Optimizing DeepSeek Prompts for Cross-Cultural English Writing Instruction: A Practical Study of Transfer Competence

Summary

This study develops a layered prompt optimization framework based on DeepSeek to enhance English writing transfer competence in cross-cultural contexts. Through a three-stage prompt chain and a quasi-experiment with 60 English majors, results show significant improvement in the experimental group compared with the control group.

Abstract

To address the limitation that traditional English writing instruction neglects systematic cultivation of language transfer and existing prompt frameworks fail to fit classroom teaching, this study constructs a hierarchical transfer training system based on the DeepSeek model covering four progressive task tiers: sentence, paragraph, discourse, and culture, with all tasks defined in triple form. A five-dimensional task library covering syntax, structure, culture, register, and logic is established, and targeted prompts are automatically generated from students’ authentic writing drafts. Integrated with the model’s Mixture of Experts (MoE) architecture, a nested prompt chain is developed to split complicated assignments into three sequential phases: semantic reorganization, linguistic revision, and cultural adaptation. Adopting a quasi-experimental design, this research recruits 60 second-year English majors from one university and assigns them to an experimental group and a control group of 30 students each for three weeks of staged intervention across three training rounds. Post-experiment results reveal that the experimental group’s cultural transfer score rises from 2.8 to 4.5, compared with a mere 0.3 increment in the control group; its average teacher-assessed score improves from 59.1 to 74.4, compared with the control group’s increase from 60.1 to 64.9. Most correlation coefficients between AI scoring and teacher evaluation exceed 0.8, and over 90% participants approve of the AI rewriting and structural optimization functions. Due to the small sample size in this trial, this optimization approach is feasible for implementation in university English writing classes.

Introduction

In the teaching of English writing in a cross-cultural context, non-native English learners often face the challenge of insufficient transfer ability as they transition from language knowledge to practical application^1,2. Although students have a certain grammatical and vocabulary foundation, they struggle to effectively convert sentence patterns, structures, and cultural expressions into real writing, resulting in formalized language output and limited thinking, which in turn affects the overall writing quality³. In addition, cross-cultural differences in language structure, logical organization, and pragmatic norms further exacerbate the complexity of writing transfer⁴. In recent years, large language models (LLMs) have provided new technical support for writing teaching⁵. Intelligent writing tools, such as DeepSeek, have demonstrated strong capabilities in language generation and reconstruction. However, in teaching practice, they are mostly limited to surface-level language correction and have not yet been deeply integrated into the teaching process, which is centered on cultivating transfer ability. They lack teaching goal orientation and controllability of generation behavior^6,7. Prevailing AI-assisted writing applications mostly confine their functions to surface-level grammatical error correction rather than targeting the cultivation of systematic transfer competence, resulting in poor teaching-oriented control over model outputs.

Existing research on ESL writing assistance falls into three mainstream categories: conventional prompt-driven writing feedback, generic AI polishing intervention, and manual-centered transfer training. First, general prompt engineering research prioritizes technical optimization of question answering and text rewriting without matching hierarchical transfer teaching goals^8,9. Second, typical ChatGPT-based ESL tutoring focuses on overall writing accuracy improvement rather than layered syntactic, structural, and cross-cultural transfer cultivation¹⁰. Third, traditional transfer pedagogy relying on imitation and contrast writing lacks closed-loop feedback supported by intelligent tools¹¹. Distinct from foregoing studies, this research customizes transfer-oriented hierarchical prompts aligned with multi-level teaching objectives and builds a sample-driven prompt auto-generation workflow, forming a unique closed-loop training mechanism combining model generation, teacher feedback, and student revision.

Prior literature hypothesizes a potential linkage spanning linguistic cultural divergence, inherent model structural constraints, and disjointed classroom practice, yet such a causal chain remains insufficiently empirically verified in existing empirical studies and becomes the core research hypothesis to be examined in this paper^12,13. As for DeepSeek’s inherent characteristics cited in this paper, its open instruction interface and MoE-based expert routing design have been documented in existing model technical reports and applied language research. Building on these documented model features, this study tailors nested prompt chains to disassemble complex cross-cultural transfer tasks and develops dynamic submodel scheduling rules for differentiated transfer assignments. Centered on structured transfer ability development, the present work explores targeted DeepSeek prompt optimization, aiming to transform large language models from universal text-editing tools into specialized transfer training systems for college English writing instruction.

