1. An adaptive language learning system (100) adapted to personalize language learning, the system (100) comprising: a learner device (102) having a learner interface (104) configured to receive learner interaction inputs including but not limited to text, speech signals, and activity responses, and to render adapted bilingual learning content in at least a first language (L1) and a second language (L2); a local processing unit (108) operatively coupled to the learner device (102), configured to process the learner interaction inputs and to transmit interaction features, wherein the local processing unit (108) further comprises: a data input module (110) configured to receive raw learner interaction inputs from the learner device (102); a data pre-processing module (112) configured to normalize, format and condition the learner interaction inputs received from the data input module (110) to generate standardized input data; a feature extraction module (114) configured to extract learner performance features from the processed learner interaction data for subsequent analysis; a communication network (106) configured to exchange the data between the components of the system (100); a central processing unit (116) communicatively coupled to the local processing unit (108) via the communication network (106), and configured to perform adaptive learning analysis and content generation based on processed learner interaction data received from the local processing unit (108), wherein the central processing unit (116) further comprises: a learner profiling module (118) configured to generate and update a learner proficiency profile and an engagement profile based on the extracted interaction features; an adaptive learning module (120) configured to dynamically determine instructional difficulty, pacing, and content sequencing based on the learner proficiency profile and the engagement profile; a scaffolding control module (122) configured to regulate a balance between L1 and L2 instructional content based on the learner proficiency profile; a language processing module (126) configured to perform semantic translation, contextual explanation, and bilingual simplification between the first language (L1) and the second language (L2) for the instructional content selected by the adaptive learning module (120); a feedback generation module (128) configured to generate real-time bilingual corrective feedback based on learner performance; an output formatting module (130) configured to transmit the adapted bilingual instructional content and generated corrective feedback to the learner device (102) for presentation to the learner; and a teacher device (134) communicatively coupled to the central processing unit (116) via the communication network (106) and configured to present learner performance analytics and to receive instructional control inputs for modifying learning parameters.

2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a data repository (136) connected to the central processing unit (116) via the communication network (106) and configured to store information, activities, and tests in more than one language tagged with difficulty level, topic and language.

3. The system (100) as claimed in claim 1, wherein the central processing unit (116) analyses real-time interaction features and stored historical learner interaction data to continuously update the learner proficiency profile thereby enabling temporally contextualized proficiency estimation and reducing adaptation latency in subsequent instructional content generation.

4. The system (100) as claimed in claim 1, wherein the central processing unit (116) further comprises a content selection module (124) configured to retrieve bilingual instructional content from a data repository (136) by matching adaptive learning module (120) output parameters with stored metadata attributes and filtering non-compliant content entries.

5. The system (100) as claimed in claim 1, wherein the central processing unit (116) further comprises a notification module (132) configured to generate learner and instructor alert data based on detecting predefined threshold deviations in proficiency score variation or engagement level variation and transmit the alert data to the central processing unit (116).

6. The system (100) as claimed in claim 1, wherein the central processing unit (116) is configured to select and transmit multimodal instructional content comprising text, audio, and video in L1 and L2 based on a control signal generated by the adaptive learning module (120), thereby dynamically switching content presentation modes in response to learner proficiency and engagement.

7. The system (100) as claimed in claim 1, wherein the scaffolding control module (122) further configured to control the bilingual scaffolding ratio and dynamically decreases L1 assistance as learner proficiency in L2 increases progressively reducing L1 explanatory content, translation frequency while increasing L2-only instructional presentation as the learner proficiency profile exceeds predefined proficiency thresholds.

8. The system (100) as claimed in claim 1, wherein the adaptive learning module (120) is configured to monitor behavioural interaction indicators comprising hesitation duration, inactivity intervals, anxiety score derived from speech characteristics, and speech confidence score, and to automatically regulate instructional difficulty, content pacing, and L1–L2 language support ratio, thereby enabling real-time adaptive control of bilingual instructional output.

9. The system (100) as claimed in claim 1, wherein the scaffolding control module (122) is configured to automatically transition the instructional presentation into an immersion mode when the learner proficiency profile exceeds a predefined proficiency threshold by progressively suppressing first-language (L1) translations and explanations and delivering second-language (L2) dominant instructional content, thereby improving autonomous L2 cognitive processing and reducing dependence on bilingual assistance during advanced learning stages.

