Computerized systems and methods for generating personalized competency-based education scheme

Abstract

Provided herein are computer implemented methods and systems for generating and providing a personalized competency based educational scheme which includes one or more content units, for a specific user or group of users, based on a personalized user profile and corresponding user specific segmented up-to date knowledge database. Further provided are uses of the systems and methods for updating the personalized competency based educational scheme(s) based, at least in part, on the progression or adherence of the user with the scheme.

Description

[0001] COMPUTERIZED SYSTEMS AND METHODS FOR GENERATING PERSONALIZED COMPETENCY-BASED EDUCATION SCHEME

[0002] FIELD OF THE INVENTION

[0003] The present disclosure relates to computerized systems and methods for generating and presenting personalized competency-based education (CBE) schemes to a user, based on a user specific personalized profile and corresponding user specific segmented up-to date knowledge database. Further provided are uses of the computerized systems and methods for updating the CBE scheme based on the progression or adherence of the user with the scheme.

[0004] BACKGROUND

[0005] Competency-based education (CBE) is an approach to teaching and learning where students progress based on their ability to demonstrate competency in a subject area

[0006] Competency-based medical education (CBME) is an approach to medical learning and / or training that focuses on the outcomes of education, specifically the competencies that students must acquire and demonstrate before progressing to the next stage of their education or entering professional practice. This model contrasts with traditional time-based education, which focuses more on the duration of training rather than the specific skills and knowledge acquired. The CBME emphasizes the assessment and development of students' clinical skills and knowledge to ensure they meet predefined professional standards before advancing.

[0007] Medical education is failing in keeping pace with the rapid advancements in medical research, technology, as well as the learners’ needs. Additionally, the number of medical students and workers is insufficient to meet growing healthcare demands. Accordingly, this leaves current and future physicians without the latest training, compromising their ability to effectively address clinical challenges.

[0008] There is therefore a need for computerized systems and methods for generating and providing personalized CBE schemes to specific users, wherein the generated personalized schemes are based on an up-to date knowledge database, and are customized in accordance with the user specific profile.

[0009] SUMMARY According to some embodiments, there are provided herein computerized systems and methods for generating and providing a personalized competency based educational scheme to a user, wherein the competency based educational scheme is adjusted / customized to a specific user, based inter alia, on a personalized user profile, which is constructed using a computation module, based on a combination of various parameters.

[0010] According to some embodiments, the computerized systems and methods disclosed herein can advantageously be used to create and deliver / pr ovide personalized educational content, ensuring each learner receives / is provided with material that is not only up-to-date and coherent but also tailored to their unique learning profile and previous educational context. In some embodiments, via assessment, adaptation, and validation, learning accessibility, process and / or outcomes are enhanced (for example, shorter time periods are required) and / or optimized, while maintaining the continuity and integrity of the educational experience.

[0011] According to some embodiments, the methods and systems disclosed herein can provide highly personalized learning experience with dynamic, responsive and current content, while enhancing the learner’s professional performance, and ensuring their readiness in handling evolving professional challenges.

[0012] According to some embodiments, utilizing the systems and methods disclosed herein can advantageously allow for the redefinition of personal and systematic goals based on changing environment, conditions and knowhow, while facilitating a flexible education path, and increasing the pace, number and quality and of professional workers (such as, for example, healthcare providers).

[0013] According to some embodiments, advantageously, systems and methods enable updating (optionally in real-time) the personalized competency based educational scheme(s) based, at least in part, on the determined progression or adherence of the user with the scheme. To this aim, for example, one or more feedback regarding the user (obtained directly from the user, or from other sources (such as, the computerized system, an education provider, a supervisor, etc.) may be obtained, and based thereon, if needed, the provided CBE may be updated or adjusted.

[0014] According to some embodiments, the CBE Al-driven platform disclosed herein enables a combination of several modules, including, real-time knowledge synthesis and generation (for example, by continuous monitoring and updating of information on key topics, thereby producing an “always-current textbook” for each area of study); deep learner profiling (for example, by building a comprehensive profile of each learner, compiling personal background, informationprocessing tendencies, learning preferences, environmental conditions, etc.); adaptive feedback mechanism, allowing a dynamic, adaptable feedback module, which tailors the educational pathway for each student; a personalized learning experience (for example, by delivering customized educational content through a variety of media and formats to suit different learning preferences); proactive content development for example, being responsive to emerging trends and challenges, ensuring that learner gain competencies necessary for the latest professional demands); and the like.

[0015] According to some embodiments, the systems and methods disclosed herein make use of one or more computational modules, that can advantageously be operative in conjunction to generate and provide the personalized competency based educational scheme (or at least one or more content units thereof) to a user.

[0016] According to some embodiments, such computational modules may include, for example, but not limited to: a user-profile module, which is configured to generate and update a personalized user profile, based on various parameters and computational models; a knowledge database module, which is configured to generate an “always-current” knowledge database, based on various resources and computational models; a segmentation module, which is configured to segment the generated always-current knowledge, based on various inputs and computational models; a presentation module, which is configured to provide / present the generated content units of the personalized competency based educational scheme to the user, using various presentation platforms and computational models. In some embodiments one or more modules may be combined in a single, unified module. In some embodiments, one or more of the modules make use of machine learning computational models.

[0017] According to some embodiments, the personalized competency based educational scheme disclosed herein may be suitable for various fields and disciplines of interest, where competency based scheme is of use. Such fields may include, for example, healthcare and medical Education (for example, medicine, nursing, health care professionals, etc.); information technology (IT) and computer science (for example, software development, cybersecurity, data science, etc.); engineering and skilled trades (for example, mechanical engineering, electrical engineering, carpentry, plumbing, etc.); education (for example, teacher, special education, principals); business and management (for example, business management, HR, marketing, etc.); creative art and design; Law; finance and accounting; and the like, or any combinations thereof. Each possibility is a separate embodiment. For example, in some embodiments, the personalized competency based educational scheme may be for medical education (i.e., competency based medical educational scheme (CBME)).

[0018] Advantageously, the herein disclosed methods and systems may apply one or more Al models, (such as machine learning (ML), Large language models, Vision Transformers, Audio Transformers, Multimodal Transformers, Vector embeddings, Reinforced learning, Supervised learning, and the like), on various parameters and data to derive features which can subsequently be used to: generate user-specific personalized profile, generate and update an always current knowledge database, generate segmented knowledge database, process and provide content unit(s) to a user, receive various types of feedbacks to thereby optionally adjust / further customize the provided content, and the like, or any combinations thereof, to ultimately generate and provide (and update, if needed) a user specific personalized competency based educational scheme including one or more content units.

