Artificial intelligence education system

Abstract

A method and system comprising inputting a combination of educational data for processing into a computer program, receiving and processing the educational data by an artificial intelligence arranged to interface with the computer program and the artificial intelligence producing corresponding output data based upon the educational data and instructions given to the artificial intelligence in relation to the educational data.

Description

[0001] ARTIFICIAL INTELLIGENCE EDUCATION SYSTEM

[0002] The present invention relates to an artificial intelligence system used in educational establishments to assist educators in those establishments.

[0003] According to one aspect of the present invention, there is provided an artificial intelligence education system comprising a computer program serving to receive an input of a combination of educational data for processing, an artificial intelligence arranged to interface with the computer program and to receive and process the educational data and subsequently produce corresponding output data based upon the educational data and instructions received by the artificial intelligence in relation to the educational data.

[0004] According to a second aspect of the present invention, there is provided a method comprising inputting a combination of educational data for processing into a computer program, receiving and processing the educational data by an artificial intelligence arranged to interface with the computer program and the artificial intelligence producing corresponding output data based upon the educational data and instructions received by the artificial intelligence in relation to the educational data.

[0005] Owing to these aspects, artificial intelligence provides tailored output data for both students and educators corresponding to the education data received and instructions given to it.

[0006] In one preferred embodiment, the educational data is a piece of work produced by one or more students combined with educational data provided by the educator in the form of a relevant marking schema along with any other relevant data that the artificial intelligence is instructed to process along with the other inputted educational data. By way of an application programming interface (API), the educator can instruct the artificial intelligence, by a single keystroke or click of a device that translates position and motion to move a cursor on a display screen and interact with websites and applications, such as a computer mouse or track-pad, to produce specific output data relating to the combination of educational input data and which meets the criteria of the specific instructions given to the artificial intelligence. In this preferred embodiment, the output data is in the form of educationally related feedback on the piece(s) of work originally input as the educational data to the individual student and / or educator.

[0007] In this way, for example, the artificial intelligence is marking individual English essays to exam standard.

[0008] In a second preferred embodiment, the educational data is a collection of feedback data based upon students’ work over a period of time and / or educational target data combined with corresponding topic data, the artificial intelligence being instructed to produce a lesson plan for a specified period of time depending on the topic data. By way of the API, possibly by a single keystroke or click of a device (as described above) the educator can instruct the artificial intelligence to produce specific lesson plans relating to the combination of the feedback data combined with the corresponding topic data. In this preferred embodiment, the output data is in the form of a proposed lesson plan over the period of time which includes proposed lesson structure(s).

[0009] As such, an educator is able to manipulate the artificial intelligence to create lesson plans over a period of time which may extend to the length of a whole topic, being a number of weeks.

[0010] In a third preferred embodiment, the educational data is a collection of feedback data based upon students’ work over a period of time for a particular topic combined with corresponding data relating to one or more learning modules for the topic and a framework of work in the topic to be set, the artificial intelligence being instructed to create new work for one or more students according to available modules and the feedback. By way of the API, a proposed work plan is provided to the educator.

[0011] It is therefore possible to create custom assignments for an individual student or a group of students.

[0012] In another preferred embodiment, the educational data is a collection of feedback data based upon students’ work over a period of time and the structure of feedback

[0013] 2

[0014] SUBST|TUTE SHEET (RULE 26) required by the educator, the artificial intelligence being instructed to produce a longitudinal analysis of student progress. By way of the API, the educator can instruct the artificial intelligence to a broad range of output data relating to student progress.

[0015] In this way such longitudinal analysis can be applied to both individual students and / or a group of students.

[0016] In yet another preferred embodiment, a two-stage process has a first training stage where the artificial intelligence learns the style of working by the educator, for example a style of marking work or a lesson plan in the style of the educator concerned and a second stage where a style guide learnt by the artificial intelligence is used to provide student feedback from the artificial intelligence in the style of the educator, with which a student is familiar. In the second stage, the educational data is a feedback style template learnt from the first stage and educator feedback and / or newly generated feedback from the artificial intelligence, the artificial intelligence being instructed to return feedback to the student to mimic the feedback style of the educator.

[0017] An artificial intelligence generated feedback can therefore be produced for a student in the style of the educator teaching a particular topic or subject and with which style the student is familiar with and readily understands.

