Standard courseware generation method for artificial intelligence learning mode

Abstract

The application discloses a standard courseware generation method for an artificial intelligence learning mode, and relates to the technical field of educational informatization. First, course standards, world language readiness frameworks and translation memories are collected, and a reference semantic vector set with a unique version fingerprint is generated by vectorization. Then, a multi-language courseware draft is mapped thereon, a weighted difference vector is output, and a time stamp is recorded. Subsequently, the difference vector is decomposed into three factors of emotional tendency, cultural symbol and term consistency, a deviation registration table is generated according to a dynamic threshold, and then, according to a rewriting energy function, a generated rewriting, template replacement or manual prompt is selected, a high-risk language block is locally revised, and a consistency mark containing a version fingerprint is written. Finally, the global residual error is rechecked, the quality is confirmed through a global consistency index, a consistency report is generated, the revised courseware is packaged as a release package for multi-end synchronization, a fingerprint is written on the chain, and a new cross-language courseware quality closed loop is constructed.

Description

Technical Field

[0001] This invention relates to the field of educational informatization technology, specifically to a method for generating standard courseware for artificial intelligence learning models. Background Technology

[0002] With the global expansion of online education, learning management systems (LMS) are increasingly adopting standards such as the Shared Content Object Reference Model (SCM) to ensure the interoperability of courseware across different platforms. This standard, through a unified data model and communication method, enables content packages to be parsed and recorded within any compliant LMS. Subsequent experiential application programming interfaces (APIs) extend the tracking scope to offline activities, virtual reality, and gaming scenarios, providing richer and more real-time data streams for learning behavior collection. Caliper Analytics further introduces a unified event vocabulary and visualization framework, enabling institutions to aggregate learning trajectories across resources. On the content production side, teachers are leveraging generative AI to rapidly generate cases and exercises, shortening courseware development cycles. Translation memory exchange standards are also being used for sentence and paragraph reuse and rapid updates in multilingual courseware. Meanwhile, a report from the Immersive Learning Research Network indicates that AR / VR environments are becoming emerging teaching spaces, placing higher demands on the real-time updating and multimodal consistency of courseware formats. A recent UNESCO document emphasizes that digital textbooks must maintain cultural appropriateness and knowledge accuracy across different languages; otherwise, educational equity will be compromised. In summary, existing technologies can ensure the cross-system flow of courseware at the structural level, but in multilingual, cross-modal, and real-time generation scenarios, they still rely on manual backend verification and debugging, lacking an automated and standardized quality consistency control link.

[0003] The most prominent problem with the current technology system is that when large language models automatically generate multilingual courseware using high-resource corpora, primarily in English, they are prone to systematic deviations in emotional inclination, use of cultural symbols, and correspondence of proper nouns. These deviations spread rapidly through the synchronization mechanism after being packaged into standard content packages. In highly equitable scenarios such as national credit recognition, cross-border remote certification, or immersive virtual classrooms, inconsistencies in the depth of description of learning objectives, evaluation standards, or cultural tone among different language versions will directly lead to imbalances in learning assessment and hinder credit transfer.

[0004] This problem arises from a combination of factors: First, large-scale language models lack sufficient high-quality corpora for low-resource languages, leading to the introduction of implicit biases from the source language during translation or direct generation. Second, the teaching content defined at the xAPI or Caliper event layer only records behavior and does not cover semantic consistency verification, making it difficult to expose biases before release. Third, current translation memory and template methods focus on word-for-word correspondence and cannot identify deeper differences in emotion or culture, especially in VR interactive scripts or real-time voice narration, where manual review is even more difficult. As a result, the same course package may output drastically different value stances and depth requirements in different regions, undermining the equivalence of international curriculum standards and posing risks to educational supervision. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a standard courseware generation method for artificial intelligence learning models. This method maps multilingual courseware drafts onto the surface, outputs a weighted difference vector, and records a timestamp. The difference vector is then decomposed into three factors: sentiment tendency, cultural symbols, and terminology consistency. A deviation registration form is generated based on a dynamic threshold. Next, generative rewriting, template replacement, or manual prompting is selected according to the rewriting energy function to locally revise high-risk language blocks and write consistency markers containing version fingerprints. Finally, a full-domain residual review is performed, and a consistency report is generated after confirming quality through a full-domain consistency index. This report is then packaged with the revised courseware into a release package and synchronized across multiple platforms, thereby solving the technical problems described in the background section.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A standard courseware generation method for AI learning models includes collecting curriculum standards, world language ready frameworks and translation memories, vectorizing and mapping to generate a set of reference semantic vectors with unique version fingerprints, which are used to build a globally robust metric baseline.

[0010] The multilingual courseware draft is mapped to the vector set through sentence-level alignment, the difference vector is calculated and contextual weights and timestamps are superimposed to form traceable offset data, and accurate measurement benchmark output is achieved;

[0011] The discrepancy vectors are decomposed according to sentiment, cultural symbols and terminology consistency to generate a deviation registration table containing fields of type, severity level and algorithm version, which can be used for subsequent revisions.

[0012] Calculate the rewrite energy based on the deviation registration form, select to generate a rewrite, replace the template, or provide manual prompts, complete the local revision, and write a consistency tag containing the version fingerprint into the courseware metadata;

[0013] Once the residual of the revised content is below the warning threshold, a final consistency report is generated. The revised courseware and the report are packaged into a release package, which is then synchronized to each terminal through the learning management system, and the release record fingerprint is stored on the blockchain for evidence.

[0014] Furthermore, the curriculum standard text, the world language ready framework text, and the translation memory dependency data fields are processed into sentences. Core semantic units are extracted through hierarchical dependency parsing. Weight estimation algorithms are used to determine the weight coefficients of each text. Kernel mapping activation functions are used to generate curriculum standard vectors, framework vectors, and translation memory vectors, respectively. The weight coefficients and each vector together constitute the basic data of the reference semantic vector set.

[0015] Furthermore, the reference semantic vector set is phase-aligned, salted hashing is used to generate version fingerprints, and semantic density index is calculated. If the density is less than the system threshold, it is written into the learning record library; otherwise, the weight coefficients are readjusted in the weight estimation stage.

[0016] Furthermore, based on the sentence index triples, corresponding vectors are retrieved in the reference semantic vector set to construct a sentence pair matrix. The matrix is ​​weighted by three factors: Euclidean distance, keyword similarity, and character-lexical edit distance, with the weights dynamically allocated according to the residual entropy.

[0017] A difference vector is generated using the three-dimensional entropy weighting method of semantics, structure and sentiment. The context weight vector and the difference vector are multiplied dimension by dimension to obtain a weighted difference vector. At the same time, the difference record fingerprint is used as a foreign key to solidify the data in the learning record library.

