NLP-supported language teacher assistant (classroom analysis and feedback)

Abstract

An intelligent classroom analysis and feedback tool for language learning, including, • a data input interface (101) configured to receive student-generated speech input consisting of written and oral contributions. • a preprocessing and normalization engine (102) for standardizing and preparing the speech inputs for analysis. • a natural language processing (NLP) pipeline (103) to perform linguistic, grammatical, pronunciation, lexical, discourse and sentiment analysis of the inputs. • a student clustering and profiling module (104) that can be used to classify students based on a common model of such error and performance patterns. • Teacher-oriented analytics dashboard (105), customized to visualize trends in language performance at the class and individual levels. • a teaching recommendation engine (106) that recommends educational interventions in order for weW patterns. • an engagement and sentiment tracking subsystem (107) adapted to monitor emotional and behavioral cues from language use; and • an LMS integration layer (8) in operational communication with the system to embed the system in educational platforms and provide feedback with an existing course content and gradebooks of the student.

Description

Field of the invention

[0001] The present invention relates to educational technology and artificial intelligence, and more particularly to an NLP-enabled (natural language processing) assistant for classroom-based analysis and feedback in language learning environments. It stores performance, language error, student, and variation histories for automatic performance tracking and error detection in student profiling and lesson adaptation. Background of the invention

[0002] Language education is a fundamental field of study that enables students to achieve academic success, communicate cross-culturally, and participate in a global workforce. As formal education institutions and training centers expand their reach through in-person, virtual, or hybrid models, educators increasingly encounter challenges in delivering, assessing, and improving language instruction. These challenges are even more pronounced in situations involving high student-teacher ratios, asynchronous learning, and diverse learning groups.

[0003] Historically, language instruction has relied on manual marking, observation, and teacher intuition to assess and test writing, speaking, and reading comprehension skills. While these systems offer some pedagogical flexibility, they are burdensome, time-consuming, and frequently inconsistent. Perhaps more seriously, they are reactive rather than proactive, alerting teachers to problems only when student performance is no longer reliable. It is rarely easy for educators to identify error patterns in instruction, track individual language changes over time, or identify early warning signs of disengaged students. The nature of the evidence—usually unstructured, multimodal, and qualitative—makes analysis even more difficult.Essays, oral responses, and text-based responses contain useful and potentially actionable information, but scalable knowledge retrieval remains a challenge.

[0004] Current educational technology, some would argue, doesn't contribute much in this regard. Tools like grammar checkers, automated grading systems, and language learning apps typically operate like a black box, offering students minimal feedback outside of the classroom context. These systems are generally unable to combine aggregated student information, fail to perform trend analysis over time, and provide few strategic recommendations to teachers. Furthermore, learning management systems (LMS) monitor administrative indicators like assignment submissions and test scores and are not designed to analyze the linguistic form of student responses or draw conclusions about emotional engagement. As a result, teachers today have only piecemeal tools that neither teach students linguistic intelligence in the classroom nor adaptive learning play.

[0005] Recent advances in artificial intelligence, particularly in areas such as natural language processing (NLP) and machine learning, offer the opportunity to overcome these deficits. NLP technologies such as POS tagging, syntactic parsing, grammatical error correction, named entity recognition, phoneme alignment, and sentiment analysis have made significant progress. Such techniques now enable the highly precise analysis of very large amounts of linguistic data, detecting not only superficial errors but also deep structural patterns in language use. Automatic speech recognition (ASR) and acoustic modeling have also contributed to a better understanding and implementation of spoken language in terms of fluency, pronunciation accuracy, and phonological awareness. In combination with unsupervised learning systems such as clustering, they enable the formation of learning groups with common linguistic problems and proficiency patterns.

[0006] However, the application of NLP in the field of real-time classroom analytics in language teaching is still underdeveloped. Most current AI-powered tools focus on individual grammar correction or machine grading and offer little ability to generate class-wide insights or pedagogically useful information. Current platforms neither support multimodal ingestion of student input (text, speech, chat), nor comprehensive linguistic analysis using custom NLP pipelines, nor class-wide behavioral analytics, feedback, affect and behavior detection (emotional tone, student behavior), nor intervention recommendations for 1:1 and 1:group instructional interventions.

[0007] This represents an urgent and previously unmet need in educational technology: the development of a centralized, intelligent platform that enables teachers to transform student language data into structured, interpretable, and actionable information. Such a platform must integrate seamlessly into teachers' existing classroom routines and connect with existing workflows by working with LMS platforms, enhancing teachers' workload rather than replacing professional judgment.