10. A method (200) for operating an adaptive language learning system (100), the method (200) comprising: receiving learner interaction inputs including text responses, speech responses, and activity responses via a learner interface (104) of a learner device (102); transmitting the learner interaction inputs from the learner device (102) to the data input module (110) of the local processing unit (108); normalizing, formatting and conditioning the learner interaction inputs to generate a standardized input data via a data pre-processing module (112) of the local processing unit (108); extracting learner performance features from the standardized input data via a feature extraction module (114) of the local processing unit (108); transmitting the learner performance features through a communication network (106) to the central processing unit (116); generating a learner proficiency profile and an engagement profile based on the learner performance features via a learner profiling module (118) of the central processing unit (116); determining instructional difficulty, pacing, and content sequencing according to the learner proficiency profile and the engagement profile via an adaptive learning module (120) of the central processing unit (116); regulating a balance between L1 instructional content and L2 instructional content based on the learner proficiency profile via a scaffolding control module (122) of the central processing unit (116); performing semantic translation, contextual explanation, and bilingual simplification of the instructional content via a language processing module (126) of the central processing unit (116); generating real-time bilingual corrective feedback via a feedback generation module (128) of the central processing system (116); formatting and transmitting the adapted bilingual instructional content and the real-time bilingual corrective feedback into presentation-ready output data via an output formatting module (130) of the central processing system (116) to the learner device (102) through the communication network (106) for presentation via the learner interface (104); and. presenting learner performance analytics and receiving instructional control inputs via a teacher device (134) communicatively coupled to the central processing unit (116).

Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to educational technology and artificial intelligence (AI) systems, more specifically, relates to an adaptive bilingual language learning system for analysing learner interaction data and dynamically personalizing instructional content based on learner proficiency and engagement.
BACKGROUND OF THE DISCLOSURE
[0002] Language learning platforms are increasingly delivered through digital devices and online educational environments. Conventional e-learning systems typically provide pre-structured lessons, fixed difficulty exercises, and static bilingual translations that remain the same for all learners regardless of their individual proficiency level, engagement behaviour, or learning pace.
[0003] Some existing computer-assisted language learning systems attempt personalization by tracking test scores or completion rates. However, such systems generally rely on limited performance indicators and do not continuously analyse multimodal learner interactions such as response time, speech responses, activity patterns, or behavioural engagement during learning sessions. As a result, the instructional content often fails to adapt in real time to the learner’s cognitive state.
[0004] Furthermore, current bilingual learning tools usually provide direct translation between a first language and a target language without dynamically regulating the proportion of the two languages. The absence of controlled language scaffolding may lead either to excessive dependence on the native language or to cognitive overload when exposed to advanced target-language content prematurely. Additionally, many platforms lack contextual explanations and adaptive pacing tailored to individual learning progress.
[0005] Consequently, existing systems suffer from several drawbacks including static or rule-based personalization that does not reflect real-time learner behaviour, inability to adjust instructional difficulty and pacing dynamically, fixed translation-based bilingual support rather than adaptive language ratio control, lack of integrated analysis of engagement and proficiency, and delayed or generic feedback that reduces learning effectiveness.
[0006] Due to these limitations, learners may experience reduced comprehension, slower language acquisition, cognitive overload, or over-reliance on translation assistance, thereby decreasing overall learning efficiency.
[0007] The present disclosure overcomes these limitations by continuously monitoring learner interactions and evaluating proficiency and engagement in real time. Based on this continuous assessment, the invention adjusts the presentation, pacing, and linguistic support of instructional content to align with the individual learner’s needs. This adaptive capability ensures that learners receive personalized guidance, appropriate language scaffolding, and timely feedback, thereby improving comprehension, enhancing learning efficiency, and reducing cognitive overload commonly experienced in static or rule-based learning platforms.
[0008] Thus, in light of the above-stated discussion, there exists a need for an adaptive language learning system with proficiency-based content adaptation.
[0009] SUMMARY OF THE DISCLOSURE
[0010] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0011] According to illustrative embodiments, the present disclosure focuses on an adaptive language learning system with proficiency-based content adaptation which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0012] An objective of the present disclosure is providing an adaptive language learning system configured to facilitate bilingual support for english-as-a-second-language (ESL) learning environments.
[0013] Another objective of the present disclosure is to improve learner comprehension and retention by providing real-time first-language (L1) assisted explanations during second-language (L2) learning activities.
[0014] Another objective of the present disclosure is to automatically adapt lesson difficulty, pacing, and sequencing using engagement indicators and performance features.
[0015] Another objective of the present disclosure is to analyse multimodal learner inputs including text, speech, and activity responses to continuously update a learner proficiency profile.
[0016] Another objective of the present disclosure is to generate real-time bilingual corrective feedback including contextual explanations and grammar guidance based on learner interaction behaviour.
