous advantages. Continuous adaptation to the latest educational standards and trends ensures that the content is always relevant, up-to-date, and of high quality. This guarantees that learners can keep pace with the latest developments in their field, which is particularly crucial in dynamic areas such as technology, science, or business. Such a system also simplifies the work of teachers and administrators, as it eliminates the time-consuming process of manually revising content. The platform automatically stays up-to-date and adapts flexibly to new requirements or curricula. Interactive elements and tasks can also be optimized to better engage learners and promote their participation. Furthermore, regular updates improve user trust in the platform, as they can be confident they are working with high-quality and up-to-date materials. For platform operators, this increases their competitiveness, as they offer a modern and effective solution that adapts to rapidly changing educational needs. Overall, such an AI-powered system significantly enhances the platform's appeal, efficiency, and learning outcomes. According to another exemplary embodiment, the procedure involves providing real-time update and revision suggestions for the learning platform, based on the continuous analysis of user behavior and internal platform metrics and / or the use of the AI ​​system via a speech processing unit to analyze texts, forum posts or other communication data from learning platform users and to suggest learning content or course structures based on these inputs. Up-to-date information, as defined in the invention, can refer to all data and content that are regularly updated to reflect the current state and developments of the learning environment. This information includes, among other things, learner progress, new course content, updated assignments or exams, changes to course schedules, and current notifications and announcements. It serves to provide users with a current and relevant learning environment by ensuring that all information provided is timely and accurate. Up-to-date information is crucial for the quality and relevance of learning resources and enables efficient adaptation of learning processes to new developments and requirements. This design offers the advantage that the platform remains relevant and up-to-date at all times. This ensures that learners have access to content that meets their current educational and developmental needs, significantly improving their preparation for exams, the job market, or further studies. Furthermore, such a system reduces the workload for instructors and administrators, as the manual revision of materials is eliminated. Instead, they can focus on supporting learners, while the platform automatically ensures its relevance and quality. By dynamically adapting to modern curricula and new technologies, the learning platform becomes not only more efficient but also more attractive to users, who benefit from interactive and innovative features. In the long term, such an AI-powered update strengthens the learning platform users' trust in the platform and promotes sustainable use. It transforms the learning platform into a flexible, modern tool that meets both the requirements of the education system and the expectations of learners, thus creating a future-oriented learning experience. According to another exemplary implementation, the AI ​​system makes suggestions to the user interface of the learning platform to improve the user experience and / or to optimize the interaction of the learning platform user with the learning platform. This significantly improves the user experience and optimizes the interaction between users and the platform. By analyzing user data and interaction patterns, the system identifies weaknesses in the user interface and makes targeted suggestions to make navigation, design, and functionality more intuitive and user-friendly. This facilitates learners' access to course materials, assignments, and other resources, enabling them to work more efficiently and with greater motivation. Furthermore, such suggestions can help adapt the platform to the individual preferences and needs of users, for example through personalized dashboards or optimized menu structures. This not only increases user satisfaction but also their loyalty to the platform, as it is perceived as a flexible and supportive tool. Platform operators also benefit: An optimized user interface reduces support requests and increases platform acceptance, which can lead to a higher number of users and a better overall rating. Overall, the AI-supported system ensures continuous improvement in user-friendliness, which increases the long-term effectiveness and attractiveness of the learning platform. According to another exemplary configuration, the digital learning content can be selected from a group that includes: word processing documents, spreadsheet documents, presentation documents, interactive learning content control documents, and certification documents. The digital learning content includes static and / or dynamically generated documents, and / or at least some documents protected against alteration. Static and / or dynamically generated data within the meaning of the invention refers to files containing information that is either fixed and unchanging (static) or updates in real time based on inputs or changes (dynamic). Static data is predetermined and remains unchanged during the use of the document, such as in a PDF document or a printed file. Dynamic data, on the other hand, can change depending on interactions, system processes, or external sources, such as in web applications, databases, or interactive forms. An example of a document with dynamic data could be an Excel file that automatically performs calculations based