In the field of second-language writing research, the transfer phenomenon has long been a core topic^14,15. Traditional language transfer theory primarily focuses on the positive and negative transfer effects of the mother tongue on the target-language acquisition process, with particular emphasis on phonetics, vocabulary, and syntax¹⁶. Mirzayev E pointed out that, in English as a Second Language (ESL) writing instruction, negative transfer from the first language remains a key factor limiting the development of students' writing abilities¹⁷. As the scope of research expands, the academic community has gradually expanded its focus to writing structure transfer¹⁸. Interlanguage writing often has language errors and fossilization due to multiple factors¹⁹. At the same time, cognitive transfer theory has also received increasing attention²⁰. This theory emphasizes the differences in cognitive organization and expression patterns in the writing process. For example, Western writing emphasizes linear reasoning and explicit logic, whereas the Chinese tradition prefers a spiral approach and semantic ambiguity. There is a significant tension between the two. Therefore, language transfer not only involves the conversion of language units but also involves the adaptation and transformation of thinking structures and cultural expression norms at a deeper level, which is an important challenge in current English writing instruction²¹.

In the practice of cultivating transfer ability, existing studies often employ strategies such as imitative writing²², contrastive writing²³, and translation training²⁴ to enhance learners' understanding and application of the target language transfer mechanism. Imitative writing helps to build basic discourse ability, but it can easily lead to structural rigidity; contrastive writing can improve language style and cultural awareness, but it lacks systematic task stratification and personalized feedback; translation training can promote cultural metaphor recognition, but without guidance, it can easily lead to semantic deviation or pragmatic inappropriateness. The above strategies primarily rely on manual demonstration and experiential guidance²⁵, lack explicit construction of transfer strategies and systematic feedback mechanisms, and are challenging to adapt to individual differences in language proficiency and cognitive styles. Although some studies have attempted to expand transfer training through diversified writing tasks^26,27, the teaching process has not yet formed a closed loop of "transfer goal-language generation-strategy feedback"; the internalization effect of transfer strategies is limited, and students' transfer ability development in real writing tasks is still insufficient²⁸.

With the development of large language model technology, its auxiliary potential in language teaching has garnered increasing attention²⁹. Generative AI tools represented by ChatGPT (Chat Generative Pre-trained Transformer)³⁰ and DeepSeek³¹ have been widely used in scenarios such as text polishing, language rewriting, and language adjustment. Diasamidze et al. found that ESL students using ChatGPT outperformed the traditional teaching group in terms of grammatical accuracy, vocabulary richness, and text organization ability³². However, existing applications are mostly limited to surface-level language processing and lack systematic guidance on transfer goals. Ranade pointed out that prompt-based projects in current educational scenarios are mostly based on general templates, and the generated content lacks contextual adaptability and task specificity³³. In addition, mainstream model training relies on an open corpus and lacks deep modeling of the differences in Chinese and English cultural expressions. Cultural inappropriateness and unnatural pragmatics are prone to occur in the generated text^34,35, which limits its in-depth application in cross-cultural writing transfer teaching.

Protocol

Ethical approval

Prior to the experiment, the authors obtained approval (Approval Number: NBCC-ETH-2026-037) from the college and distributed informed consent documents to all student participants. All students were clearly informed of the research objectives, experimental procedures, and data-use regulations. Participation was voluntary, and participants could withdraw from the study at any time. All personal data and written works were anonymized to protect privacy and were used only for academic analysis. All 60 participants signed the consent form before taking part in the research. Participants were selected from two parallel sophomore English writing classes at the same higher vocational college, recruited using convenience sampling. Inclusion criteria included: passing CET-6 with a score > 425, no absences this semester, and average scores in the last two unit writing tests. Exclusion criteria included: long-term use of commercial English AI writing software outside of class and severe dyslexia.

Variability for all continuous outcome variables was reported as mean ± standard deviation (SD). All analyses were conducted with a sample size of n = 30 participants per group (experimental group vs. control group), total N = 60. For longitudinal repeated-measures data, a repeated-measures analysis of variance (RM-ANOVA) was used to examine main effects of group and time, and the group-by-time interaction. Independent-samples t-tests were used for post-hoc pairwise comparisons at individual time points. The control group received traditional writing instruction plus a generic, free, uncustomized AI writing tool (no tiered prompts, no transfer-oriented templates). The instruction hours, writing tasks, feedback frequency, and training texts were identical to those of the experimental group, except for the lack of the customized nested prompt chain and five-level task library intervention used in this study. The experimental group used instructions based on the DeepSeek model to optimize writing instruction, including nested prompt-chain guidance, cross-cultural adaptation output, and the "generation-reflection-regeneration" mechanism. The control group, in contrast, employed traditional teaching methods or unoptimized AI-assisted tools. By comparing the teaching effects of the two groups, the effectiveness of the transfer training method based on the large language model in improving students' writing transfer ability was verified.