[0017] Yet another objective of the present disclosure is to progressively transition a learner from bilingual assistance to predominantly L2-based instruction as learner proficiency increases.
[0018] In light of the above, in one aspect of the present disclosure, an adaptive language learning system with proficiency-based content adaptation is disclosed herein. The system comprises a learner device having a learner interface configured to receive learner interaction inputs including but not limited to text, speech signals, and activity responses, and to render adapted bilingual learning content in at least a first language (L1) and a second language (L2). The system also includes a communication network configured to exchange the data between the components of the system. The system also includes a local processing unit operatively coupled to the learner device the local processing unit configured to process the learner interaction inputs and to transmit interaction features. The local processing unit comprising a data input module configured to receive raw learner interaction inputs from the learner device, a data pre-processing module configured to normalize, format and condition the learner interaction inputs received from the data input module to generate standardized input data, a feature extraction module configured to extract learner performance features from the processed learner interaction data for subsequent analysis. The system also includes a central processing unit communicatively coupled to the local processing unit via the communication network and configured to perform adaptive learning analysis and content generation based on processed learner interaction data received from the local processing unit. The central processing unit further comprises a learner profiling module configured to generate and update a learner proficiency profile and an engagement profile based on the extracted interaction features, an adaptive learning module configured to dynamically determine instructional difficulty, pacing, and content sequencing based on the learner proficiency profile and the engagement profile, a scaffolding control module configured to regulate a balance between L1 and L2 instructional content based on the learner proficiency profile, a language processing module configured to perform semantic translation, contextual explanation, and bilingual simplification between the first language (L1) and the second language (L2) for the instructional content selected by the adaptive learning module (120, a feedback generation module configured to generate real-time bilingual corrective feedback based on learner performance, an output formatting module configured to transmit the adapted bilingual instructional content and generated corrective feedback to the learner device for presentation to the learner. The system also includes a teacher device communicatively coupled to the central processing system via the communication network, and configured to present learner performance analytics and to receive instructional control inputs for modifying learning parameters.
[0019] In one embodiment the system further comprises a data repository connected to the central processing unit via the communication network configured to store information, activities, and tests in more than one language tagged with difficulty level, topic and language.
[0020] In one embodiment, the central processing unit further analyses real-time interaction features and stored historical learner interaction data to continuously update the learner proficiency profile thereby enabling temporally contextualized proficiency estimation and reducing adaptation latency in subsequent instructional content generation.
[0021] In one embodiment, the central processing unit further comprises a content selection module configured to retrieve bilingual instructional content from a data repository by matching adaptive learning module output parameters with stored metadata attributes and filtering non-compliant content entries.
[0022] In one embodiment, the central processing unit further comprises a notification module configured to generate learner and instructor alert data based on detecting predefined threshold deviations in proficiency score variation or engagement level variation and transmit the alert data to the central processing unit.
[0023] In one embodiment, the central processing unit is configured to select and transmit multimodal instructional content comprising text, audio, and video in L1 and L2 based on a control signal generated by the adaptive learning module, thereby dynamically switching content presentation modes in response to learner proficiency and engagement.
[0024] In one embodiment, the scaffolding control module further configured to control the bilingual scaffolding ratio and dynamically decreases L1 assistance as learner proficiency in L2 increases progressively reducing L1 explanatory content, translation frequency while increasing L2-only instructional presentation as the learner proficiency profile exceeds predefined proficiency thresholds.
[0025] In one embodiment, the adaptive learning module is configured to monitor behavioural interaction indicators comprising hesitation duration, inactivity intervals, anxiety score derived from speech characteristics, and speech confidence score, and to automatically regulate instructional difficulty, content pacing, and L1–L2 language support ratio, thereby enabling real-time adaptive control of bilingual instructional output.