[0018] In a still further preferred embodiment, the educational data is a collection of feedback data based upon students’ work over a period of time and data relating to a variety of progress report templates, the artificial intelligence being instructed to produce a progress report for a student on a particular one of the variety of templates.

[0019] Personalized progress reports are therefore creatable by the artificial intelligence on a desired template which may be a particular template used by a local education authority which is particularly useful if the student is not educated in a local authority run school, such as those students who are home-educated but have to provide the local authority or another educational authority with progress reports.

[0020] 3

[0021] SUBST|TUTE SHEET (RULE 26) Each preferred embodiment hereinabove described are advantageously options to be run in any chosen combination from the computer program, which is preferably a web-based software program that students and educators have secured access to.

[0022] The artificial intelligence system can be used for children of all abilities, with tailored work being planned for each student.

[0023] In order that the present invention can be clearly and completely disclosed, reference will now be made, by way of example only, to the accompanying drawings in which:-

[0024] Figure 1 is a flow diagram and system architecture of a first preferred embodiment of an artificial intelligence system for use in an educational environment,

[0025] Figure 2 is a flow diagram and system architecture similar to Figure 1 but of a second preferred embodiment,

[0026] Figure 3 is a flow diagram and system architecture similar to Figure 1 but of a third preferred embodiment,

[0027] Figure 4 is a flow diagram and system architecture similar to Figure 1 but of a fourth preferred embodiment,

[0028] Figure 5 is a flow diagram and system architecture similar to Figure 1 but of a fifth preferred embodiment shown in two stages, and

[0029] Figure 6 is a flow diagram and system architecture similar to Figure 1 but of a sixth preferred embodiment.

[0030] In the Figures, the work-flow is shown on the left-hand side, the system architecture shown centrally and instructions given to an artificial intelligence on the right-hand side.

[0031] Referring to Figure 1 , the first step of using an artificial intelligence to generate high- quality student feedback comprises a student or educator (i.e. teacher / teaching assistant) submitting 2, via an education computer program which is advantageously securely accessed via the internet and is a web-based software program, a piece of work for marking which could be, for example, an English essay on a particular topic. The piece of work may be scanned with OCR software, copied and pasted from one software program to the education software program or is typed directly into the education software program. The educator is able to select 4 via the education computer program a specific marking schema for the particular topic the piece of work has been prepared for. Such a marking schema is relevant to a particular examining body responsible for qualifications the student is studying to achieve. If the piece of work is an English essay the data will be in a text format. The data sources in this embodiment are therefore the student essay, the question which the essay is proposed to answer and the relevant marking schema (usually obtained from the examining body). Once the marking schema has been selected, the educator can instruct 6 the artificial intelligence, which advantageously in the form of a natural language processing tool, to interface with the education computer program via an API call, possibly by a single keystroke or click of a device that translates position and motion to move a cursor on a display screen and interact with websites and applications, such as a computer mouse or track-pad, by way of a prompt to perform specific tasks relating to the piece of work. For example, and as shown in Figure 1 , the prompt from the educator might be to provide short motivating feedback in British English for the student on the essay and to keep the feedback to a certain number of words, the number of words being set by the educator. In addition, the prompt to the artificial intelligence includes the title of the essay and a request for a particular student feedback format and focusing only on particular criteria. The output data sent from the artificial intelligence to the education computer program to enable the student to see the requested feedback follows the prompt(s) given to the artificial intelligence by the educator and in this instance, that output data comprises the Al- generated information being returned 8 to the student. That feedback might, for example say what the student did well, for example “You used powerful adjectives to describe the setting, such as ‘huge’ sandcastle, ‘shiny’ sun and ‘sparkly blue’ water” and “Your spelling is accurate throughout the essay” and it might also suggest what could be even better, such as the essay could be even better if “You included a description of a character using powerful adjectives”.

[0032] In this way, the system takes a student essay and uses the artificial intelligence to mark it against a marking schema and then feeds tailored results back to the student which the educator can also see.