Overall research idea

This study aimed to improve students’ English writing transfer ability and develop effective instructional design methods, with a particular focus on adapting DeepSeek for educational use. First, it proposed an instruction optimization strategy to redesign model prompts. This improved the generated output's alignment with multiple transfer goals in writing instruction, including syntactic structure, paragraph logic, and cultural expression. Second, teachers’ writing tasks were converted into structured and goal-oriented prompt templates. A mapping relationship was then established between teaching tasks and model behaviors. This improved the model's controllability and ensured that the generated content was pedagogically relevant. Finally, the study developed a closed-loop transfer training process, “generation–reflection–rewriting–regeneration.” This process encouraged students to identify transfer strategies and actively refine their language expression with the support of the model. Through repeated iterations, students’ cross-linguistic and cross-cultural writing transfer abilities were continuously strengthened. The steps for sentence structure transformation are shown in Figure 1A–D.

To clarify the complete operation logic of the AI-assisted writing training system and improve replicability, the overall implementation workflow is summarized in Table 1, covering core links including raw input, teacher interventions, prompt application, student activities, final outputs, and corresponding evaluation criteria.

Transfer-oriented task construction strategy

This study focused on cultivating transfer ability in English writing. It constructed a transfer task path that progresses from shallow to deep, systematically guiding students in mastering and practicing multi-level language transfer strategies. To describe the path structure as a whole, it was defined as a four-level task set:

T = {T₁,T₂,T₃,T₄} (1)

T₁ represents the sentence-level transfer task, which focuses on basic grammatical structure operations, such as short-sentence expansion, voice change, and clause replacement, to help students master diverse methods of sentence structure. T₂ is the paragraph-level transfer task, which guides students to pay attention to the organization and connection of information within the paragraph, and trains them to improve the structural clarity and expression coherence of the paragraph through the extraction of the main sentence and the adjustment of the logic between sentences. T₃ represents the discourse-level task, which further extends to the overall logical layout and argumentation rhythm of the chapter, and guides students to construct an English article with a complete argumentation system by adjusting the order of paragraphs and optimizing the structural juxtaposition relationship. T₄ is the cultural-level transfer task, which requires students to identify implicit cultural expression characteristics in native-language writing, such as implicit wording and semantic hints, and transform them into direct expressions or logical explicit forms that conform to the target-language culture. Its evolution process is shown in Figure 2. To enhance the operability and evaluation of task design, this study further defined each level of task as a triple form:

T_i = (S_i,G_i,E_i) (2)

S_j represents the task input sample, G_i represents the target transfer operation, and E_i represents the supporting evaluation criteria. Through this structured expression, it ensured that each level of the task was built upon the mastery of the strategy from the previous level and was equipped with corresponding samples, target descriptions, and performance criteria, providing a mechanism for the steady advancement of the transfer path. In the process of constructing the transfer training system, to achieve systematic coverage of task types and efficient management of teaching calls, this study built a structured task library covering five types of transfer goals based on the common language conversion dimensions in writing transfer. The task library was formally expressed as:

M = {(D_j,T_Dj)∣_j = 1,2,3,4,5} (3)

D_j corresponds to five transfer dimensions: syntactic transfer, structural transfer, cultural transfer, register transfer, and logical transfer; the task set T_Dj associated with each dimension represents its operation subset. In the process of developing the task library, first, the authors summarized and classified common transfer difficulties and language error types based on a large number of real student writing samples, and combined with the actual teaching needs and evaluation standards, divided the different transfer types into functional areas, and established the core training objectives and operation modes of each task. Each task was abstractly represented as the following triple structure:

T_i,j = (S_i,j,G_i,j,C_i,j) (4)

S_i,j represents the task input sample, G_i,j is the transfer target description of the task, and C_i,j is the evaluation and control dimension of the task. In the syntactic transfer task, this study designed a sentence reorganization sequence that progressed from simple to difficult, covering sentence connection, clause nesting, passive-voice conversion, and related concepts, aiming to improve students' abilities in sentence expansion and grammatical transformation. The structural transfer task focused on optimizing paragraph logic, strengthening information organization, and enhancing logical construction ability through rewriting the main sentence, sorting supporting sentences, and reconstructing the cause-and-effect and example relationships. The cultural transfer task was based on a cross-cultural corpus and targets pragmatic deviations, such as implicit expressions and euphemisms. It designed expression conversions that are close to the real context to promote the development of cultural adaptability. The register transfer task guided students in transforming oral expressions into academic language and mastering strategies for switching registers. The logical transfer task focused on constructing reasoning chains and ensuring clarity of expression while conducting training on the use of conjunctions, explicitation of logical relationships, and paragraph transitions.