[0018] Furthermore, weighted difference vectors are projected onto the sentiment, culture, and terminology mapping tensors respectively. The projection results are then normalized by quantiles and input into an adjustable slope logic function to calculate the severity of each factor, generating bias candidate entries containing sentence pair indexes.

[0019] Furthermore, the severity vector of the candidate item is aligned with the historical baseline mean vector and variance vector, the severity level is divided according to the dynamic threshold vector, and the revised strategy code is generated according to the level mapping table and written into the deviation registration table.

[0020] Furthermore, a rewrite energy function is established, with severity level, context weight, and context window width as independent variables. When the energy value is higher than the preset generation threshold, generative rewriting is invoked; when it is between the two thresholds, template replacement is performed; and when it is lower than the low threshold, only manual prompts are generated.

[0021] Furthermore, semantic alignment loss is calculated for the generative rewritten text, the candidate text with the minimum loss is selected, and the associated speech script or image description is synchronously updated using the cross-modal mapping matrix. Subsequently, a consistency tag containing version fingerprint, difference record fingerprint and revision strategy is generated and written into the metadata.

[0022] Furthermore, the revised courseware is embedded into the reference semantic vector space, the residual matrix and global consistency index are calculated, and if the residual vector magnitude is greater than the warning threshold, a residual warning set is generated and a residual heatmap is attached to the final consistency report.

[0023] Furthermore, the revised courseware, the final consistency report, and the revision mapping table are encapsulated into a release package, a dependency description file is generated, and the corresponding hash is calculated. After receiving the release package, the client needs to verify the release package hash and the dependency file hash in turn. Only after they pass can the release package be unpacked and loaded. At the same time, the smart contract is called to record the release event fingerprint.

[0024] (III) Beneficial Effects

[0025] This invention provides a standard courseware generation method for artificial intelligence learning modes, which has the following beneficial effects:

[0026] By constructing a unique set of reference semantic vectors, the curriculum standards, the World Language Ready Framework, and translation memory are aligned in the same embedded coordinates, achieving an integrated measurement baseline for cross-language courseware. This enables subsequent algorithms to accurately compare the content of each language on a unified scale, creating a new paradigm for textbook standardization driven by a universal semantic space.

[0027] By combining difference vectors with sentence pair matrices and multi-scale offset measurements, minute deviations in the three modalities of text, speech, and image are quantified into traceable data. Then, the deviation registration form is used to break down the three factors of emotion, culture, and terminology. For the first time, a three-dimensional profile diagnosis of the quality of cross-language teaching materials is achieved, laying a data foundation for precise revision.

[0028] The rewritten energy function calculates severity level, context weight, and context window in conjunction, automatically switching between generative rewriting, template replacement, and manual prompting. This breaks the traditional "one-size-fits-all" revision model, precisely coupling revision actions with teaching weights, combining creativity and practicality, and significantly reducing the amount of manual intervention.

[0029] The cross-modal mapping matrix updates the voice script and image descriptions synchronously based on contrastive learning, ensuring that multimedia elements still fall within the same semantic neighborhood after text rewriting. This solves the common problem of text-image mismatch in immersive scenarios and achieves a dual improvement in content consistency and learning immersion.

[0030] The final consistency report uses residual matrix review and global consistency index to provide quantitative endorsement for the entire courseware. Combined with on-chain release record fingerprints, it forms a verifiable and tamper-proof quality proof link, which improves the transparency of textbook supervision and the credibility of institutional brands. Attached Figure Description

[0031] Figure 1This is a schematic diagram of the standard courseware generation method for artificial intelligence learning mode according to the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see Figure 1 This invention provides a standard courseware generation method for artificial intelligence learning models, including:

[0034] As the trend of global sharing of teaching content becomes increasingly prominent, digital courseware has evolved from the early static single-package form of shareable content object reference models to an AI-driven form that can be generated on demand and updated synchronously in multiple languages. However, the training corpus of large language models is extremely unevenly distributed; if models trained directly on the main English corpus are used for other languages, they are prone to producing hidden biases at the levels of terminology, emotion, and even cultural symbols, thereby undermining consistent learning goals across systems, credits, and cultures.

[0035] For subsequent deviation detection, discrepancy correction, and quality auditing to truly function effectively, the first step is to provide a unified semantic coordinate system for all languages—this is the mission of the reference semantic vector set. It must be compatible with three heterogeneous knowledge sources: curriculum standards, the World Language Ready Framework, and open-source translation memories; it must support multi-source expansion, version traceability, and real-time interoperability with the learning record repository; and it must ensure that the vectorized high-dimensional space maintains semantic topological stability, providing a unified and differentiable metric for sentence-level alignment algorithms in subsequent steps. Only in this way can subsequent processes such as discrepancy vector generation, deviation registration table splitting, and consistency marker writing proceed smoothly and seamlessly.

[0036] Step 1: Construct a unique reference semantic vector set through hierarchical acquisition, weight alignment, and unified mapping to establish a semantic common parameter baseline for the entire system.

[0037] Step 101: Curriculum standard documents are regarded as learning objectives and evaluation benchmarks at the educational administration level, while the World Language Readiness Framework defines the cognitive level of different languages; if these two types of texts cannot form an overlayable relationship in the vector space, any difference measurement will be distorted.

[0038] To ensure that the contribution of the three source texts—curriculum standards, the World Language Readiness Framework, and open-source translation memory—is determined by its pedagogical impact rather than text length in the subsequent integration stage, standard semantic extraction and weight estimation first perform hierarchical syntactic dependency expansion on the three source texts. Then, a weight estimation strategy based on the correlation between target difficulty and learning performance is introduced. The core of the weight estimation is based on a semantic coverage function, which is calculated as follows:

[0039]

[0040] Among them: Curriculum Standard Weighting Coefficient Based on semantic coverage Derivation of the expected ascending gradient; weight coefficients of the world language ready framework The degree of cross-linguistic complementarity shall be used as the criterion.

[0041] Open source translation memory weight coefficient Through balance and The residuals are determined, and the three conditions are satisfied. ;

[0042] semantic coverage : To measure the adequacy of the coverage of the core teaching objectives by the three source texts, with a range of values. The higher the value, the more comprehensive the coverage; coverage indicator : Boolean variable, indicating whether the sentence contains core teaching terms, and its possible values. ;

[0043] Sentence length : Number of lexical units in a sentence, a positive integer; Curriculum standard weighting coefficient : Control the intensity of the impact of curriculum standards on embedded integration, and the range of values. World Language Readiness Framework Weight Coefficient Same as above, range of values Open source translation memory weight coefficient Same as above, range of values .