[0033] 5

[0034] SUBST|TUTE SHEET (RULE 26) The system architecture comprises a database 10 of questions (which includes past Examination questions if necessary), including the question asked to the student and for which the essay is produced, a second database 12 which holds the text format of the essay once entered to the education computer program, a third database 14 holding data relating to relevant marking schema from relevant examining bodies (this data is also in text format), and a fourth database 16 including a specific sub-set of the data in the third database and being specific marking selections such as being able to compare different writers’ ideas and perspectives, as well as how these are conveyed, across two or more texts, to communicate clearly, effectively and imaginatively, selecting and adapting tone, style and register for different forms, purposes and audiences, and to demonstrate presentation skills in a formal setting. For example:-

[0035] • at Key Stage 2 level (students aged 7 to 11 in the United Kingdom) this could reflect national curriculum criteria such as:

[0036] - Use of powerful adjectives to describe characters

[0037] - Grammar

[0038] - Use of past tense

[0039] • at GCSE-level (students aged around 15 to 16) this could reflect the examboard criteria, such as “Read, understand and respond to text, using textual references and quotations” and “Analyse the language, form and structure”.

[0040] A combination of the input data from the first, second, third and fourth databases are sent via an API call 18 to the artificial intelligence which is prompted by the educator to process the data. The educator has to specifically instruct the artificial intelligence to process the combination of data to create the specifically requested feedback on the essay returned by the API.

[0041] Educators save on average 2 to 4 hours per writing assignment with the present system.

[0042] 6

[0043] SUBST|TUTE SHEET (RULE 26) Educators have control over both the execution of the artificial intelligence (having to request it process the data input into the system) and in approving feedback before it is returned to the student. No experience of using an artificial intelligence is required since the step of executing the artificial intelligence can be a one-click process on the user interface provided by the web-based education software program.

[0044] Referring to Figure 2, in this embodiment the artificial intelligence is manipulated to deliver a proposed lesson structure to the educator. The data to produce such a proposal to be input to the education computer program is based upon, firstly, feedback 20 (gathered by the educator or the artificial intelligence from previous use) gathered over a period of time for an individual student or a group of students and, secondly, on topic data 22 including how often in a period of time such as a week that topic is to be taught. The feedback data is based on one or more pieces of submitted work submitted over a period of time, for example, over a school term and may take the form of information such as “students should work on exploring further the broader themes of the play, specifically the conflict between parental expectations and individual desires” and “it would be beneficial for students to delve deeper into how Shakespeare’s portrayal of characters reflects these broader themes”. Topic data includes information such as “Lady Capulet in Romeo and Juliet” to directly relate to the feedback data and also skeleton information of the proposed lesson structure such as timetabling information or infrastructure which can include one or more of the following information

[0045] • Lesson objective

[0046] • Starter question / information

[0047] • Main teaching content

[0048] • Independent task / group activity

[0049] • Plenary

[0050] • Differentiation tasks

[0051] • SEN information

[0052] • Challenge for more able students

[0053] • Resources required

[0054] • Finish (with 3 key questions to consider)

[0055] 7

[0056] SUBST|TUTE SHEET (RULE 26) That combination of data is subsequently submitted 24 to the artificial intelligence by way of the API. The result of the data processing by the artificial intelligence is a return 26 of the proposed lesson structure to the educator.

[0057] This process takes student feedback and uses the artificial intelligence to create a lesson plan over a period of time and thus advantageously a multi-lesson plan to cover that period of time. This may be a lesson plan for an individual student or a group of students.

[0058] The system architecture comprises a database 28 of the feedback data and a second database 30 which holds the topic data. The educator can then prompt the artificial intelligence to, for example, produce a UK lesson plan on the topic of (to be specified by the educator) and building on (a specified) feedback; to use British English targeted at (specified ages of students); and to define a lesson plan according to a specified structurewhich may, for example, be based on the following:-

[0059] 1 . Objective: what is lesson is trying to achieve?

[0060] 2. Materials: required materials

[0061] 3. Introduction: what the teacher should do at the start of the lesson

[0062] 4. Activity: an activity for the students to do

[0063] 5. Guided Practice: working as a class to develop knowledge

[0064] 6. Independent Practice: independent work

[0065] 7. Close: wrap-up at the end of the lesson

[0066] 8. Homework: work for the students to do after the lesson

[0067] 9. Evaluation: how the students will be assessed.

[0068] A combination of the input data from the first and second databases 28 and 30 are sent via an API call 32 to the artificial intelligence which is prompted by the educator to process the data, possibly by a single keystroke or click of a device (as described above). The educator has to specifically instruct the artificial intelligence to process the combination of data to create the proposed lesson plan which is returned by the API.

[0069] In this embodiment, the ability of the artificial intelligence to understand development needs and to develop corresponding lesson plans is particular advantageous.