[0026] In one embodiment, the scaffolding control module is configured to automatically transition the instructional presentation into an immersion mode when the learner proficiency profile exceeds a predefined proficiency threshold by progressively suppressing first-language (L1) translations and explanations and delivering second-language (L2) dominant instructional content, thereby improving autonomous L2 cognitive processing and reducing dependence on bilingual assistance during advanced learning stages
[0027] In light of the above, in another aspect of the present disclosure, a method for operating an adaptive language learning system is disclosed herein. The method comprises receiving learner interaction inputs including text responses, speech responses, and activity responses via a learner interface of a learner device. The method also includes transmitting the learner interaction inputs from the learner device to a local processing unit. The method also includes normalizing, formatting and conditioning the learner interaction inputs to generate standardized input data via a data pre-processing module of the local processing unit. The method also includes extracting learner performance features from the standardized input data via a feature extraction module of the local processing unit. The method also includes transmitting the learner performance features through a communication network to a central processing system. The method also includes generating a learner proficiency profile and an engagement profile based on the learner performance features via a learner profiling module of the central processing system. The method also includes determining instructional difficulty, pacing, and content sequencing according to the learner proficiency profile and the engagement profile via an adaptive learning module of the central processing system. The method also includes performing semantic translation, contextual explanation, and bilingual simplification of the instructional content via a language processing module of the central processing system. The method also includes regulating a balance between L1 instructional content and L2 instructional content based on the learner proficiency profile via a scaffolding control module of the central processing system. The method also includes generating real-time bilingual corrective feedback via a feedback generation module of the central processing system. The method also includes formatting and transmitting the adapted bilingual instructional content and the real-time bilingual corrective feedback into presentation-ready output data via an output formatting module of the central processing system to the learner device through the communication network for presentation via the learner interface. The method also includes presenting learner performance analytics and receiving instructional control inputs via a teacher device communicatively coupled to the central processing system.
[0028] These and other advantages will be apparent from the present application of the embodiments described herein.
[0029] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0030] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0031] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0032] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0033] FIG. 1 illustrates a block diagram of an adaptive language learning system, in accordance with an embodiment of the present disclosure;
[0034] FIG. 2 illustrates a flowchart of a method, outlining the sequential steps for operating an adaptive language learning system, in accordance with an exemplary embodiment of the present disclosure.
[0035] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0036] The adaptive language learning system with proficiency-based content adaptation is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0037] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure.
[0038] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0039] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0040] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0041] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0042] Referring now to FIG. 1 to FIG.2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of an adaptive language learning system 100 , in accordance with an embodiment of the present disclosure.
[0043] The system 100 may include a learner device 102 having a learner interface 104, a local processing unit 108, a communication network 106, a central processing unit 116, a teacher device 134 and a data repository 136.
[0044] The learner device 102 having a learner interface 104 configured to receive learner interaction inputs including but not limited to text, speech signals, and activity responses, and to render adapted bilingual learning content in at least a first language (L1) and a second language (L2). The learner interface 102 enables selectable language presentation modes including bilingual assistance mode and english-only immersion mode, thereby allowing gradual transition from assisted learning to independent second-language comprehension. Additionally, the learner interface 104 supports interactive feedback display, corrective suggestions, and performance prompts for both learners and instructors to facilitate guided pedagogical interaction.
[0045] The communication network 106 is configured to enable data transmission between components of the system 100. The communication network 106 may comprise one or more wired or wireless communication channels enabling exchange of interaction data, instructional content, and control signals among system entities.
[0046] In a preferred embodiment of the present invention the communication network 106 comprises wireless communication technologies including, but not limited to, wireless fidelity (Wi-Fi), Bluetooth, or internet-based communication protocols, thereby facilitating interaction between the system entities.
[0047] The local processing unit 108 operatively coupled to the learner device 102 configured to process the learner interaction inputs and to transmit interaction features. The local processing unit 108 performs preliminary computational processing on the received inputs, it captures raw interaction data including text entries, speech signals, and activity responses, performs signal conditioning and structuring operations, and derives intermediate interaction features representing learner behaviour characteristics.
[0048] The local processing unit 106 have specialized modules including a data input module 110, a data preprocessing module 112 and a feature extraction module 114.
[0049] The data input module 110 configured to receive raw learner interaction inputs from the learner device 102. The data input module 110 acquires multimodal inputs including typed text responses and captured speech signals.
[0050] The data pre-processing module 112 configured to normalize, format and condition the learner interaction inputs received from the data input module 110 to generate standardized input data. The pre-processing module 112 module performs noise reduction on captured speech signals and spelling normalization of textual responses to preserve interaction context. Further, speech inputs are converted into machine-processable representations including phoneme or transcript sequences, and text inputs are standardized through punctuation correction, and linguistic segmentation. The pre-processing module 112 module removes incomplete interaction records and applies temporal alignment.
[0051] The feature extraction module 114 configured to extract learner performance features from the processed learner interaction data for subsequent analysis. The feature extraction module 114 derives behavioural and linguistic indicators including response accuracy, response latency, hesitation, error frequency, pronunciation deviation, fluency patterns, vocabulary, and anxiety. The extracted features collectively represent learner comprehension ability, confidence level, and learning progression.