[0019] According to some embodiments, there is provided a computer implemented method for generating a personalized competency based educational scheme (CBE) which includes one or more content units, for a specific user, the method includes the steps of: generating a user specific personalized profile which includes personal background, information-processing tendencies, learning preferences, geographical condition(s), geographical location and / or learning environmental conditions; generating an “always-currenf ’ knowledge database; segmenting the knowledge database based on the user specific personalized profile, a competency selected by the user; and / or predefined requirements; generating personalized competency based educational scheme content units, based on the segmented knowledge database; and providing the generated personalized competency based educational scheme to user. According to some embodiments, providing the competency based educational scheme to the user may include providing one or more personalized content unit(s) to the user.

[0020] According to some embodiments, the method may further include receiving feedback regarding engagement and / or progress of the user with the personalized competency based education content unit and / or scheme, and based thereon maintain, adjust or update the content unit and / or scheme.

[0021] According to some embodiments, the knowledge database may be generated based on information obtained from one or more information sources including: literature publications, presentation(s), lecture(s), human expert knowledge, informal relevant communication, info provided during morning rounds, team meetings, social networks, and the like, or any combinations thereof. Each possibility is a separate embodiment. According to some embodiments, each relevant information source may be classified / graded based on its relative impact on the knowledge database.

[0022] According to some embodiments, the always-current knowledge database may be continuously updated in real time.

[0023] According to some embodiments, the personalized user profile may be generated based on input obtained directly from the user and / or based on user derived information obtained directly or indirectly from the user.

[0024] According to some embodiments, segmenting the knowledge database and / or generating the personalized content unit may be updated during progression of the content unit and / or the scheme.

[0025] According to some embodiments, segmenting the knowledge database and / or generating the personalized content unit and / or scheme may be updated based on the feedback.

[0026] According to some embodiments, providing the generated personalized competence based education content unit to the user may include tactile presentation, olfactory presentation, visual presentation and / or audible presentation of the content units. Each possibility is a separate embodiment.

[0027] According to some embodiments, the visual and / or audible presentation may include, for example, but not limited to: text, image, video, audio, narrated presentation, extended reality ( XR) presentation, augmented reality (AR) presentation, mixed reality (MR) presentation, Virtual reality (VR) presentation, simulations, or any combinations thereof. Each possibility is a separate embodiment.

[0028] According to some embodiments, providing the generated personalized competence based education content unit to the user may be facilitated based on the user profile and / or user specific preference(s).

[0029] According to some embodiments, the personal background may include age, gender, expertise, experience, education level, socio-economic status, marital status, family status, place of birth, native language, and the like, or any combinations thereof. Each possibility is a separate embodiment.

[0030] According to some embodiments, information-processing tendencies may include user preference for: type of media for receiving information, information load, cognitive load, duration of learning session, amount or quantity of data provided to the user, and the like, or any combinations thereof. Each possibility is a separate embodiment.

[0031] According to some embodiments, learning preferences may include user preferences for: length of learning session(s), timing of learning (for example, time of day).

[0032] According to some embodiments, learning environmental conditions may include: location of learning (including, for example, but not limited to: library, home, office, outdoor, cafeteria, coffee shop, car, on the move, and the like), quite environment, noisy environment, and the like, or any combination thereof.

[0033] According to some embodiments, the personalized competency based educational scheme may include personalized competency based medical educational scheme (CBME).

[0034] According to some embodiments, wherein the knowledge database is generated based on information obtained from medical publication sources, including: scientific literature, medical literature, medical related information, medical presentations, human experts, informal relevant communication or any combinations thereof.

[0035] According to some embodiments, each relevant publication may be classified / graded based on its relative impact on the knowledge database, wherein the relative impact is determined based on one or more of: citation index, impact factor, level of evidence of publication, H-index of authors, and the like, or any combinations thereof.

[0036] According to some embodiments, the method includes applying one or more machine learning algorithms for executing one or more of the steps.

[0037] According to some embodiments, there is provided a computerized system for generating a personalized competency based education educational scheme including one or more content units for a specific user, the system includes one or more processors configured to execute the method disclosed herein.

[0038] According to some embodiments, there is provided a computerized system for generating a personalized competency based education educational scheme comprising one or more content units for a specific user, the system includes: a first module configured to generate an “always-current” knowledge database; a second module configured to generate a personalized user profile including, personal background, information-processing tendencies, learning preferences, geographical conditions, geographical location and / or learning environmental conditions; and a third module configured to segment the knowledge database based on the user specific personalized profile, a competency selected by the user, and / or predefined requirements, and to generate competency based educational scheme content units, based on the segmented knowledge database and to provide the generated personalized competence based medical education scheme to the user; wherein the first, the second and the third modules are functionally and / or communicatively associated therewith.

[0039] According to some embodiments, the system may further optionally include a fourth module, configured to generate a new competency.

[0040] According to some embodiments, at least one of the modules may be remotely based (for example, server based, cloud based, etc.).

[0041] According to some embodiments, the system may include one or more processing units configured to execute one or more of the modules According to some embodiments, the system may further include a user interface and / or a communication unit.

[0042] According to some embodiments, the user interface may include a display, a keyboard, a microphone, a speaker, a tactile unit, goggles, a wearable unit, a headset, headphone, or any combinations thereof.

[0043] According to some embodiments, providing the competency based educational scheme to the user may include providing one or more personalized content unit(s) to the user.

[0044] According to some embodiments, the system may be further configured to receive feedback regarding engagement and / or progress of the user with the personalized competency based education content unit and / or scheme, and based thereon maintain, adjust and / or update the content unit and / or the scheme.

[0045] According to some embodiments, the feedback may be received directly or indirectly from the user and / or from an external source (such as, a supervisor, lecturer, teacher, education provider, co-workers, the computational system itself, etc.).

[0046] According to some embodiments, the first module may be configured to generate a knowledge database, based on information obtained from one or more information sources including: literature publications, presentation(s), lecture(s), human expert knowledge, formal and / or informal relevant communication, or any combinations thereof.

[0047] According to some embodiments, the first module may be configured to classify, assess and / or grade each relevant information source, based on its relative impact on the knowledge database.

[0048] According to some embodiments, the first module may be configured to continuously update the always-current knowledge, in real time (i.e., as an updated information is available and / or as an updated information is deemed necessary for a content unit or a competency scheme).

[0049] According to some embodiments, the second module may be configured to generate the personalized user profile based on input obtained directly from the user and / or based on user derived information obtained directly or indirectly from the user. According to some embodiments, the first module and / or the second module may be configured to update the segmented knowledge database and / or the personalized content unit during progression of the content unit and / or the scheme.

[0050] According to some embodiments, the third module may be configured to provide the generated personalized competency based education content unit(s) to the user as visual presentation and / or audible presentation of the content units.

[0051] According to some embodiments, the system disclosed herein may be used for generating a personalized competency based medical education scheme including one or more content units.