[0008] The present invention fulfills the need for such a system and incorporates a new, NLP-based teacher assistance system specifically designed for language instruction. This system automatically processes student-generated content—text responses, voice recordings, and discussion threads—and feeds it through a complex, educationally optimized NLP pipeline. It detects grammatical, syntactical, and phonological errors; measures lexical diversity and fluency; analyzes emotional tone and engagement; and groups learners into profiles based on their proficiency level and recurring error patterns. This data is then communicated via a user-friendly teacher dashboard that displays class-wide trends, identifies students with learning difficulties, and offers concrete intervention suggestions such as targeted mini-lessons or practice sessions.

[0009] The core of this innovation is transforming unstructured, heterogeneous language data into actionable information for individual students and the classroom. Teachers can track learning progress in real time, intervene early, and tailor lessons to meet their specific needs. This leads to improved student performance and greater teaching effectiveness. This innovation has broad application in education—from elementary school to higher education, in language training centers, and in-company language programs. It transforms the function of technology in language education—from passive feedback to active, intelligent support elements for teachers. This enables teachers to design data-driven, effective lessons. Summary of the invention

[0010] The present invention provides an intelligent educational system called an "NLP-powered Language Teacher Assistant" that supports and reinforces language instruction by automatically analyzing language information input by students and providing actionable feedback in the classroom. This AI-powered solution is compatible with learning management systems (LMS) and instructional platforms and provides on-demand, actionable information on learner performance, language acquisition, engagement, and instructional needs.

[0011] At a high level, the system processes heterogeneous student modalities such as written (e.g., essays, short answers, discussion contributions), oral (e.g., recorded oral assignments, pronunciation exercises), and chat content (e.g., chat or forum posts). This data is then passed through a powerful, modular natural language processing (NLP) pipeline tailored to educational contexts and the model. This pipeline leverages advanced techniques, including syntactic parsing, grammar and pronunciation error detection, lexical profiling, speech-to-text conversion, and sentiment / emotion analysis. Such analyses enable the system to derive diverse, rich linguistic, structural, and behavioral information from unstructured input.

[0012] A key innovation is a class-level analytics engine that combines data from individual students across an entire class to identify learning patterns, recurring language error patterns, and fluctuations in language proficiency and dropout rates over the course of the class period. This enables teachers to gain insights at the macro (class) and micro (individual) levels in an engaging dashboard interface. The system automatically groups students into dynamic ability groups based on common error patterns and language characteristics using unsupervised machine learning techniques (e.g., K-Means, DBSCAN). These clusters are amenable to ability grouping, allowing teachers to tailor their instructional systems to each individual cluster.

[0013] In addition to error detection and clustering, the system also features an adaptive instructional recommendation mechanism that provides pedagogical recommendations as needed. These can be targeted mini-lessons (e.g., grammar review), vocabulary exercises, pronunciation exercises, or personal revision tasks. The guidelines correspond to observed trends such as the frequent occurrence of errors in subject-verb agreement or linguistic redundancy in the classroom. Unlike conventional assessment tools that only detect correctness, the present invention transforms linguistic behavior into instructional intelligence to support lesson design.

[0014] The platform also features an engagement and mood tracking feature. This uses NLP-powered sentiment analysis to identify disengaged or underperforming students based on linguistic affect. For example, a low tone, lack of fluency, disjointed sentences, or low lexical diversity can indicate a lack of confidence or cognitive fatigue. Teachers are alerted to these risks and can proactively intervene before performance declines.

[0015] Furthermore, the system enables a two-way exchange with the teacher. Teachers can validate, override, or further refine system-generated insights, thus bringing professional judgment into the feedback loop. Automation does not replace the role of the teacher, but rather expands it. It contributes to more contemporary, scalable, and data-driven teaching, freeing teachers from the tyranny of "average" without compromising the quality of teaching.

[0016] The system is a cloud-based, modular service that integrates seamlessly with modern LMSs and features a plug-and-play design based on standard APIs. It works online and offline and is ideal for in-person classes, virtual courses, and blended learning models, as well as nationwide distance learning programs. Its architecture is responsive and enables real-time processing. It is also compatible with data protection and educational standards such as FERPA and GDPR.

