Data communications network and method of assisting educational outcomes

Abstract

The present invention relates to a computer-implemented data communications network and method of utilizing same to assist education outcomes. In particular, the network and method provides a platform that, according to an embodiment, enables an educator to create and dynamically adjust programs and resources, in accordance with characteristics of enrolled students, managing course materials and marking assignments, including automatically checking for any Artificial Intelligence (AI) generated or plagiarised content

Description

DATA COMMUNICATIONS NETWORK AND METHOD OF ASSISTING EDUCATIONAL OUTCOMESFIELD OF THE INVENTION

[0001] The present invention relates to a computer-implemented data communications network and method of utilizing same to assist educational outcomes. In particular, the network and method provides a platform that enables an educator to create and dynamically adjust programs and resources, in accordance with the individual characteristics of enrolled students, managing course materials and marking assignments, including automatically checking for any Artificial Intelligence (Al) generated or plagiarised content.BACKGROUND OF THE INVENTION

[0002] Educators (eg. teachers, lecturers, etc) are often confronted with the difficult and time-consuming task of creating, managing and executing educational programs for students. The creation, management and execution of educational programs on behalf of students often requires educators to perform a multitude of tasks including, but not limited to, scheduling tasks for students, creating programs and arranging resources, generating and managing course materials, reviewing and assessing submissions (eg. assignments, projects, etc), and assessing submissions for plagiarism. Such requirements are exacerbated in circumstances where there are students with different learning needs, which introduces an additional complexity to the number of complex tasks confronting educators.

[0003] The problem of plagiarism in submitted work by students has become more prevalent in recent times with the advent of generative Al tools such as ChatGPT. In the past, plagiarism involved copying the work of other students or work produced by other authors such as books and online resources. However, generative Al now enables content to be created for students. In other words, students are able to access such tools using a data communications network such as the internet and utilise that network to generate content for assignments and other work (eg. group projects). In this regard, the only effort required from a student is the submission of a basic question or query to the generative Al tool. Accordingly, there is a need for educators to be able to monitor andrecognize when generative Al tools have been used by their students to create content on their behalf.

[0004] Another problem associated with existing techniques for creating, managing and executing educational programs for students relates to providing feedback to students within an appropriate timeframe of assignment submission for assessment, and the ability for educators and students to communicate with one another in relation to tasks, programs, resources, course materials, assessments, results, performance and feedback learning.

[0005] A technical problem also arises when educators and students are required to utilise their fixed and portable computing devices to access multiple different platforms, resources and websites in an effort to complete all tasks required to achieve particular results (in the case of educators, the successful creation, management and execution of an educational program, and in the case of students, the successful completion of an educational program). Increased use of devices gives rise to substantial utilization of data communications network bandwidth. Inefficient use of available resources, including bandwidth of data communications networks, is undesirable since slow and / or unreliable data communications are frustrating and inefficient use of computing resources including data communications bandwidth is increasingly consuming a finite resource. This problem is further exacerbated during times of high network traffic which consume available bandwidth which may cause latency problems regarding communications.

[0006] The present invention seeks to mitigate the problems discussed herein, or at least provide an alternative solution to existing systems, networks and methods .

[0007] The reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any suggestion, that the prior art forms part of the common general knowledge.SUMMARY OF THE INVENTION

[0008] In one aspect, the present invention provides a data communications network including connected data communications devices and a method of operating same for assisting educational program execution, the method including a first connected data communications device, receiving, by one or more processors, details from a plurality of educators and students, and establishing an account profile for each educator and student for whom details have been verified, retrieving or generating, by one or more processors, a program for each educator and prompting the educator to personalize the program according to at least one or more characteristics of the student(s) enrolled in the program, enabling, by one or more processors, the student(s) enrolled in the program to access content and / or materials associated with the received or generated program including one or more tasks to be completed, receiving, by one or more processors, one or more submissions from data communications device(s) of the student(s) enrolled in the program in response to the one or more tasks to be completed, applying, by one or more processors, one or more artificial intelligence (Al) techniques to assess the authenticity of the one or more submission(s) to thereby generate an authenticity result; and providing, by one or more processors, for display on a data communications device associated with the educator, the student’s submission(s) and the authenticity result generated in respect of the submission(s).

[0009] Accordingly, the solution proposed above provides a platform for use by educators and students, assisted by the use of Al techniques and, in particular, techniques to assist educators to create and dynamically adjust educational programs, and to automatically assess the presence of any generated or plagiarised content submitted by a student, thereby presenting a deterrent to students who may otherwise seek to utilise generative Al techniques or plagiarise another person’s work to assist development of content in respect of their assignments and / or any other submitted work.

[0010] In an embodiment, the educator is a teacher or lecturer.

[0011] In an embodiment, retrieving or generating a program for each educator includes one or more of, receiving a program which has been uploaded by the educator, receiving a program which has been automatically selected from a database of available programs on behalf of the educator according to details received from the educator and / orstudent(s), or creating a program on behalf of the educator according to the details received from the educator and / or student(s), and / or one or more queries submitted by the educator.

[0012] In an embodiment, the method further includes providing to the educators, by one or more processors, resources regarding course content and course materials, including access to libraries and / or databases comprising teaching resources to assist the educator to create and upload the educational program. For example, the teaching resources may include course materials for use in creating a program for one or more subjects.

[0013] In an embodiment, the program relates to one or more of curriculum, programming, or lesson plans.

[0014] In an embodiment, prompting the educator to personalize the program according to at least one or more characteristics of the student(s) enrolled in the program includes one or more of, providing links to course content and materials, enabling access to libraries and databases comprising teaching resources, or providing suggestions to the educator, the suggestions generated using one or more artificial intelligence techniques and aligning with requirements and / or interests of the student(s).

