Mass generation of content for educational applications

Abstract

A system, apparatus, method, and instruction for generating content for an educational application, comprising: periodically web scraping a repository of learning content; saving the learning content and corresponding information; processing the learning content using a neural network architecture; summarizing the learning content using artificial intelligence or machine learning; and generating one or more questions and answers for an educational application based on the learning content using artificial intelligence or machine learning.

Description

Technical Field

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 459,995, filed Apr. 17, 2023, which is hereby incorporated by reference in its entirety.

[0002] Embodiments of the present invention generally relate to online learning, and more specifically to the mass generation, storage, and retrieval of content for educational applications (e.g., continuing education applications). In various embodiments, an open market concept of crowdsourcing may be adopted to generate content. In various embodiments, artificial intelligence and / or machine learning may be used to generate content.

Background Art

[0003] In medicine and other specialized fields, it is well known that the rate of research progress far exceeds the rate at which experts practicing continuing education can reasonably learn. For example, in 2005, a systematic review published in the Annals of Internal Medicine found that in most studies, there was a correlation between the increase in years of clinical experience and the decline in the quality of medical care, which became a topic of discussion. The article suggested that as a physician's clinical practice period lengthens, the physician is less likely to follow updated clinical guidelines, which may lead to a deterioration of patient outcomes. Based on this, a 2011 study estimated that the doubling time of medical knowledge was 50 years in 1950, 7 years in 1980, and 3.5 years in 2010. In 2020, it was estimated that the doubling time was only 73 days. This statistic means that what was learned in the first three years of medical school represents only 6% of what was known at the end of the 10-year period from 2010 to 2020.

Summary of the Invention

Problems to be Solved by the Invention

[0004] These examples illustrate the problem that it is nearly impossible for doctors and other professionals to keep up with the pace of progress in their fields. Educational applications (e.g., continuing education applications) and programs have limited supply of educational materials and cannot keep up with the rapid pace of progress occurring in various fields.

[0005] To the best of the inventor's knowledge, free-market crowdsourcing models and artificial intelligence and / or machine learning systems are not being used to develop content for educational applications aimed at doctors and other professionals. As a result, the scope and depth of content offered by existing educational applications and programs are significantly lacking.

[0006] Accordingly, embodiments of the present invention relate to the mass generation of content for educational applications that substantially eliminates one or more problems arising from the limitations and weaknesses of related technologies. In various embodiments, a crowdsourcing open market concept may be employed to generate the content. In various embodiments, artificial intelligence and / or machine learning may be used to generate the content.

[0007] Other features and advantages of the present invention are set forth below, will be partially apparent from the following description, or will become apparent through the practice of the present invention. The objectives and other advantages of the present invention are achieved and realized, in particular, by the configurations shown herein, in the claims, and in the accompanying drawings.

[0008] To achieve the objectives and other advantages of the present invention, and according to the objectives of the present invention as broadly and illustratively described, the mass generation of content for educational applications such as continuing medical education (CME) includes systems, apparatus, methods, and instructions for generating content for educational applications, which include periodically web scraping a repository of learning content, storing learning content and corresponding information, processing the learning content using a neural network architecture, summarizing the learning content using artificial intelligence or machine learning, and generating one or more questions and answers for educational applications based on the learning content using artificial intelligence or machine learning.

[0009] In another example, the mass production of content for educational applications (e.g., medical continuing education) using the open market concept of crowdsourcing includes systems, apparatus, methods, and instructions for mass production of continuing education content using crowdsourcing.

[0010] In another example, the mass generation of content for educational applications (e.g., medical continuing education) using the open market concept of crowdsourcing includes systems, apparatus, methods, and instructions for mass-producing continuing education content using crowdsourcing and machine learning and / or artificial intelligence.

[0011] In another example, the mass generation of content for educational applications (e.g., medical continuing education) using the open market concept of crowdsourcing includes systems, apparatus, methods, and instructions for mass-producing continuing education content using machine learning and / or artificial intelligence.