[0052] The central processing unit 116 communicatively coupled to the local processing unit 106 via the communication network 106, and configured to perform adaptive learning analysis and content generation based on processed learner interaction data received from the local processing unit 108. The central processing unit 116 applies rule-based artificial intelligence driven decision mechanisms to determine instructional difficulty level, pacing, and sequencing of learning activities. Based on the determined learning state, the central processing unit 116 generates bilingual instructional content, explanations, and corrective feedback tailored to the learner’s current comprehension ability.
[0053] The central processing unit 116 have specialized modules including a learner profiling module 118, an adaptive learning module 120, a scaffolding control module 122, a content selection module 124, a language processing module 126, a feedback generation module 128, an output formatting module 130, and a notification module 132.
[0054] In one embodiment of the present invention, the central processing unit 116 is configured to select and transmit multimodal instructional content comprising text, audio, and video in L1 and L2. thereby dynamically switching content presentation modes in response to learner proficiency and engagement. The system 100 dynamically switches content presentation modes, adjusts modality emphasis, and varies explanatory support so as to match the learner’s current comprehension state and maintain effective instructional delivery.
[0055] In one embodiment of the present invention, the central processing unit 116 analyses real-time interaction features and stored historical learner interaction data to continuously update the learner proficiency profile, enabling temporally contextualized proficiency estimation and reducing adaptation latency in subsequent instructional content generation. The analysis evaluates performance trends including accuracy progression, response consistency, error patterns, and fluency variation across learning sessions. Based on the updated proficiency profile, the system 100 dynamically refines instructional difficulty, pacing, and language support level, thereby enabling personalized adaptation of bilingual instructional content in accordance with the learner’s evolving language competence.
[0056] The learner profiling module 118 configured to generate and update a learner proficiency profile and an engagement profile based on the extracted interaction features. The learner profiling module 118 interprets behavioural and linguistic performance indicators to estimate learner competence level and learning participation patterns for adaptive instructional control.
[0057] In a preferred embodiment of the present invention the learner profiling module 118 applies artificial intelligence based analytical algorithms to interpret the extracted learner performance features and to generate a dynamic learner proficiency profile and engagement profile representing the learner’s language competence and behavioural learning patterns.
[0058] The adaptive learning module 120 configured to dynamically determine instructional difficulty, pacing, and content sequencing based on the learner proficiency profile and the engagement profile. The adaptive learning module 120 evaluates the learner state against predefined thresholds and progression criteria to select subsequent instructional units, adjust repetition frequency, and regulate task complexity.
[0059] In a preferred embodiment of the present invention, adaptive content selection by machine learning (ML) enables the system 100 to automatically choose appropriate instructional material based on learner performance and behaviour patterns.
[0060] In one embodiment of the present invention, the adaptive learning module 120 is configured to monitor behavioural interaction indicators comprising hesitation duration, inactivity intervals, anxiety score derived from speech characteristics, and speech confidence score, and to automatically regulate instructional difficulty, content pacing, and L1–L2 language support ratio, thereby enabling real-time adaptive control of bilingual instructional output.
[0061] The scaffolding control module 122 configured to regulate a balance between L1 and L2 instructional content based on the learner proficiency profile. The scaffolding control module 122 adjusts language substitution level so that L1 assistance decreases and L2 exposure increases as learner proficiency improves.
[0062] In one embodiment of the present invention, wherein the scaffolding control module 122 further configured to control the bilingual scaffolding ratio and dynamically decreases L1 assistance as learner proficiency in L2 increases progressively reducing L1 explanatory content, translation frequency while increasing L2-only instructional presentation as the learner proficiency profile exceeds predefined proficiency thresholds.
[0063] In one embodiment of the present invention, the scaffolding control module 122 is configured to automatically transition the instructional presentation into an immersion mode when the learner proficiency profile exceeds a predefined proficiency threshold by progressively suppressing first-language (L1) translations and explanations and delivering second-language (L2) dominant instructional content, thereby improving autonomous L2 cognitive processing and reducing dependence on bilingual assistance during advanced learning stages.
[0064] In one embodiment of the present invention, the central processing unit 116 further comprises content selection module 124 configured to retrieve bilingual instructional content from a data repository 136 by matching adaptive learning module 120 output parameters with stored metadata attributes including difficulty level, topic category, language support level, and instructional format. The module filters non-compliant content entries and prioritizes contextually relevant learning material, thereby ensuring delivery of instruction aligned with the learner’s current proficiency and engagement state.
[0065] The language processing module 126 configured to perform semantic translation, contextual explanation, and bilingual simplification between the first language (L1) and the second language (L2) for the instructional content selected by the adaptive learning module 120. The language processing module 126 analyzes linguistic structure and contextual meaning to generate equivalent expressions, adaptive explanations, and graded vocabulary substitutions suitable for the learner proficiency level.
