Systems and methods for ai-assisted employability analysis

Abstract

A system and method of analyzing employability of an entity is provided. The method comprises receiving information associated with the entity; receiving a target employment position associated with the entity; and performing machine learning algorithms to conduct an employability assessment for the entity based on the information associated with the entity and the target employment position associated with the entity. The method provides a platform to comprehensively assess entities’ strengths and / or deficits, which may be helpful for strategically planning their career paths. Because the assessment is conducted based on the information associated with the entity and the target employment position, the analysis of the employability of the entity is performed with greater accuracy. What is more, a personalized action plan is customized for each entity based on the assessment to achieve job-readiness.

Description

SYSTEMS AND METHODS FOR AI-ASSISTED EMPLOYABILITY ANALYSIS TECHNICAL FIELD

[0001] Example embodiments relate generally to employability facilitation systems. BACKGROUND

[0002] Nowadays, in the job market, job hunters face multiple employability challenges, such as underemployment, not securing jobs in chosen fields. Especially, for a job hunter moving to a country with hopes and dreams of applying his skills within a local professional engineering landscape, they struggle to find a job that aligns with their field of study when transitioning and assimilating into foreign countries.

[0003] There are many reasons for the challenges. Firstly, although job hunters may have varieties of skills, knowledge, and experience gaps, they might be unaware of discrepancies ofthe skill gaps and do not know how to relate the discrepancies to industry demands. Secondly, even if they are aware of skill gaps, they lack a clear plan to address the skill gaps. Lastly, they lack guidance and resources to access, in order to overcome the deficiencies.

[0004] There is thus a need for an improved system and method to address employability challenges. SUMMARY

[0005] Example embodiments include a method of providing an Al-driven platform to improve entities’ employability. Such a method enables employability assessment to be accurately implemented to identify an entity’s skill gaps / knowledge deficits / experience gaps between their competencies and requirements of a target employment position. In some applications, each entity’s areas for growth are pinpointed, which may help to create an employability score to gauge the entity’s job market readiness.

[0006] Furthermore, the platform may be able to provide customized action plans, which may offer customized training to help entities to address the gaps. The 1customized action plans may be generated based on each entity’s uniqueness. In some examples, the customized action plans include personalized roadmaps, which align with students’ unique competency and employability scores. Because the gaps are identified with accuracy, the customized action plans based on the gaps are generated with greater accuracy and certainty.

[0007] Additionally, the platform could provide multiple different kinds of flexible learning modules to facilitate the entities’ learning journey efficiently.

[0008] In some applications, the machine learning algorithms may include at least one convolutional neural network (CNN).

[0009] According to at least one embodiment, there is disclosed a method for analyzing employability of an entity. The method comprises: receiving information associated with the entity; receiving a target employment position associated with the entity; and performing machine learning algorithms to conduct an employability assessment for the entity based on the information associated with the entity and the target employment position associated with the entity.

[0010] In another example embodiment of the method of any of the above, the information includes skills associated with the entity, and the employability assessment of the entity is conducted by identifying skill gaps between the skills associated with the entity and required skills of the target employment position.

[0011] In another example embodiment of the method of any of the above, the information includes academic background of the entity, and the employability assessment of the entity is conducted by identifying knowledge deficits between the academic background and required knowledge of the target employment position.

[0012] In another example embodiment of the method of any of the above, the information includes experience associated with the entity, and the employability assessment of the entity is conducted by identifying experience gaps between the experience associated with the entity and required experience of the target employment position. 2

[0013] In another example embodiment of the method of any of the above, the method further comprises generating an assessment report based on the employability assessment.

[0014] In another example embodiment of the method of any of the above, the assessment report includes at least one of an employability score, analysis of the entity’s competencies, and one or more areas for the entity’s growth.

[0015] In another example embodiment of the method of any of the above, the information further comprises a profile of the entity, the method further comprising generating a customized roadmap based on the entity’s profile and the employability assessment.

[0016] In another example embodiment of the method of any of the above, the customized roadmap comprises at least one customized action plan for the entity.

[0017] In another example embodiment of the method of any of the above, the method further comprises generating learning modules that are to address gaps or deficits identified in the employability assessment.

[0018] In another example embodiment of the method of any of the above, the learning modules provides educational resources and guidance.

[0019] In another example embodiment of the method of any of the above, the method further comprises providing an interactive module where the entity receives mentorship from one or more mentors.

[0020] In another example embodiment of the method of any of the above, the method further comprises providing a community learning module where the entity learns lessons with a group of entities.