[0012] Please understand that the above general description and the following detailed description are illustrative and explanatory, and are intended to provide further explanation of the claimed invention. [Brief explanation of the drawing]

[0013] To further understand the present invention, the accompanying drawings included as part of this specification illustrate embodiments of the present invention and, together with this specification, illustrate the principles of the present invention. [Figure 1A] This diagram shows a flowchart of the functionality for generating educational content according to an exemplary embodiment of the present invention. [Figure 1B] A flowchart of the functionality for generating educational content according to another exemplary embodiment of the present invention is shown. [Figure 2] An example of a user interface according to an exemplary embodiment of the present invention is shown. [Figure 3] A flowchart of the functions for continuing education according to an exemplary embodiment of the present invention is shown. [Figure 4] An example of a user interface according to an exemplary embodiment of the present invention is shown. [Figure 5] This shows a typical configuration of a portable electronic device according to an exemplary embodiment. [Figure 6] An example of a user interface according to an exemplary embodiment of the present invention is shown. [Modes for carrying out the invention]

[0014] Embodiments of the present invention will be described in detail below (examples of which are shown in the accompanying drawings). The following detailed description includes numerous specific details to ensure a full understanding of the invention. However, it will be apparent to those skilled in the art that the invention can be carried out without these specific details. Known methods, procedures, components, and circuits may not be described in detail to avoid unnecessarily obscuring the aspects of the embodiments. Wherever possible, identical elements will be given the same reference numerals.

[0015] Embodiments of user interfaces and related methods of using the device are described. In some embodiments, the device is a portable communication device (e.g., a mobile phone or tablet). The user interface may include a touchscreen, a gyroscope or other accelerometer, and / or other input / output devices. In the following description, a portable communication device is referred to as an exemplary embodiment. However, it should be understood that the user interface and related methods may also apply to other devices (such as a personal computer or laptop) which may include one or more other physical user interface devices such as a keyboard or mouse.

[0016] A portable communication device may support a variety of applications, such as telephone, text messenger, calendar, and (continuing) education applications. These various applications that may run on the device may use at least one common physical user interface device, such as a touchscreen. One or more functions of the touchscreen and corresponding information displayed on the device may be coordinated and / or modified between applications and within each application. In this way, a common physical configuration of the device can support a variety of applications with an intuitive and transparent user interface. In the following description, an educational application (e.g., a continuing education application) is mentioned as an exemplary embodiment, but it should be understood that the user interface and related methods can be applied to a variety of educational applications (e.g., professional, academic) and other applications. Although medical education is described as an example, the embodiment can be readily applied to other professional education (e.g., law, finance, regulation, data privacy, accounting, engineering, etc.).

[0017] Currently, educational applications and programs are struggling to keep pace with the rapid pace of progress in their respective fields due to the limited supply of educational materials. Historically, content for educational applications has been generated through laborious manual processes. As a result, continuing education in highly specialized fields is limited.

[0018] In recent years, advances in artificial intelligence and machine learning have begun to demonstrate the ability of computers to mimic human communication. For example, ChatGPT is a chatbot developed by OpenAI and released on November 30, 2022. ChatGPT uses a large-scale language model to refine and develop conversations to the length, format, style, level of detail, and language desired by the user. Despite these recent advances, ChatGPT and other AI tools can sometimes provide feedback that sounds plausible but is ultimately incorrect. Therefore, the need for validation of content generated by artificial intelligence and / or machine learning by human users remains.

[0019] In an exemplary embodiment, an educational application (e.g., a continuing education application) is a flea market-based system for crowdsourcing large volumes of continuing education content for physicians and other professionals. In this embodiment, experts in each field (or industry) are guided through the content submission process by effectively utilizing guidelines published in open access research and peer-reviewed, influential academic journals. Submissions are peer-reviewed, and upon approval, the submitter is compensated. Certain articles may be offered higher compensation, thus ensuring that the “flea market” works to guarantee that content on the required subject matter is obtained.

[0020] Reference is made to journal articles as examples of academic content (or learning content), although the various embodiments of the present invention are readily applicable to any academic content such as medical / scientific journals, engineering journals, textbooks, textbook chapters, white papers, legal opinions, regulations, business case studies, accounting rules, and the like.

[0021] Experts are recruited as contractors and participate using a web-based interface or the interface of a mobile application. Since these experts are currently engaged in their duties (here limited to medicine for the sake of explanation, although the embodiments are not so limited), they have expertise in one or more medical fields. Such experts are the ones who submit content to the platform and are referred to herein as "submitters".