[0015] In an embodiment, the method further includes one or more of, prompting, by one or more processors, the educator to adjust the program before or during delivery of the program to the student(s), or automatically adjusting the program before or during delivery of the program to the student(s).

[0016] In an embodiment, adjustments to the program are suggested or automatically implemented based on one or more of, student details including study patterns, student feedback, student performance, or student progress.

[0017] In an embodiment, the adjustments to the program include one or more of adjustments to one or more of the educator’s programming, lesson plans, course content and / or course materials, suggesting additional review material prior to delivery of the program, including one or more of video tutorials, interactive simulations, and practice quizzes, suggesting additional reading material for the student(s), suggesting supplementary exercises, or incorporating additional interactive exercises during deliveryof the program including one or more of augmented reality (AR) or virtual reality (VR) simulations.

[0018] In an embodiment, the one or more Al techniques applied to assess authenticity of the one or more submissions includes at least a natural language processing (NLP) technique to analyse text in each submission.

[0019] In an embodiment, the one or more Al techniques applied to assess authenticity of the one or more submissions further includes training one or more machine learning models to, compare each submission against published articles and / or a historical library of student work to determine the existence of plagiarized content, and / or identify any content in the submission that has been developed using generative artificial intelligence.

[0020] In an embodiment, the method includes training, by one or more processors, a first machine learning model to identify submissions as lacking authenticity according to a pre-defined authenticity assessment threshold, wherein training data used to train the first model is based on submissions provided by the plurality of students with authenticity results regarding any current submissions generated according to the pre-defined authenticity threshold.

[0021] In an embodiment, the method further includes providing one or more student submissions as input for the first machine learning model, and receiving, from the first model, an indication regarding whether the one or more student submissions is authentic.

[0022] In an embodiment, the method further includes, assigning, by one or more processors, an authenticity score to each student submission, and based on the authenticity score of a submission satisfying the pre-defined authenticity assessment threshold, recording, by one or more processors, the submission on a blockchain ledger.

[0023] In an embodiment, the method further includes receiving, by one or more processors, assessment results relating to the one or more student submissions, the assessment result according to one or more reviews of each student submission by one or more educators.

[0024] In an embodiment, the method further includes training, by one or more processors, a second machine learning model to provide recommendations to theeducator regarding adjustments to the teaching program, wherein training data used to train the second model is based upon one or more of, the submissions provided by the plurality of students, the authenticity assessment results generated in respect of the submissions, and the educator’s assessment results in respect of the submissions.

[0025] In an embodiment, the method further includes, providing the one or more student submissions as input to the second machine learning model, and receiving, from the second model, an indication regarding those aspect(s) of the program for which a recommendation is provided and those aspect(s) of the program that are recommended for adjustment by the educator.

[0026] In an embodiment, the method further includes, based on the NLP technique processing and analysing the text in each student’s submission, generating personalized feedback and recommendations to the educator according to the individual needs and learning style of the student.

[0027] In an embodiment, the method further includes providing, by one or more processors, a communication facility that enables students and educators to remotely communicate with each other.

[0028] In an embodiment, the communication facility provides a means of communication between students, educators, and between students and educators, using real-time chat and messaging functionality incorporating one or more types of media including written text, recorded audio and / or recorded video.

[0029] In an embodiment, the method further includes one or more of, receiving, by one or more processors, a request from an educator for a report regarding student progress with respect to the educational program, the request specifying a particular time period, and generating, by one or more processors, a report regarding student progress with respect to the program over the particular time period, and providing the report for display on the data communications device of the educator.

[0030] In an embodiment, the method further includes generating, by one or more processors, notifications to educators and / or students when students enrolled in the program upload submissions, educators issue assessments and additional feedback regarding uploaded submissions, or adjustments to the program and / or amendments toinformation regarding pre-requisites with respect to any particular program component are implemented.

[0031] According to another aspect, the present invention provides a computer- implemented system for assisting educational program execution, the system including one or more computer processors configured to, receive details from a plurality of educators and students, and establishing an account profile for each educator and student for whom details have been verified, retrieve or generate a program for each educator and prompting the educator to personalize the program according to at least one or more characteristics of the student(s) enrolled in the program, enable the student(s) enrolled in the program to access content and / or materials associated with the received or generated program including one or more tasks to be completed, receive one or more submissions from data communications device(s) of the student(s) enrolled in the program in response to the one or more tasks to be completed, apply one or more artificial intelligence (Al) techniques to assess the authenticity of the one or more submission(s) to thereby generate an authenticity result, and provide, for display on a data communications device associated with the educator, the student’s submission(s) and the authenticity result generated in respect of the submission(s).

[0032] According to a still further aspect, the present invention provides a non-transitory computer-readable medium including computer instruction code that, when executed on a computer, causes one or more processors of the computer to perform steps to assist execution of an educational program, including receiving details from a plurality of educators and students, and establishing an account profile for each educator and student for whom details have been verified, retrieving or generating a program for each educator and prompting the educator to personalize the program according to at least one or more characteristics of the student(s) enrolled in the program, enabling the student(s) enrolled in the program to access content and / or materials associated with the received or generated program including one or more tasks to be completed, receiving one or more submissions from data communications device(s) of the student(s) enrolled in the program in response to the one or more tasks to be completed, applying one or more artificial intelligence (Al) techniques to assess the authenticity of the one or more submission(s) to thereby generate an authenticity result, and providing for display on adata communications device associated with the educator, the student’s submission(s) and the authenticity result generated in respect of the submission(s).BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Embodiments of the invention will now be described in further detail with reference to the accompanying figures in which:

[0034] Figure 1 provides an overview of a data communications network according to an embodiment of the present invention showing, in particular, the interaction between various network components.