on the entered values. This approach offers a multitude of advantages, which are important depending on the application. Static data is characterized by its consistency and stability, as it remains unchanged and thus offers high security and reliability. It is easy to distribute and requires minimal system resources, since no real-time calculations or updates are necessary. Dynamic data, on the other hand, offers flexibility and up-to-dateness, as it can be updated in real time, enabling a rapid response to changes. It promotes process automation, reduces manual input, and allows for interactive and customizable user experiences. Dynamic data offers particularly valuable advantages in areas that require real-time data, such as financial markets or logistics.Often, both approaches are combined to unite the advantages of stability and flexibility, thus ensuring efficient and adaptable document creation. According to another exemplary embodiment, the procedure also includes authentication of the learning platform user, whereby authentication is preferably carried out using a middleware device. This design offers numerous advantages, particularly in terms of security and efficiency. It enhances security by ensuring that only authorized users access the platform and enables the integration of advanced authentication protocols. Furthermore, it simplifies user access management by centralizing various authentication methods. The middleware improves scalability, remaining efficient even with a growing user base, and ensures consistent application of security policies. Overall, it provides secure, efficient, and user-friendly authentication. According to another exemplary implementation, the authentication of the learning platform user takes place on a client. Furthermore, the procedure involves identifying the client at the middleware device before transmitting authentication information. Authentication on a learning platform ensures that only authorized users have access to the platform's functions and content. It is usually done via username and password, but can be supplemented by additional methods such as two-factor authentication or single sign-on. Secure and user-friendly authentication is crucial for protecting the platform from unauthorized access and ensuring a positive user experience. Authentication, as defined in the invention, can refer to the process by which a user's identity is verified to ensure that only authorized persons have access to the platform and its functions. It is an essential security mechanism that ensures only authorized users (e.g., learners, teachers, administrators) can access the platform's content and services. Authentication on a learning platform ensures that only authorized users can access the content and features. The most common method is using a username and password, with the system comparing the entered data against the information stored in the database. To increase security, two-factor authentication (2FA) is frequently used. With 2FA, after entering their password, the user must provide an additional authentication factor, such as a one-time password (OTP) sent via SMS or an authenticator app like Google Authenticator. Another common method is single sign-on (SSO), where the user logs in once to an identity provider like Google or Microsoft and can then seamlessly access various platforms, including the learning platform, without having to log in again.Some platforms also offer the option to authenticate via existing social media accounts such as Facebook or LinkedIn, which simplifies the registration process. To further control access, role-based access control (RBAC) is frequently used, where, after successful authentication, users can access specific platform functions based on their role (e.g., learner, instructor, administrator). In larger organizations or educational institutions, authentication can also be performed via centralized directory services such as LDAP or Active Directory to centrally manage user identities. Additionally, security settings such as encryption of authentication data and password hashing are used to enhance security and prevent attacks. Sessions can expire automatically after a set period, with token-based authentication often used to secure the session until it needs to be renewed. All these methods contribute to making authentication on learning platforms both secure and user-friendly. Authentication information for a learning platform user, as defined in the invention, refers to the specific information used to verify a user's identity before granting them access to the platform. This information serves to securely and unambiguously verify the user and ensures that only authorized persons have access to the platform's functions and content. A middleware device within the meaning of the invention refers to a software component or system that acts as an intermediary between different applications, services, or databases to enable their communication and interaction. It serves as a mediator, forwarding and coordinating data, requests, and commands between the various systems without requiring them to be directly connected. Middleware simplifies the exchange of information by providing standardizations, protocols, and interfaces that allow different, often heterogeneous, systems to communicate with each other. In a learning platform, middleware could, for example, act as an intermediary between the user interface and the database to process course registration requests or control access to specific learning content. It can also handle functions such as authentication, security, transaction management, or logging, thereby improving the platform's functionality and scalability. This design offers several advantages. It enhances security by ensuring that authentication data is