[0022] When a submitter logs in to the interface, a list of free-market rewards in tabular form is presented. The rewards are paid for each approved / accepted submission. Factors that can affect (increase or decrease) the reward amount for a submission include the medical specialty and subspecialty (e.g., a higher reward is presented for specialties with a shortage of content), submission from a highly influential (more well-known) medical journal, special topics in the medical field (e.g., appropriate prescribing of opioids), the number of citations of the article, the evaluation of the submitter, etc. The submitter can select which topic to submit content on based on their specialty and the free-market rewards.

[0023] In the medical field, PubMed can be utilized. Other sources can also be used, including the websites of academic journals and publishers. PubMed is a free database mainly containing references and abstracts on topics related to life sciences and biomedicine. The site is operated by the National Library of Medicine and the National Institutes of Health of the United States. The submitter searches for research papers, meta-analyses, review articles, and practice guidelines on the site that match their areas of interest and / or desired rewards. The submitter selects a research article and reads it carefully.

[0024] After carefully reading the article, the submitter is guided to enter the main information about the article (e.g., PubMed ID to facilitate automatic download of the article content), the medical specialty and subspecialty to which the article is most applicable, and the relevant keywords of the article according to the "Submitter" workflow within the application, and finally write a series of multiple-choice questions to be used as part of the continuing medical education activity focused on the article.

[0025] The academic article and its corresponding data (e.g., title, abstract, author, keywords, metadata, article text and figures, submitter information, questions created by the submitter) may be stored in one or more web servers and / or one or more cloud-based storage systems.

[0026] The submitted article is subject to a multi-step review process, and upon obtaining final approval of the content, a reward according to the free market rate is automatically sent to the submitter.

[0027] In another exemplary embodiment, an educational application (e.g., a continuing education application) includes an artificial intelligence and / or machine learning system to generate a large amount of content. For example, the educational application may be a continuing education application for physicians and other professionals.

[0028] Using one of various commercial web scraping applications (e.g., PubMed Web Scraper, ParseHub, Diffbot, etc.), scholarly articles published in peer-reviewed, influential journals and downloadable from internet-based repositories (e.g., databases) can be retrieved regularly (e.g., daily, weekly, monthly, quarterly). The retrieved scholarly articles and their corresponding data (e.g., title, abstract, author, keywords, metadata, article text, and figures, etc.) may be stored on one or more web servers and / or one or more cloud-based storage systems. Other scholarly content and learning materials may also be retrieved and stored. While scholarly articles are mentioned as an example, the embodiments are not limited thereto.

[0029] In this embodiment, instead of utilizing experts in each field (industry), one of various commercial artificial intelligence and / or machine learning tools (e.g., ChatGPT, ChatDoc, ChatPDF, Unriddle, etc.) is used to process academic articles obtained using, for example, a neural network architecture. In many artificial intelligence or machine learning models, the neural network enables language processing using complex mathematical functions that take numerical data as input. Therefore, the input text is encoded into numerical data by language processing before being fed into the neural network. Similarly, the numerical output of the neural network is decoded into natural language for the user.

[0030] The content of the acquired academic articles may be summarized in a coursework or seminar format (e.g., a series of slides and / or a lecture read aloud by an educational application). In addition to summarizing the content of the acquired academic articles, artificial intelligence and / or machine learning tools are configured to generate one or more questions and answers (e.g., multiple-choice questions with one or more correct answers) based on the content of the acquired academic articles. The generated questions are used to test the user's understanding of the acquired academic articles. The acquired academic articles and generated questions may be peer-reviewed and approved before being used as part of a continuing education application.

[0031] Experts may be recruited and participate as contractors to review acquired academic articles and generated questions through a web-based interface or a mobile application interface. These experts are currently engaged in their work (for the sake of explanation, this is limited to medicine, but the implementation is not limited in this way) and therefore possess expertise in one or more areas of the medical field. Furthermore, acquired academic articles and the corresponding generated questions may be subjected to a multi-stage peer review process.

[0032] Figure 1A shows a flowchart of function 100A for generating educational content according to an exemplary embodiment of the present invention.

[0033] The functions in the flowcharts of Figure 1A (and Figures 1B and 3 below) are implemented by software stored in memory or other non-temporary computer-readable storage media and may be executed by one or more processors. Alternatively, the functions may be executed by hardware (e.g., using application-specific integrated circuits (ASICs), programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), processors, etc.), or by any combination of hardware and software.

[0034] First, function 100 may optionally initialize the portable electronic device in 101. For example, function 100 may determine or initialize the settings of various components of the portable electronic device in order to run an educational application.