[0044] By dynamically adjusting the weights, the contribution of the three knowledge sources to the semantic space is linked to the matching degree of the core teaching objectives, thus overcoming the distortion problem of the traditional equal-weighted averaging strategy in educational scenarios.

[0045] After weight estimation is completed through multi-source vector fusion and spatial consistency, the three source texts are aligned according to sentence-level annotations, and the weights are used as scaling factors to input into the shared embedding network to generate three sets of temporary vectors. , , To ensure that the final fused vector maintains both local semantic density and global alignment, a vector fusion formula based on kernel mapping harmonics is proposed:

[0046]

[0047] Wherein: reference semantic vector set The high-dimensional vector set after the fusion of the three source texts provides a benchmark for subsequent sentence-level alignment;

[0048] Kernel mapping activation function : Ensure that the vector elements are distributed in Within the interval, to improve spatial differentiability, you can choose either a hyperbolic tangent kernel (preserving local linearity) or a Gaussian radial basis kernel (preserving global smoothness).

[0049] splicing operation : Concatenate vectors from different sources by expanding their dimensions to maintain information independence; temporary vectors , , These correspond to the embedding results of the curriculum standards, the World Language Readiness Framework, and the open-source translation memory, respectively.

[0050] This fusion strategy preserves semantic proximity relationships in the new space through the smoothness of kernel mapping, thereby avoiding semantic drift caused by traditional linear fusion.

[0051] Step 102: After vector fusion is completed, without strict version identification and quality verification, subsequent difference comparisons will become invalid due to benchmark drift. Therefore, this step focuses on the generation of version hashes and the quantitative verification of semantic density to ensure that the reference semantic vector set can be accurately referenced in subsequent steps.

[0052] First, the reference semantic vector set... Phase alignment correction is performed, followed by the generation of a version fingerprint that meets traceability requirements based on a salted hashing method. The hash generation formula is as follows:

[0053]

[0054] Where: version fingerprint : An irreversible identifier that guarantees the uniqueness of a vector set;

[0055] Phase alignment correction function Sort the vector elements in descending order of their weights to ensure consistency before and after sorting.

[0056] Salinity Randomly generated security parameters, with a value range of... Within; splicing operation : String concatenation symbol;

[0057] Phase alignment correction is a deterministic vector rearrangement method used to ensure that the dimensional order of the same sentence embedding vector is completely consistent across different operating environments, making the hash results of subsequent version fingerprints reproducible and traceable. Specifically, it works as follows: First, the global weight of each vector dimension is calculated based on the curriculum standard weights, the World Language Readiness Framework weights, and the translation memory weights. Then, a permutation matrix consisting only of 0s and 1s is generated according to the weights from high to low. This matrix is ​​then multiplied left by the original embedding vector to obtain the "weight descending" corrected vector. Since the permutation matrix only changes the position of elements without changing their values, the semantic distance and topological relationship remain unchanged before and after correction. Simultaneously, it ensures that any platform using the same weight table can obtain vectors of the same order, thus making the salted hash of the version fingerprint unique, stable, and publicly verifiable.

[0058] By sorting vector elements through phase alignment correction and then adding salt hashing, fingerprint collisions caused by isomorphic rearrangement are avoided, and version-unique tracking is achieved without revealing the vector content. Semantic density evaluation and admission threshold determination: To prevent sparse dead zones in the vector space, the system defines a semantic density index based on high-dimensional average neighbor distance. The calculation formula is:

[0059]

[0060] When semantic density index Less than the density threshold If the vector space is deemed dense and sufficiently covered, writing to the learning record library is allowed; if the value exceeds a threshold, the process returns to the weight adjustment phase for re-estimation. , , .

[0061] Where: semantic density index : Measures the local density of a vector space; the smaller the value, the denser the vector; the number of vectors. : Reference semantic vector set size, positive integer; vector elements : No. Sentence-level vectors; nearest neighbor index : No. The first vector A neighbor of the Euler family;

[0062] Nearest Neighbors The value is adaptively adjusted according to the vector dimension and is a positive integer; Euclidean distance. : Measuring the straight-line distance between vectors; density threshold : Preset upper bound, used to determine whether sparsity exists.

[0063] By setting a limit on the average neighbor distance, we can ensure that the vector space is not excessively sparse due to low-frequency terms or noisy text, thereby ensuring that the similarity measurement of subsequent sentence-level alignment is stable and reliable.

[0064] The entire construction process, from multi-source corpus collection to vector fusion, version fingerprint generation, and semantic density verification, has been completed. The reference semantic vector set not only has a unique version fingerprint, but also has controllable quality through density threshold screening. This provides a stable and high-resolution semantic benchmark for the next step of generating difference vectors, enabling the difference vector generation process to quickly locate high-risk language blocks and ultimately achieve consistency and traceability of courseware quality across all languages.

[0065] To truly leverage the value of this coordinate system in measuring course consistency, the draft content of the AI-generated multilingual courseware must be projected onto the same coordinate system immediately after being written into the learning record library, and the semantic vector set calculated and referenced must be used. The semantic offset between them, and solidify this offset into a difference vector. Only after this operation is completed can the subsequent deviation registration form be segmented into three factors—sentimental tendency, cultural symbols, and terminological consistency—based on accurate and consistent deviation data.

[0066] Step 2: Generate a timestamped difference vector through a collaborative mechanism of sentence-level alignment, multi-scale offset measurement, and weighted temporal labeling. This lays a precise and unified measurement foundation for the decomposition of downstream deviation factors.

[0067] Step 201: The draft multilingual courseware generated by artificial intelligence contains text, voice scripts, interactive prompts, and even image descriptions; these heterogeneous units must be integrated with the reference semantic vector set. The sentence-level vectors in the matrix must correspond one-to-one; otherwise, the difference measure will be distorted due to missing values. Therefore, a robust sentence-pair matrix must first be established to ensure mapping consistency. Then, a composite metric function is introduced to compress the multidimensional offset into scalar difference values, which are finally concatenated into a high-dimensional difference vector. .

[0068] To ensure that the multilingual draft is consistent with the reference semantic vector set at the sentence level One-to-one correspondence: First, the draft is divided into sentences and the first keywords, emotional indicators and cultural symbols of each sentence are extracted to form sentence index triplets;

[0069] Then, the fast stack vectorizer is invoked to map each draft sentence to a temporary vector space, generating a set of draft sentence vectors. Meanwhile, in the reference semantic vector set Internal retrieval of reference vectors isomorphic to sentence index triples generates a set of reference sentence vectors. Finally, a sentence pair matrix is ​​constructed using Euclidean distance and keyword matching degree as dual weighting factors. The matrix The construction formula is:

[0070]

[0071] Where: Euclidean distance weight : The influence of control vector distance on the matching score, and its value range ;

[0072] Keyword similarity weight : Controlling the impact of keyword matching degree on matching score, value range And satisfy ;

[0073] Normalized distance The Euclidean distance between the draft sentence vector and the reference sentence vector is obtained by mini-normalization and takes the following values: ;

[0074] Keyword similarity : Match degree based on Jaccard coefficient, with values ​​ranging from 0 to 10. .