[0021] In another example embodiment of the method of any of the above, the method further comprises providing a customized training program based on the employability assessment. 3

[0022] In another example embodiment of the method of any of the above, the customized training program comprises at least one instructor-led guidance, jobreadiness training, and market analytics training.

[0023] In another example embodiment of the method of any of the above, the method further comprises providing a job placement module where the entity is configured to connect with one or more employers.

[0024] In another example embodiment of the method of any of the above, the method further comprises providing real-time job market analytics.

[0025] In another example embodiment of the method of any of the above, the method further comprises providing an interview module where mock interviews are performed.

[0026] In another example embodiment of the method of any of the above, the information includes at least one of academic background, resumes, report cards, skills, and audio and / or video interviews.

[0027] In another example embodiment of the method of any of the above, the information is received via a user interface.

[0028] In another example embodiment of the method of any of the above, the entity is an international student.

[0029] In another example embodiment of the method of any of the above, the entity is an immigrant.

[0030] In another example embodiment of the method of any of the above, the machine learning algorithms comprise at least one convolutional neural network (CNN).

[0031] Another example embodiment is a computer system comprising: a processing unit configured to execute instructions to cause the computing system to perform the method of any of the above. 4

[0032] Another example embodiment is a non-transitory computer-readable medium storing machine-executable instructions which, when executed by one or more processors, cause the processors to perform steps of the method of any of the above. BRIEF DESCRIPTION OF DRAWINGS

[0033] Reference will now be made, by way of example, to the accompanying drawing which show example embodiments, and in which:

[0034] FIG. 1 is a schematic diagram of an example employability analysis system architecture in accordance with example embodiments;

[0035] FIG. 2 is a block diagram illustrating an example processing system suitable for implementing a server or a cloud in the employability analysis system architecture system of FIG.1;

[0036] FIG.3 is a block diagram illustrating an example processing system suitable for implementing an electronic device in the employability analysis system architecture system of FIG.1;

[0037] FIG.4 is a flowchart illustrating a method of analyzing employability of an entity in accordance with example embodiments;

[0038] FIG. 5 is a schematic diagram illustrating a convolutional neural network (CNN) suitable for implementing machine learning algorithms in the employability analysis system architecture system of FIG.1;

[0039] In the drawings, embodiments are illustrated by way of example. It is to be expressly understood that the description and drawings are only for purposes of illustrating certain embodiments and are an aid for understanding. They are not intended to be a definition of the limits of the invention. 5DETAILED DESCRIPTION

[0040] For illustrative purposes, specific example embodiments will now be explained in greater detail below in conjunction with the figures.

[0041] The embodiments set forth herein represent information sufficient to practice the claimed subject matter and illustrate ways of practicing such subject matter. Upon reading the following description in light of the accompanying figures, those of skill in the art will understand the concepts of the claimed subject matter and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the example embodiments.

[0042] Moreover, it will be appreciated that any module, component, or device disclosed herein that executes instructions may include or otherwise have access to a non-transitory computer / processor readable storage medium or media for storage of information, such as computer / processor readable instructions, data structures, program modules, and / or other data. A non-exhaustive list of examples of nontransitory computer / processor readable storage media includes magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, optical disks such as compact disc read-only memory (CD-ROM), digital video discs or digital versatile discs (i.e. DVDs), Blu-ray Disc™, or other optical storage, volatile and nonvolatile, removable and non-removable media implemented in any method or technology, random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology. Any such non-transitory computer / processor storage media may be part of a device or accessible or connectable thereto. Computer / processor readable / executable instructions to implement an application or module described herein may be stored or otherwise held by such non-transitory computer / processor readable storage media.

[0043] Aspects of example embodiments are directed to an artificial intelligence (Al)- powered platform, which implements methods of performing machine learning 6algorithms to assess employability of an entity (e.g., an international student or an immigrant to a country). The assessment could identify strengths and weaknesses of the entity accurately.

[0044] Furthermore, the platform may implement the machine learning algorithms to provide a customized roadmap for the entity based on the employability assessment. Because the entity’s skill gaps could be identified accurately, an action plan can be customized for the entity based on the identified skill gaps, which may help entities to overcome employability challenges and strategize their career paths effectively.

[0045] In some applications, the platform may provide dynamic workplace-simulating courses that foster skill development and career success for the entity.

[0046] In some applications, the system can be applied in a variety of educational areas, including, but not limited to, colleges hoping to support their international students, employers who are looking for qualified international talent, careers coaching institution, education consultancies, or online student platforms, etc.