[0017] In summary, the NLP-supported language teaching assistant will revolutionize the way languages are taught and learned. It moves from reactive and manual assessment mechanisms to proactive and intelligent feedback systems. Teachers can continuously track their students' progress, diagnose learning difficulties early, provide evidence-based instruction, and provide an enriching experience for students at all levels of language development. The present invention thus closes a technological and pedagogical gap in the state of the art and provides a commercially viable, patentable solution for intelligent, adaptive language instruction in the 21st-century classroom. Description of the invention

[0018] Fig. : Block diagram of the NLP-supported language teacher assistant (classroom analysis and feedback). describes an artificial intelligence-based educational system that promotes language acquisition by automatically assessing the language content offered by students and providing intelligent feedback in the classroom. The system uses NLP, language analysis, machine learning, and sentiment monitoring to analyze students' writing, speech, and communication data and derive structured insights that personalize and improve instruction. Fundamentally, the invention enables classroom analytics and dynamic pedagogical adjustments, flanked by real-time feedback loops specifically for language teachers. In this case, the system of the present application is referred to as System 100.The main components of System 100 are the data collection interface (101), the preprocessing and normalization engine (102), the NLP analysis pipeline (103), the student clustering and profiling module (104), the teacher analysis and feedback dashboard (105), the recommendation engine (106), the engagement and sentiment monitoring subsystem (107), and the LMS integration layer (108).

[0019] The architecture consists of a set of intelligent, integrated modules that handle data collection, analysis, interpretation, and feedback. The Data Ingestion Interface (101) is the first interface to create secure channels for student content submitted from multiple locations. This can include written texts such as essays, forum posts, written responses and fill-in-the-blanks, quizzes, as well as oral contributions in the form of oral exams, recorded reading prompts, and even pronunciation exercises. The interface enables synchronous and asynchronous data ingestion and interacts with generic LMSs via API endpoints, CSV batch uploads, and plug-ins.

[0020] The recorded data is then passed to the preprocessing and normalization engine (102) to convert multimodal inputs into a form suitable for linguistic analysis. For text, this includes tasks such as tokenization, normalization, and speech recognition. For speech, this may include signal cleanup, speaker segmentation, and automatic speech recognition (ASR) to transcribe spoken language into aligned transcripts. The sent normalized data is then standardized and stored for further linguistic processing.

[0021] The post-processed data is fed into the NLP analysis pipeline (103). This module extracts deep linguistic features using state-of-the-art NLP models optimized for educational applications. The system features annotators for such wide-ranging linguistic phenomena as grammatical errors (e.g., article usage and verb agreement), lexical, syntactic, and cohesive difficulties, lexical scope, word repetition, cohesive markers, and rhetorical structure. For spoken language, it verifies pronunciation accuracy using phoneme alignment models and fluency features such as speech rate, pause duration, and intonation contours. The pipeline also includes an affect perception and tone detection sub-module that detects affective cues indicating student engagement, frustration, or disinterest.

[0022] All extracted features are summarized into learner-specific language profiles and sent to the Student Clustering and Profiling module (104). This module uses unsupervised learning algorithms, including K-means clustering or hierarchical agglomerative clustering, to group learners over a period of time into dynamic cohorts based on shared proficiency patterns, error profiles, and language performance profiles. These clusters are updated with new data to track students' development and regression longitudinally. This confirms a key aspect of the innovation: classroom analyses are not simply conducted by averaging performance, but by identifying patterns and trajectories within (and across) many different learner groups.

[0023] The information obtained from these modules is available to teachers via the Teacher Analysis and Feedback Dashboard (105). Fig.: A secondary view of the invention with a dashboard. While not required, this dashboard doubles as the primary report for displaying and interpreting class analytics. The heatmap with common class-wide errors, lexical trends, and pronunciation distributions is displayed in the dashboard. It offers advanced and detailed graphical representations such as writing complexity trend lines, a fluency graph, engagement metrics, and an interactive comparison of students and cohorts. Finally, the training dashboard identifies high-risk students who show signs of decline, stagnation, or non-response. This feature helps teachers understand individual student performance as well as overall class trends over time, enabling data-driven lesson planning and individualized feedback.To translate data analysis into actionable pedagogical interventions, the system includes a teaching recommendation engine (106). This component leverages data models from the analysis pipeline and the cluster module to suggest personalized interventions. These can be short, targeted materials on common grammatical errors, inevitably mispronounced phoneme groups (pronunciation exercises), continuous vocabulary expansion, or collaborative learning activities. The system can generate individual feedback messages or comments that instructors can share with students via the LMS or embedded in messaging systems.

[0024] Another important tool for classroom analysis is the Engagement and Sentiment Monitoring Subsystem ( 107 ), which continuously monitors the affective content of students' speech. It also captures changes in tone, vocabulary strength, writing volume, and emotional polarity, allowing teachers to identify emotionally withdrawn or stressed students. For example, a sustained decline in lexical diversity combined with negativity can signal a struggling student to the system, which then triggers an alert recommending further support.