[0075] By using a dual-weighted factor design, spatial distance and keyword semantics are balanced. Especially in the context of short sentences or abstract sentences, keyword matching degree can effectively compensate for the distortion of distance indicators.

[0076] Multi-scale composite metrics generate difference vectors in obtaining sentence pair matrices Subsequently, for each pair of sentences, three types of offset indicators—semantic offset, structural complexity offset, and sentiment offset—were calculated, and the weights contributing to these indicators were dynamically calculated using the entropy weight method. , , Final difference vector The The dimension is given by the following formula:

[0077]

[0078] Where: difference vector : A high-dimensional vector summing the multidimensional offsets of each sentence pair, with a length equal to the total number of sentence pairs;

[0079] Semantic offset weight The semantic shift index distribution entropy value is determined by the entropy weight method, and the value is set accordingly. ;

[0080] Structural offset weight Same as above, value ;

[0081] Emotional Shift Weight Same as above, value and satisfy ;

[0082] Draft sentence vector Draft No. Vector representation of a sentence;

[0083] Reference sentence vector : Refer to the first Vector representation of a sentence;

[0084] Structural complexity offset value Offset based on the difference in nested clause levels, a non-negative real number;

[0085] The structural complexity offset value measures the syntactic difference between a draft sentence and a reference sentence. The calculation steps are as follows: First, perform dependency parsing on both sentences separately, recording the level of the longest dependency chain to obtain the "syntactic depth difference." Next, count the number of nested clauses (such as relative clauses and adverbial clauses) in each sentence to obtain the "clause quantity difference." Then, add the two differences using a preset coefficient, determined based on the average sentence length and average number of clauses in a large number of textbooks, ensuring that the two contributions are of the same order of magnitude. Finally, take the 95th percentile of all sentences within the same courseware as the denominator, normalizing the linear result of each sentence to between zero and one. The result is the structural complexity offset value. The closer the value is to one, the greater the difference between the draft and the reference in terms of hierarchical nesting or complex sentence structure, and it can be directly used for subsequent risk level classification and revision strategy selection.

[0086] Sentiment bias offset value : Offset based on cross-linguistic changes in emotional polarity, real number interval .

[0087] Multi-scale metrics, while preserving Euclidean distance information, extend the interpretability of offset metrics through structural and sentiment dimensions, thus enriching the discrepancy vector. It can accurately capture the comprehensive differences in meaning and emotion of cross-language content.

[0088] Step 202, Difference Vector It is merely a static description of the offset; if a reference semantic vector set cannot be considered... Weighting coefficients , , Furthermore, considering the impact of draft generation time on the learning path, the offset cannot reflect the true risk level during downstream factor decomposition. Therefore, in [the following context, it will be necessary to] Inject context weight vectors into the data and use monotonically increasing timestamps. The label retention process is traceable:

[0089] First, according to the weighting coefficients defined in step 101... , , Calculate the context weight vector , its first Dimension is defined as:

[0090]

[0091] in: , , The first The coverage indicator factor of a sentence in the three source texts. The weighted difference vector is obtained by multiplying the context weight vector and the difference vector dimension by dimension. :

[0092]

[0093] Where: Context weight vector : Reflects the importance of each sentence to the overall teaching objectives, with a range of values. ;

[0094] Coverage indicator If the first If a sentence directly maps to the curriculum standards, then the value is 1; otherwise, it is 0.

[0095] Coverage indicator If the first If a sentence is mapped to a core entry of the World Language Ready Framework, the value is 1; otherwise, it is 0.

[0096] Coverage indicator If the first If the sentence appears in the open-source translation memory, it is set to 1; otherwise, it is set to 0.

[0097] Follow the steps The product of elements of the same dimension of a vector results in a new vector.

[0098] By explicitly injecting upstream weight information, the offset of important sentences is amplified after weighting, while the offset of weakly related sentences is suppressed, thus allowing subsequent factor decomposition to focus on core teaching risks.

[0099] After completing the weighted difference vector calculation, a difference record entry is created in the learning record library, whose core fields include the version fingerprint. Weighted Difference Vector With timestamp Timestamp The generation strategy uses a monotonically increasing logical clock synchronized with the network clock to ensure the uniqueness of data write order across nodes. The hash digest of the final structured entry written to disk is defined as follows:

[0100]

[0101] In the formula: difference record fingerprint Used to uniquely identify the result of this difference vector generation, avoiding duplicate calculations;

[0102] Timestamp : A logic clock output that conforms to International Atomic Time correction, in ISO 8601 format;

[0103] Summary function Use a variable output hash algorithm to generate a fixed-length digest, ensuring that the original vector is not leaked.

[0104] By creating independent fingerprints for difference records The system can backtrack the difference data and verify its integrity at any time, ensuring that the deviation factor decomposition process can accurately reference and trace the source of the original offset.

[0105] Step 2: Pair matrix with sentences To bridge the gap, the AI-generated multilingual drafts are projected onto a reference semantic vector set. On the constructed coordinate system, the difference vector of the joint measurement of semantics, structure, and sentiment is then calculated. Subsequently, based on the weighting coefficients... , , Inject contextual weights to form a weighted difference vector that highlights core differences in teaching. By using version fingerprints and monotonically increasing timestamps Common hashing generates differential record fingerprints Step two not only ensures the complete traceability of the discrepancy data, but also provides highly focused and verifiable input for the next step of forming a deviation registration form.

[0106] When downstream factor decomposition reads the weighted difference vector At that time, the dimensional offset value and context weight information can be directly used to quickly decompose the sentiment bias offset. Cultural symbol offset and terminology consistency offset, based on timestamps. Determine the revision priority to ultimately achieve efficient and accurate deviation identification and rating.

[0107] In step two, the sentence pair matrix has already been used. Map multilingual drafts to reference semantic vector sets The constructed unified coordinate system produced a weighted difference vector with weights and timestamps. fingerprints were recorded using differences. The tracing path is fixed. Next, if the weighted difference vector is not... Without detailed analysis, it becomes impossible to distinguish the different root causes of the deviations, let alone provide a precise scope for revision of local rewrites.

[0108] Step 3 utilizes a triple mechanism of multi-tensor mapping, comprehensive risk quantification, and versioned storage to transform the weighted difference vector. The decoupling process creates a traceable and measurable deviation register, laying a precise baseline for subsequent local rewriting.

