WO2025034938A1 - Personal digital tutor integrated with 3d educational interactives - Google Patents

Abstract

Disclosed is the implementation of a digital tutoring system within a network-based learning platform, where the platform may be accessed by students in a self-directed environment. The digital tutoring system utilizes AI techniques to specifically ascertain and respond to questions posed by a student in real time. In preferred embodiments, the digital tutoring system is configured to have access to the same 3D manipulatives as used by the student and, therefore, may "take over" the manipulation of a specific object (in much the same way as a classroom teacher) to assist the student in understanding the presented material.

Description

PERSONAL DIGITAL TUTOR INTEGRATED WITH 3D EDUCATIONAL INTERACTIVES

Cross-Reference to Related Applications

This application claims the benefit of U.S. Provisional Application 63/531,660 filed August 9, 2023, and U.S. Provisional Application 63/531,665 also filed August 9, 2023, with both applications herein incorporated by reference.

Technical Field

The present invention is directed to the implementation of a networkbased distance learning platform and, more particularly, to the implementation of a digital tutoring system as a module within the learning platform to provide assistance to individual students.

Background of the Invention

Various prior art educational platforms exist for use in a virtual learning environment. One prior art offering provides a seamless platform that utilizes live lectures (such as via Zoom, YouTube, or the like) in combination with prerecorded lectures. In addition, it provides features and tools that are of use to a teacher in this environment (e.g., enabling remote questioning of students, giving tests, and the like) .

The need for excellence in a “distance learning” mode has become quite evident during the recent time of pandemic isolation. One existing distance learning methodology is based on self-directed instruction initiated by a student without live teacher involvement and includes the use of three- dimensional (3D) models of various objects (referred to as “manipulatives” in academic parlance) that are observable by a student wearing 3D stereoscopic glasses (or similar technology including, but not limited to “glasses-free” 3D devices utilizing lenticular lenses, lightfield, or the like). One limitation to many of these distance learning platforms is the inability for individual students to receive one-on-one additional assistance. While some services provide the opportunity for live tutoring systems via Zoom, there may be additional cost involved, as well as the need to schedule a time when both the student and an approved tutor in the specific subject matter may be available. The possibility of the student and tutor residing in different time zones is another concern in terms of scheduling.

Beyond the field of distance learning, there are in-person school environments where a teacher is instructing a group and an individual student is in need of a one-on-one tutor. The need for some supplemental type of tutoring system that is able to address this concern can greatly free-up a teacher’s time, while also improving the student’s understanding of presented material (as well as perhaps tracking individual student’s needs for additional instruction).

A need remains, therefore, for a personalized tutoring capability to be offered in a self-directed learning platform (whether in a remote situation or as part of in-class environment).

Summary of the Invention

The needs remaining in the prior art are addressed by the present invention, which relates to a network-based learning platform and, more particularly, to the implementation of a digital tutoring system within the platform to provide assistance to individual students.

The digital tutoring system takes the form of a module that is included within the learning platform itself and utilizes Al techniques to specifically ascertain and respond to questions posed by a student in real time. In preferred embodiments, the digital tutoring system is configured to have access to the same 3D manipulatives as used by the student and, therefore, may “take over” the manipulation of a specific object (in much the same way as a classroom teacher) to assist the student in understanding the presented material. The digital tutoring system may be configured to provide assistance in multiple languages, and the tutoring sessions may be recorded for further use.

An exemplary embodiment may take the form of a network-based learning platform configured to provide individual instruction for subscribed students, where the learning platform includes at least a service management component, a knowledge base, a 3D imaging system, and a digital tutoring system. The service management component is utilized as a communication interface between subscribed students and the learning platform, and is configured to include a validation element to limit student access to previously- authorized learning material, including confirmation of a student’s computing system to process and manipulate 3D objects. The knowledge base may take the form of a plurality of separate databases, each database associated with a different academic discipline and including a plurality of individual lesson modules. The 3D imaging system is utilized to render selected 3D objects associated with a particular lesson module, and is also configured to transmit information related to the rendering to the student’s computing system. The digital tutoring system communicates with both the knowledge base and the 3D imaging system. More particularly, the digital tutoring system includes an Al-driven model component configured to recognize the content of a student question and develop a tutorial response based on information retrieved from either one or both of the knowledge base and the 3D imaging system.