[0047] Example embodiments of the methods and systems may be applied in various applications, including employability analysis of entities (e.g., international talents / students, immigrant).

[0048] FIG. 1 is a schematic diagram illustrating an example employability analysis system architecture 100 in a scenario where an entity (e.g., an international student) may access an Al-powered platform in accordance with a non-limiting example. In various non-limiting embodiments, in the job-hunting industry, the entity 106 may be an international student who will graduate from a college or an immigrant moving to a country with hopes to find a job that matches with his qualification.

[0049] The employability analysis system architecture 100 comprises a network 110, which can be a public data network such as the internet, and an electronic device 102. The electronic device 102 may be utilized by the entity 106 and is connected to the network 110, enabling the electronic device 102 to access one or more services through the network 110. The employability analysis system 100 can include multiple different 7types of communication networks (not shown) in communication with the electronic device 102, and each of these communication networks can be connected directly or indirectly to the network 110.

[0050] In some applications, data related to one or more services (e.g., educational resources and guidance, job alerts, market analytics, etc.,) may be stored within a cloud 104, including one or more servers 104(1 )-104(3) accessible over the network 110. The servers 104(1 )-104(3) may be reachable over the network 110 in various non-limiting ways, such as a wired link, via cellular communication links, or by using Wi-Fi wireless network which conforms to IEEE 802.11x standards (sometimes referred to as Wi-Fi®, although in other examples, other communication protocols may be used for the Wi-Fi wireless network).

[0051] In some applications, the electronic device 102 may establish any suitable communication link (e.g., wired communication link, cellular network communication link, or Wi-Fi wireless communication link) with the network 110 through which the respective servers 104(1 )-104(3) are accessed to retrieve different respective services and data source.

[0052] The server 104(1)-104(n) are collectively referred to as a cloud 104 and in some applications, the server 104 may also be referred to as information source. It should be appreciated that although the drawing shows three network-connected server 104(1 )- 104(3), there is no particular limitation on the number of servers and types of the network connections.

[0053] In some cases, the server 104(1) may receive the information associated with the entity 106 via the network 110 and store the received information associated with the entity 106. Additionally, the server 104(1) may receive a target employment position associated with the entity 106 via the network 110 and store the received employment position associated with the entity 106. Furthermore, the server 104(1) may additionally receive profile information associated with the entity and store the profile information. However, in some examples, some of the information stored by the server 104(1) may be stored additionally or alternatively at other entities. This is illustrative and not intended to 8be limiting. In other possible configurations, the electronic device 102 may access any other entity of the employability analysis system architecture 100 (even those not shown) to retrieve any suitable information.

[0054] In the embodiment of FIG. 1, the electronic device 102 may be any component (or collection of components) capable of carrying out a graphical user interface (GUI) and communicating with the server 104. To this end, as shown in FIG. 1, the electronic device 102 may be mobile phones, however, it is not intended to be limiting. In other possible configurations, the electronic device 102 could be a desktop, console, a tablet and a laptop that is configured with a user interface.

[0055] A graphical user interface (GUI) can be defined as functionality for displaying information for visualization by a user, receiving input from the user pertaining to the displayed information, associating the user input to the displayed information and taking an action based on the displayed information associated with the user input. In some cases, a GUI can be implemented by a combination of a display, a mouse / keyboard and an input / output controller, whereas in other cases, a GUI can be implemented by a touchscreen and touchscreen controller. Other embodiments are of course possible.

[0056] While in the embodiment of FIG. 1, the functions of implementing the GUI and communicating with the server 104 are carried out by a same electronic device, such as the electronic device 102, this functionality can be distributed among separate devices. For example, corresponding to the electronic device 102, one electronic device may implement the GUI and another electronic device (not shown) communicates with the network cloud 104. For simplicity, the electronic devices can be collectively referred to as the electronic device 102 for the entity 106 to provide Al-empowered employability analysis services in the below description.

[0057] FIG. 2 is a block diagram of an example simplified processing system 200, which may be used to implement any or each of the servers 104. A given one of the servers 104 may also be referred to as a “centralized device”, which implements machine learning algorithms to perform employability assessment to identify the entity’s strengths and weakness. Furthermore, the servers 104 may provide learning modules, interactive 9modules, community learning modules, and customized training program to help the entity to improve their competencies in the job market. In addition, the server 104 may provide educational resources and guidance, to the electronic device 102, either via the network 110 or via a direct communication link.