[0109] Step 301, Weighted Difference Vector While having the same number of sentence pairs in terms of dimensionality, it internally incorporates three types of offset components: emotional, cultural, and terminological. Without vector space partitioning, subsequent revisions cannot identify specific factors or select targeted rewriting strategies. Therefore, it is necessary to construct three independent but homologous mapping tensors and weight the difference vectors... It is decomposed into three comparable metric chains, and a preliminary severity level is given through a unified threshold function.

[0110] To ensure that the difference vector is expressed independently and faithfully across all three factors, we first use the context weight vector from step two. With reference semantic vector set Generate three mapping tensors: sentiment mapping tensor Cultural mapping tensor Terminology mapping tensor The mapping tensor not only records sentence-level positions but also the semantic ordering index of the original vector in the corresponding factor corpus. Taking sentiment mapping as an example, its projection formula is:

[0111]

[0112] In the formula: Emotion mapping tensor A third-order tensor, where the first dimension corresponds to the sentence pair index, the second dimension to the sentiment polarity feature, and the third dimension to the weight adjustment coefficient. The element values... Weighted difference vector : Output vector from step two; Emotional offset projection The larger the element value, the more significant the sentiment shift.

[0113] The emotion tensor is derived from a multilingual emotion corpus of 400,000 sentences; the culture tensor is derived from the UNESCO cultural dictionary; and the terminology tensor is derived from a self-built terminology list in the course.

[0114] By using tensor transpose multiplication, while preserving sentence-level indices, the difference values ​​are projected onto the corresponding factor space, forming factor-specific offsets. After offset magnitude normalization and projection using the severity function design, the dimensions of the factor offsets become inconsistent, leading to distortion in subsequent threshold comparisons. Factor normalization coefficients are then introduced. , , Map the three types of projection results to a unified map. Taking the sentiment factor as an example, the normalization formula is:

[0115]

[0116] Then, the severity function is defined. :

[0117]

[0118] Where: factor normalization coefficient : Determined by the quantiles of the sentiment offset samples, positive real numbers; threshold offset : Upper bound of the emotional safety interval, value Slope adjustment parameters : Steepness of rise of the control function, a positive real number; Severity of emotion : No. Sentence sentiment shift severity, value .

[0119] Using a logic function with an adjustable slope, the normalized offset is mapped to a severity score, which is both interpretable and differentiable.

[0120] Severity of emotions Cultural severity Terminology severity Merged into a preliminary deviation entry vector Entry format:

[0121]

[0122] If obtained Exceeding the total threshold If the entry is not found, it will be pushed to the deviation candidate cache pool for further verification.

[0123] In the formula: For sentence pair indexing; deviation candidate threshold : The set initial screening threshold, with a value of [value]. ; Deviation entry vector Record the severity of a single sentence based on three factors and index.

[0124] High-risk phrases are quickly identified through three-factor aggregation and threshold filtering, while ensuring consistency between the entry format and subsequent storage scheme. Initial entries still need to be combined with historical baselines and algorithm versions to assign a final severity level to each deviation and record suggested handling strategies. Otherwise, priority mismatch may occur during document rewriting due to baseline drift or algorithm upgrades.

[0125] First, retrieve the baseline severity mean vector for similar topics in historical versions of the current course. Compared with the baseline severity variance vector Calculate the dynamic threshold vector:

[0126]

[0127] In the formula: This is a dynamically thresholded scaling vector, with elements taking values... Baseline severity mean vector Mean deviation of similar courses over time; baseline severity variance vector : Variance of each bias; Dynamic threshold vector Used for final severity level determination;

[0128] Dynamic correction using mean and variance can automatically adjust thresholds based on historical fluctuations, preventing excessive alarms or omissions.

[0129] For each candidate entry Calculate the three-factor severity vector If the three-factor severity vector Any element exceeding the corresponding dynamic threshold Then set the severity level:

[0130]

[0131] And generate strategy codes based on the level mapping table. For example, Level 1 corresponds to Lightweight revision prompts, and Level 3 corresponds to Mandatory revisions.

[0132] Where: Severity level Integer, range of values Mapping amplification factor Smoothing factor, a positive real number; strategy code : Character type, identifies the recommended revision strategy. , No. The sentence in the first Severity scores on each factor, of which These correspond to emotional inclination, cultural symbols, and terminological consistency, respectively.

[0133] Continuous severity is mapped to discrete levels, and a corresponding strategy code is assigned to facilitate subsequent automatic or semi-automatic revision processes, ultimately creating entries in the deviation registration table:

[0134]

[0135] Where: Algorithm version : String, recording the model and rule set version used in this decomposition.

[0136] Entries record fingerprints using differences Foreign key, timestamp Use this as the sorting key and write it to the primary key index of the deviation registration table. Algorithm version Each item corresponds one-to-one with the model weight label, facilitating future backtracking. A dual-key index using foreign keys and timestamps ensures that entries from multiple difference decompositions can be written in parallel and retrieved sequentially, forming a traceable and concurrent registration system.

[0137] Step 3 uses the factor mapping tensor as a bridge to connect the weighted difference vector. The risk was broken down into three independent offset chains: sentiment, culture, and terminology. A normalization function and a severity function were used to convert the offsets into measurable risk scores. Subsequently, a dynamic threshold vector was constructed by combining this with historical baselines. The algorithm performs hierarchical determination on candidate deviation items and generates a strategy code that is bound to the algorithm version.

[0138] Ultimately, all the information was solidified into fingerprints recorded using differences. The deviation registration table, which serves as a foreign key, ensures both data traceability and revision priority reliability. At this point, a multi-dimensional mapping from semantic offsets to risk levels and strategy recommendations has been completed, providing clear revision blocks, revision strengths, and referenced versions for writing consistency tags in step four. This achieves cross-step, cross-dimensional parameter threading and data closure.

[0139] Following step three, factor decomposition and severity level determination, the severity level has been marked for each high-risk phrase in the deviation registration table. With policy code And record fingerprints by difference With timestamp A traceable index has been established. The next task is to perform differential revisions and write consistency tags into the courseware metadata to ensure that all affected chunks are restored to semantic, sentiment, and terminological equivalence across multilingual versions.

[0140] Driven by multi-source evidence, three revision strategies—generative rewriting, template replacement, and manual prompting—are automatically selected according to severity levels. A weight inheritance mechanism is then used to ensure that the reference semantic vector set... Version information is integrated into metadata, enabling subsequent quality audits to quickly pinpoint the scope and factors of revisions.

[0141] Step 4: Using the triple mechanism of rewriting energy function, peer consistency tagging and version mapping, perform local revisions on high-level deviation blocks and write them into metadata to form a traceable and unified set of consistency tags.