Other and further features and aspects of the present invention may become apparent during the course of the following discussion and by reference to the accompanying drawings.

Brief Description of the Drawings

Referring now to the drawings,

FIG. 1 contains a diagram of an exemplary learning system platform including digital tutoring capabilities in accordance with the principles of the present invention; FIG. 2 shows another embodiment of the present invention, in this case incorporating translation capabilities useful in providing information to a student in his/her preferred language; and

FIG. 3 is a flow chart of an exemplary process for creating a translation of multi-media material for presentation to a student during a tutoring session.

Detailed Description

For clarity of explanation, in some instances, the principles of the present invention may be presented in the form of individual functional blocks, where these functional blocks may comprise devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.

Moreover, any of the steps, operations, functions, or processes described herein may be performed or implemented by a combination of hardware and software services, alone or in combination with other devices. In some embodiments, a tutoring functionality may utilize software embedded within a student computing device as well as modules resident on the network-based learning platform. In some cases, the tutoring functionality is embodied in a program or collection of programs that carry out specific instructions, perhaps using information stored in a memory (where the memory may take the form of a non-transitory computer-readable medium).

The tutoring functionality as described in detail below may be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. The executable computer instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during utilization of the learning platform include solid-state memory devices, flash memory, USB devices provided with non-volatile memory, networked storage devices, and the like.

Devices implementing methods according to this disclosure can comprise hardware, firmware and/or software, and can take any of variety of form factors. Typical examples of such form factors include servers. Laptops, smartphones, small form factor personal computers, and so on. The functionality described herein may also be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different integrated circuits, including ASICs, or different processes executing in a single device, by way of further example.

The instructions, media for conveying such instructions, computer resources for executing them, and other structures for supporting such computing resources are considered to be means for providing the functions described in detail hereinbelow.

FIG. 1 is a diagram of an exemplary architecture within which a networkbased learning platform 10 is configured to allow students to interact with stored lesson material, including 3D models (i.e., manipulatives). In this exemplary embodiment, learning platform 10 is shown as including a service management component 12, a knowledge base 14, a 3D imaging system 16, and a digital tutoring system 18. In this particular architecture, the individual elements forming the learning platform are capable of sharing data and other communications with each other via a communication bus 19.

Also shown in FIG. 1 is a student computer site 20 that is able to communicate with learning platform 10 over a communication network 30 (where communication network may comprise the internet, or any suitable public or private communication network). It is this configuration that permits a student’s computer site 20 (also referred to as a student instruction site 20) to interact with pre-existing lesson modules stored within knowledge base 14 of learning platform 10. It is an aspect of the present invention that student instruction site 20 has installed a required educational software application that includes the capabilities to view and manipulate 3D objects as included in specific lesson modules.

Service management component 12 of learning platform 10 functions as the communication interface between network 30 and the other elements within learning platform 10. Additionally, service management component 12 may be used to control a student’s access to learning platform 10 (via a subscription, for example) as well as the grade level and specific subject matter areas that a student may access.

Knowledge base 14 of learning platform 10 includes sets of lesson modules developed for a number of well-defined academic disciplines. For explanatory purposes only (and thus not considered to limit the scope of the applicability of the present invention), knowledge base 14 is shown in FIG. 1 as including sets of lesson modules for the disciplines of mathematics, science, and history. Shown as database systems 14.1, 14.2, and 14.3, respectively, each general discipline area may be further divided into different subject matter areas, with topics defined for each subject matter area, and lesson modules (typically including a multiple number of individual sessions) associated with each subject matter area. It is to be understood that the specific architecture of knowledge base 14 is not particularly relevant to the subject matter of the present invention, as long as students are able to follow the hierarchy and access the specific modules required to follow their assigned curriculum.

3D imaging system 16, also included in learning platform 10, provides the ability to add the third dimension to the presented material and give the student a more “real world” setting within which to learn the material being presented. As described in our co-pending application PCT/US2019/057289, filed October 21, 2019 (entitled “On-Line Instructional System and Tools for Student-Centered Learning”} and herein incorporated herein by reference, the provision of 3D tools is considered to enhance the learning experience by “breaking through” the barrier of a computer display screen, allowing a student to engage with the presented subject matter in a hands-on fashion.