[0035] Figure 2 illustrates a diagram associated with an exemplary server component within the network illustrated in Figure 1 .

[0036] Figure 3 illustrates an exemplary flow diagram of a process that enable users to download and install a software application and subsequently access, or register to use, the software application for interaction with the network illustrated in Figure 1 , including to enable educators to upload or create an educational program with the ability to personalize the program according to characteristics of the students enrolled.

[0037] Figure 4 illustrates a diagram associated with an example interface of the software application that enables students to access content and materials associated with the educational programs including one or more tasks to be completed, and to make submissions in response to the one or more tasks.

[0038] Figure 5 illustrates a diagram associated with additional example interfaces of the software application, including an interface that enables educators to view the students’ submissions as well as authenticity results generated in respect of a pre-defined authenticity threshold, and an interface to receive recommendations including adjustments to the teaching program.

[0039] Figure 6 illustrates a diagram associated with a further example interface of the software application that enables additional functionality including provision of a chat facility, payment gateway and report generation facility.DETAILED DESCRIPTION OF EMBODIMENT(S) OF THE INVENTION

[0040] For simplicity and clarity, the present disclosure is described by referring to embodiment (s) thereof. In the following paragraphs, numerous specific details are described to provide a better understanding of the present disclosure. It will be readily apparent, however, that the current disclosure may be practiced without adherence to the specific details in any one or more of the embodiments described. In other instances, some features have not been described in detail to avoid obscuring the present disclosure.

[0041] The present invention, according to the embodiment depicted in Figure 1 , relates to a data communications network and a method of utilizing same to assist educational program execution, including by providing educators (30A) (eg. teachers) and their students (30B) with a means of communication, including to conduct various transactions assisted by the use of Al to automatically determine the presence of any plagiarized content submitted by students.

[0042] The network and method provide a platform that hosts a computer implemented software application (40), wherein the application (40) is accessible by the users (30), including educators (30A), seeking to generate and personalize educational programs (60) for enrolled students (30B) who are seeking to efficiently access content and materials associated with educational programs tailored according to their learning needs.

[0043] General reference to “users (30)” herein is intended to indicate reference to either one or both of the educators (30A) or students (30B).

[0044] The platform is provided by a central server (20) which maintains one or more processors and / or databases for performing functions, including receiving details from a plurality of educators (30A) and students (30B), and establishing an account profile for the educators (30A) and students (30B) in respect of whom details have been verified. The server (20) also receives (or may automatically generate) an educational program (60) for each educator (30A) and prompt the educator (30A) to personalize the program (60) according to one or more characteristics of the student(s) (30B) enrolled or proposed to be enrolled in the program (60). The server (20) is further configured to enable thestudent(s) (30B) enrolled in the program (60) to access content and / or materials (70) associated with the educational programs, including one or more tasks to be completed by the student(s) (30B), and receive one or more submissions (80) from the enrolled student(s) (SOB) in response to the one or more tasks. The authenticity of the submission(s) (80) are subsequently assessed by applying one or more Al techniques to generate an authenticity result (90) in relation to each submission (80), wherein the educator (30A) is provided with a display on their data communications device (50) substantially in real time regarding the submission(s) (80) of relevant students along with the authenticity result (90) generated in respect of each submission (80).

[0045] The person skilled in the relevant field of technology will appreciate that a platform according to the present invention provides a solution to existing problems associated with educators (30A) spending substantial time scheduling tasks, creating programs and resources, managing course materials and marking assignments including checking for plagiarism during the process of creating, managing and executing educational programs for their students (30B). Furthermore, students (30B) have different needs which introduces an additional complexity to an already complex task confronting educators. The person skilled in the relevant field of technology will appreciate that a platform according to the present invention provides a solution to this problem by utilizing Al techniques to suggest to educators (30A) program and resource differentiation and adjustments for students (30B). Additionally, the platform provides educators (30A) with a means of automatically determining the presence of any plagiarized content submitted by students (30B), thereby presenting a deterrent to students (30B) who may otherwise seek to utilize generative Al techniques to assist development of content in respect of assignments and related submissions in respect of an educational program.

[0046] The details received from the educators (30A) and / or students (30B) may include real-time data based on monitoring the results of delivering an educational program to students (30B). For example, tests may be performed during the delivery of an educational program to monitor the engagement level of students. An example of such a test involves the provision of computer tablet devices to students (30B) with neurofeedback sensors that monitor engagement levels (as well as other pertinent information such as stress, anxiety or fatigue levels of the student (30B)), and such data may be used to adapt the further delivery of the educational program (eg. by slowing thepace of the program, by adding additional elements, etc). In this way, lesson delivery may be based on cognitive feedback. In addition, when students (30B) start to show signs of disengagement, the platform may introduce interactive elements to enhance the student’s interest, including by introducing Augmented Reality (AR), virtual reality (VR) and similar enhancements to the learning experience. In a particular example, in a chemistry class, students may use AR on their devices, or a VR headset, to visualize molecular structures and chemical reactions, thereby enhancing their understanding through interactive 3D models.

[0047] One or more artificial intelligence techniques may also be used to detect confusion during implementation of an educational program (eg. a virtual lesson) such that suggestions and alternative explanations or resources may be provided to the educators (30A) and / or students (30B).

[0048] Upon completion of an educational program, students (30B) may also receive digital certificates stored on a blockchain, ensuring verifiable and tamper-proof credentials that may be shared with employers and academic institutions.