[0142] Step 401: The severity level alone is not enough to determine the specific revision method; items of the same severity level still differ in contextual dependence, instructional weight and media type. If a single rewriting strategy is adopted, it may lead to semantic fragmentation or a decrease in instructional coherence.

[0143] Therefore, a rewriting priority function needs to be established based on context window, instructional weight, and media coupling, and then the revision method should be dynamically selected accordingly. First, based on severity level... Context weights and context window width Using the input, construct a rewritten energy function. :

[0144]

[0145] Where: Severity level Integer from the deviation registration form, range Context weights The teaching importance and scope of corresponding sentences. Context window width The dependency depth of a sentence within a paragraph refers to the range of preceding and following contexts that must be included to maintain semantic integrity, clarity of reference, and logical coherence; it is a non-negative real number; an exponential adjustment coefficient. , Real numbers, regulating different factors on elasticity; energy threshold , : positive real number, and .

[0146] When rewriting the energy function Exceeding the energy threshold At that time, a generative rewriting strategy is triggered; due to the energy threshold With energy threshold When the energy level is between 10 and 20, a template replacement strategy is triggered; when the energy level is below the energy threshold... Manual prompts will only be provided in the editing interface.

[0147] By transforming discrete variables of severity levels into continuous energy metrics, and then combining context and weights, fine-grained scheduling of revision methods can be achieved. This is achieved by rewriting the energy function. When a generative rewriting is deemed necessary, the controlled text generator is invoked, with input including a reference vector. Draft vectors Based on the factor label hints, generate candidate rewrite statements. Subsequently, based on semantic alignment loss... :

[0148]

[0149] In the formula: candidate rewrite statements : No. Candidate texts; sentence vector mapping function Embedding functions that are homologous to those in step two; semantic alignment loss : The squared Euclidean distance between the candidate and the reference vector.

[0150] The sentence vector mapping function is a fixed process that converts single-sentence text in any language into a unified 256-dimensional semantic vector. First, the input sentence undergoes basic cleaning and word segmentation: English and French use sub-word segmentation, while Chinese uses word fragment concatenation, ensuring comparable sub-word sequences across different languages. Next, the sequence is input into a Transformer encoder that has been further trained in multiple languages. This encoder has been jointly optimized using curriculum standard corpora, cultural dictionary corpora, and translation memory corpora, thus mapping the expression of the same concept in different languages ​​to similar positions. After the encoder output, the whole-sentence representative vector (CLS bits) is extracted and weighted and averaged with the sub-word vectors within the sentence, then fused according to a fixed ratio. Finally, it is compressed to 256 dimensions and normalized through a fully connected network. After completing these steps, regardless of the language of the sentence, whether it is text, speech-to-text, or image OCR, a vector located in the same semantic coordinate system can be obtained, providing standardized input for subsequent difference detection, semantic alignment, and residual re-examination.

[0151] Select the one with the least loss As the final rewritten text, the best rewrite is selected by minimizing the semantic alignment loss, ensuring that the text is semantically consistent with the reference. The first one selected by semantic alignment loss Final revised text of the sentence;

[0152] If the deviation item involves a speech script or image description, after the rewritten text is determined, it is mapped through a cross-modal mapping matrix. Map text vectors to speech or image embedding spaces to generate revision scripts or replacement instructions, ensuring that the three modalities are in the same semantic neighborhood:

[0153]

[0154] Where: cross-modal mapping matrix : Cross-modal mapping matrix. Trained on text-speech or text-image paired samples through contrastive learning, its size matches the dimensions of the text vector and the target modality vector, used to map text semantic embeddings to the target modality embedding space; target modality vector Used for synthesizing speech or retrieving corresponding images.

[0155] Vector mapping allows images and speech revisions to be automatically aligned with text, reducing the cost of manual matching.

[0156] Step 402: After completing the multimodal rewriting, a consistency tag needs to be written at the courseware metadata level to ensure that the learning management system can perceive the revision scope and reference baseline; at the same time, the version fingerprint of the reference semantic vector set must be maintained. fingerprint with difference record The mapping relationship is used to support subsequent version upgrades and audits.

[0157] Technical Point 1: Structured writing of consistency tags generates consistency tags for each revision block. :

[0158]

[0159] Where: Consistency tag Record revision metadata; revision factor : Policy code from step three; rewrite policy : Text type, records the rewriting method used.

[0160] Among them, rewriting strategy .

[0161] GenRewrite: Generative rewriting. It invokes a controlled generative model to automatically generate new sentences or scripts based on reference semantic vectors and factor labels, used to repair content with high severity or large semantic shifts.

[0162] TemplateSwap: Template replacement. It replaces the core structure of the original sentence with a proven and secure fixed expression template, adjusting only variable words to quickly eliminate moderate severity and regularization biases while maintaining consistent formatting.

[0163] HumanHint: Human-assisted suggestions. When the deviation severity is low or involves specialized contexts, the system only prompts in the editing interface that manual review is required, allowing experts to make manual adjustments and avoiding information distortion caused by over-automation.

[0164] After the tags are written into the revision mapping table of the courseware metadata, an index key that can be retrieved in the LMS is immediately generated. Thus, the structured tags provide precise anchor points for subsequent differential updates and monitoring; to prevent referencing outdated vector versions, they also provide anchor points for the revised text. Calculate the new vector ,like:

[0165]

[0166] Then maintain version fingerprint The reference remains unchanged; otherwise, a new fingerprint is generated incrementally. And form a version mapping chain in the revision mapping table.

[0167] Where: version difference threshold Small positive numbers control vector drift tolerance; new version fingerprint If triggered, a new one will be added. Intent: To control whether to roll over versions by using a threshold, thus avoiding increased auditing costs caused by frequent fingerprint changes.

[0168] For each consistency tag Generate digital signature :

[0169]

[0170] Where: digital signature : An immutable verification code; Private key: Stored in a secure hardware module and used for signing operations.

[0171] By attaching the signature to the tag and writing it into the metadata, the learning management system can quickly verify that the tag has not been tampered with when loading using the public key. Digital signatures provide tamper protection and trusted traceability, meeting compliance and auditing requirements.

[0172] Step four begins by rewriting the energy function. By comprehensively considering severity level, context weight, and context window width, three revision strategies are finely scheduled to ensure that high-risk chunks receive the most appropriate processing; subsequently, semantic alignment loss is utilized. Filter the best generative rewritten text and use a cross-modal mapping matrix Ensure that text, speech, and images are updated uniformly within the same semantic neighborhood.