For some students, the approach of moving through a guided study of a curriculum without the direct involvement of a teacher is sufficient. For many others, however, it is inevitable that a question may arise based on how the material is presented (or presumptions based on a certain knowledge base, etc.) that can best be addressed by having the question answered in real time. The Al-driven digital tutor as configured in accordance with the present invention is intended to meet that real-time need for answers/ explanations.

In particular and as shown in FIG. 1, a digital tutoring system 18 is included within learning platform 10 and is configured to interact with students and provide supplemental instruction in various ways, in some cases advantageously utilizing 3D objects to illustrate and clarify certain learning objectives. As will be described below, the power of 3D content is made more engaging and effective in accordance with the principles of the present invention by integrating it within Al-driven tutor-student interactions provided by digital tutoring system 18.

In particular, digital tutoring system 18 includes an Al-driven model component 18.1 that communicates with both knowledge base 14 and 3D imaging system 16 in a manner that manifests a tutor emulation that may engage with students in a variety of ways. One way this can be accomplished is by effectively capturing the relevant aspects of a selected 3D simulation in code, based on the tool used to render the image within 3D imaging system 16, and providing the code as input to Al-driven model component 18. 1 for interpretation of the task at hand, based on the question(s) raised by the student. An example engagement in this context may include the student being shown or referred to specific 3D content (and in particular, specific aspects or parts of a 3D object). Additionally, digital tutoring system may submit queries to knowledge base 14 to search for video segments that would be most relevant to include in response to a question posed by the student. By digital tutoring system 18 being able to track the subject matter currently under study, module 18 is able to select a 3D object from within that module (or, perhaps, a 3D object from a previous module, or, in general, any 3D objects that are relevant in explaining the concept to the student) .

In an exemplary flow process, a student may be able to access digital tutoring system 18 via a displayed UI element 22 on the screen of student computer system 20 (e.g., clicking on a link on the screen). UI element 22 may be labelled with a phrase such as “ask a question”, or the like. Accessing digital tutoring system 18 may result in the system taking over the student’s screen or, alternatively, filling only a portion of the screen so that the lesson material remains visible to the student. The student may opt to “start a new chat” with a new topic, or continue an existing chat for the topic currently under study. The student may speak the question (in situations where speech- to-text conversion is available) or type it in.

Digital tutoring system 18 may limit its retrieval of relevant content based on the subscription of the student (i.e., to restrict to permissible topics and grade level). For example, if a student is requesting assistance with a question in a geometry lesson session, digital tutoring session 18 may restrict its search for supplemental material to grade-level appropriate geometry topics and not, for example, retrieve higher-level information from a calculus course. In some cases digital tutoring system 18 may be configured to permit a student to ask an off-topic question that is still relevant to the active lesson session with which the student is involved.

Upon receipt of a question, digital tutoring system 18 functions to provide an engaging answer to the student’s question, as well as a set of supplemental reference material, drawing from both knowledge base 14 and 3D imaging system 16. The material may take the form of written descriptions, videos (cued to a specific time associated with the question), specific 3D models, simulations, and the like. Further, with direct access to the code that generates the 3D objects within 3D imaging system 16 and because the underlying structure of the 3D objects and related elements of the simulation are captured in code (such as, for example, a 3D object’s location in 3D space in the rendering portion of the software) are available to Al-driven model component 18.1, digital tutoring system 18 can take control of the 3D model and rotate or zoom in on specific portions or take pieces apart to convey a particular concept to the student. In other words, Al-driven model component 18.1 of digital tutoring system 18 may itself interact with the 3D object to help the student better understand the concept. This can be taken further, where digital tutoring system 18 is able to modify portions (to an extent controllable by a teacher or content creator) of the 3D object, or create a new 3D simulation using the same software or a different one that creates and renders 3D models, in order to convey different concepts. For example, digital tutor system 18 can present a newly-rendered 3D object where a portion of the object is removed and the tutor asks the student - “what is missing here?”. The capabilities of digital tutoring system 18 may be further configured to generate new video segments and new images to supplement the tutoring session.

An alternative to mirroring the actions of Al-driven model component 18.1 and the student back and forth to each other is creating two different screens (split screen) where one screen shows the manipulation of a 3D object by Al-driven model component 18.1 and the other shows a student’s manipulation of the same 3D object so that comparisons can be directly viewed and misconceptions uncovered with this complementary approach.