transmitted only from trusted devices, minimizing the risk of attacks. Simultaneously, it protects user privacy, as sensitive data is only shared with authorized devices. The authentication process becomes more efficient because the middleware quickly verifies whether the client is a trusted device, thus avoiding redundant checks. Furthermore, the middleware enables centralized security monitoring and rapid response to suspicious activity. Finally, this method contributes to the platform's scalability and flexibility, as different security policies can be implemented flexibly without requiring individual configuration of each client device. The invention will be explained in more detail below with reference to the figures. These show: Fig. 1 a schematic representation of a proposed method according to an exemplary embodiment of the invention; Fig. 2 a schematic representation of a proposed method according to a further exemplary embodiment of the invention; Fig. 3 a schematic representation of a proposed method according to a further exemplary embodiment of the invention; and Fig. 4 a schematic representation of a further proposed method according to a further exemplary embodiment of the invention. Fig. 1 shows a schematic representation of a proposed method according to an exemplary embodiment of the invention. Figure 1 shows a schematic representation of a method for updating and revising a learning platform 100, wherein the learning platform 100 is stored and used digitally, comprising the following: Reading 10 of initial platform structure data 110 using a platform reading device 120. Furthermore, reading 20 of additional platform structure data 130 using another platform reading device 120. The platform structure data 110 contains general data 131 for the structure of the learning platform 100. The additional platform structure data 130 contains individual data 132 for the individualized structure of the learning platform 100. The additional platform structure data 130 depends on the authentication of a learning platform user 200.Furthermore, the process involves building the learning platform 100 based on the initial platform setup data 110 and subsequent platform setup data 130, such that a digital, interactive, and individualized visualization of the learning platform 100 is created, through which the authenticated learning platform user 200 can interact to digitally use and / or provide learning content 140. Furthermore, the process involves analyzing user interactions 150 and / or learning progress of a learning platform user 200 on the learning platform 100. Furthermore, it involves identifying patterns, needs, and / or potential improvements based on the analyzed user interactions 150 and / or learning progress on the learning platform 100. Finally, it involves implementing an artificial intelligence (AI) system 400. Furthermore, the Analyze 55 procedure identifies platform architecture data 110 using the AI ​​system 400. Additionally, the Generate 60 procedure identifies suggestions 160 for improving and / or adapting the learning content 140, the structure and / or user interface of the learning platform 100, based on the analysis of the collected platform architecture data 111. Furthermore, the Prioritize 65 procedure identifies suggestions 160 based on the analyzed user interactions 150 and / or learning progress on the learning platform 100. Fig. 2 shows a schematic representation of a proposed method according to a further exemplary embodiment of the invention. Fig. 2 shows a schematic representation of a method extended compared to the method shown in Fig. 1. What was said previously regarding Fig. 1 applies accordingly to Fig. 2. As can be seen in Fig. 2, the method further integrates 90 feedback loops in which the AI ​​system 400 continuously considers new user data and dynamically adjusts the platform update suggestions to ensure continuous optimization of the learning platform 100. Furthermore, the method provides 95 personalized suggestions for learning platform users 200, based on their learning progress, preferences, and previous interactions, to recommend targeted, individually tailored learning paths and materials. Fig. 3 shows a schematic representation of a proposed method according to a further exemplary embodiment of the invention. Figure 3 shows a schematic representation of a method extended compared to the method shown in Figures 1 and 2. The statements made previously regarding Figures 1 and 2 apply accordingly to Figure 3. As can be seen in Fig. 3, the procedure includes as a further step authentication 7 of the learning platform user 200), wherein the authentication 70 is preferably carried out by means of a middleware device. Fig. 4 shows a schematic representation of another proposed method according to a further exemplary embodiment of the invention. Figure 4 shows a schematic representation of a method extended compared to the method shown in Figures 1, 2 to 3. The statements made previously regarding Figures 1, 2 to 3 apply accordingly to Figure 4. As can be seen in Fig. 4, the procedure includes a further step of identifying client 80 at the middleware device before transmitting authentication information. Reference symbol list 10 Reading platform setup data 20 Reading further platform setup data 30 Building the learning platform 40 Analyzing user interactions and / or learning progress 45 Identifying patterns, needs, and / or improvement potential 50 Implementing an artificial intelligence system 55 Analyzing platform setup data 60 Generating suggestions 65 Prioritizing suggestions 70 Authenticating the learning platform user 80 Identifying the client 90 Integrating feedback loops 95 Providing personalized suggestions for learning platform users 100 Learning platform 110 Initial platform setup data 120 Platform input tools 130 Further platform setup data 131 General data 132 Individual data 140 Learning content 150 User interactions 160 Suggestions for improvement 200 Learning platform users 300 A client 400 AI system