[0058] The example processing system 200 described below, or variations thereof, may be used to implement certain functionality of the server 104. However, other processing system architectures may be suitable for implementing the server 104 and may include components different from those discussed below. Although FIG. 2 shows a single instance of each component in the processing system 200, there may be multiple instances of each component.

[0059] The processing system 200 may include one or more processing devices 202, such as a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), a neural processing unit (NPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a dedicated logic circuitry, or combinations thereof. The processing system 200 may include one or more input / output (I / O) controllers 204, to enable interfacing with one or more input devices 220 and / or output devices 222.

[0060] The processing system 200 may further include one or more network interfaces 206 for wired or wireless communication with the communication network 110 or peer-topeer communication with other processing systems, such as a processing system 300 (see FIG. 3) of an electronic device 102, to receive an entity’s (e.g., an international student) input. The network interface 206 may include wired links (e.g., Ethernet cable) and / or wireless links (e.g., one or more antennas) for intra-network and / or inter-network communications. The network interface 206 may be connected to one or more antennas 218, which are configured to facilitate wireless communication that may be implemented by the network interface 206.

[0061] The processing system 200 may also include or have access to one or more storage units 208, which may include a mass storage unit such as a solid-state drive, a hard disk drive, a magnetic disk drive and / or an optical disk drive. In some examples, 10the storage units 208 may store information 2081 associated with the entity 106 and machine learning algorithms 2082. In some examples, the information includes at least one of academic background, resumes, report cards, skills, and audio and / or video interviews.

[0062] The processing system 200 may include one or more non-transitory memories 210, which may include a volatile or non-volatile memory (e.g., a flash memory, a random-access memory (RAM), and / or a read-only memory (ROM)). The non-transitory memory 210 may store instructions for execution by the processing device 202, such as to carry out example methods in example embodiments. The memory 210 may store other software (e.g., instructions for execution by the processing devices 202), such as an operating system and other applications / functions. In some examples, one or more data sets and / or modules may be provided by an external memory (e.g., an external drive in wired or wireless communication with the processing system 200) or may be provided by a transitory or non-transitory computer-readable medium. Examples of non-transitory computer readable media include a RAM, a ROM, an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a CD-ROM, or other portable memory storage.

[0063] There may be a bus 217 providing communication among components of the processing system 200, including the processing device 202, I / O controller 204, network interface 206, storage unit 208, and / or memory 210. The bus 217 may be any suitable bus architecture including, for example, a memory bus, a peripheral bus or a video bus.

[0064] In FIG. 2, the server 104 may do not comprise an input device and an output device. In other words, the main function of the server 104 includes performing machine learning algorithms to perform an employability assessment for an entity (e.g., the entity 106) based on the information associated with the entity and the target employment position associated with the entity. Furthermore, the server 104 may collect, manage, and store profile information of each entity and store marketing analytics on the job market in different industries. 11

[0065] Configurations of the electronic device 102 will be discussed in greater detail below with reference to the simplified block diagram of FIG. 3. FIG. 3 shows a block diagram of an example simplified processing system 300, which may be used to implement the electronic device 102. Components of the processing system 300 are similar to those of the processing system 200 as shown in FIG. 2. A notable exception is presence of an input device and an output device. That is to say, as shown in FIG. 3, an input device 320 and an output device 322 are shown as external to the processing system 300. The input device 320 may include at least one of a keyboard, a mouse, and a microphone, which receives input (e.g., information associated with the entity, a target employment position associated with the entity) from the entity 106. The output device 322 may include at least one of a display and a loudspeaker, which may provide audio and visual output to the entity 106. In other examples, one or more of the input device 320 and / or the output device 322 may be integrated together and / or with the processing system 300. For example, the input device 320 and the output device 322 may be integrated as a single component, such as a touch-sensitive display screen which, together with the I / O controller 304 being implemented as a touchscreen controller, carries out a graphical user interface. In that case, the entity 106 may interact with the GUI primarily through finger contacts and gestures on the touch-sensitive display, such as input his information (e.g., academic background, resumes, report cards, skills, and audio and / or video interviews) and a target employment position.

[0066] Reference is now made with respect to FIG. 4, which illustrates an example method 400, which is performed by any server 104 in the cloud, to provide a platform that is able to perform employability assessments, such as to identify an entity’s strengths and weakness accurately. The method 400 comprises:

[0067] Step 402, the server 104 receives information associated with the entity from an entity. The entity 106 may be an international student, an immigrant, or any person who is configured to seek employability analysis and assistance.