Beyond modifying and creating 3D objections or simulations, digital tutoring system 18 is further configured to help answer a student’s question by using a particular screen shot or video segment from knowledge base 14, where Al-driven model component 18.1 is able to improvise and generate a similar presentation or video that can be an extension of the original that is modified slightly and tailored to explain or probe the student on a specific concept.

An aspect of this engagement is one in which digital tutoring system 18 asks the student questions to assess if he or she has mastered or understood what he or she was initially asking about. For example, digital tutor system 18 may ask “Can you move [this 3D model] to show [this feature]?” This movement as given by a student may be processed by digital tutoring system 18 as a series of images or video, or as a change in location of the 3D object. Based on the student’s response, his or her understanding may be rated quantitatively and refined with follow-up questions from digital tutoring system 18. The student can also be asked to draw something using their computer system 20, which digital tutoring system 18 can process as an image or video. Understanding can also be quantified and determined further by evaluating how well the student applies what they learned to other, similar problems (generalization). The number of other similar problems the student answers correctly can increase their “generalizability” score for a concept.

In one example system, digital tutoring system 18 may be configured to automatically take the student to a “most relevant” content reference from knowledge base 14 (using metrics developed by digital tutoring system 18, perhaps), highlight the relevant portion for the student and explain the relevant material in one or more ways to the student so that he or she can best understand given their background (as tracked by the student’s progress and performance in working with learning platform 10). After this, digital tutoring system 18 may step through the generated list of references to the “next” most relevant content and proceed in a similar fashion. In the case where the relevant content is a video clip, digital tutor 18 also provides a short, but engaging, summary of the video to supplement the student’s understanding. Following this, a subsequent content reference may be a 3D object manipulation, using the prompts discussed above. The order of the presented content material may be under the direct control of digital tutor 18, selected by the student, or developed by digital tutor 18 for a given student based upon previous interactions.

It is contemplated that various features of digital tutoring system 18 may also be of use as a teacher’s aide, suggesting to a teacher various alternatives for presenting a particular subject, providing references to top pieces of content as well as how to use the content. This may include, for example, the real-time creation of a quiz that may be tailored to a certain set of students within a given performance level, background, or set of interests. Another aspect of the engagement between a student and digital tutoring system 18 of the present invention is that the conversational nature of the feedback or response that Al-driven model component 18.1 gives can range between a very focused response to the student’s question and an open-ended broad response (e.g., exploratory), bringing in other related topics and areas with the purpose of expanding a student’s interest in that topic while also increasing their understanding of it. Moreover, it is preferred that a student is given the ability to interrupt an on-going session by asking a new question. The conversational nature of the interaction is considered to create an environment where this give-and-take between tutor and student can occur.

Another aspect of the personalization of digital tutor system 18 is the capability to present answers and provide explanations in the student’s preferred language. Beyond providing a multi-lingual tutoring system, the ability to alter the voicing of any audio response of the tutor may be used to ensure that the student is comfortable.

FIG. 2 illustrates a specific embodiment of the present invention in the form of a learning platform 10A that incorporates the use of a multi-lingual translator module 40 in addition to digital tutor 18. It is to be understood that while this embodiment shows these elements as separate and distinct components, the multi-lingual translation functionality may be directly incorporated within digital tutor system 18. In either configuration, the language translation is configured to remain in sync with any material presented by digital tutor 18, including with the capabilities of 3D object manipulation by the student and/or digital tutor. As will be described below, the language translation capabilities may preferably extend into modifying the text presented in an accompanying video so as to appear in the language of choice.

Indeed, the proposed multi-lingual translation capabilities may extend into text-based material such as theory descriptions, quizzes, tests, mind maps, and reference documents. Suitable translations associated with images, models (both 2D and 3D), simulations (2D, 3D) and videos (2D, 3D) are contemplated as well.

FIG. 3 is a generalized flow chart illustrating the process as used by multi-lingual translator 20 to accomplish the task of translating original language learning modules into a language best-suited for a particular student. The process begins at step 100 with obtaining the original audio track associated with a specific lesson, or set of lessons creating a learning module. From this audio track, a speech-to-text conversion is performed (step 110) to create a written transcript of the audio file. The transcript is “time stamped” to maintain a synchronization with the individual frames of the original video file.

Subsequent to this, multi-lingual translator performs a “natural language” translation of generated transcript into a “conversion transcript” into the language of choice and performs a text-to-speech translation (step 120).