Based on students' actual writing samples, this study developed a prompt generation process closely tied to teaching tasks, aiming to enhance the relevance of instructions and the practicality of generated content. The process was led by teachers, who selected representative student text fragments during the teaching process, focusing on typical transfer problems such as redundant sentences, chaotic structures, and inappropriate cultural expressions. They then annotated the samples with transfer types through the system interface to clarify the training objectives. After receiving the annotation information, the system automatically activated the transfer task identification module. It then matched the input with the predefined task library and prompt template library. The matching process considered factors such as transfer type, linguistic features, and text structure. Based on these factors, the system selected the most appropriate template and generated an initial prompt. The system also supported structured enhancement processing. Teacher annotations were converted into a standardized format that can be easily interpreted by the model. Relevant instructional cues were embedded into the prompt to help the model accurately understand both the teacher’s intentions and the student’s questions. To improve the flexibility and controllability of instructions, the system included a teacher assistance module. This module allowed teachers to refine prompts by modifying instructional guidance, adjusting contextual settings, and customizing tone and style. As a result, teachers gained greater control over the generation process and better adapted it to different teaching scenarios.

Deepseek command optimization mechanism

Based on the practical needs of English writing transfer training, this study developed five types of transfer-oriented prompt templates. These templates were designed to guide and regulate the generation behavior of the large language model. The structural transfer template focused on paragraph organization and discourse structure. It helped students reorganize texts by adopting structures such as introduction–body–conclusion, and by improving the logical flow of ideas. The syntactic transfer template emphasized sentence-level complexity. It encouraged the use of diverse grammatical structures, including passive voice, parallel constructions, and complex clauses, to enhance the sophistication of written expression. The cultural transfer template addressed cultural appropriateness in writing. It guided students in adapting expressions influenced by their native language and culture to forms that were more natural and acceptable in English-speaking contexts. The register transfer template focused on style adaptation and register control. It helped students adjust their language use according to different writing purposes, audiences, and genres. The logic transfer template aimed to strengthen logical organization. It promoted the effective use of relationships such as cause and effect, comparison and contrast, and parallel development, thereby improving coherence and argumentative rigor. Together, these five templates covered the major dimensions of writing transfer. They provided structured instructional support for both teaching task design and model-based text generation.

Each template type followed a unified format to ensure its universality and adaptability across different teaching situations. The template contained four core elements: the first was the instruction target, which clearly indicated the required transfer task; the second was the sample input, which provided students with real language fragments as the corpus basis for the effectiveness of the instruction; the third was the target output, which showed the standard style of ideal language output after transfer for students to compare and use for model tuning; the fourth was the usage suggestion, which explained the language level, text type and usage strategy applicable to the template, and provided adjustable parameter prompts to enhance generation control and teaching guidance effect.

To further illustrate the design logic and practical application of the prompt templates, representative examples of five transfer-oriented prompts are presented below. All templates complied with the unified framework, which consisted of an instruction target, sample input, target output, and usage suggestion. Students were guided to expand simple sentences, convert sentence voices, and embed clauses to enrich syntactic diversity. For instance, given the student draft "I study English writing. It is very important for cross-cultural communication", the optimized output is "Learning English writing plays a vital role in improving learners’ ability of cross-cultural communication". This prompt was suitable for sentence-level training for sophomore English majors at a moderate difficulty level, focusing on basic syntactic reconstruction and voice transformation. Another type of prompt targeted cultural transfer, aiming to identify Chinglish expressions influenced by native cultural thinking and to rewrite sentences into natural English pragmatic expressions that conform to Western cultural norms. Taking the student sentence "Many people think you must work hard if you want to succeed" as an example, the revised version was "People generally believe that diligence is the key to success". This prompt was applied to cultural-level transfer training, focusing on eliminating implicit Chinese thinking patterns and fostering explicit expression in the English context.