[0068] In some examples, the entity 106 may create an account on a platform or an application and set up his profile information via a user-friendly interface. 12

[0069] Additionally, the entity 106 may provide information associated with the entity via the user interface. The information may include academic background, job aspirations, resume, and skills associated with the entity, which are utilized for subsequent steps (e.g., personalized recommendations). In particular, the entity 106 is prompted to input details regarding his academic background, job aspirations, report cards, resume, interest and skills, which may help to improve accuracy of the subsequent personalized recommendations.

[0070] Step 404, the server 104 receives a target employment position associated with the entity from the entity. The entity 106 may input / enter his target employment position via the user interface. The target employment position may be a job that aligns with entity’s professional field or professional job expectation.

[0071] Step 406, the platform performs machine learning algorithms to conduct an employability assessment for the entity based on the information associated with the entity and the target employment position associated with the entity. The employability assessment may include evaluation of language proficiency, cultural understanding, jobmarket readiness, and professional skills. In some examples, the machine learning algorithms may be implemented in a convolutional neural network (CNN) that includes multiple layers. FIG.5 shows a schematic structure of an example CNN 500. The CNN 500 may include input layers 502, hidden layers 504 and an output layer 506. In a convolutional neural network, the hidden layers 504 include one or more layers that perform convolutions. Typically, the hidden layers 504 include a layer that performs a dot product of the convolution kernel with the layer's input matrix. As the convolution kernel slides along the input matrix for the layer, the convolution operation generates a feature map, which in turn contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers.

[0072] In the case where the information includes skills associated with the entity, the platform performs the machine learning algorithms to identify skill gaps between the skills associated with the entity and required skills of the target employment position. Such a method may help entities to identify the skill gaps between their skills and the 13required skills of the target employment position. The identified gaps may help to provide in-depth assessment to pinpoint the strengths and areas of improvement for each entity, which may be beneficial to build subsequent customized employability scores and to provide a career roadmap for career enhancement.

[0073] In the case where the information includes academic background of the entity, the platform performs the machine learning algorithms to identify knowledge deficits between the entity’s academic background and required knowledge of the target employment position. Such a method may help entities to identify knowledge deficits, which may help to guide the entities towards strategic career development.

[0074] In the case where the information includes experience associated with the entity, the platform may perform the machine learning algorithms to identify experience gaps between the experience associated with the entity and required experience of the target employment position. Such a method may help entities to identify experience limitations of the entities (e.g., international students, immigrants, etc.), which may be beneficial to evaluate the entities’ competencies in multiple angles.

[0075] The platform provides an Al-powered employability assessment for an entity based on the information associated with the entity and the target employment position associated with the entity, which may help to navigate job hunters’ success in job seeking market.

[0076] Optionally, at step 408, the platform may generate an assessment report based on the employability assessment. In some examples, the platform may perform in-depth analysis and generate a detailed report of the entity’s employability score, competencies, and areas needing improvement. This report is designed to guide the entity's journey towards job-readiness. In other words, the assessment report includes at least one of an employability score, analysis of the entity’s competencies, and one or more areas for the entity’s growth. The employability score may help to gauge job market readiness for the entity. The assessment report provides a report of entities’ current skills / educational background / experience compared with those required for the target employment position associated with the entity. In other words, the entity’s 14current skills / educational background / experience are compared with necessary required skills / educational background / experience for their target job. Therefore, the entity’s areas for growth could be pinpointed.

[0077] In order to give a full picture of entity’s career potential, the generated employability score may consider industry trends and employer expectations, which may help to deliver a comprehensive understanding of each entity’s career potential.

[0078] Optionally, at step 410, the platform may generate a customized roadmap based on the entity’s profile and the employability assessment. The profile of the entity may be included in the information received from the entity. The customized roadmap comprises at least one customized action plan for the entity. The customized roadmap is a tailored roadmap designed to cater to the entity’s unique needs and goals. Based on the report, the platform provides a personalized and step-by-step pathway for each entity to achieve job-readiness. The plan may include providing courses and learning paths to improve the skills / education knowledge / experiences. Suh a method may help to tailor recommendations to improve job prospects based on each entity’s profile information.

[0079] In some applications, the platform may apply some tools to help the entities to find a perfect career path.

[0080] Optionally, at step 412, the platform may generate learning modules that are to address gaps or deficits identified in the employability assessment. The learning modules is one type of training programs, which may offer multiple dynamic, workplacesimulating courses that are customized based on the identified gaps and / or knowledge deficits. The learning modules foster skill development and career success. In some examples, the learning modules provide educational resources and guidance, which are flexible and allow entities (e.g., international students) to explore diverse and comprehensive content at their own pace. The lessons may help to address language, culture, and key professional skills. In some applications, the courses are interactive and could track the entity’s progress and performance. 15

[0081] Such a platform delivers practical and dynamic courses that stimulate real-world workplace challenges, which may help the entities to develop relevant skills, leading to tangible career success.