[0049] The person skilled in the relevant field of technology will also appreciate that the efficiencies resulting from the use of such a platform will give rise to conservation of computer processing, memory and network resources since educators (30A) and students (30B) are no longer required to utilize their fixed and portable computing devices to access multiple platforms, resources and websites in an effort to address the above problems, noting that increased use of such devices gives rise to substantive utilization of such resources (eg. use of smartphones to conduct phone calls and search various databases and / or internet websites to retrieve relevant information), which is also likely to give rise to increased instances of human error and / or retrieval of inaccurate information. Furthermore, the infrastructure may be caused to scale during peak times, such as exam periods, to support potentially thousands of simultaneous users during district-wide online examinations thereby ensuring uninterrupted service. In other words, the platform may dynamically adjust server loads according to user activity, thereby optimizing resource usage whilst reducing cost. In a particular example, during a peak exam period, the platform may automatically reallocate computing resources to handleincreased demand for online testing and assignment submissions, ensuring consistent performance.

[0050] In view of the above observable effects arising from an implementation of the present invention, the use of data communications devices (50) on which the software application (40) described herein is operated, in combination with the functionality of server (20), represents a more efficient use of computing resources as compared with conventional computing technology utilized for assisting the development and execution of educational programs.

[0051] Figure 1 is divided into Segments which are further expanded in subsequent Figures 2 to 6. In particular, Segment 200 of Figure 1 illustrates the server component (20) with which a software application (40) operating on data communication devices (50) of individual users (30) are configured to communicate. It will be apparent to the person skilled in the relevant field of technology that the software application (40) may be a mobile application or a web-based application, and similarly, the device (50) utilized by each user (30) may be a portable device such as a mobile (cell) phone, laptop, or tablet, or alternatively, a fixed location computer device such as a workstation or personal computer (not shown). The server component (20) is further detailed in Figure 2. Irrespective of the device used, it will be appreciated that the platform may synchronise across all devices to ensure that an educational program may proceed uninterrupted irrespective of the particular device the user chooses to engage the platform.

[0052] The person skilled in the relevant field of technology will appreciate that the steps described herein may be executed by the devices (50), wherein such operations are facilitated by the software application (40) operating on each device (50). According to another implementation of the present invention, the server (20) may be programmed to provide all of the functions described herein, particularly where they cannot be provided locally on the user devices (50) or where it may be impracticable or commercially infeasible to operate such a configuration. In other words, the steps described herein as performed by the devices (50), or components thereof, may be associated with hardware that is located externally of the devices, such as the remote central server (20) (ie. in a distributed architecture). Different arrangements are possible in this regard and alternate variations will be apparent to the person skilled in the relevant field of technology.

[0053] Segment 300 of Figure 1 illustrates how the server (20) may be configured for communication (140) with the devices (50) associated with the user (30). As detailed in Figure 3 in one example, the server (20) may receive data from the devices (50) for the purpose of creating a user profile, (eg. based upon entry of details in interface (160) from the educators (30A) as well as students (30B)). The server (20) may also verify such details prior to the establishment of an account and account profile. Segment 300 of Figure 1 further illustrates a user (30) downloading and installing the application (40) and subsequently accessing the application (40) to establish their account and user profile, with educators (30A) also provided with an opportunity to select an existing educational program (60) or create a new educational program (60) in respect of which students (30B) may enroll. Segment 400 of Figure 1 illustrates an example interface (170) accessible by students (30B) for accessing content and / or material associated with an educational program (60) in which they are enrolled (or for which enrollment is currently being considered), including one or more tasks to be completed to satisfy the requirements of the program (60), and further, to make submissions (80) in response to program tasks, as further detailed in Figure 4.

[0054] Segment 500 of Figure 1 illustrates example interfaces including interface (180) that enables educators (30A) to view and assess submissions (80) provided by students (30B) in respect of a particular educational program (60) in which students (30B) are enrolled, including an authenticity result (90) that has been generated in respect of each submission (80) using data processing functionality (115) associated with the server (20), and interface (190) in which educators (30A) may receive personalized feedback and recommendations according to individual needs and learning styles of students (30B), as further detailed in Figure 5. Segment 600 of Figure 1 illustrates an additional example interface (210) in which additional functionality associated with the software application (40) may be accessed by each user (30), as further detailed in Figure 6.

[0055] As mentioned above, Figure 2 illustrates in greater detail Segment 200 of Figure 1 and, in particular, Figure 2 illustrates the server component (20) which includes infrastructure upon which the platform the present invention operates. The infrastructure may be local or cloud-based. In a particular embodiment, the infrastructure utilizes a hybrid cloud approach (eg. combing Microsoft Azure and Amazon Web Services (AWS)) to achieve optimal scalability, reliability and performance. The central server (20) mayoperate one or more computer processors and maintain one or more databases to enable the following functionality and / or storage;• User account and data register (100) storing details relating to registered users (30) (eg. name, address, contact details and any additional detail that may be relevant for the purpose of identifying each user). Additional details may be stored in the register (100) including account profiles, and details relating to information which has undergone verification according to a predetermined standard to enable the creation of a user account and / or profile;• Educational program database (105) storing details relating to programs (60) which have been created for, selected and / or edited by educators (30A), as well as details relating to students (30B) enrolled in such programs (60). The database (105) may also store the content and materials associated with each stored program (60) including tasks to be completed, and it is to be understood that the database (105) may be regularly updated with the most up to date details and / or personalizations made to programs (60) by educators (30A);• Submissions database (110) for storing submissions (80) by students (30B) or records relating to those students (30B), wherein each submission may include a date / time stamp. The database (1 10) may also store the authenticity result (90) generated in relation to each submission (80);• Data processing functionality (115) for processing functions including processing user input commands and any additional data received for the purpose of generating output(s) in response. For example, functionality (115) may apply one or more Al techniques (eg. natural language processing, etc) to interpret and assess the authenticity of one or more submissions (80) and thereby generate an authenticity result (90) in respect of each submission (80) relating to the presence of any generated or plagiarized content submitted. The data processing functionality (115) may also use Al techniques to process and analyze human language data present in the submission (80) and access a historical library of student work (95) when conducting assessments regarding authenticity to generate personalized feedback and recommendations to educators (30A). The functionality (115) may also be responsible for training a machine learning modelto identify submissions as lacking authenticity according to a pre-defined authenticity assessment threshold as further described herein;• Payment gateway functionality (120) to manage all financial transactions that may be required through the platform including handling payments in respect of subscription fees as well as payments in respect of access to educational programs (60) or content and / or materials required to create such programs, etc;• Chat / messaging functionality (125) enabling communications between registered users (30) including amongst educators (30A), amongst students (30B), and between educators (30A) and students (30B), as well as any communications that may be required with a platform administrator;• Alerts / notifications functionality (130) to manage in a timely manner the generation and dispatch of alerts and / or notifications to users (30) of the platform. For example, alerts and / or notifications may be generated based on the receipt of communications from other users, the receipt of submissions from students (30B) or the receipt of authenticity results (90);• Report generation functionality (135) that may be utilized for processing user requests for particular reports and generating such reports, including for example in relation to student progress with respect to educational programs (60).