[0052] According to some embodiments, one or more of the modules of the system may utilize one or more machine learning algorithms.

[0053] Certain embodiments of the present disclosure may include some, all, or none of the above advantages. One or more other technical advantages may be readily apparent to those skilled in the art from the figures, descriptions, and claims included herein. Moreover, while specific advantages have been enumerated above, various embodiments may include all, some, or none of the enumerated advantages.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In case of conflict, the patent specification, including definitions, governs. As used herein, the indefinite articles “a” and “an” mean “at least one” or “one or more” unless the context clearly dictates otherwise.

[0055] BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Some embodiments are herein described, by way of example only, with reference to the accompanying drawings. With specific reference to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced. Atention is now directed to the drawings, where like reference numerals or characters indicate corresponding or like components. In the drawings:

[0057] FIG. 1 schematically shows an outline of a process for generating a personalized competency based educational scheme, according to some embodiments;

[0058] FIG. 2 schematically shows an outline of a process for determining and providing a personalized CBE scheme, according to some embodiments;

[0059] FIG. 3 schematically shows an outline of a computerized method for generating a personalized CBE scheme, according to some embodiments;

[0060] FIG. 4 shows a schematic illustration of a computerized system for generating a personalized CBE scheme, according to some embodiments; and

[0061] FIGs. 5A-5B schematically show parts of a process for generating a personalized user profile, according to some embodiments. Fig. 5A shows an outline of a first part of a process for generating a personalized user profile, by determining values or parameters of various categories; Fig. 5B shows an outline of second part of the process, of determination of the effect / impact of an exemplary category X on the personalized user profile.

[0062] DETAILED DESCRIPTION

[0063] The following detailed description is of the best currently contemplated modes of carrying out the invention. The description is not to be taken in a limiting sense, but is made merely for the purpose of illustrating the general principles of the invention, since the scope of the invention is best defined by the appended claims.

[0064] According to some embodiments, there are provided herein computer-based methods and systems for creating (generating), updating and presenting personalized, customized competency based educational schemes (each scheme (process) includes or made of one or more content units), based on specifically generated (and optionally continuously updated) user profile; specifically generated (and continuously updated) up-to-date (“always current”) relevant knowledge database; specific segmentation of the up-to-date knowledge database based on various parameters and inputs; specific presentation (at various customized and personalized means) of the generated scheme (or any portion thereof); or any combinations thereof. According to some embodiments, the systems and methods disclosed herein are exemplified for a competency based medical educational scheme (CBME), however, as detailed herein, such computerized systems and methods can apply to CBE schemes for various applicable fields and disciplines, where mastery of specific skills and knowledge is needed.

[0065] As used herein, the term "competency based educational scheme”, and “CBE” are interchangeable. The terms relate to a personalized / customized competency based education process including one or more content units, generated specifically for a specific user, regarding a specific selected or requested competence (including specific skills and knowledge) for a specific topic / field. The term “content unit” relates to specific chapters / portions of knowledge, expertise, skills which are related to a specific competency, and collectively make-up the educational path of the competency. Each of the content units may be separated into corresponding subunits.

[0066] According to some embodiments, a competency based educational scheme may be used for various fields and / or topics, including, for example, but not limited to: healthcare and medical education, information technology, engineering, education (for example, teacher, special education, principals); business and management, creative art and design, law, finance; and the like, or any combinations thereof. Each possibility is a separate embodiment.

[0067] According to some embodiments, a user may include, for example, a student (for example, a medical student, a nursing student, etc.), a trainee, a medical resident, an intern, a service provider (such as a healthcare provider), a trained professional, and the like. In some embodiments, the term user refers to any individual or entity receiving or interacting with the personalized competency-based medical education (CBME) scheme generated by the disclosed system and methods. A user may include, for example, a medical student, nursing student, medical resident, intern, healthcare professional, allied health practitioner, or any other learner engaged in acquiring, improving, or mastering one or more medical competencies. In some embodiments, a user may also include a supervisor, educator, or administrator who accesses the system to review, assign, or manage educational content for other learners. A user may interact with the system individually or as part of a group, and may consume the generated educational content through any supported interface or presentation modality as described herein. Each user may be associated with a respective personalized user profile, which is generated and updated by the system based on user-specific data and ongoing performance feedback. In some embodiments, the user may learn or acquire a new competency, master or improve an existing competency, develop a competency, and the like, or any combinations thereof.

[0068] In some embodiments, the competency is selected by the user. In some embodiments, the competency may be selected for the user by a 3rdparty (for example, an education provider, a university, a health care facility). In some embodiments, the competency may be selected by or for the user in accordance with a curriculum.

[0069] Reference is now made to Fig. 1, which schematically shows an outline of a process for generating a personalized competency based educational scheme, according to some embodiments. As generally shown in Fig. 1, process 100 includes several components, that are collectively operated, in order to generate and provide personalized CBE content to a use. At initial phase, several resources (modules) are utilized, in order to form the base for the CBE scheme. As shown in Fig. 1, such resources include an Educational Blueprint (“educational pathway”) 110, which is configured to determine / organize foundational knowledge in alignment with professional competencies for a selected field. In some embodiments, the educational pathway is the scheme that may include multiple competencies as milestones towards a predefined goal. Further resources include a cutting-edge knowledge database (also referred to as “always current knowledge database” or “up to date knowledge database”) 120, which includes the most updated and latest relevant publications and resources (for example, latest scientific and medical discoveries, for the medical field). Further resources include the user’s profile 130, which includes users’ individual preferences, knowledge context, evaluation outcomes, and the like, as further detailed hereinbelow. As detailed herein, each of the resources may be generated using one or more algorithms, includes, ML algorithms, which make use of various resources as input, in order to generate the required content to be used as a resource in the generation of the personalized CBE. Next, input from these resource is used for segmenting the generated knowledge database based on the learner’s (user) profile and the education blueprint, in order to generate extracted fragments 140. For the segmentation, the comprehensive educational material is divided into smaller, manageable “fragments”, which are tailored to align with the learner’s current educational journey, while taking into account, for example, past learning experiences and performance. Each of the fragments include personalized CBE content (in the form of one or more content units), which are the most updated and suitable for the specific user (learner) and the competency topic. Further, each of the generated fragments undergo a coherence check (validation) 142, to ensure its integrity, comprehensibility and clarity of concepts thereof. In addition, essential knowledge points 144 within each fragment may be mapped and documented, thereby ensuring alignment with the users’ personalized content. The knowledge points are considered the distilled values of the extracted segment, as they define the ground truth and serve as the basis for the adapted fragment generation. In some embodiments, after the adaptation process, the system is configured to refer back to the knowledge points in the process of assessing the integrity in the generated fragment. In some embodiments, knowledge points are used to evaluate the student interaction to generate an intake score that will be used to update the student profile. Additionally, in some embodiments, interactive tools for engagement with the user (for example, quizzes, activities or simulations) may be utilized, to assess the understating and retention of core concepts). According to some embodiments, the fragments may be adapted, to generate adapted fragments 146, to better suit the learner’s profile, by modifying presentation styles, formats, and structures to enhance engagement and comprehension. It is noted that the adaptation process rigorously maintains the integrity of fundamental knowledge points. For example, in cases where the student knowledge’s assessment reflects lack of understanding of core concepts, the relevant knowledge points may be incorporated in the following fragments with a different adaptation strategy. In some embodiments, post adaptation quality control may be executed, whereby adapted content may be evaluated for its fidelity to education objectives. In some embodiments, the fidelity may be provided in the form of a score (knowledge fidelity score), which may be a quantitative measurement of the adherence to the core knowledge. In some embodiments, the fidelity score may be above a threshold value, in order to ensure quality of content. The customized content may then be provided (delivered) to the user for engagement 148. Interactive tools may be utilized to validate the effectiveness of the learning fragment, by performing intake assessment 150. Based on the assessment and by measuring the users’ comprehension, an intake score / evaluation 152 may be calculated / determined, in order to provide a qualitative and / or quantitative measure of the personalized adapted fragment / content unit. In some embodiments, the determined intake score, may further be used for enhancing, costuming and / or updating the learning profile of the user, for enhancing the customization and personalization of the generated CBE.