[0025] All modules are connected to and work with the LMS Integration Layer (108). This backend service ensures compatibility with common educational software systems such as Moodle, Blackboard, Canvas, Google Classroom, and similar. This enables real-time assignment tracking, automatic integration of feedback into student gradebooks, and embedded visualization widgets in instructor dashboards. The system architecture is designed for secure and scalable deployment, whether in the cloud or on-premises, and complies with all data protection regulations such as FERPA, COPPA, and GDPR.

[0026] The system can be used in a variety of educational settings, including elementary schools, middle schools, college language institutes, ESL schools, test preparation courses, and online tutoring websites. In each case, the invention offers teachers a new way to make teaching more intelligent. This includes the ability to "read" linguistic information across the entire class and to diagnose and treat common linguistic behavioral constellations in a pedagogically sound manner.

[0027] Unlike existing educational tools that focus exclusively on individual grading or grammar correction, the NLP-powered Language Teacher Assistant offers a set of components for systematic language instruction, supporting real-time classroom analytics and adaptive feedback. This is not only a technological but also a pedagogical breakthrough, transforming the way teachers interact with student language data to achieve better learning outcomes at scale.

Claims

[1] An intelligent classroom analysis and feedback tool for language acquisition, including, • a data input interface (101) configured to receive student-generated speech input consisting of written and oral contributions. • a preprocessing and normalization engine (102) for standardizing and preparing the speech inputs for analysis. • a natural language processing (NLP) pipeline (103) to perform linguistic, grammatical, pronunciation, lexical, discourse and sentiment analysis of the inputs. • a student clustering and profiling module (104) that can be used to classify students based on a common model of such error and performance patterns. • Teacher-oriented analytics dashboard (105), customized to visualize trends in language performance at the class and individual levels. • a teaching recommendation engine (106) that recommends educational interventions in order for weW patterns. • an engagement and sentiment tracking subsystem (107) adapted to monitor emotional and behavioral cues from language use; and • an LMS integration layer (8) in operational communication with the system to embed the system in educational platforms and provide feedback with an existing course content and gradebooks of the student. [2] The system of claim 1, wherein the data ingestion interface (101) is further operable to receive both synchronous and asynchronous data from learning management systems, student devices, or third-party applications via RESTful application program interfaces or file-based delivery. [3] The system of claim 1, wherein the preprocessing and normalization engine (102) transcribes spoken responses by automatic speech recognition (ASR) into time-aligned phoneme sequences for pronunciation analysis. [4] The system of claim 1, wherein the NLP analysis pipeline (103) comprises: Rule-based and machine learning-based grammatical error detection; lexical profiling based on vocabulary diversity assessment; discourse structure analysis based on transition and coherence detection; deep learning-based sentiment detection trained on educational corpora. [5] The system of claim 1, wherein the student clustering and profiling module (104) uses unsupervised machine learning algorithms such as k-means, DBSCAN, or Gaussian mixture models to categorize students based on similarities in speech patterns and error types. [6] The system of claim 1, wherein the analytics dashboard (105) includes classroom-wide heatmaps showing error frequency, individual or group performance trend charts, mood timelines, and alerts for behavioral anomalies (e.g., disinterest or reduced language complexity) as they occur. [7] The system of claim 1, wherein the lesson recommendation engine (106) is programmed to suggest user-defined mini-lessons, grammar tutorials, pronunciation exercises, vocabulary exercises, and written feedback templates according to general trends within the class or the particular needs of the user. [8] The system of claim 1, wherein the engagement and mood monitoring subsystem (107) is adapted to detect affective states such as frustration, demotivation, or confusion through mood polarity, lexical tone, and expressive language markers in the student's input. [9] The system of claim 1, wherein the LMS integration layer (108) enables secure integration with external platforms and data protection regulations such as FERPA, COPPA and GDRP. [10] The system of claim 9, wherein the system is implemented on a cloud-based infrastructure and supports multilingual processing and is capable of supporting thousands of users simultaneously. [11] The system of claim 10, further comprising prioritizing the recommendations based at least in part on instructional level, urgency, level of risk to students, and educational impact. [12] The system of claim 10, wherein the system updates learner profiles and cluster assignments in real time as new data is received, allowing dynamic instructional adaptation over time. [13] The system of claim 1, wherein the pronunciation analysis module in the NLP pipeline (103) is configured to identify phonological differences by using a phoneme alignment model, longitude, acoustic profiles of the native language.

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