[0070] 8

[0071] SUBST|TUTE SHEET (RULE 26) Referring to Figure 3, creation of new student assignments are achieved by utilizing the artificial intelligence. At the start of this task, subject-specific feedback data is gathered 34 along with data 36 corresponding to the learning modules in the specific subject which includes information regarding assignment structure for the subject. In this embodiment the feedback data includes progress information on individual students within one or more specific subject modules, for example:-

[0072] • Student 1 : excellent progress in fractions and algebra, needs to work harder on trigonometry

[0073] • Student 2: struggling with the basics in all areas

[0074] • Student 3: outstanding trigonometry, needs to work harder on basics in other areas

[0075] The learning module data will simply include module titles such as “Basic Fractions (2 modules of 10 mins)”, “Advanced Fractions (2 modules of 10 mins)”.

[0076] Assignment structure data relates to a desired framework of an assignment, such as the time given for an assignment in the particular module, for example, “Three 20 minute homework assignments”.

[0077] That combination of data is subsequently submitted 38 to the artificial intelligence by way of the API call. The result of the data processing by the artificial intelligence is a return 40 of a proposed assignment plan to the educator.

[0078] The system architecture comprises a database 42 of the feedback data and a second database 44 which holds the data of learning modules and assignment framework. The educator can then prompt the artificial intelligence to, for example, create assignments for individual students that fit with a specified assignment framework. A combination of the input data from the databases 42 and 44 are sent via the API call to the artificial intelligence which is prompted to process the data.

[0079] In this way, the artificial intelligence serves to take feedback and source assignments which address student development goals.

[0080] 9

[0081] SUBST|TUTE SHEET (RULE 26) Referring to Figure 4, in this embodiment the artificial intelligence is manipulated to deliver a longitudinal (or long-term) analysis of student progress for individual students and student groupings in order to create a timeline of student progress. The data to produce such an analysis to be input to the education computer program is based upon, firstly, feedback 46 gathered over a period of time by way of multiple assignments for an individual student or a group of students and, secondly, on feedback structure 48 which might be a topic structure, for example; fiction writing; non-fiction writing; reading comprehension; spelling; grammar, or it might be progress structure, for example; significant advances; modest progress; no progress.. The feedback data is based on one or more pieces of submitted work submitted over a period of time, for example, which can include information along the following lines:-

[0082] • Student 1 , assignment 1 : weak algebra, moderate trigonometry, okay fractions

[0083] • Student 1 , assignment 2: moderate trigonometry, okay fractions

[0084] • Student 1 , assignment 3: okay algebra, good trigonometry, okay fractions

[0085] • Student 1 , assignment 4: okay algebra, good trigonometry, okay fractions

[0086] • Student 2, week 1 : strong algebra

[0087] • Student 2, week 2: moderate trigonometry

[0088] • Student 2, week 3: okay trigonometry

[0089] • Student 2, week 4: strong algebra and trigonometry, weak fractions

[0090] • Student 2, week 5: moderate fractions

[0091] • Student 3, week 1 : okay algebra

[0092] • Student 3, week 2: okay trigonometry

[0093] • Student 3, week 3: moderate trigonometry

[0094] • Student 3, week 4: strong algebra and trigonometry, strong fractions

[0095] • Student 3, week 5: moderate fractions

[0096] That combination of data is subsequently submitted 50 to the artificial intelligence by way of the API call. The result of the data processing by the artificial intelligence is a return 52 of the long-term progress of the student(s).

[0097] The system architecture comprises a database 54 of the feedback data over time and a second database 56 which holds the data relating to educator-defined io

[0098] SUBST|TUTE SHEET (RULE 26) feedback structure. The educator can then prompt the artificial intelligence to, for example, produce student progress and also define that, for example, merely an analysis and overall progress is required. As shown in Figure 4, the educator making the request can also instruct the artificial intelligence not to do defined acts such as recommendations for further development. A combination of the input data from the databases 54 and 56 are sent via an API call to the artificial intelligence which is prompted by the educator to process the data possibly by a single keystroke or click of a device (as described above).

[0099] Thus, the artificial intelligence creates a time axis, showing how student(s) progress over time.