[0070] Reference is now made to Fig. 2, which shows an outline of a process for determining and providing a personalized CBE scheme, according to some embodiments. It is noted that steps of the method may be executed at varying orders. As shown in Fig. 2, process 200 includes at step 210 selecting or determining the field or subject of the requested education scheme. For example, such selection may include a specific topic in a designated scientific field, as further detailed below. Next, At step 220, a personalized, multilayer user profile is generated. As detailed herein below, the user profile is generated based on a plurality of categories, layers, variables and parameters, wherein each of these may be attributed a different weight in the determination of the user profile. The unique user profile is ultimately used as one of the key parameters in the generation of the personalized / customized CBE. It is noted that in some instances, step 220 may be performed in conjunction with or prior to step 210. At step 230, dynamic, up to-date relevant content (database) is generated, as detailed herein, based on the most current knowledge, while accessing a variety of relevant sources. At step 240, required competencies are defined, such that the key points / topic / knowledge / experience of the competency are determined and are confirmed to be part of the provided CBE. It is noted that in some instances, step 240 may precede step 210. At step 250, based on the user profile (generated in step 220), based on the dynamic content (generated at step 230), based on required competencies (defined in step 240), a personalized (individualized) education plan (scheme / process) is generated. The education scheme includes one or more content units, which are customized to the specific user and the specific competency. At step 260, the generated individualized scheme (in particular, content units thereof) is provided to a user, via a tutoring system, that may include any type of presentation means, which may also be adjusted to the specific user. Such presentation means may include any accessible presentation type, including audible means (for example, recordings, podcasts); visual means (for example, slideshow, video, images, VR, AR); tactile means (for example, simulator); and the like, or any combinations thereof. Each possibility is a separate embodiment. At step 270, after provided to the user, the competency and / or adherence to and / or performance (knowledge and / or actual activity) of the user may be assessed. The assessment may be used to enhance the CBE (for example, by adjusting the content units) and / or to enhance the competency experience of the user. In some embodiments, assessment may be in the form of a calculated score, that may be indicative of the quality of the CBE, adherence of the user to the CBE, performance of the user with respect of the acquired competency, and the like, or any combinations thereof.

[0071] Reference is now made to Fig. 3, which shows an outline of a computerized method for generating a personalized CBE scheme, according to some embodiments. As shown in Fig. 3, computerized method 300 includes, at step 310 the generation of a user specific personalized profile. As detailed below herein, the user specific generated profile is generated based on a combination of a plurality of categories, layers, and / or variables, wherein each of these parameters may be attributed a different weight, in order to generate the most relevant and customized user profile. The generated personalized user profile is an important element in the generation of the CBE, as each user may be provided with a different personalized educational scheme, that may be provided in a different manner, adjusted to the specific user profile. At step 320, an always-current knowledge database is generated. The generated knowledge database may be compiled based on information retrieved from a verity of sources, including, for example, but not limited to: scientific publications, textbooks, presentations, lectures, relevant communications, and the like. In some embodiments, the knowledge database is generated from scratch for each selected competency. In some embodiments, the knowledge database is continuously updated, so as to ensure that the most recent and up-to date relevant information is included within the generated database. In some embodiments, the knowledge database may be generated using various algorithms, including, for example, datamining, ML and / or Al models. At step 330, the knowledge database generated in step 320 is segmented into fragments, in accordance with and based on the personalized user profile (generated in step 310), a selected competency and / or based on predefined requirements (for example, requirements related to a specific competency). The segmentation may be performed using various computational models, including ML and Al algorithms. In some embodiments, the segmented material may be validated and may be attributed with a validation score, indicative of the quality thereof. At step 340, based on the segmented knowledge database, the personalized CBE content unit(s), which make up the educational scheme are generated. In some embodiments, the segmentation may be performed continuously and may be updated during the progression of the user with the education scheme. In some embodiments, the segmentation may be updated in accordance with the adherence, engagement and / or progression of the user with the educational scheme. In some embodiments, feedback from the user or from other sources may be utilized for updating or adjusting the segmentation and accordingly, the content unit(s) and / or the scheme. At step 350, the generated content unit(s) are provided / presented to the user. Providing the content unit(s) may be facilitated in various means and formats, which are adjusted and customized to the user. Such means may include, for example, audible means, visual means and / or tactile means. In some embodiments, presentation formats may include, for example, but not limited to: text, image, video, audio, narrated presentation, virtual reality (VR) video, augmented reality (AR) video, simulation, and the like, or any combinations thereof. It is noted that at least some of the steps of method may be performed continuously or repeatedly, at any desired order. In some embodiments, at least some of the steps of the method may be updated in real-time. In some embodiments, steps of the method may be performed by one or more modules, that may be communicatively and / or physically associated therebetween.

[0072] According to some embodiments, in order to create and / or update a competency or a scheme, CBE, one or more modules may be utilized.