[0035] Next, in step 102, one or more contributors log in to the educational application, and a list of subjects (or articles) for content generation is presented (in tabular or list format). Each article may be linked to a reward for the contributor. Contributors are typically professionals currently engaged in their respective occupations and possess expertise in one or more subjects / educational fields. Educational content is crowdsourced by recruiting contributors for specific subject areas.

[0036] For example, academic articles published in peer-reviewed, influential academic journals may be retrieved regularly (e.g., daily, weekly, monthly, quarterly, etc.) using various commercial web scraping applications (e.g., PubMed Web Scraper, ParseHub, Diffbot, etc.). Other academic content and learning materials may also be retrieved and stored. While academic articles are mentioned as an example here, the embodiments are not limited to this.

[0037] Subsequently, in step 103, the submitter selects the subject matter for which they wish to submit content, based on their area of ​​expertise and desired compensation. In the medical field, for example, PubMed is used. The submitter identifies one or more research papers, meta-analyses, reviews, and / or practice guidelines that match their area of ​​interest and / or desired compensation. Here, the submitter selects research papers, etc., and carefully reads them to create educational content. In addition to or instead of this, the submitter may create the subject matter in the form of coursework or seminars.

[0038] In 104, after the submitter has thoroughly reviewed the article for educational content, they are guided through the submitter workflow of the educational application. Here, key information about the article (e.g., PubMed ID (to facilitate automatic download of the article content), the medical specialty and subspecialty to which the article applies (e.g., as tags or metadata), one or more relevant keywords, a set of multiple-choice questions to be used as part of medical continuing education activities focused on the article, etc.) is obtained from the submitter and stored electronically on an application server or cloud-based device. Here, the key information may be suggested by artificial intelligence and / or machine learning, and the suggested key information may be reviewed and verified by the submitter.

[0039] Finally, in 105, submitted educational content and related information undergo a multi-stage peer-review process, and once the content is finally approved, a reward based on the flea market rate is automatically sent to the submitter.

[0040] Figure 1B shows a flowchart of function 100B for generating educational content according to another exemplary embodiment of the present invention. In this embodiment, the continuing education application is an artificial intelligence and / or machine learning system for generating large quantities of continuing education content for physicians and other professionals.

[0041] First, under paragraph 110, scholarly articles published in peer-reviewed, influential academic journals and available for download from internet-based repositories (e.g., databases) may be retrieved periodically (e.g., daily, weekly, monthly, quarterly, etc.) using one of various commercial web scraping applications (e.g., PubMed Web Scraper, ParseHub, Diffbot, etc.). Under paragraph 120, the retrieved scholarly articles and their corresponding data (e.g., title, abstract, author, keywords, metadata, article text, and figures, etc.) may be stored on one or more web servers and / or cloud-based storage systems. Other scholarly content and learning materials may also be retrieved and stored. While scholarly articles are mentioned here as an example, the embodiments are not limited thereto.

[0042] In this embodiment, instead of utilizing experts from various fields (or industries) in 130, one of various commercial artificial intelligence or machine learning tools (e.g., ChatGPT, ChatDoc, ChatPDF, Unriddle, etc.) is used to process the acquired academic articles using a neural network architecture. In many artificial intelligence or machine learning models, the neural network enables language processing using complex mathematical functions that take numerical data as input. Therefore, before the input text is fed into the neural network, the input text is encoded into numerical data by language processing. Similarly, the numerical output of the neural network is decoded into natural language for the user.

[0043] Next, in 140, the content of the acquired academic articles may be summarized in a coursework or seminar format (e.g., a lecture consisting of a series of slides and / or read aloud by an educational application). In addition to summarizing the content of the academic articles, in 150, artificial intelligence and / or machine learning tools are configured to generate one or more questions (e.g., multiple-choice questions with one or more correct answers) based on the content of the acquired academic articles. The generated questions are used to test the user's understanding of the acquired academic articles. The acquired academic articles and generated questions may be used as part of a continuing education application after being peer-reviewed and approved.

[0044] Finally, in 160, experts may be recruited as contractors to review scholarly articles and generated questions obtained using a web-based interface or a mobile application interface. These experts possess expertise in one or more areas of the medical field, as they are currently engaged in their work (for convenience of explanation, this is limited to medicine, but the embodiments are not limited thereto). Furthermore, the obtained scholarly articles and the corresponding generated questions may be subjected to a multi-stage peer review process.