Claims

A method for updating and revising a learning platform (100), wherein the learning platform (100) is stored and used digitally, comprising the method of: - reading (10) initial platform setup data (110) using a platform read-in device (120); - reading (20) further platform setup data (130) using a further platform read-in device (120); wherein the platform setup data (110) includes general data (131) for the setup of the learning platform (100); the further platform setup data (130) includes individual data (132) for the individualized setup of the learning platform (100); wherein the further platform setup data (130) depend on authentication of a learning platform user (200);- Building (30) the learning platform (100) based on the initial platform setup data (110) and the subsequent platform setup data (130) such that a digital, interactive, and individualized visualization of the learning platform (100) is created, by means of which the authenticated learning platform user (200) can interact to digitally use and / or provide learning content (140); - Analyzing (40) user interactions (150) and / or learning progress of a learning platform user (200) on the learning platform (100); - Identifying (45) patterns, needs, and / or potential improvements based on the analyzed user interactions (150) and / or learning progress on the learning platform (100); - Implementing (50) an artificial intelligence (AI) system (400); - Analyzing (55) the platform setup data (110) using the AI ​​system (400);- Generating (60) suggestions (160) for improving and / or adapting the learning content (140), the structure and / or the user interface of the learning platform (100), based on the analysis of the collected platform architecture data (111); - Prioritizing (65) the suggestions (160), based on the analyzed user interactions (150) and / or learning progress on the learning platform (100).; The method according to claim 1, wherein: - A data processing unit configured to analyze and process large amounts of data and to recognize patterns and relationships within that data; - A machine learning algorithm configured to learn from the processed data, make predictions, and make decisions without requiring explicit programming for each individual decision; - A neural network and / or other learning model configured to recognize complex, nonlinear relationships in the processed data and to generate solutions or suggestions based thereon; - A continuous improvement processing unit configured to learn from new data and results and to adapt over time to optimize performance;- An interaction interface designed to communicate with other systems, devices and / or users for the purpose of presenting results, receiving feedback and / or making decisions. The method according to claim 1 or 2, wherein the method further comprises the following steps: - Integrating (90) feedback loops in which the AI ​​system (400) continuously considers new user data and dynamically adjusts the platform update suggestions to ensure continuous optimization of the learning platform (100); - Providing (95) personalized suggestions for learning platform users (200) based on their learning progress, preferences and previous interactions to recommend targeted, individually tailored learning paths and materials. The method according to any of the preceding claims, wherein AI system (400) comprises a revision (?) of the learning platform (100) by updating (?) course content, tests, assignments and / or interactive elements, preferably in accordance with the latest educational standards and trends. The method according to any of the preceding claims, wherein the update and revision suggestions for the learning platform (100) are provided in real time, based on the continuous analysis of user behavior and internal platform metrics and / or the AI ​​system (400) uses a language processing unit to analyze texts, forum posts or other communication data from learning platform users (200) and to suggest learning content (140) or course structures based on these inputs. The method according to any of the preceding claims, wherein the AI ​​system (400) makes suggestions for the user interface of the learning platform (100) to improve the user experience and / or to optimize the interaction of the learning platform user (200) with the learning platform (100). The method according to any of the preceding claims, wherein the digital learning content (140) is selectable from a group comprising: - word processing documents, - spreadsheet documents, - presentation documents, - interactive learning content control documents, - certification documents; and wherein the digital learning content (140) comprises static and / or dynamically created documents, and / or the digital learning content (140) comprises at least some documents protected against alteration. The method according to any of the preceding claims, the method further comprising: - Authenticating (70) the learning platform user (200), wherein the authentication (70) is preferably carried out by means of a middleware device. The method according to claim 8, wherein the authentication (70) of the learning platform user (200) is performed on a client (300); and the method further comprises: - identifying (80) the client (300) at the middleware device prior to transmitting authentication information. A computer program product comprising program code which, when executed on a programmable processor, causes the execution of a method according to any one of the preceding claims 1 to 9.

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