[0073] Reference is now made to Fig. 4, which shows a schematic illustration of a computerized system for generating a personalized CBE scheme, according to some embodiments. As shown in Fig. 4, computerized system 400 includes one or more processors, configured to execute a method for generating a personalized CBE scheme. As shown in Fig. 4, system 400 includes three modules, each module configured to generate a different aspect of the method, to collectively and coordinately generate a personalized CBE. As shown in Fig. 4, a first module 410, is configured to generate an always current knowledge database. As detailed herein, the knowledge database is generated based on current and updated resources, that may include any type of retrievable / accessible publication / communi cation. The generation of the database may be automatic, using a variety of algorithms and models. In some embodiments, the database may be generated once and continuously updated. In some embodiments, the database is generated a-new for every competency, or for every requested CBE. In some embodiments, the database is updated in real-time. In some embodiments, the database is compiled and stored in a server, which continuously being updated at designated intervals, or in real-time, as additional relevant publications are available. In some embodiments, the generation of the database includes classifying, ranking or otherwise scoring the sources / publications, based on their relative impact. In some embodiments, the database may be generated using a large language model (LLM), or other suitable algorithms. As shown in Fig. 4, system 400 further includes a personalized user profile module 420. As detailed herein, the personalized user profile is a multi-layer profile, which includes a variety of categories and variables and is determined based on various inputs, including, direct or in-direct input from the user. The user profile module makes use of various parameters, in order to determine a highly personalized user profile, which takes into account a plurality of personal and other user related information, as detailed below. Additionally, system 400 further includes a segmentation, generation and presentation module 430. Module 430 is configured to segment the knowledge database, based on the selected / requested competency and the personalized user profile, generate the content units of the CBE scheme, and provide the content unit(s) to the user, in a suitable, customized manner, as detailed herein. In some embodiments, modules 410-430 of system 400 are communicatively, functionally and / or physically associated. In some embodiments, the modules are comprised in a single module. In some embodiments, each of the modules is executed by a different processing unit. In some embodiments, the modules are collectively executed by a same processor. In some embodiments, at least one of the modules may be executed in a remote location (for example, a remote server, that may be cloud based).

[0074] According to some embodiments, system 400 may further include a user interface (UI) 440, allowing a user to interact with the system. In some embodiments, a UI may include a keyboard, a touch screen, a mouse, a microphone, a speaker, and the like, or any combinations thereof. In some embodiments, the competency CBE may, at least partially be presented to the user via the UI of the system. In some embodiments, the system may further optionally include a communication unit, configured to communicate with one or more servers, one or more systems and / or one or more end units (for example, a computer, a monitor, a mobile device of the user, etc.).

[0075] In some embodiments, system 400 may further include a memory 450, configured to store data or information related to the modules and / or the generated CBE. In some embodiments, at least some of the data utilized or generated by the system is stored on a remote location, such as, a server, a remote server, a cloud-based server, and the like. Each possibility is a separate embodiment.

[0076] According to some embodiments, the system (such as system 400 of Fig. 4) may include one or more additional modules. In some embodiments, the system may include a fourth module, which is the competency-generator module that, based on the “always-current” knowledge database, user-profile module and / or content-generator module, is configured to identify a need or demand for a new competency, articulate the need, define the competency framework and goals, and when authorized, generate the new competency. It some embodiments, the module may adapt an existing competency to the new situation by restructuring it or updating its content. For example, in some embodiments, two or more existing competencies may be meshed and remixed into a new competency. In some embodiments, new competency may be incubated from scratch. In some embodiments, the generated competency can then be evaluated and tested, and if approved, added into the competencies pool.

[0077] According to some embodiments, the personalized user profile is generated based on one or more parameters of various categories, that may be acquired from the user, for example, based on a questionnaire. In some embodiments, the categories may include, for example, but not limited to: personal information, psychological profile, learning preferences, learning goals, physical environment of studies, daily schedule, academic profile, personal goals, cultural competency, technological proficiency, individual needs, life interest, hobbies, and the like, or any combination thereof.

[0078] In some embodiments, the systems and methods disclosed herein are further configured to support collaborative or multi-user learning environments. In such embodiments, the system may generate and maintain a plurality of personalized learner profiles, each corresponding to a respective user within a defined group (for example, students enrolled in the same medical training module, residents within the same hospital department, or members of an interdisciplinary care team). The system may segment the always-current medical knowledge database both on an individual basis and at a group level, and may further generate group-adapted content units that incorporate shared competencies, collective performance metrics, and inter-user interaction data. In some embodiments, the system dynamically adapts the group content units based on aggregated performance feedback (for example, average intake scores, group activity participation metrics, or collaborative task outcomes) while also maintaining personalization by adapting each individual user’s content path according to their individual profile and performance. This configuration advantageously enables synchronous or asynchronous collaborative learning while preserving individualized competency progression for each user.

[0079] In some embodiments, each of the categories may have a different weight, value or score. In some embodiments, the weight of the category is adjustable in accordance with a specific user, or category of users. In some embodiments, the weight of a category is constant. In some embodiments, the weight of a category is variable over time.

[0080] According to some embodiments, each category may include one or more variables, wherein each of the variables may have a different weight with respect of impact thereof on the category overall score, value and / or weight. In some embodiments, the profiling module is configured to determine a score / weight / value for each selected category, based on the personalized input regarding the category and respective variables thereof.

[0081] Reference is now made to Figs. 5A-5B, which schematically show parts of a process for generating a personalized user profile, according to some embodiments. Fig. 5A shows an outline of a first part of a process for generating a personalized user profile, by determining values or parameters of various categories. As shown in Fig. 5A, for generation of a personalized user profile, an interactive personal information acquisition process 510 is executed. The process includes obtaining information directly or indirectly from the user, and processing said information. To this aim, the information may be divided / categorized into a plurality of categories (also referred to as layers) 512. The calculations may take into account information from one or more viable categories, while facilitating the use of a minimal number of viable categories (514), while maximizing security and transparency of the acquisition. For example, as shown in Fig. 5A, exemplary categories / layers may include such categories as, but not limited to: layer 1- personal information (personal background); layer 2- psychological profile of the user, layer 3- learning preferences of the user, layer 4- learning goals of the user, layer 5- physical environment of the user (living environment and / or study environment, geographical condition(s) and / or geographical location), layer 6- daily schedule of the user, layer 7- academic profile of the user, layer 8- personal goals of the user, layer 9- cultural competency required / requested by / from the user, layer 10- technological proficiency of the user (information-processing tendencies), layer 11- individual needs of the user, layer 12- life interest / hobbies of the user. It is noted that any additional categories may be used. According to some embodiments, for the calculation or determination of a personalized user profile, any number of viable categories may be used, provided that at least a minimal number of categories are included to enable accurate profiling. The election of the viable categories to be used may be affected, for example, based on their impact on the personalized profile, as detailed herein. In some embodiments, one or more of the following categories may be used: personal background, information-processing tendencies, learning preferences, geographical condition(s), geographical location, learning environmental conditions, or any combinations thereof.