[0100] Referring to Figure 5, a two-stage process for creating feedback information to the student(s) that mimics the feedback style of the individual educator is shown. In a first learning stage on the left-hand side of Figure 5, initially, examples 58 of previous written feedback instances from the individual educator are entered into the education computer program which is then submitted 60 to the artificial intelligence with which it interfaces, and the result of that interfacing is a database 62 in the form of a feedback style template, unique to the individual educator. In the second application stage, shown on the right-hand side of Figure 5, the feedback style template 62 is combined with feedback data 64 either already existing from the artificial intelligence or from a new request for feedback (such as in the marking of a new assignment). In combining the data input, the feedback returned 66 will now be in the style of the individual educator which is effectively personalized and which the student(s) are familiar with and understands.

[0101] This two-stage process creates a style guide matching an individual educator’s style of marking. This allows the artificial intelligence to generate future student feedback in the style of the individual educator. An advantageous feature is that the feedback style template 62 can be directly applied to new student feedback requests so that it does not need to be applied to adapt existing feedback each time.

[0102] Referring to Figure 6, the process shown details how the artificial intelligence can be utilized to generate official information required by Government / local Authority

[0103] 11

[0104] SUBST|TUTE SHEET (RULE 26) establishments on student progress. This embodiment may be useful for parent feedback on a child that is, for example, home educated and data needs to be submitted to the relevant educational authority in a defined way. In this instance, information regarding student activities and progress are entered 68 and the educator (whether a parent or private tutor or other person) further selects 70 an educational authority-defined reporting template from a database 72 thereof.

[0105] That combination of input data is subsequently submitted to the artificial intelligence by way of the API call, possibly by a single keystroke or click of a device (as described above). The result of the data processing by the artificial intelligence is a return 74 of a proposed official progress report to the educator for review and subsequent submission to the relevant authority in the correct format.

[0106] For example, unstructured feedback might be text information that says:- “Jane is an excellent student. We have worked hard learning together this term. She has used online learning resources and textbooks to focus on English, and Maths, particularly trigonometry. Additionally, she has helped in the kitchen with measuring materials. For Science we have watched TV programmes on space and satellites. On holiday we visited Paris and practiced French. We also visited cathedrals to learn about religion.”

[0107] Correspondingly, the database 72 might include the following information:- “1 ) Our approach to education: brief philosophy on your approach to education.

[0108] 2) About child: brief paragraph about the student, their interests and how they learn. Also mention the student’s needs, attitudes and aspirations.

[0109] 3) Education: Write about the activities completed and how students have learnt from them. Include numeracy and reading. Include a clear structure by subject. Write in a clear style that it easy to read. Include education throughout all aspects of life.

[0110] 4) Resources: these include books, clubs, trips, websites, TV programmes, arts and crafts materials, electronics, and physical activity.”

[0111] In this way, the process can take unstructured feedback for a student who may be home educated and convert it into structured feedback in a format which matches that required by the relevant educational authority. The API call may accompany an

[0112] 12

[0113] SUBST|TUTE SHEET (RULE 26) instruction from a home educator that request the artificial intelligence for help in creating a report for a student from unstructured data feedback contained in a particular template submitted and to be converted to a structure that matches a user- defined template for the appropriate authority.

[0114] The artificial intelligence can therefore help home educating parents by turning unstructured feedback into high-quality report information.

[0115] In all the embodiments described above, the artificial intelligence is a chat bot such as Chat GPT for example.

[0116] In use, an educator, parent or student can log-in to their personal virtual space on the web-based computer program using personal log-in details at the requisite security level desired. In that virtual space, taking an educator as an example, the user interface will enable various tasks to be carried out. One of those tasks will be to ‘Set an Assignment’ and activation of that option would open a software window in which various data options are presented for completion, such as:-

[0117] • the Class to be selected (for example, Year 6 English),

[0118] • the title of the Assignment

[0119] • the date the task was set

[0120] • the time the task was set on the set date

[0121] • the date the Assignment is due

[0122] • the time the Assignment is due on the due date

[0123] • any further details (for the educator, for example, to enter any instructions to the student(s))

[0124] • the option to add attached electronic files

[0125] • the artificial intelligence options which are either in an ‘off’ or ‘on’ condition

[0126] The artificial intelligence options are independently selectable by the educator and in the example of a Year 6 English Assignment being set, such options might include the following filter and ciriteria:-

[0127] 13

[0128] SUBST|TUTE SHEET (RULE 26) FILTER CRITERIA

[0129] Composition Adjectives for setting Composition Adjectives for character Composition Powerful verbs Composition Sentence structure Transcription Suffixes Transcription Prefixes Transcription Spelling Transcription Adverbials

[0130] Once the relevant information has been entered, a student for whom the Assignment has been set sees the Assignment in their secure virtual space and completes the Assignment directly within the software program or uploads a document to the software program. The educator just needs to press or click on one key or virtual button in the software program to allow the artificial intelligence to interface with the software program and, in this instance, mark the Assignment according to the Filter and Criteria selected earlier.