[0056] Figure 2 also depicts that server (20) is configured to enable communication (140) with the devices (50) and, in particular, the software application (40) operating on each device (50). Such communications may occur via the internet or similar network.

[0057] Figure 3 illustrates in greater detail Segment 300 of Figure 1 and, in particular, the steps associated with a user (30) (educators (30A) or students (30B)) installing (150) the software application (40) on their devices (50), and subsequently accessing a user login and registration interface (160) associated with the application (40). Such access may be granted after the user (30) has installed the application (40) which may be achieved by downloading the application (40) from an application store. Each user (30) may create an account (which may include a user profile) using the application (40) basedupon verification of user details, and the account / profile information may be stored in the user account and data register (100).

[0058] Once each user (30) has accessed the application (40), the user (30) may be presented with one or more interfaces that the user (30) may utilize to create and maintain their profile and subsequently enable other users to view such information. In the example shown in Figure 3, the interface (160) is also used by educators (30A) to upload, select or create a program (60) and to edit (personalize) the program (60) according to one or more characteristics of the students (30B) enrolled in the program or who are likely to be enrolled in the program (60). In this regard, interface (160) may also provide assistance to such educators (30A), including by providing links (65) to course content and material to assist the educator (30A) when generating and / or editing educational programs (60). For example, adjusting a standard program may include adjusting one or more of the educator’s lesson plans, course content, and / or course materials. Ultimately, the program (60) may include curriculum, programming and lesson plans sufficient to enable enrolled students (30B) to improve and increase their knowledge regarding the particular subject matter and to address the requirements of any one particular educational authority governing the educational program (60).

[0059] The links regarding course content and / or course materials may be provided to educators (30A) seeking to create or personalize educational programs (60), and such links may enable access to libraries and databases comprising teaching resources to assist the educator (30A). For example, the teaching resources may include course materials for use when creating a program for one or more subjects. In a particular example, a history teacher preparing a teaching unit regarding ancient civilisations may receive Al-generated suggestions for articles, documentaries, and virtual museum tours that align with the curriculum and the students’ requirements and / or interests. In another example, an elementary school teacher may input the topic “solar system”, and a week long lesson plan including videos, interactive AR activities, and quizzes all tailored to the student(s) may be generated. Where the program (60) is not created by an educator (30A) and is either a generic or automatically generated program, such libraries and / or databases may be automatically interrogated in order to retrieve relevant course content and course materials according to the particular educator (30A) and / or the student (30B) enrolled or proposed to be enrolled.

[0060] It will be appreciated that educational programs created or generated on behalf of educators may undergo changes (implemented by the educators (30A) or automatically implemented) not only prior to, but also during, the program. In a particular example, an educator (30A) may be provided with a means of creating or editing a history curriculum that includes AR-based simulations of historical events. The platform may automatically suggest additional reading materials and interactive exercises based on student feedback and engagement data. In another example, an educator (30A) may develop a maths program that adapts to each student’s understanding in substantially real-time. If a student struggles with a particular concept, the platform may introduce supplementary exercises or visual aids to clarify the material.

[0061] Feedback and recommendations may also be provided to students (30B) in substantially real-time before or during an educational program. In one example, a student preparing for a biology exam may receive personalized recommendations for review materials, such as video tutorials, interactive simulations, and practice quizzes, according to their performance and current study patterns. A student’s progress may be analysed and their learning path tailored to provide a focus on areas where the student requires greater assistance and guidance.

[0062] Figure 4 illustrates in greater detail Segment 400 of Figure 1 and, in particular, an example interface (170) which is accessible by students (30B), thereby enabling such students (30B) to access the relevant educational programs (60) in which they are enrolled, including the course content and materials (70) and one or more tasks to be completed (80), and to make submissions (90) in response to those tasks. For example, a student (30B) may submit an essay in respect of environmental science. The platform may utilize natural language processing techniques to analyse the text, compare same with published articles and previous student work (which may be stored on a blockchain), detecting potential plagiarism and thereby ensuring originality. The platform may also apply machine learning models trained on large datasets to identify Al-generated content in student submissions, alerting educators to potential academic misconduct.

[0063] Subtle forms of plagiarism may also be detected according to the training of machine learning models using historical data based upon past student submissions. For example, models may be trained to detect paraphrasing in submitted text that does notinclude citations. Such machine learning models may be continuously updated based on feedback from educators (30A), refining their ability to detect new types of plagiarism and academic dishonesty.