[0082] Reference is now made to Fig. 5B, which shows an outline of determining the effect / impact of an exemplary category X on the personalized user profile. As shown in Fig. 5B, for each category X, a category weight is determined. Each category 520 may include a plurality (N) of variables (VI.. N) 530, which are used for the category weight determination 540. The step may include numerical quantification 542, use of binary indicators 544 (for example, “Yes - No”, ’’Agree - Disagree”), and / or categorical values 546. For example, an assigned value for a category (e.g., “physical environment”) may be based on its weight (relative to the user and the other categories), number of categories used in this instance, and the like. The obtained data of step 540 for some or for each category is then processed at step 550, including, for example, normalization of the data, statistical analysis of the data, and classification (for example, using ML tools), to generate a compiled score 560, indicative of the impact of the categories. At the final step of the applied decision making 570, in which the calculations are condensed into a unified value or (evaluation) of the profiling process that can be used or applied to determine the learning roadmap, type and order of competencies, etc. exemplary types of such profile scores are presented. The personalized user profile may be determined, for example, based on a threshold value 572 (for example, if the user’s final score crosses a certain threshold, then Path A may be selected vs. Path B. for example, if it is determined that most of the users learning should be via audio vs text, or a recommendation to learn in a group vs. individually, etc.), category parameters 574 (for example, for a certain user a particular category is extremely dominant (e.g., “tech proficiency” or certain learning disabilities )- that will make a significant impact on which learning path will be selected, and / or situation based 576 (for example, for a certain user a particular situation / context is extremely dominant (for example, change of location, illness, a child is born, personal crisis, severe weather, etc.)). Thus, using a combination of variables and the respective weight of each variable, the weight of a category may be calculated For example, for a single, unmarried 20 years old student, the weight of related categories (such as, for example, but not limited to: personal information and / or individual needs) may be different than the corresponding categories for a 30 years old married + 2 student.

[0083] According to some embodiments, the methods and system disclosed herein may be utilized to identify new competencies and / or to adjust current competencies.

[0084] According to some embodiments, the CBE generated by the systems and methods disclosed herein are continuously updated, based on various inputs or triggers, including, for example, but not limited to: direct or indirect feedback from the user, direct or indirect feedback from a supervisor, updates of the sources used for the generation of knowledge database, changes or modifications requested or determined for specific competencies, and the like, or any combinations thereof.

[0085] According to some embodiments, the systems and methods disclosed herein utilize artificial intelligence tools to provide a fully personalized learning experience. In some embodiments, advanced machine learning algorithms, including, for example, natural language processing (NLP), Large language models, and the like are used, to adapt content in real-time based on individual student performance and evolving professional knowledge.

[0086] According to some embodiments, the systems and methods disclosed herein may include one or more of: i.) scalable data infrastructure, facilitating managing user data, learning progress records, and extensive medical datasets; ii.) comprehensive content management system: hub for content storage, retrieval, and management, facilitating curriculum development and updates, and managing a vast repository of educational materials; iii.) Advanced Al learning engine, integrating fine-tuned Al Large Language Models (LLM), utilizing deep and reinforcement learning to dynamically adjust learning paths and to optionally provides predictive analytics; iv.) Interactive user interface, including an intuitive, accessible user interface that can support complex interactions and data visualizations, while integrating user feedback, optionally in cross-platforms or devices (such as, PCs, mobile devices, tablets, etc.); v.) analytics and reporting dashboard, allowing generating and presenting customizable reports for educators, administrators, and stakeholders.

[0087] According to some embodiments, a competency may include, for example, but not limited to: Professional Competencies, including, Commitment to learning and growth, cultural awareness, cultural humility, empathy and compassion, ethical responsibility to self and others, resilience and adaptability; thinking and reasoning competencies, including, critical thinking, scientific enquiry, quantitative reasoning; science competencies, including human behavior, living systems, etc.

[0088] According to some embodiments, a competency may be a medical related competency.

[0089] According to some exemplary embodiments, a medical competency may include evaluating the cause of seizures in the Pediatric Intensive Care Unit (PICU). A CBE scheme generated by the systems and methods disclosed herein includes a structure for a given content, based on the user profile and intended goal, wherein the content is presented to the user in accordance with a preferred media, to enable user optimized learning. Such a setting involves a comprehensive approach due to the complex and varied potential triggers. The competencies include: 1. Clinical Assessment and History: Initial evaluation starts with a detailed clinical history to identify any known neurological conditions, family history of seizures, or recent events such as trauma or illness that could precipitate seizures; 2. Laboratory Tests: Blood tests to check for electrolyte imbalances (such as abnormalities in sodium, calcium, or glucose levels), liver and kidney function tests, and toxicology screening are crucial. These tests help in identifying metabolic causes and potential toxic causes of seizures; 3. Neuroimaging: Techniques like MRI or CT scans are employed to look for structural causes such as hemorrhages, tumors, or congenital brain malformations; 4. Lumbar Puncture: This is done to test cerebrospinal fluid (CSF) for infections like; meningitis or encephalitis, which can cause seizures; 5. Electroencephalogram (EEG): Continuous EEG monitoring is critical to detect and characterize seizure activity, particularly to identify non-convulsive seizures which are common in critically ill patients and may not be clinically apparent; 6. Genetic Testing: Considered when a genetic syndrome or metabolic disorder is suspected based on the patient’s history and initial evaluations. The evaluation is often multidisciplinary, involving neurologists, intensivists, and other specialists to comprehensively address the potential causes and tailor appropriate treatments for seizures in the PICU setting.

[0090] According to some embodiments, the computerized models disclosed herein may utilize Machine learning (ML) and Artificial intelligence (Al) tools, including any type of suitable algorithms, such as, for example, but not limited to: transformers, artificial neural network(s) (ANN), such as convolutional neural network (CNN), recurrent neural network (RNN), long-short term memory (LSTM), auto-encoder (AE), generative adversarial network (GAN), Reinforcement-Learning (RL), support vector machine (SVM), decision tree (DT), random forest (RF), and the like. Both “supervised” and “unsupervised” methods may be implemented. As further detailed herein, the ML / Al algorithms may utilize various types of data, information, parameters, values and / or any information derived therefrom, for the creation, updating and presenting of CBE schemes.

[0091] According to some embodiments, there is provided a computer-implemented method for generating a personalized competency-based medical education (CBME) scheme for a medical learner, the method includes: obtaining user data describing the learner’s personal background, psychological profile, learning preferences, and environmental conditions; generating a multilayer learner profile by processing the user data with a machine learning model configured to assign weighted values to a plurality of profile categories; generating an always-current medical knowledge database by retrieving and classifying medical information from scientific publications, medical literature, presentations, lectures, and expert communications, wherein the classification is based on relative impact metrics comprising at least one of citation index, impact factor, or H-index; segmenting the knowledge database into content fragments based on the learner profile and a selected target medical competency; assembling a set of personalized content units from the segmented fragments; delivering the content units to the learner in a presentation format selected according to the learner profile; and receiving performance feedback from the learner’s interactions with the content units and dynamically updating the content units based on the performance feedback.