[0066] In a preferred embodiment of the present invention, the system 100 employs natural language processing (NLP) algorithms to interpret learner inputs including textual and spoken responses and to derive linguistic intent, grammatical structure, and contextual meaning.
[0067] The feedback generation module 128 configured to generate real-time bilingual corrective feedback based on learner performance. The feedback generation module 128 identifies response errors, pronunciation deviations, and comprehension gaps and produces corrective suggestions, hints, and reinforced examples in L1 and/or L2 according to the learner proficiency level to facilitate immediate learning correction.
[0068] In a preferred embodiment of the present invention, the feedback generation module 128 processes collected interaction and performance data to identify usage patterns, behavioural trends, and learning effectiveness indicators. The module generates analytical insights and feedback reports which assist administrators and instructors in evaluating system performance and learner progress.
[0069] The output formatting module 128 configured to transmit the adapted bilingual instructional content and generated corrective feedback to the learner device 102 for presentation to the learner. The output formatting module 128 structures multimodal elements including text, audio, and visual cues, applies interface-compatible encoding, and synchronizes instructional content with feedback prompts to ensure coherent interactive presentation.
[0070] In one embodiment of the present invention, the central processing unit 116 further comprises a notification module 132 configured to generate learner and instructor alert data based on detecting predefined threshold deviations in proficiency score variation or engagement level variation and transmit the alert data to the central processing unit 116. The notification module 132 monitors performance trends across interaction sessions and, upon identifying abnormal decline, stagnation, or inactivity patterns, produces alert signals and transmits the alert data to the central processing unit 116 for presentation to the teacher.
[0071] The teacher device 134 communicatively coupled to the central processing unit 116 via the communication network 106, the teacher device 134 configured to present learner performance analytics and to receive instructional control inputs for modifying learning parameters. The teacher device 134 displays proficiency trends, engagement indicators, and error distribution metrics, and allows an instructor to adjust difficulty level, language support settings, and content selection preferences, which are transmitted back to the central processing unit for adaptive instructional regulation.
[0072] In one embodiment, the system 100 further comprises a data repository 136 connected to the central processing unit 116 via the communication network 106 configured to store information, activities, and tests in more than one language tagged with difficulty level, topic and language. The stored content is indexed using metadata attributes including difficulty level, topic category, language support level, and instructional format to enable efficient retrieval by the content selection module 124. The data repository 136 further maintains versioned learning resources and historical performance-linked content references to support adaptive content delivery.
[0073] In a preferred embodiment of the present invention, artificial intelligence (AI) based analytical algorithms are applied to evaluate learner performance and identify areas of comprehension difficulty through analysis of response accuracy, error patterns, and interaction behaviour.
[0074] In the preferred embodiment of the invention, the system 100 may be deployed within an online classroom environment, integrated into e-learning platforms, or implemented as a standalone mobile application. The architecture supports both network-connected and device-centric operation.
[0075] In a preferred embodiment of the present invention, the system 100 provides bilingual pedagogical support for effective second-language acquisition. It provides contextual translation of key terms and phrases by presenting meaning equivalents within the instructional context rather than isolated word substitution.
[0076] In a preferred embodiment of the present invention, the system 100 offers interactive bilingual exercises and vocabulary mapping tools that visually associate related concepts across the first language (L1) and the second language (L2), thereby strengthening retention and comprehension.
[0077] In a preferred embodiment of the present invention, the system 100 reduces learner anxiety by providing familiar language (L1) support alongside second-language instruction, thereby improving confidence and encouraging active participation during learning activities.
[0078] In a preferred embodiment of the present invention, the architecture of the system 100 is suitable for deployment in classroom environments, web-based learning platforms, and self-paced study applications, enabling flexible adoption across different educational settings.
[0079] In a preferred embodiment of the present invention, the system 100 allows selectable bilingual and English-only instructional modes, wherein the presentation of L1 assistance may be enabled or disabled based on learner preference or system-determined proficiency level.
[0080] FIG. 2 illustrates a flow chart of a method 200, outlining the sequential steps for operating an adaptive language learning system 100, in accordance with an embodiment of the present disclosure.
[0081] At step 202, receiving learner interaction inputs including text responses, speech responses, and activity responses via a learner interface 104 of a learner device 102.
[0082] At step 204, transmitting the learner interaction inputs from the learner device 102 to the data input module 110 of the local processing unit 108.
[0083] At step 206, normalizing, formatting and conditioning the learner interaction inputs to generate a standardized input data via a data pre-processing module 112 of the local processing unit 108.