[0064] Students may also be alerted to the likelihood that their work, if submitted in its current form, will be determined as satisfying the authenticity assessment threshold. In this regard, the platform may provide substantially instant feedback regarding originality (as well as issues such as grammar, sentence structure, etc), thereby assisting the student (30B) to refine their work prior to final submission.

[0065] Accordingly, the person skilled in the relevant field of technology will appreciate that a student (SOB) may elect to plagiarize content created by other individuals or utilize generative Al techniques to assist development of content in respect of submissions (eg. assignments and other submitted works). In this regard, based upon receipt of a submission (80), the submission (80) will be automatically assessed for authenticity including through the use of Al techniques (eg. one or more natural language processing techniques as described above) to process and analyze the language of each student’s submission (80) to determine whether plagiarism and / or generative Al techniques have been utilized to formulate the content of the student’s submission (80).

[0066] The Al techniques that may be applied to assess authenticity of the one or more submissions (80) may vary according to the particular application. However, it will be appreciated that most techniques will utilize machine learning algorithms to process and analyze human language data from the submissions of students (30B), which may also include accessing a historical library of student work (95) for comparative purposes. In other words, if a newly submitted student work is determined to be significantly different to the student’s previous work in terms of style, grammar, etc, then this may contribute to an overall determination that the new work is likely to have been plagiarized. Educators (30A) may be provided with detailed insights into the assessment, including by receiving direction to areas of the work considered original as compared with those areas likely to be plagiarized.

[0067] In circumstances where the student’s work is assessed as comprising original work of the student, the submission may be recorded on a blockchain ledger, creating an immutable trail that verifies the originality and authorship of the academic work.

[0068] The platform may also continuously learn and improve outputs regarding authenticity based upon historical data including, for example, the accuracy or otherwise associated with previous determinations regarding authenticity. In this regard, a machine learning model may be trained to identify submissions (80) as lacking authenticity according to a pre-defined authenticity assessment threshold, wherein training data used to train the model may be based upon submissions (80) provided by the students (30B) with authenticity results (90) regarding any current submissions generated according to the predefined authenticity threshold. Accordingly, the student’s submissions may be provided as input for the machine learning model, wherein the output will be an indication regarding whether the submission (80) is authentic.

[0069] A level of authenticity may be determined in respect of newly submitted works, and according to an embodiment, an authenticity score may be assigned to submitted works. For example, the score may be based on the presence of original thought and appropriate citations. It will be appreciated that assigning a score to submitted work provides a means by which authenticity can be assessed against an authenticity assessment threshold. The threshold itself may be a score upon which the authenticity score associated with newly submitted work may be compared.

[0070] Figure 5 illustrates in greater detail Segment 500 of Figure 1 and, in particular, a further example interface (180) that educators (30A) may utilize to view submissions (80) presented by students (30B) and to also view an authenticity result (90) generated by the software application (40) in respect of the submissions (80). It will be appreciated that at this point, the educator (30A) is provided with a real-time indication regarding a likelihood that the materials submitted by a particular student (30B) is not the original work of the student and instead may be plagiarized content or content generated with the assistance of generative Al techniques.

[0071] Figure 5 also illustrates an interface (190) configured to provide educators (30A) with feedback and recommendations. In this regard, the language of each student’s submission (80) may be processed and analyzed using one or more natural language processing techniques to generate personalized feedback and recommendations for the educator (30A) according to individual needs and learning styles of the student (30B) as understood and recognized by the educator (30A). Al techniques applied to assessauthenticity of the one or more submissions may also use machine learning algorithms to process and analyze human language data from the submissions of students and access the historical library of student work (95) when conducting assessments regarding authenticity and when generating the personalized feedback and recommendations to educators (30A).

[0072] Educators (30A) may also utilize the interface (180) to provide feedback to students including providing assessment results in relation to the submissions, and any comments in relation to the authenticity result (90) generated and displayed to the educator (30A) through interface (180). In this regard, it is to be understood that the recommendations to educators regarding adjustments to the teaching program (60) may be based upon training a machine learning model, where the training data comprises one or more submissions provided by the students (30B), the authenticity result (90) generated in respect in the submissions, and the educators assessment result in respect of the submission (80). The output of the machine learning model may be an indication regarding those aspects of the program for which a recommendation is provided, and those aspects of the program recommended for adjustment by the educator (30A). Such feedback and recommendations may be automatically integrated into the educational program such that the platform continuously adjusts the program according to real-time feedback and performance data.

[0073] In circumstances where the educator (30A) uses the interface to grade work submitted by students (30B), the platform may suggest grades to be applied by the educator (30A) according to predefined rubrics which will save time for the educator (30A) and also ensure consistency. It will also be appreciated that any feedback provided by educators (30A), and indeed any communications provided through the platform, may take into account the different linguistic backgrounds of users (30) such that communications may undergo automatic translation to avoid impeding collaboration.

[0074] Figure 6 illustrates in greater detail Segment 600 of Figure 1 and, in particular, a further example interface (210) that may be utilized to provide general functionality to users (30). For example, the payment gateway functionality (120) may be provided through interface (210) in circumstances where any one user (30) is required to make a financial payment to another user (30) or to a platform administrator. Further, thecommunication functionality (125) may be provided through interface (210) in the form of a chat facility that enable users (30) to communicate with one another. Such a communication facility may provide a means of communication between students (30B), educators (30A), and an administrator, using real-time chat and messaging functionality incorporating one or more types of media including written texts, recorded audio and / or recorded video.