[0092] According to some embodiments, the machine learning model may include a neural network trained to predict content preferences from the user data.

[0093] According to some embodiments, the performance feedback may include an intake score derived from assessment quizzes, simulation outcomes, or recorded user interactions.

[0094] According to some embodiments, the segmenting may include mapping essential knowledge points within each content fragment and validating coherence of the fragment based on said knowledge points.

[0095] According to some embodiments, the content units may be delivered as one or more of narrated presentations, virtual reality simulations, or augmented reality visualizations.

[0096] According to some embodiments, the method may further include updating the learner profile based on the performance feedback and re-segmenting the knowledge database according to the updated learner profile.

[0097] According to some embodiments, there is provided a computerized system for generating a personalized competency-based medical education (CBME) scheme for a medical learner, the system includes: a profile generation module configured to process learner data to produce a multilayer learner profile, wherein the module applies a machine learning model to assign weighted values to a plurality of profile categories; a knowledge database module configured to generate and maintain an always-current medical knowledge database by retrieving and classifying medical information from scientific publications, medical literature, presentations, lectures, and expert communications; a segmentation module configured to segment the knowledge database into content fragments based on the learner profile and a selected target medical competency; a content generation module configured to assemble personalized content units from the segmented fragments; a presentation module configured to deliver the content units to the learner in a format selected according to the learner profile; and a feedback module configured to receive performance feedback from the learner’s interaction with the content units and update the content units based on the performance feedback.

[0098] According to some embodiments, the knowledge database module may assign impact scores to each information source based on at least one of citation index, impact factor, or H-index of its authors. According to some embodiments, the feedback module may update the learner profile and causes the segmentation module to re-segment the knowledge database based on the updated learner profile.

[0099] According to some embodiments, the presentation module may deliver the content units as one or more of narrated presentations, virtual reality simulations, or augmented reality visualizations.

[0100] According to some embodiments, the modules are communicatively coupled and executed by one or more processors. In some embodiments one or more of the processors are cloud-based. According to some embodiments, the segmentation module may validate each content fragment by mapping essential knowledge points and assigning a coherence score to the fragment.

[0101] According to some embodiments, there is provided a non-transitory memory device, wherein modules of instruction code are stored, and at least one processor associated with the memory device, and configured to execute the modules of instruction code, whereupon execution of said modules of instruction code, the at least one processor is configured to execute one or more of the methods disclosed herein, including, generation of personalized CBE content units and CBE scheme, generation and / or updating a personalized user profile, generation and / or updating a knowledge database, generation of presentation, and the like, or any combinations thereof.

[0102] According to some embodiments, there is provided a system for generating personalized CBE content units and CBE scheme, which includes one or more processors and, optionally, RAM and / or non-volatile memory components associated with the one or more processors, the processors are configured to execute the method of generating personalized CBE content units and CBE scheme.

[0103] According to an aspect of some embodiments, there is provided a non-transitory computer- readable storage medium. The storage medium stores instructions that cause one or more processors to implement one more of the Al engine specified methods for generating personalized CBE content units and CBE scheme.

[0104] According to some embodiments, there is provided a computer-readable storage medium having stored therein machine learning software, executable by one or more processors for generating personalized CBE content units and CBE scheme, as disclosed herein.

[0105] According to some embodiments, there is provided a computer-readable storage medium having stored therein machine learning software, executable by one or more processors for generating personalized CBE content units and CBE scheme, as disclosed herein.

[0106] In the description and claims of the application, the words “include” and “have”, and forms thereof, are not limited to members in a list with which the words may be associated.

[0107] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. In case of conflict, the patent specification, including definitions, governs. As used herein, the indefinite articles “a” and “an” mean “at least one” or “one or more” unless the context clearly dictates otherwise.

[0108] It is appreciated that certain features of the disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination or as suitable in any other described embodiment of the disclosure. No feature described in the context of an embodiment is to be considered an essential feature of that embodiment, unless explicitly specified as such.

[0109] Although stages of methods according to some embodiments may be described in a specific sequence, methods of the disclosure may include some or all of the described stages carried out in a different order. A method of the disclosure may include a few of the stages described or all of the stages described. No particular stage in a disclosed method is to be considered an essential stage of that method, unless explicitly specified as such.

[0110] Although the disclosure is described in conjunction with specific embodiments thereof, it is evident that numerous alternatives, modifications and variations that are apparent to those skilled in the art may exist. Accordingly, the disclosure embraces all such alternatives, modifications and variations that fall within the scope of the appended claims. It is to be understood that the disclosure is not necessarily limited in its application to the details of construction and the arrangement of the components and / or methods set forth herein. Other embodiments may be practiced, and an embodiment may be carried out in various ways.

[0111] The present invention may be a system, a method, and / or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

[0112] A computer program (also referred to as a program, software, software application, script or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a standalone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A computer program can be stored in a portion of a file that holds other programs or data, in a single file dedicated to the program in question, or in multiple coordinated files (for example, files that store one or more modules, sub programs or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

[0113] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0114] Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, for example, JavaScript, Smalltalk, C, C++, TypeScript, Python and R.

[0115] The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server (such as, a cloud based). In the latter scenario, the remote computer (or cloud) may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) including wired or wireless connection (such as, for example, Wi-Fi, BT, mobile, and the like). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. Moreover, a computer can be embedded in another device, for example, a mobile phone, a tablet, a personal digital assistant (PDA, or a portable storage device (for example, a USB flash drive). Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including semiconductor memory devices, for example, EPROM, EEPROM, random access memories (RAMs), including SRAM, DRAM, embedded DRAM (eDRAM) and Hybrid Memory Cube (HMC), and flash memory devices; magnetic discs, for example, internal hard discs or removable discs; magneto optical discs; read-only memories (ROMs), including CD-ROM and DVD-ROM discs; solid state drives (SSDs); and cloud-based storage. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions.

[0116] These computer readable program instructions may be provided to a processor of a general- purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function / act specified in the flowchart and / or block diagram block or blocks.

[0117] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0118] The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

[0119] The processes and logic flows described herein may be performed in whole or in part in a cloud computing environment. For example, some or all of a given disclosed process may be executed by a secure cloud-based system comprised of co-located and / or geographically distributed server systems. The term “cloud computing” is generally used to describe a computing model which enables on-demand access to a shared pool of computing resources, such as computer networks, servers, software applications, and services, and which allows for rapid provisioning and release of resources with minimal management effort or service provider interaction.