[0082] Optionally, at step 414, the platform provides an interactive module where the entity receives mentorship from one or more mentors. The interactive module may be facilitated by seasoned professionals to provide live feedback and interview preparation. In the interactive module, the entity could have live sessions with experienced mentors who provide immediate feedback. The interactive module focuses on interview preparation to enhance student confidence and job market navigation.

[0083] In some examples, the platform may offer interview modules, which apply Alpowered mock interview technologies to assess entity’s video / audio responses. The mock interview modules do not only enhance entities’ communication skills but also amplify the chances of interview success through insightful and valuable feedback. This unique feature gives entities hands-on interview experience, which significantly boosts their communication skills and interview performance.

[0084] In some examples, the interactive module is a hybrid combined learning model which balances self-paced learning with timely mentor guidance, delivers courses that hone practical and job-relevant skills. The hybrid combined learning module delivers lessons to address language obstacles, culture discrepancies, and improves essential professional skills. In other words, the hybrid combined learning model balances independent learning with timely mentor guidance.

[0085] In some applications, the courses are tailored for each entity to develop practical skills pertinent to the job market.

[0086] Optionally, at step 416, the platform provides a community learning module where the entity learns lessons with a group of other entities in a community. The community learning module grants access to a community forum, which allows for peer interaction and networking. The community learning module enables the entities to study in a group session, which helps to foster vibrant discussions, collective problem- 16solving, and shared knowledge enrichment. Therefore, the community learning module may help to improve collaborative problem-solving and shared learning skills.

[0087] Optionally, at step 418, the platform may provide a customized training program based on the employability assessment. In some examples, the customized training program comprises at least one instructor-led guidance, job-readiness training, and market analytics training. In some examples, the instructor-led guidance simplifies regulatory compliance and job-specific requirements with hands-on mentorship. The mentors, alongside external service providers, deliver comprehensive and hands-on training. This includes regulatory compliance guidance, which simplifies comprehension ofjob-specific licenses and certifications.

[0088] In some applications, a job-readiness training program provides practical instructions on behavioral norms, safety standards, and cross-disciplinary skills, which help to simplify job application processes. The job-readiness training program provides compact, hands-on training focusing on behavioural norms, safety standards and interdisciplinary skills. Such a program helps clarifying the job application process and contracts.

[0089] With respect to market analytics training, this program equips entities with realtime insights on in-demand skills, industry trends, and salaries, ensuring that they stay ahead of market shifts. Such a program offers real-time job market and valuable insights, which help students understand in-demand skills, emerging industries, and salary trends. Furthermore, because the market analytics training provides adaptable market strategies, entities are kept ahead of trends for continuous adaptability and progression. Analytics highlight trends in skills, sectors and salaries enable entities to refine profiles and make informed career decisions, which help to smooth their transition to the workforce.

[0090] Optionally, at step 420, the platform may provide a job placement module where the entity is configured to connect with one or more employers. The job placement module is a program that is integrated with job boards and networking sites, or any other suitable hiring platform. The job placement module enhances entities' exposure to 17potential employers. With seamless integration with job boards and professional networking sites, job hunting is simplified, and students' exposure is increased to potential employers with our preferred employer network. In other words, the job placement module helps to prepare students for networking, attracting employers, and transitioning seamlessly from academics to professional life. Such a comprehensive services and job placement strategies equip entities to network effectively and stand out to find right opportunities among other employers. The platform facilitates seamless transition from theory to job placement. What is more, the job placement module focuses on networking and negotiation skills, which are critical for improving job prospects and achieving career goals.

[0091] Optionally, at step 422, the platform may provide real-time job market analytics. In particular, the platform provides real-time insights on in-demand skills, industry trends, salaries, which may help the entities (e.g., international students, immigrant, job hunters, etc.,) to keep updated of market information. For example, the offered real-time job market insights may help students understand in-demand skills, emerging industries, and salary trends. Such a method of providing adaptable market strategies may help to keep students ahead of trends for continuous adaptability and progression.

[0092] Moreover, the real-time job market analytics may offer career growth strategies, which combine industry updates with customized development programs. The entities are empowered with job-ready expertise and guided towards successful career paths.