[0075] Interface (210) may also be accessed by users (30) to receive alerts and / or notifications (130). For example, alerts and / or notifications may be provided to educators (30A) and students (30B) in response to detection of instances such as amendments to programs (60), schedules, curriculum, lesson plans, course content and course material, and amendments to information regarding prerequisites with respect to any particular program components. Additional examples regarding instances include an alert and / or notification generation message receipt from another user (30), submission by a student, or when an assessment result is available for access by a student.

[0076] Yet another function that may be available through an interface such as interface (210) relates to report generation. For example, an educator (30A) may request a report regarding student progress with respect to the educational program (60), wherein the request specifies a particular time period, and a report regarding student progress with respect to the program over the particular time period may be generated and provided for display by interface (210). In another example, an educator (30A) may be provided with useful reporting information prior to the commencement of an educational program (eg. the results of predictive analytics pertaining to a student (30B) or a cohort of students, including insights on the student(s) at risk of underperforming and recommendations for intervention such as personalized tutoring). It is to be understood that a general searching function may also be provided through one or more interfaces described herein, that may enable searches to be conducted by students (30B) in relation to available educators, and educational programs.

[0077] The functionality described above may also integrate into existing learning platforms, such as an existing learning management system (LMS) of a university. This allows educators (30A) and students (30B) to access Al-driven tools and resources directly through the existing LMS interface, enhancing the overall learning experience.Further, the platform may use application programming interfaces (APIs) to directly exchange data with external systems, such as student information systems (SISs) and assessment platforms, ensuring that all relevant information is synchronized and up-to- date. Any data communicated may be encrypted and securely transmitted over the network, ensuring privacy and compliance with data protection regulations.

[0078] As used herein, the term “server”, “system", “computer”, “computing system” or the like may include any processor-based or microprocessor-based system including systems using microcontrollers, reduced instruction set computers (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuit or processor including hardware, software, or a combination thereof capable of executing the functions described herein. Such are exemplary only, and are thus not intended to limit in any way the definition and / or meaning of such terms.

[0079] The one or more processors as described herein are configured to execute a set of instructions that are stored in one or more data storage units or elements (such as one or more memories), in order to process data. For example, the one or more processors may include or be coupled to one or more memories. The data storage units may also store data or other information as desired or needed. The data storage units may be in the form of an information source or a physical memory element within a processing machine.

[0080] The set of instructions may include various commands that instruct the one or more processors to perform specific operations such as the methods and processes of the various embodiments of the subject matter described herein. The set of instructions may be in the form of a software program. The software may be in various forms such as system software or application software. Further, the software may be in the form of a collection of separate programs, a program subset within a larger program or a portion of a program. The software may also include modular programming in the form of object- oriented programming. The processing of input data by the processing machine may be in response to user commands, or in response to results of previous processing, or in response to a request made by another processing machine.

[0081] The diagrams of embodiments herein illustrate one or more control or processing units. It is to be understood that the processing or control units may represent circuits,circuitry, or portions thereof that may be implemented as hardware with associated instructions (e.g., software stored on a tangible and non-transitory computer readable storage medium, such as a computer hard drive, ROM, RAM, or the like) that perform the operations described herein. The hardware may include state machine circuitry hardwired to perform the functions described herein. Optionally, the hardware may include electronic circuits that include and / or are connected to one or more logic-based devices, such as microprocessors, processors, controllers, or the like.

[0082] Optionally, the one or more processors may represent processing circuitry such as one or more of a field programmable gate array (FPGA), application specific integrated circuit (ASIC), microprocessor(s), and / or the like. The circuits in various embodiments may be configured to execute one or more algorithms to perform functions described herein. The one or more algorithms may include aspects of embodiments disclosed herein, whether or not expressly identified in the figures or a described method.

[0083] It will be appreciated by persons skilled in the relevant field of technology that numerous variations and / or modifications may be made to the invention as detailed in the embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all aspects as illustrative and not restrictive.

[0084] Throughout this specification and claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated feature or step, or group of features or steps, but not the exclusion of any other feature or step or group of features or steps.

Claims

The claims defining the invention are as follows:1 . A data communications network including connected data communications devices and a method of operating same for assisting educational program execution, the method including a first connected data communications device: receiving, by one or more processors, details from a plurality of educators and students, and establishing an account profile for each educator and student for whom details have been verified; retrieving or generating, by one or more processors, a program for each educator and prompting the educator to personalize the program according to at least one or more characteristics of the student(s) enrolled in the program; enabling, by one or more processors, the student(s) enrolled in the program to access content and / or materials associated with the received or generated program including one or more tasks to be completed; receiving, by one or more processors, one or more submissions from data communications device(s) of the student(s) enrolled in the program in response to the one or more tasks to be completed; applying, by one or more processors, one or more artificial intelligence (Al) techniques to assess the authenticity of the one or more submission(s) to thereby generate an authenticity result; and providing, by one or more processors, for display on a data communications device associated with the educator, the student’s submission(s) and the authenticity result generated in respect of the submission(s).

2. A data communications network according to claim 1 , wherein retrieving or generating a program for each educator includes one or more of: receiving a program uploaded by the educator, receiving a program automatically selected from a database of available programs on behalf of the educator according to details received from the educator and / or student(s), orcreating a program on behalf of the educator according to the details received from the educator and / or student(s), and / or one or more queries submitted by the educator.

3. A data communications network according to claim 2, wherein the method further includes providing to the educators, by one or more processors, resources regarding course content and course materials, including access to libraries and / or databases comprising teaching resources to assist the educator to create and upload the educational program.

4. A data communications network according to any one of the preceding claims, wherein the program relates to one or more of: curriculum, programming, or lesson plans.