[0173] After completing the content revision, use the version fingerprint. fingerprint with difference record For double keys, a structured consistency tag is generated and written to the metadata after threshold verification, along with a digital signature. To prevent tampering, this sequential operation not only ensures the accuracy and multimodal consistency of the partial rewrite, but also establishes a tagging system that can be instantly identified at the learning management system level. This lays a reliable version and security foundation for the generation of the final consistency report, ensuring that the semantics, sentiment, and terminology of cross-language courses are fully consistent when displayed on the user's end.

[0174] In step four, three types of revisions have been completed for high-risk phrases: generative rewriting, template replacement, or manual prompting, and consistency tags have been used to achieve these revisions. Version fingerprint Differential fingerprint recording With revision strategy Write the courseware metadata to ensure that the courseware in each language is restored to equivalence and consistency at the content level.

[0175] However, the entire deviation governance chain is only closed when these revisions are formally confirmed and released at the learning management system level; otherwise, all the efforts of the preceding steps remain offline. Step five bears the heavy responsibility of solidifying the revision results into auditable, traceable, and quantifiable official documents and synchronizing them to all terminals.

[0176] Specifically, it requires: 1. Generating a final consistency report after multi-dimensional residual review, and then combining the report metrics with the weighted difference vector. Second, re-align to ensure no omissions or deviations; third, package the report and revised courseware together into a compliant release package, push it to desktop, mobile and virtual reality terminals through the synchronization interface of the learning management system, and record the audit fingerprint on the blockchain.

[0177] Step 5: Through a triple mechanism of full-domain residual review, compilation of indicator quantification reports, and multi-terminal synchronous on-chain audit, a final consistency report is generated and the courseware is released in compliance with regulations, achieving a complete closure from offline revision to online update.

[0178] Step 501: Although all high-risk phrases have been revised in Step 4, cascading residuals may occur between different phrases due to contextual coupling. Without a full residual review, the overall consistency after revision cannot be guaranteed. Therefore, this step designs a residual matrix. All text blocks are re-compared, and a global consistency index is proposed. As the final quality threshold, a final consistency report is generated, which includes quantitative indicators, revision ranges, and residual plots.

[0179] First, the revised courseware is re-embedded into the reference semantic vector space to obtain the revised vector matrix. Subsequently, using the reference semantic vector set Calculate the residual matrix based on the baseline. :

[0180]

[0181] Next, we define the global consistency index. :

[0182]

[0183] Where: residual matrix : Set of sentence-by-sentence residual vectors; elements For the first Sentence residual vector; revised vector matrix The revised courseware's set of sentence-level vectors; The reference semantic vector set The baseline vector of the sentence, whose second norm is used to normalize the residuals, makes the indicators comparable for different sentence lengths and languages;

[0184] Global Consistency Index Percentage index for measuring overall consistency, with a range of values. The closer the number is to 1, the higher the consistency; number of sentences Total number of sentences in the presentation slides.

[0185] The residual matrix and consistency index can be used to quickly assess the overall revision effect and provide core quantitative indicators for the report. If there are elements in the residual matrix that satisfy:

[0186]

[0187] Index of corresponding sentences Add residual warning set It also generates a residual heatmap; the heatmap uses color depth to represent the magnitude of the residuals, supplemented by three factors of sentiment, culture, and terminology, which facilitates quick review by experts.

[0188] Where: Warning threshold Small positive numbers set by experience; residual warning set : Record the set of indexes for sentences with high residuals;

[0189] This visualizes the significant residuals, intuitively revealing edge cases that still require manual attention. The final consistency report comprises four main parts: summary metrics, residual heatmap, revision range table, and version mapping chain. A hash fingerprint is calculated for the complete report.

[0190]

[0191] In the formula: report fingerprint : A hash value that uniquely identifies this consensus report; digest function Output a short summary string of the alert set.

[0192] The digest function is a fixed step in this scheme used to generate the "fingerprint." Its purpose is to quickly compress text of arbitrary length into a fixed-length, unique, and irreversible verification string, facilitating subsequent integrity verification. The implementation process is as follows: First, the text to be processed is uniformly encoded in UTF-8 to obtain a byte stream that is consistent across platforms; then, the publicly available standard SHA-256 one-way hash algorithm is called to calculate a 256-bit hash value for this byte stream; if the system only needs a shorter fingerprint, the high bits can be truncated according to fixed rules, such as taking the first 128 bits; finally, the obtained bit string is converted into printable characters using hexadecimal or Base64 format, and this string is the output of the digest function. Due to the avalanche effect of SHA-256, even if a single character in the input is modified, the output will be completely different, thus ensuring that the fingerprint can reliably detect tampering, determine version consistency, and provide a unique identifier when storing evidence on the blockchain.

[0193] Generated report fingerprint This will serve as the unique identifier for the report, stored on the blockchain, thereby generating a fingerprint for the report, which can be used to verify the report's integrity and origin at any point in time.

[0194] Step 502: After the consistency report is completed, the revised courseware and report still need to be packaged together into a multi-platform unpackable release package and pushed through the secure channel of the learning management system. At the same time, a release record fingerprint is written to the blockchain to ensure that any version can be traced back in the future. This step revolves around three main aspects: packaging, synchronization, and auditing, emphasizing cross-platform consistency and on-chain verifiability.

[0195] The release package is generated using the revised courseware file set, the final consistency report, and the revision mapping table as core files. Dependency description files are then automatically generated. Record version fingerprint Report fingerprint With revision timestamp Depends on file hash The certificate is stored along with the release package. Where: release package : Compressed archive file containing all necessary resources; dependency description file hash : Hash value to ensure the integrity of dependent files.

[0196] Dependency description files allow any endpoint to verify the integrity of resources and version dependencies within a package. (Release package) After being uploaded to the learning management system, it is pushed to desktop, mobile, and virtual reality devices via a synchronization interface. Upon receiving the packet, the client first verifies:

[0197]

[0198] If they match, continue verification:

[0199]

[0200] Both conditions must be met before the package can be unpacked and the revised content loaded; otherwise, a rollback mechanism will be triggered.

[0201] Where: Release packet hash : Sent by the server along with the packet body; Rollback mechanism: The client automatically retains the old version when verification fails.

[0202] Thus, double verification ensures that the transmission process has not been tampered with and prevents the loading of corrupted files. After client synchronization is complete, the smart contract is invoked. Record the published event:

[0203]

[0204] Contract automatically generates release record fingerprint This will trigger an event notification to the monitoring node; if content disputes are discovered in the future, they can be reported through [the relevant mechanism]. Let's go back to the specific report and package. It is deployed on the educational resource consortium blockchain smart contract An event logging function is used to write key information about each official release of courseware onto the blockchain.

[0205] In the formula: Publish record fingerprint On-chain event hash, used to uniquely identify this release; smart contract : An on-chain protocol specifically used to record the release of educational resources.