[0045] Figure 2 shows an example of a user interface 200 according to an exemplary embodiment of the present invention. As shown in Figure 2, the submitter submits an article and a set of problems.

[0046] Figure 3 shows a flowchart of function 300 for continuing education according to an exemplary embodiment of the present invention.

[0047] First, function 300 may optionally initialize the portable electronic device in 301. For example, function 300 may determine or initialize the settings of various components of the portable electronic device in order to run an educational application.

[0048] Next, in 302, one or more professionals who wish to pursue continuing education log in to the educational application, where they are presented with a list of continuing education subjects (or articles). Professionals seeking continuing education are typically currently employed and wish to broaden and / or update their skills in one or more subjects / educational areas.

[0049] Next, in section 303, each expert identifies articles for continuing education. In section 304, experts may be given a pre-quiz before reading the articles in order to assess their knowledge in the subject area.

[0050] Subsequently, in section 305, the expert is presented with an article for continuing education. Finally, in section 306, after the expert has read the article, they are presented with a quiz, and based on the results of the quiz, it may be determined whether the expert is eligible for continuing education credits.

[0051] Most CME programs conduct a course evaluation after completion to assess the usefulness of the course. However, physicians' responses to course evaluations are quite subjective. In this method, the pre-course quiz questions may be a subset of the same questions as the quiz. In this embodiment, by comparing the answers to the pre-course quiz with the answers to the quiz, it is possible to quantitatively evaluate the improvement in the professional's knowledge, abilities, and competence.

[0052] Figure 4 shows an example of a user interface 400 according to an exemplary embodiment of the present invention. As shown in Figure 4, an article is presented to an expert, who then takes a quiz to determine whether it qualifies for continuing education credits.

[0053] Figure 5 shows a typical configuration of a portable electronic device according to an exemplary embodiment of the present invention.

[0054] The portable electronic device 500 may include a touchscreen interface 511, a processing unit 512, a memory 513, and an input / output module 514. The touchscreen interface 511 may include a display (e.g., a touchscreen) that can display data to the user of the portable electronic device 500. The portable electronic device 500 may further include one or more educational modules 515 that generally implement the functions of an educational application. The components and functions of one or more educational modules 515 are described herein.

[0055] Although not shown, the touchscreen may include sensors (e.g., capacitive touch sensors) configured to detect and track motion on and / or near the display. The sensors may be connected to signal processing circuits configured to identify, locate, and / or track the motion of objects based on data obtained from the sensors. The input / output module 514 manages the functionality of the touchscreen interface 511. For example, the input / output module 514 may include the functionality to identify component sections within an educational application. An alternative component section may be selected by touching it.

[0056] The memory 513 may include a non-temporary computer-readable storage medium that stores application modules, which may include instructions related to applications and modules of the portable electronic device 500.

[0057] The portable electronic device may include a processing unit 512, a memory 513, and a communication device (not shown), all of which may be interconnected via a system bus. In various embodiments, the device 500 may have a configuration having a modular hardware and / or software system, the software system including another and / or different systems communicating over one or more networks via one or more communication devices.

[0058] The communication device may enable a connection between the device 500's processing unit 512 and other systems by encoding data transmitted from the processing unit 512 to other systems via the network and decoding data received from other systems via the network for use with the processing unit 512.

[0059] In one embodiment, the memory 513 may include different components for data acquisition, presentation, modification, and storage, and may include a computer-readable medium. The memory 513 may include various memory devices such as dynamic random access memory (DRAM), static RAM (SRAM), flash memory, cache memory, and other memory devices. Furthermore, for example, the memory 513 and the processing unit 512 may be distributed across several separate computers that come together to form a system. The memory 513 can store not only user input and selections, but also customized displays and templates.

[0060] The processing unit 512 may perform calculation and system control functions and may include a suitable central processing unit (CPU). The processing unit 512 may include a single integrated circuit such as a microprocessor, or it may include any number of integrated circuit devices and / or circuit boards that cooperate to achieve the functions of the processing unit. The processing unit 512 may execute a computer program (e.g., an object-oriented computer program) in the memory 513.