[0093] In some applications, the real-time job market analytics may further provide regular industry updates, which could be combined with custom development programs and key performance indicator (KPI) guidance to equip entities with job-ready expertise. Thus, the platform provides comprehensive career paths to guide entities towards professional success.

[0094] In some examples, even after job placement, the platform is configured to provide continued support for each entity (e.g., help each entity with career progression, promotion negotiation, and performance tracking). 18

[0095] Because an Al-powered platform is provided to assess an entity’s employability and competency, the entity could be able to be aware of deficits and gaps between their competencies and the requirements of their target employment position. Such platform may help to pinpoint entities’ strengths. Furthermore, based on the identified deficits and gaps, a customized action plan (e.g., strategical career paths planning) could be set for the entity to help the entity to address the discrepancies between the competencies of the requirements and to identify growth area. Thus, this Al-powered assessment could offer personalized career guidance based on an in-depth analysis of each entity’s profile, thus enhancing job search efficiency.

[0096] In some applications, the comprehensive assessment may facilitate job role alignment as well.

[0097] What is more, the platform provides various modules (e.g., learning modules, interactive modules, community learning modules, customized training programs, etc.,) to provide guidance and resources to help the entity to address gaps or deficits identified in the employability assessment.

[0098] Therefore, an entity could be assisted with a plurality of services from the platform to secure a career success effectively. To name a few non-limiting examples, the plurality of services may include language and cultural assimilation, internships, part-time job opportunities, professional networking, interview preparation, understanding workplace norms, job search strategies, and resume writing. The plurality of services may be ongoing support for career advancement, networking, and continued cultural assimilation support.

[0099] Furthermore, institutions using the comprehensive platform disclosed here may help entities to assimilate into culture of a new country, enhancing their academic and job success and attracting more global talent.

[0100] In some applications, the machine learning algorithms (e.g., trained artificial intelligent (Al) model) may include at least one convolutional neural network (CNN), which help to facilitate with greater accuracy and efficiency. 19

[0101] In examples, the terms “a” or “an” are defined to mean “at least one”, that is, these terms do not exclude a plural number of items, unless stated otherwise.

[0102] In examples, terms such as “substantially”, “generally” and “about”, which modify a value, condition or characteristic of a feature of an example embodiment, should be understood to mean that the value, condition or characteristic is defined within tolerances that are acceptable for the proper operation of the example embodiment for its intended application.

[0103] In examples, unless stated otherwise, the terms “connected” and “coupled”, and derivatives and variants thereof, refer herein to any structural or functional connection or coupling, either direct or indirect, between two or more elements. For example, the connection or coupling between the elements can be acoustical, mechanical, optical, electrical, thermal, logical, or any combinations thereof.

[0104] In examples, expressions such as “match”, “matching” and “matched”, including variants and derivatives thereof, are intended to refer herein to a condition in which two or more elements are either the same or within some predetermined tolerance of each other. That is, these terms are meant to encompass not only “exactly” or “identically” matching the two elements but also “substantially”, “approximately” or “subjectively” matching the two or more elements, as well as providing a higher or best match among a plurality of matching possibilities.

[0105] In examples, the expression “based on” is intended to mean “based at least partly on”, that is, this expression can mean “based solely on” or “based partially on”, and so should not be interpreted in a limited manner. More particularly, the expression “based on” could also be understood as meaning “depending on”, “representative of’, “indicative of’, “associated with” or similar expressions.

[0106] In examples, the terms "system" and "network" may be used interchangeably in different embodiments of this application. "At least one" means one or more, and "a plurality of means two or more. The term "and / or" describes an association relationship of associated objects, and indicates that three relationships may exist. For example, A 20and / or B may indicate the following three cases: Only A exists, both A and B exist, and only B exists, where A and B may be singular or plural. The character 7" indicates an "or" relationship between associated objects. "At least one of the following items (pieces)" or a similar expression thereof indicates any combination of these items, including a single item (piece) or any combination of a plurality of items (pieces). For example, "at least one of A, B, or C" includes: only A; only B; only C; A and B; A and C; B and C; or A, B, and C, and "at least one of A, B, and C" may also be understood as including: only A; only B; only C; A and B; A and C; B and C; or A, B, and C. In addition, unless otherwise specified, ordinal numbers such as "first" and "second" in embodiments of this application are used to distinguish between a plurality of objects, and are not used to limit a sequence, a time sequence, priorities, or importance of the plurality of objects.