With the “language X” audio file, a following step 130 is performed to match this audio file to the original video file, using the timestamps as guides to the transition. Once completed, the merging of the original video file with the translated audio file may be stored in a “translation partition” 18-T of digital tutor 18 (step 140). Additional features, shown as steps A and B in FIG. 3 provide additional capabilities. For example, step A utilizes further speech processing capabilities to mimic the style/ voicing/ pace of the original audio file to that of the newly-created file in language X. Step B provides additional textbased modifications, as mentioned above, to translate the text appearing in various presentations associated with the lesson (e.g., ppts, tests, quizzes, etc.) to coordinate with the converted language of choice. When the translation is fully prepared, it is presented to the student (step 150) as part of the tutoring session. It is to be understood that translation module 40 may also be used to create multi-lingual versions of the original material as stored in knowledge base 14.

In another embodiment, the translated audio and content can be directly generated by feeding the original audio to Al-drive model component 18.1 which is further trained to generate scientifically-accurate translations into language “X” given an input language.

For each such tutorial session / chat, the topics or concepts that were covered in trying to answer the initial question may be used to generate a concept map that can be used as a visual for an educator or parent to comprehend what the student needed in order to better understand something. The topics and concepts covered can also be logged to inform the next session between digital tutor system 18 and that student. Concept maps generated from a chat can also highlight concepts according to the level of understanding the student exhibited at the beginning vs. the end of the tutorial chat, and in general, can be updated over time. Such logs of a student’s level of understanding can also be utilized by digital tutoring system 18 to generate a quiz/test for the student to further quantify their level of comprehension. In particular, a detailed testing structure may include specific questions associated with details of different aspects of a concept in order to better reveal areas of remaining weakness (as well as improved proficiency) of the student.

In another aspect of the present invention, at the completion of a given tutorial session, the student may have the ability to rate how helpful the session was, and this may be used along with the topics and concepts covered to update the responses that digital tutoring system 18 provides to other students so that better answers can be given (for students of similar background perhaps).

Besides comprehension, other metrics of the tutorial interaction can be quantified based on the chat. These metrics may include, but are not limited to elements such as: (1) the student’s level of engagement (which can be determined from something like the duration of the chat), or (2) the number of times a student uses digital tutoring system 18.

In cases where digital tutoring system 18 is unable to answer a student’s question sufficiently at the time (determined, for example, by directly asking the student), digital tutoring system 18 is tasked with flagging the session with an alert such that an email, perhaps automatically, is sent to the student’s teacher to indicate the need for further assistance.

In an effort to better engage the student, an additional aspect of the present invention relates to configuring digital tutoring system 18 to “pop up” in an on-going learning module and interject interesting and relevant information, images, and 3D interactives that relate to the student’s usage of the module. For example, an Al-generated tutor may appear with a “Did you know....” as a student is going over a specific portion within a topic that makes apparent a direct application of the concept they are learning.

Another aspect of the Al-driven digital tutor system of the present invention is that it can appear more engaging within the learning module by taking on more human-like features. For example, the “tutor” may speak in a natural-sounding voice rather than, or in addition to, interacting with the student via text. The accent and language can be tailored to the student based on his or her preference or location. Moreover, the tone of the tutor can be modified (by the student, parent, or teacher, for example) to serve different effective roles in mentoring - authoritative (where the tutor gives more direct instructions) to explorative (where the tutor sounds more investigative, curious, and reflective, like a student or peer, in suggesting something, such as, “what if this were to happen?...”). In particular, Al-driven model component 18.1 of digital tutoring system 18 may serve the role of a virtual third party (e.g., another student) who does not understand a concept and requests the student to explain the concept to the virtual third party. As the student explains, digital tutoring system 18 may ask more questions, allowing the student to think about different aspects of the concept in order to clarify the explanation being given to this virtual third party. This aspect of the inventive methodology takes advantage of the concept of learning-by-teaching, another tool that may be successfully utilized by digital tutoring system 18.

In addition, the student may wish to speak to the Al-driven tutor, in which case speech (and speaker) recognition is built into the interaction. In one embodiment, a real-time tutor session could occur over video where the tutor may be an actual teacher talking to the student and guiding them through his or her query.