[0120] The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

[0121] While certain embodiments of the invention have been illustrated and described, it will be clear that the invention is not limited to the embodiments described herein. Numerous modifications, changes, variations, substitutions and equivalents will be apparent to those skilled in the art without departing from the spirit and scope of the present invention as described by the claims which follow.

Claims

CLAIMSWhat is claimed is:

1. A computer implemented method for generating a personalized competency based educational scheme comprising one or more content units, for a user, or group of users, the method comprising: generating a user personalized profile comprising personal background, informationprocessing tendencies, learning preferences, geographical condition(s), geographical location and / or learning environmental conditions; generating an “always-current” knowledge database; segmenting the knowledge database based on the user personalized profile, a competency selected by the user; and / or predefined requirements; generating personalized competency based educational scheme content units, based on the segmented knowledge database; and providing the generated personalized competency based educational scheme to the user.

2. The method according to claim 1, wherein providing the competency based educational scheme to the user comprises providing one or more personalized content unit(s) to the user.

3. The method according to any one of claims 1-2, further comprising receiving feedback regarding engagement and / or progress of the user with the personalized competency based education content unit and / or scheme, and based thereon maintain, adjust or update the content unit and / or scheme.

4. The method according to any one of claims 1-3, wherein the knowledge database is generated based on information obtained from one or more information sources comprising: literature publications, presentation(s), lecture(s), human expert knowledge, relevant communication, or any combinations thereof.

5. The method according to claim 4, wherein each relevant information source is classified / graded based on its relative impact on the knowledge database.

6. The method according to any one of claims 1-5, wherein the always-current knowledge database is continuously updated in real time.

7. The method according to any one of claims 1 -6, wherein the personalized user profile is generated based on input obtained directly from the user and / or based on user derived information obtained directly or indirectly from the user.

8. The method according to any one of claims 1-7, wherein segmenting the knowledge database and / or generating the personalized content unit is updated during progression of the content unit and / or the scheme.

9. The method according to any one of claims 3-8, wherein segmenting the knowledge database and / or generating the personalized content unit and / or scheme is updated based on the feedback.

10. The method according to any one of claims 1-9, wherein providing the generated personalized competence based education content unit to the user comprises tactile presentation, olfactory presentation, visual presentation, and / or audible presentation of the content units.

11. The method according to claim 10, wherein the visual and / or audible presentation comprise: text, image, video, audio, narrated presentation, extended reality ( XR) presentation, augmented reality (AR) presentation, mixed reality (MR) presentation, Virtual reality (VR) presentation, simulations, or any combinations thereof.

12. The method according to any one of claims 1-11, wherein providing the generated personalized competence based education content unit to the user is facilitated based on the user profile and / or user specific preference(s).

13. The method according to any one of claims 1-12, wherein personal background comprises age, gender, expertise, experience, education level, socio-economic status, marital status, family status, place of birth, native language, or any combinations thereof.

14. The method according to any one of claims 1-13, wherein information-processing tendencies comprises user preference for: type of media for receiving information, information load, cognitive load, duration of learning session, amount / quantity of data provided to the user, or any combinations thereof.

15. The method according to any one of claims 1-14, wherein learning preferences comprises user preferences for: length of learning session(s), timing of learning.

16. The method according to any one of claims 1-15, wherein learning environmental conditions comprises: location of learning, quite environment, noisy environment, or any combination thereof.

17. The method according to any one of claims 1-16, wherein the personalized competency based educational scheme comprises personalized competency based medical educational scheme.

18. The method according to claim 17, wherein the knowledge database is generated based on information obtained from medical publication sources, comprising: scientific literature, medical literature, medical related information, medical presentations, human experts, informal relevant communication or any combinations thereof.

19. The method according to claim 18, wherein each relevant publication is classified / graded based on its relative impact on the knowledge database, wherein the relative impact is determined based on one or more of: citation index, H-index and / or impact factor.

20. The method according to any one of claims 1-19, comprising applying one or more machine learning algorithms for executing one or more of the steps.

21. A computerized system for generating a personalized competency based educational scheme comprising one or more content units for a specific user, the system comprising one or more processors configured to execute the method of any one of claims 1-20.

22. A computerized system for generating a personalized competency based educational scheme comprising one or more content units for a specific user or group of users, the system comprising: a first module configured to generate an “always-currenf ’ knowledge database; a second module configured to generate a personalized user profile comprising personal background, information-processing tendencies, learning preferences, geographical conditions, geographical location and / or learning environmental conditions; and a third module configured to segment the knowledge database based on the user personalized profile, a competency selected by the user, and / or predefined requirements,and to generate competency based educational scheme content units, based on the segmented knowledge database and to provide the generated personalized competence based medical education scheme to the user; wherein the first, the second and the third modules are functionally and / or communicatively associated therewith.

23. The computerized system according to claim 22, wherein at least one of the modules is remotely based.

24. The computerized system according to any one of claims 22-23, comprising one or more processing units configured to execute one or more of the modules25. The computerized system according to any one of claims 22-24, further comprising a user interface and / or a communication unit.

26. The computerized system according to claim 25, wherein the user interface comprises a display, a keyboard, a microphone, a speaker, a tactile unit, a wearable unit, a headset, goggles, headphones, or any combinations thereof.

27. The system according to any one of claims 22-26, wherein providing the competency based educational scheme to the user comprises providing one or more personalized content unit(s) to the user.

28. The system according to any one of claims 22-27, further configured to receive feedback regarding engagement and / or progress of the user with the personalized competency based education content unit and / or scheme, and based thereon maintain, adjust and / or update the content unit and / or the scheme.

29. The system according to claim 28, wherein the feedback is received from the user and / or from an external source.

30. The system according to any one of claims 22-29, wherein the first module is configured to generate knowledge data base, based on information obtained from one or more information sources comprising: literature publications, presentation(s), lecture(s), human expert knowledge, relevant communication, or any combinations thereof.

31. The system according to claim 30, wherein the first module is configured to classify or grade each relevant information source, based on its relative impact on the knowledge database.

32. The system according to any one of claims 22-31, wherein first module is configured to continuously update the always-current knowledge, in real time.

33. The system according to any one of claims 22-32, wherein the second module is configured to generate the personalized user profile based on input obtained directly from the user and / or based on user derived information obtained directly or indirectly from the user.

34. The system according to any one of claims 22-33, wherein first module and / or the second module are configured to update the segmented knowledge database and / or the personalized content unit during progression of the content unit and / or the scheme.

35. The system according to any one of claims 22-34, wherein the third module is configured to provide the generated personalized competence based education content unit(s) to the user as one or more types of presentation, comprising olfactory, tactile, and / or audible presentation.

36. The system according to any one of claims 22-35, for generating a personalized competency based medical educational scheme comprising one or more content units.

37. The system according to any one of claims 22-36, wherein one or more of the modules utilize one or more machine learning algorithms.

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