[0107] A person skilled in the art should understand that embodiments of this application may be provided as a method, an apparatus (or system), computer-readable storage medium, or a computer program product. Therefore, this application may use a form of a hardware-only embodiment, a software-only embodiment, or an embodiment with a combination of software and hardware. Moreover, this application may use a form of a computer program product that is implemented on one or more computer-usable storage media (including but not limited to a disk memory, an optical memory, and the like) that include computer-usable program code.

[0108] This application is described with reference to the flowcharts and / or block diagrams of the method, the device (system), and the computer program product according to this application. It should be understood that computer program instructions may be used to implement each process and / or each block in the flowcharts and / or the block diagrams and a combination of a process and / or a block in the flowcharts and / or the block diagrams. The computer program instructions may be provided for a general-purpose computer, a dedicated computer, an embedded processor, or a processor of another programmable data processing device and enable a machine to execute the instructions. When executed by any computer or the processor of a programmable data processing device, the instructions cause the 21apparatus to implement specific functions as described in one or more procedures in the flowcharts and / or one or more blocks in the block diagrams. The computer program instructions may alternatively be stored in a computer-readable memory that can indicate a computer or another programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate an artifact that includes an instruction apparatus. The instruction apparatus implements a specific function in one or more procedures in the flowcharts and / or one or more blocks in the block diagrams.

[0109] The computer program instructions may alternatively be loaded onto a computer or another programmable data processing device, so that a series of operations and steps are performed on the computer or the another programmable device, so that computer-implemented processing is generated. Therefore, the instructions executed on the computer or on another programmable device provide steps for implementing specific functions as described in one or more procedures in the flowcharts and / or one or more blocks in the block diagrams.

[0110] It is clear that a person skilled in the art can make various modifications and variations to this application without departing from the scope of example embodiments. Example embodiments are intended to cover these modifications and variations, and their equivalent technologies. 22

Claims

What is claimed is:

1. A method of analyzing employability of an entity, the method comprising: receiving information associated with the entity; receiving a target employment position associated with the entity; and performing machine learning algorithms to conduct an employability assessment for the entity based on the information associated with the entity and the target employment position associated with the entity.

2. The method of claim 1, wherein the information includes skills associated with the entity, and the employability assessment of the entity is conducted by identifying skill gaps between the skills associated with the entity and required skills of the target employment position.

3. The method of claim 1, wherein the information includes academic background of the entity, and the employability assessment of the entity is conducted by identifying knowledge deficits between the academic background and required knowledge of the target employment position.

4. The method of claim 1, wherein the information includes experience associated with the entity, and the employability assessment of the entity is conducted by identifying experience gaps between the experience associated with the entity and required experience of the target employment position.

5. The method of claim 1, further comprising generating an assessment report based on the employability assessment.

6. The method of claim 5, wherein the assessment report includes at least one of an employability score, analysis of the entity’s competencies, and one or more areas for the entity’s growth.

237. The method of claim 1, wherein the information further comprises a profile of the entity, the method further comprising generating a customized roadmap based on the entity’s profile and the employability assessment.

8. The method of claim 7, wherein the customized roadmap comprises at least one customized action plan for the entity.

9. The method of claim 1, the method further comprising generating learning modules that are to address gaps or deficits identified in the employability assessment. 10.The method of claim 9, wherein the learning modules provide educational resources and guidance. 11.The method of claim 1, the method further comprising providing an interactive module where the entity receives mentorship from one or more mentors. 12.The method of claim 1, the method further comprising providing a community learning module where the entity learns lessons with a group of entities. 13.The method of claim 1, the method further comprising providing a customized training program based on the employability assessment. 14.The method of claim 13, wherein the customized training program comprises at least one instructor-led guidance, job-readiness training, and market analytics training. 15.The method of claim 1, the method further comprising providing a job placement module where the entity is configured to connect with one or more employers. 16.The method of claim 1, the method further comprising providing real-time job market analytics. 17.The method of claim 1, the method further comprising providing an interview module where mock interviews are performed. 2418.The method of claim 1, wherein the information includes at least one of academic background, resumes, report cards, skills, and audio and / or video interviews. 19.The method of claim 1, wherein the information is received via a user interface. 20.The method of claim 1, wherein the entity is an international student.

21. The method of claim 1, wherein the entity is an immigrant. 22.The method of claim 1, wherein the machine learning algorithms comprise at least one convolutional neural network (CNN).

23. A computer system comprising: a processing unit configured to execute instructions to cause the computing system to perform the method of claim 1.

24. A non-transitory computer-readable medium storing machine-executable instructions which, when executed by one or more processors, cause the processors to perform steps of the method of claim 1. 25