[0084] At step 208, extracting learner performance features from the standardized input data via a feature extraction module 114 of the local processing unit 108.
[0085] At step 210, transmitting the learner performance features through a communication network 106 to the central processing unit 116.
[0086] At step 212, generating a learner proficiency profile and an engagement profile based on the learner performance features via a learner profiling module (118) of the central processing unit 116.
[0087] At step 214, determining instructional difficulty, pacing, and content sequencing according to the learner proficiency profile and the engagement profile via an adaptive learning module 120 of the central processing unit 116.
[0088] At step 216, regulating a balance between L1 instructional content and L2 instructional content based on the learner proficiency profile via a scaffolding control module 122 of the central processing unit 116.
[0089] At step 218, performing semantic translation, contextual explanation, and bilingual simplification of the instructional content via a language processing module 126 of the central processing unit 116.
[0090] At step 220, generating real-time bilingual corrective feedback via a feedback generation module 128 of the central processing system 116.
[0091] At step 222, formatting and transmitting the adapted bilingual instructional content and the real-time bilingual corrective feedback into presentation-ready output data via an output formatting module 130 of the central processing system 116 to the learner device 102 through the communication network 106 for presentation via the learner interface 104.
[0092] At step 224, presenting learner performance analytics and receiving instructional control inputs via a teacher device 134 communicatively coupled to the central processing unit 116.
[0093] In best mode of operation of the present invention, the system 100 operates in a structured processing sequence to provide adaptive bilingual learning assistance. Learner interaction inputs comprising text responses, speech signals, and activity responses are received through the learner interface 104 of the learner device 102 and transmitted to the local processing unit 108 via the communication network 106. The data input module 110 acquires the interaction inputs which are normalized and conditioned by the data pre-processing module 112 to generate standardized interaction data. The feature extraction module 112 derives learner performance indicators which are forwarded to the central processing unit 116. The learner profiling module 118 evaluates the extracted features to generate a proficiency profile and an engagement profile, and the adaptive learning module 120 determines instructional difficulty, pacing, and sequencing parameters. Based on these parameters, the, the scaffolding control module 122 regulates the proportion of L1 and L2 support, the content selection module 124 retrieves suitable instructional material from the data repository 136 the language processing module 126 generates contextual bilingual explanations, the feedback generation module 128 produces real-time corrective feedback and the output formatting module 130 delivers the adapted instructional contents, and the notification module 132 generates alert data upon detecting predefined deviations in proficiency or engagement levels for instructor awareness and notifies both learners device and teachers device. The system 100 utilises artificial intelligence and natural language processing techniques to analyse learner performance and dynamically personalise instruction. The system 100 continuously monitors response accuracy, error patterns, hesitation behaviour, and interaction consistency to detect comprehension difficulty and adjust language assistance accordingly. Bilingual scaffolding is increased when learning difficulty is detected and gradually reduced as proficiency improves, enabling a transition toward independent second-language usage. The system 100 further provides performance analytics, engagement indicators, and instructional recommendations to instructors, enabling evidence-based instructional decisions. The architecture supports real-time interactive learning across classroom, online learning platforms, and mobile environments, thereby improving learning effectiveness, engagement, and retention.
[0094] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0095] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0096] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0097] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0098] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An adaptive language learning system (100) adapted to personalize language learning, the system (100) comprising:
a learner device (102) having a learner interface (104) configured to receive learner interaction inputs including but not limited to text, speech signals, and activity responses, and to render adapted bilingual learning content in at least a first language (L1) and a second language (L2);
a local processing unit (108) operatively coupled to the learner device (102), configured to process the learner interaction inputs and to transmit interaction features, wherein the local processing unit (108) further comprises:
a data input module (110) configured to receive raw learner interaction inputs from the learner device (102);
a data pre-processing module (112) configured to normalize, format and condition the learner interaction inputs received from the data input module (110) to generate standardized input data;
a feature extraction module (114) configured to extract learner performance features from the processed learner interaction data for subsequent analysis;
a communication network (106) configured to exchange the data between the components of the system (100);
a central processing unit (116) communicatively coupled to the local processing unit (108) via the communication network (106), and configured to perform adaptive learning analysis and content generation based on processed learner interaction data received from the local processing unit (108), wherein the central processing unit (116) further comprises:
a learner profiling module (118) configured to generate and update a learner proficiency profile and an engagement profile based on the extracted interaction features;
an adaptive learning module (120) configured to dynamically determine instructional difficulty, pacing, and content sequencing based on the learner proficiency profile and the engagement profile;
a scaffolding control module (122) configured to regulate a balance between L1 and L2 instructional content based on the learner proficiency profile;
a language processing module (126) configured to perform semantic translation, contextual explanation, and bilingual simplification between the first language (L1) and the second language (L2) for the instructional content selected by the adaptive learning module (120);
a feedback generation module (128) configured to generate real-time bilingual corrective feedback based on learner performance;
an output formatting module (130) configured to transmit the adapted bilingual instructional content and generated corrective feedback to the learner device (102) for presentation to the learner; and
a teacher device (134) communicatively coupled to the central processing unit (116) via the communication network (106) and configured to present learner performance analytics and to receive instructional control inputs for modifying learning parameters.