[0131] The result is achieved in a matter of seconds and, if feedback for the student has been requested, the educator then goes through the step of approving the feedback generated by the artificial intelligence before it is sent to the student.

Claims

CLAIMS1 . An artificial intelligence education system comprising a computer program serving to receive an input of a combination of educational data for processing, an artificial intelligence arranged to interface with the computer program and receive the educational data, processing the educational data by the artificial intelligence and producing corresponding output data based upon the educational data and instructions given to the artificial intelligence in relation to the educational data.

2. An artificial intelligence education system according to claim 1 , wherein the educational data is a piece of work produced combined with a relevant marking schema that the artificial intelligence is instructed to process and wherein by way of an application programming interface, the artificial intelligence receives instructions to produce specific output data relating to the combination of educational input data and which meets the criteria of the instructions given to the artificial intelligence.

3. An artificial intelligence education system according to claim 2, wherein the output data is in the form of educationally related feedback on the piece of work.

4. An artificial intelligence education system according to claim 2 or 3, wherein the artificial intelligence is marking the piece of work to an exam standard.

5. An artificial intelligence education system according to any preceding claim, wherein the educational data is a collection of feedback data based upon pieces of work over a period of time and / or educational target data combined with corresponding topic data, the artificial intelligence being instructed to produce a lesson plan for a specified period of time depending on the topic data, and wherein by way of the application programming interface the artificial intelligence is instructed to produce specific lesson plans relating to the combination of the feedback data combined with the corresponding topic data.

6. An artificial intelligence education system according to claim 5, wherein the output data is in the form of a proposed lesson plan over the period of time which includes proposed lesson structure(s).

7. An artificial intelligence education system according to any preceding claim, wherein the educational data is a collection of feedback data based upon15SUBST|TUTE SHEET (RULE 26)pieces of work completed over a period of time for a particular topic combined with corresponding data relating to one or more learning modules for the topic and a framework of work in the topic to be set, the artificial intelligence being instructed to create new work assignments for one or more students according to available modules and the feedback, and wherein by way of the application programming interface, a proposed work plan is provided.

8. An artificial intelligence education system according to claim 7, wherein customized assignments for an individual student or a group of students are created.

9. An artificial intelligence education system according to any preceding claim, the educational data is a collection of feedback data based upon pieces of work generated over a period of time and the structure of feedback required by an educator, the artificial intelligence being instructed to produce a longitudinal analysis of student progress and wherein by way of the application programming interface, the educator can instruct the artificial intelligence to a broad range of output data relating to student progress.

10. An artificial intelligence education system according to any preceding claim, and including a first training stage where the artificial intelligence learns the style of working by an educator, and a second stage where a style guide learnt by the artificial intelligence in the first step is used to provide feedback from the artificial intelligence in the style of the educator.11.An artificial intelligence education system according to claim 10, wherein during the second stage, the educational data is a feedback style template learnt from the first stage and educator feedback and / or newly generated feedback from the artificial intelligence, the artificial intelligence being instructed to return feedback to the student to mimic the feedback style of the educator.

12. An artificial intelligence education system according to any preceding claim, wherein the educational data is a collection of feedback data based upon pieces of work over a period of time and data relating to a variety of progress report templates, the artificial intelligence being instructed to produce a progress report based upon a particular one of the variety of templates.16SUBST|TUTE SHEET (RULE 26)13. An artificial intelligence education system according to claim 12, wherein personalized progress reports are creatable by the artificial intelligence on a desired template.

14. A method comprising inputting a combination of educational data for processing into a computer program, receiving and processing the educational data by an artificial intelligence arranged to interface with the computer program and the artificial intelligence producing corresponding output data based upon the educational data and instructions given to the artificial intelligence in relation to the educational data.17SUBST|TUTE SHEET (RULE 26)

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