[0061] The foregoing descriptions are provided for illustrative and illustrative purposes only. They are not exhaustive and do not limit embodiments of this disclosure to those disclosed. For example, although the processing unit 512 is shown separately from modules 514, 515 and the touchscreen interface 511, the processing unit 512 and one or both of the touchscreen interface 511 and / or modules 514 and 515 may be functionally integrated and perform their respective functions.

[0062] Figure 6 shows an example of a user interface 600 according to an exemplary embodiment of the present invention. As shown in Figure 6, based on the language processing of the learned content (e.g., academic articles), content such as "What you can learn after reading" and "Who should read this research" is generated by artificial intelligence and / or machine learning.

[0063] It will be apparent to those skilled in the art that various modifications and variations may be made to the mass generation of educational application content according to the present invention, without departing from the spirit or scope of the invention. Accordingly, the present invention is intended to encompass modifications and variations that fall within the scope of the appended claims and their equivalents.

Claims

1. A device for generating content for educational applications, wherein the device is Processor and The system includes a non-temporary memory that stores one or more programs executed by the processor, The one or more programs mentioned above are A command to periodically web scrape the learning content repository, A command to save the aforementioned learning content and corresponding information, Using a neural network architecture, instructions are provided to process the learned content in language, An instruction to summarize the learned content using artificial intelligence or machine learning, The command includes, using the artificial intelligence or machine learning described above, to generate one or more questions and answers for the educational application based on the learned content, A device for generating content for educational applications.

2. The aforementioned correspondence information includes one or more of the following: title, abstract, author, keywords, metadata, and article text and figures. An apparatus for generating content for educational applications as described in claim 1.

3. The aforementioned educational application is a continuing education application. An apparatus for generating content for educational applications as described in claim 1.

4. The aforementioned learning content will be summarized in a coursework or seminar format. An apparatus for generating content for educational applications as described in claim 1.

5. The aforementioned summary includes a series of slides and / or a lecture read aloud by the educational application. An apparatus for generating content for educational applications as described in claim 4.

6. The aforementioned one or more problems are of the multi-choice type. An apparatus for generating content for educational applications as described in claim 1.

7. A non-temporary computer-readable storage medium configured to be executed by a processor and storing one or more programs for generating content for educational applications, The one or more programs mentioned above are A command to periodically web scrape the learning content repository, A command to save the aforementioned learning content and corresponding information, Using a neural network architecture, instructions are provided to process the learned content in language, An instruction to summarize the learned content using artificial intelligence or machine learning, The system includes commands that use artificial intelligence or machine learning to generate one or more questions and answers for the educational application based on the learned content, A non-temporary, computer-readable storage medium for generating content for educational applications.

8. The aforementioned correspondence information includes one or more of the following: title, abstract, author, keywords, metadata, and article text and figures. A non-temporary computer-readable storage medium for generating content for educational applications as described in claim 7.

9. The aforementioned educational application is a continuing education application. A non-temporary computer-readable storage medium for generating content for educational applications as described in claim 7.

10. The aforementioned learning content will be summarized in a coursework or seminar format. A non-temporary computer-readable storage medium for generating content for educational applications as described in claim 7.

11. The aforementioned summary includes a series of slides and / or a lecture read aloud by the educational application. A non-temporary computer-readable storage medium for generating content for educational applications as described in claim 10.

12. The aforementioned one or more problems are of the multi-choice type. A non-temporary computer-readable storage medium for generating content for educational applications as described in claim 7.

13. A device for generating content for educational applications, wherein the device is Processor and The system includes a non-temporary memory that stores one or more programs executed by the processor, The one or more programs mentioned above are Commands to retrieve learning content from the repository, A command to save the aforementioned learning content and corresponding information, An order to receive a summary of the learning content from the submitter, The command includes receiving from the submitter one or more questions and answers for the educational application based on the learning content, A device for generating content for educational applications.

14. The aforementioned correspondence information includes one or more of the following: title, abstract, author, keywords, metadata, and article text and figures. An apparatus for generating content for educational applications as described in claim 13.

15. The aforementioned educational application is a continuing education application. An apparatus for generating content for educational applications as described in claim 13.

16. The aforementioned learning content will be summarized in a coursework or seminar format. An apparatus for generating content for educational applications as described in claim 13.

17. The aforementioned summary includes a series of slides and / or a lecture read aloud by the educational application. An apparatus for generating content for educational applications as described in claim 16.

18. The aforementioned one or more problems are of the multi-choice type. An apparatus for generating content for educational applications as described in claim 13.

Images