[0206] On-chain publication records provide immutable timestamps and fingerprints, meeting policy compliance requirements and simplifying external audits.

[0207] Step 5 first uses the residual matrix Consistency Index A comprehensive review of the revised courseware was conducted to ensure that the residual offset of multilingual content in the semantic space was below the warning threshold; at the same time, residual heatmaps and warning sets were generated. This allows for rapid review by experts. Subsequently, the summary metrics, residual heatmap, revision range table, and version mapping chain are integrated into a final consistency report, and a report fingerprint is used. Ensure the uniqueness and completeness of the report.

[0208] During the packaging phase, the release package Merge courseware, reports, and mapping tables, and include dependency description file hashes. The client double-checks the package body and dependency file hashes to prevent file tampering at the source. Finally, a smart contract is used. Write the release record fingerprint This not only provides real-time notifications to regulatory nodes but also offers verifiable evidence for any future version disputes. Ultimately, it achieves end-to-end control from offline semantic baselines to compliant on-chain releases, ensuring that multilingual courseware in AI learning models maintains comprehensive semantic, emotional, and terminological consistency and audit credibility globally.

[0209] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0210] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0211] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0212] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0213] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A standard courseware generation method for artificial intelligence learning modes, characterized in that: include, We collect curriculum standards, world language ready frameworks, and translation memories, vectorize and map them to generate a set of reference semantic vectors with unique version fingerprints, which are used to build a globally robust metric baseline. The multilingual courseware draft is mapped to the vector set through sentence-level alignment, the difference vector is calculated and contextual weights and timestamps are superimposed to form traceable offset data, and accurate measurement benchmark output is achieved; The difference vector is decomposed according to sentiment, cultural symbols and terminology consistency to generate a deviation registration table containing fields of deviation type, severity level and algorithm version. The deviation type is used to characterize the deviation category corresponding to the decomposition, and includes at least one of sentiment deviation, cultural symbol deviation and terminology consistency deviation. Contextual weights are calculated based on pre-defined correlations between curriculum standard weight coefficients, world language readiness framework weight coefficients, and translation memory weight coefficients. The rewrite energy is calculated based on the deviation registration table. The rewrite energy is calculated by severity level, context weight and context window width. Generative rewrite, template replacement or manual prompting is selected according to the rewrite energy. Local revision is completed and a consistency tag containing version fingerprint is written into the courseware metadata. Once the residual of the revised content is below the warning threshold, a final consistency report is generated. The revised courseware and the report are packaged into a release package, which is then synchronized to each terminal through the learning management system, and the release record fingerprint is stored on the blockchain for evidence.

2. The standard courseware generation method for artificial intelligence learning modes according to claim 1, characterized in that: The curriculum standard text, the World Language Ready Framework text, and the translation memory dependency data fields are segmented into sentences. Core semantic units are extracted through hierarchical dependency parsing. Weight estimation algorithms are used to determine the weight coefficients of each text. Kernel mapping activation functions are used to generate curriculum standard vectors, framework vectors, and translation memory vectors, respectively. The weight coefficients and each vector together constitute the basic data of the reference semantic vector set.

3. The standard courseware generation method for artificial intelligence learning modes according to claim 2, characterized in that: Phase alignment correction is performed on the reference semantic vector set. Phase alignment correction is a deterministic vector rearrangement that ensures that the dimensional order of the embedding vectors of the same sentence is completely consistent in different operating environments. Salt hashing is used to generate version fingerprints, and semantic density index is calculated. If the density is less than the system threshold, it is written into the learning record library; otherwise, the weight coefficients are readjusted in the weight estimation stage.

4. The standard courseware generation method for artificial intelligence learning modes according to claim 3, characterized in that: Based on the sentence index triplet, the corresponding vector is retrieved in the reference semantic vector set to construct a sentence pair matrix. The matrix is ​​weighted by three factors: Euclidean distance, keyword similarity and character-lexical edit distance. The weighting weights are dynamically allocated according to the residual entropy. A difference vector is generated using the three-dimensional entropy weighting method of semantics, structure, and sentiment. The context weight vector and the difference vector are multiplied dimension by dimension to obtain a weighted difference vector. At the same time, the difference record fingerprint is used as a foreign key to solidify the data in the learning record library. The difference record fingerprint is obtained based on the version fingerprint, the weighted difference vector, and the timestamp.

5. The standard courseware generation method for artificial intelligence learning modes according to claim 4, characterized in that: Weighted difference vectors are projected onto the sentiment mapping tensor, culture mapping tensor, and terminology mapping tensor, respectively. The projection results are normalized by quantiles and input into an adjustable slope logic function to calculate the severity of each factor, generating bias candidate entries containing sentence pair indexes.

6. The standard courseware generation method for artificial intelligence learning modes according to claim 5, characterized in that: Align the severity vector of the candidate item with the historical baseline mean vector and variance vector, classify the severity level according to the dynamic threshold vector, and generate a revision strategy code according to the level mapping table and write it into the deviation registration table.

7. The standard courseware generation method for artificial intelligence learning modes according to claim 6, characterized in that: Establish a rewrite energy function with severity level, context weight, and context window width as independent variables. When the energy value is higher than a preset generation threshold, generative rewrite is invoked; when it is between the two thresholds, template replacement is performed; and when it is lower than the low threshold, only human prompts are generated. Specifically, set a generation threshold and a low threshold. When the rewrite energy is higher than the generation threshold, generative rewrite is invoked; when the rewrite energy is between the generation threshold and the low threshold, template replacement is performed; and when the rewrite energy is lower than the low threshold, only human prompts are generated.

8. The standard courseware generation method for artificial intelligence learning modes according to claim 7, characterized in that: The semantic alignment loss is calculated for the generative rewritten text. The candidate text with the minimum loss is selected, and the associated speech script or image description is updated synchronously using the cross-modal mapping matrix. Then, a consistency tag containing version fingerprint, difference record fingerprint and revision strategy is generated and written into the metadata.

9. The standard courseware generation method for artificial intelligence learning modes according to claim 8, characterized in that: The revised courseware is embedded into the reference semantic vector space, and the residual matrix and global consistency index are calculated. If the magnitude of the residual vector is greater than the warning threshold, a residual warning set is generated and a residual heatmap is attached to the final consistency report.

10. The standard courseware generation method for artificial intelligence learning modes according to claim 9, characterized in that: The revised courseware, the final consistency report, and the revision mapping table are encapsulated into a release package, a dependency description file is generated, and the corresponding hash is calculated. After receiving the release package, the client needs to verify the hash of the release package and the hash of the dependency description file in turn. Only after they pass can the package be unpacked and loaded. At the same time, the smart contract is called to record the release event fingerprint.

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