5. A data communications network according to any one of the preceding claims, wherein prompting the educator to personalize the program according to at least one or more characteristics of the student(s) enrolled in the program includes one or more of: providing links to course content and materials, enabling access to libraries and databases including teaching resources, or providing suggestions to the educator, the suggestions generated using one or more artificial intelligence techniques and aligning with requirements and / or interests of the student(s).

6. A data communications network according to any one of the preceding claims, wherein the method further includes one or more of: prompting the educator, by one or more processors, to adjust the program before or during delivery of the program to the student(s), orautomatically adjusting the program before or during delivery of the program to the student(s).

7. A data communications network according to claim 6, wherein adjustments to the program are suggested or automatically implemented according to one or more of: student details including study patterns, student feedback, student performance, or student progress.

8. A data communications network according to either claim 6 or claim 7, wherein the adjustments to the program include one or more of: adjustments to one or more of the educator’s programming, lesson plans, course content and / or course materials, suggesting additional review material prior to delivery of the program, including one or more of video tutorials, interactive simulations, and practice quizzes, suggesting additional reading material for the student(s), suggesting supplementary exercises, or incorporating additional interactive exercises during delivery of the program including one or more of augmented reality (AR) or virtual reality (VR) simulations.

9. A data communications network according to any one of the preceding claims, wherein the one or more Al techniques applied to assess authenticity of the one or more submissions includes at least a natural language processing (NLP) technique to analyse text in each submission.

10. A data communications network according to claim 9, wherein the one or more Al techniques applied to assess authenticity of the one or more submissions further includes training one or more machine learning models to:compare each submission against published articles and / or a historical library of student work to assess the existence of plagiarized content, and / or identify any content in the submission that has been developed using generative artificial intelligence.

11. A data communications network according to claim 10, wherein the method includes training, by one or more processors, a first machine learning model to identify submissions as lacking authenticity according to a pre-defined authenticity assessment threshold, wherein training data used to train the first model is based upon submissions provided by the plurality of students with authenticity results regarding any current submissions generated according to the pre-defined authenticity threshold.

12. A data communications network according to claim 11 , wherein the method further includes providing one or more student submissions as input for the first machine learning model, and receiving, from the first model, an indication regarding whether the one or more student submissions is authentic.

13. A data communications network according to claim 12, wherein the method further includes: assigning, by one or more processors, an authenticity score to each student submission, and based upon the authenticity score of a submission satisfying the pre-defined authenticity assessment threshold, recording, by one or more processors, the submission on a blockchain ledger.

14. A data communications network according to any one of claims 9 to 13, wherein the method further includes receiving, by one or more processors, assessment results relating to the one or more student submissions, the assessment result according to one or more reviews of each student submission by one or more educators.

15. A data communications network according to claim 14, wherein the method further includes training, by one or more processors, a second machine learning model to provide recommendations to the educator regarding adjustments to the teachingprogram, wherein training data used to train the second model is based upon one or more of: the submissions provided by the plurality of students, the authenticity assessment results generated in respect of the submissions, and the educator’s assessment results in respect of the submissions.

16. A data communications network according to claim 15, wherein the method further includes: providing the one or more student submissions as input to the second machine learning model, and receiving, from the second model, an indication regarding those aspect(s) of the program for which a recommendation is provided and those aspect(s) of the program that are recommended for adjustment by the educator.

17. A data communications network according to any one of claims 9 to 16, wherein the method further includes: generating personalized feedback and recommendations for the educator according to the NLP technique processing and analysing the text in each student’s submission, and according to the individual needs and learning style of the student.

18. A data communications network according to any one of the preceding claims, wherein the method further includes one or more of: receiving, by one or more processors, a request from an educator for a report regarding student progress with respect to the educational program, the request specifying a particular time period, and generating, by one or more processors, a report regarding student progress with respect to the program over the particular time period, and providing the report for display on the data communications device of the educator; orgenerating, by one or more processors, notifications to educators and / or students when students enrolled in the program upload submissions, educators issue assessments and additional feedback regarding uploaded submissions, or adjustments to the program and / or amendments to information regarding pre-requisites with respect to any particular program component are implemented.

19. A computer-implemented system for assisting educational program execution, the system including one or more computer processors configured to: receive details from a plurality of educators and students, and establishing an account profile for each educator and student for whom details have been verified; retrieve or generate a program for each educator and prompting the educator to personalize the program according to at least one or more characteristics of the student(s) enrolled in the program; enable the student(s) enrolled in the program to access content and / or materials associated with the received or generated program including one or more tasks to be completed; receive one or more submissions from data communications device(s) of the student(s) enrolled in the program in response to the one or more tasks to be completed; apply one or more artificial intelligence (Al) techniques to assess the authenticity of the one or more submission(s) to thereby generate an authenticity result; and provide, for display on a data communications device associated with the educator, the student’s submission(s) and the authenticity result generated in respect of the submission(s).

20. A non-transitory computer-readable medium including computer instruction code that, when executed on a computer, causes one or more processors of the computer to perform steps to assist execution of an educational program, including: receiving details from a plurality of educators and students, and establishing an account profile for each educator and student for whom details have been verified;retrieving or generating a program for each educator and prompting the educator to personalize the program according to at least one or more characteristics of the student(s) enrolled in the program; enabling the student(s) enrolled in the program to access content and / or materials associated with the received or generated program including one or more tasks to be completed; receiving one or more submissions from data communications device(s) of the student(s) enrolled in the program in response to the one or more tasks to be completed; applying one or more artificial intelligence (Al) techniques to assess the authenticity of the one or more submission(s) to thereby generate an authenticity result; and providing for display on a data communications device associated with the educator, the student’s submission(s) and the authenticity result generated in respect of the submission(s).

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