2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a data repository (136) connected to the central processing unit (116) via the communication network (106) and configured to store information, activities, and tests in more than one language tagged with difficulty level, topic and language.
3. The system (100) as claimed in claim 1, wherein the central processing unit (116) analyses real-time interaction features and stored historical learner interaction data to continuously update the learner proficiency profile thereby enabling temporally contextualized proficiency estimation and reducing adaptation latency in subsequent instructional content generation.
4. The system (100) as claimed in claim 1, wherein the central processing unit (116) further comprises a content selection module (124) configured to retrieve bilingual instructional content from a data repository (136) by matching adaptive learning module (120) output parameters with stored metadata attributes and filtering non-compliant content entries.
5. The system (100) as claimed in claim 1, wherein the central processing unit (116) further comprises a notification module (132) configured to generate learner and instructor alert data based on detecting predefined threshold deviations in proficiency score variation or engagement level variation and transmit the alert data to the central processing unit (116).
6. The system (100) as claimed in claim 1, wherein the central processing unit (116) is configured to select and transmit multimodal instructional content comprising text, audio, and video in L1 and L2 based on a control signal generated by the adaptive learning module (120), thereby dynamically switching content presentation modes in response to learner proficiency and engagement.
7. The system (100) as claimed in claim 1, wherein the scaffolding control module (122) further configured to control the bilingual scaffolding ratio and dynamically decreases L1 assistance as learner proficiency in L2 increases progressively reducing L1 explanatory content, translation frequency while increasing L2-only instructional presentation as the learner proficiency profile exceeds predefined proficiency thresholds.
8. The system (100) as claimed in claim 1, wherein the adaptive learning module (120) is configured to monitor behavioural interaction indicators comprising hesitation duration, inactivity intervals, anxiety score derived from speech characteristics, and speech confidence score, and to automatically regulate instructional difficulty, content pacing, and L1–L2 language support ratio, thereby enabling real-time adaptive control of bilingual instructional output.
9. The system (100) as claimed in claim 1, wherein the scaffolding control module (122) is configured to automatically transition the instructional presentation into an immersion mode when the learner proficiency profile exceeds a predefined proficiency threshold by progressively suppressing first-language (L1) translations and explanations and delivering second-language (L2) dominant instructional content, thereby improving autonomous L2 cognitive processing and reducing dependence on bilingual assistance during advanced learning stages.
10. A method (200) for operating an adaptive language learning system (100), the method (200) comprising:
receiving learner interaction inputs including text responses, speech responses, and activity responses via a learner interface (104) of a learner device (102);
transmitting the learner interaction inputs from the learner device (102) to the data input module (110) of the local processing unit (108);
normalizing, formatting and conditioning the learner interaction inputs to generate a standardized input data via a data pre-processing module (112) of the local processing unit (108);
extracting learner performance features from the standardized input data via a feature extraction module (114) of the local processing unit (108);
transmitting the learner performance features through a communication network (106) to the central processing unit (116);
generating a learner proficiency profile and an engagement profile based on the learner performance features via a learner profiling module (118) of the central processing unit (116);
determining instructional difficulty, pacing, and content sequencing according to the learner proficiency profile and the engagement profile via an adaptive learning module (120) of the central processing unit (116);
regulating a balance between L1 instructional content and L2 instructional content based on the learner proficiency profile via a scaffolding control module (122) of the central processing unit (116);
performing semantic translation, contextual explanation, and bilingual simplification of the instructional content via a language processing module (126) of the central processing unit (116);
generating real-time bilingual corrective feedback via a feedback generation module (128) of the central processing system (116);
formatting and transmitting the adapted bilingual instructional content and the real-time bilingual corrective feedback into presentation-ready output data via an output formatting module (130) of the central processing system (116) to the learner device (102) through the communication network (106) for presentation via the learner interface (104); and.
presenting learner performance analytics and receiving instructional control inputs via a teacher device (134) communicatively coupled to the central processing unit (116).