Various embodiments of the invention have been described. It will, however, be evident to those of skill in the art that various modifications and changes may be made thereto, and additional embodiments may be implemented, without departing from the broader scope of the invention as set forth in this disclosure. Indeed, this specification is to be regarded in an illustrative rather than a restrictive sense, with the scope of the invention defined by the claims appended hereto.

Claims

What is claimed is:

1. A network-based learning platform configured to provide individual instruction for subscribed students, comprising: a service management component utilized as a communication interface between subscribed students and the learning platform, the service management component configured to include a validation element to limit student access to previously-authorized learning material, including confirmation of a student’s computing system to process and manipulate 3D objects; a knowledge base including a plurality of separate databases, each database associated with a different academic discipline and including a plurality of individual lesson modules; a 3D imaging system for rendering selected 3D objects associated with a particular lesson module and transmitting information related to the rendering to the student’s computing system; and a digital tutoring system in communication with both the knowledge base and the 3D imaging system, where the digital tutoring system includes an AI- driven model component configured to recognize the content of a student question and develop a tutorial response based on information retrieved from either one or both of the knowledge base and the 3D imaging system.

2. The network-based learning platform as defined in claim 1 wherein the retrieved information includes one or more elements selected from the group consisting of: images, video clips, and 3D object manipulation.

3. The network-based learning platform as defined in claim 1 wherein the digital tutoring system further comprises a multi-lingual element for translating the tutorial response information into a student’s preferred language when necessary.

4. The network-based learning platform as defined in claim 1 wherein the digital tutoring system is further configured to limit the retrieved information to a known grade level of a student requesting a tutoring session.

5. The network-based learning platform as defined in claim 1 wherein the digital tutoring system is further configured to limit the retrieved information content based on a subscription level of a student requesting a tutoring session.

6. The network-based learning platform as defined in claim 1 wherein the response developed by the Al-driven model component includes 3D object manipulation by the digital tutoring system.

7. The network-based learning platform as defined in claim 6 wherein the digital tutoring system is configured to take over control of a student’s computing device to present an Al-driven manipulation of a 3D object in response to the student’s question.

8. The network-based learning platform as defined in claim 7 wherein the digital tutoring system is further configured to create a split screen configuration of the student’s computing device to present both the Al-driven rendering and a student rendering for instructional purposes.

9. The network-based learning platform as defined in claim 1 wherein the digital tutoring system is further configured to develop tests for confirming a student’s comprehension of supplied material in response to one or more questions.

10. The network-based learning platform as defined in claim 9, wherein the digital tutoring system is also configured to develop metrics regarding the effectiveness of the Al-driven model component based on test results related to student comprehension.

11. The network-based learning platform as defined in claim 10, wherein the digital tutoring system is further configured to generate a concept map depicting an initial level of understanding of the student at a beginning of a tutoring session vs. a final level of understanding of the student at a completion of a tutoring session.

12. The network-based learning platform as defined in claim 11, wherein the concept map generated by the digital tutoring system further depicts remaining areas of weakness and areas of improved proficiency of the student at the completion of the tutoring session.

13. The network-based learning platform as defined in claim 1, wherein the Al-driven model component of the digital tutoring system is further configured to modify a voicing of an audio tutorial response.

14. The network-based learning platform as defined in claim 13 wherein the Al-driven model component is configured to provide a natural-sounding voicing of the audio tutorial response.

15. The network-based learning platform as defined in claim 13 wherein the Al-drive model component is configured to provide one of an authoritative tone and an explorative tone in generating the audio tutorial response.

16. The network-based learning platform as defined in claim 1, wherein the digital tutoring system is further configured to monitor a student’s level of comprehension during a tutoring session and generating an alert message when the student’s level of comprehensions falls below a defined threshold during the tutoring session.

17. The network-based learning platform as defined in claim 12 wherein the generated alert message is transmitted as an email to an instructor.

18. The network-based learning platform as defined in claim 1 wherein the Al-driven model component is further configured to generate new 3D models, images, or video clips to supplement a tutorial response.

19. The network-based learning platform as defined in claim 18 wherein the digital tutoring system is further configured to store elected ones of any newly-generated 3D models, images, or video clips created for the tutorial response.

20. The network-based learning platform as defined in claim 1 wherein the digital tutoring system is further configured to store selected portions of prepared tutorial responses for use in subsequent tutoring sessions.