This study also developed a three-stage nested prompt chain that took students’ paragraph drafts as unified input and ran in sequential phases. The first phase was semantic reorganization, with the prompt requiring the participant to rearrange sentences of the given paragraph, optimize the logical order of ideas, and form a complete paragraph with clear topic sentences. The second phase was linguistic revision, which asked to rewrite the revised paragraph into formal academic English, replace colloquial words, and appropriately use complex sentences and passive structures. The third phase was cultural adaptation, whose prompt guided users to check for cultural-pragmatic expressions in the text, remove native-language cultural interference, and polish the content to fit English cross-cultural writing conventions. The above examples fully demonstrated the operational modes of single-prompt templates and nested prompt chains. On this basis, the study designed a complete phased prompt chain combined with DeepSeek’s MoE architecture.

To overcome the limitations of single-round prompts in terms of generation control and task response depth, this study designed a nested prompt chain structure with phased and continuous characteristics, enabling hierarchical guidance and the gradual generation of complex transfer tasks. This mechanism broke down a complete transfer task into several sub-goals and designed corresponding prompt templates for each sub-goal, which were connected in series in the logical order of semantic reconstruction, language style adjustment, and cultural pragmatic adaptation, forming a generation chain with context dependence. This study implemented nested prompt chain optimization for DeepSeek through its standard open API. The DeepSeek model natively adopted a Mixture of Experts (MoE) architecture equipped with an automatic backend expert routing system, which operates as an inherent internal reasoning module independent of external prompts. The customized multi-stage prompts only adjusted the content characteristics of model inputs. Corresponding to the semantic, stylistic, and cultural requirements embedded in the optimized inputs, the model’s native MoE routing mechanism autonomously activated the most suitable expert modules during text generation. The three-stage prompt design focusing on semantic reorganization, stylistic adjustment, and cultural adaptation merely refined input texts and did not interfere with the model’s internal expert selection and allocation logic.

In terms of the operational process, the system received student texts uploaded by teachers, transferred target annotations, and automatically determined whether to enable the nested chain structure. If enabled, the system executed three stages in sequence: the first stage was semantic reorganization, calling the structure transfer template to optimize paragraph organization and logical clues; the second stage was language style adjustment, using the language template to convert colloquial expressions into academic written language; the third stage was cultural adaptation, calling the cultural transfer template to identify and replace expressions that do not conform to the target language culture, and generated more authentic text.

Each round of generation took the output of the previous round as input and retained the context vector cache to ensure semantic coherence. Teachers customized the prompt chain structure in the system interface, including template selection, instruction modification, and sequence adjustment. The system also supported the "template combination package" function, which was convenient for teachers to reuse or assign tasks in batches. Additionally, the system featured a built-in dynamic interruption and feedback mechanism that provided intermediate results after each generation. Teachers annotated or adjusted subsequent instructions in real time, forming a "generation-adjustment-regeneration" teaching closed loop.

Model generation, evaluation

In the text generation and evaluation process based on the DeepSeek model, this study constructed a multidimensional automatic scoring mechanism to systematically analyze and quantitatively evaluate generated text across three aspects: syntactic complexity, structural standardization, and discourse coherence. This study employed the spaCy dependency parser and the Coreferee coreference resolution extension module for text feature extraction and automated evaluation. The spaCy dependency parser parsed the syntactic structure of English text, extracting key indicators such as average sentence length and the number of nested clauses, enabling quantitative analysis of syntactic complexity. The Coreferee coreference resolution extension module, running within the spaCy framework, primarily identified internal referential relationships in text and assessed discourse referential coherence, providing technical support for automated writing quality scoring. Due to network limitations, the aforementioned overseas open-source websites were inaccessible.

First, the system evaluated syntactic complexity. It extracted features such as average sentence length, clause embedding depth, and sentence variety. These features were obtained through dependency parsing and deep syntactic tree modeling. Based on the results, the system assessed whether students have successfully progressed from simple sentence patterns to more complex structures. Second, the system evaluated paragraph organization. It combined BERT-based text representations with classification models to identify key paragraph elements, including topic, supporting, and concluding sentences. A paragraph structure map was then constructed to determine whether the organization conformed to the conventions of English academic writing. Third, the system assessed textual coherence. It examined the use of logical connectives, semantic consistency between sentences, and the quality of referential links throughout the text. These indicators were used to evaluate the clarity of logical development and the smooth progression of ideas. To measure topic consistency, the system calculated semantic similarity using vector-based representations. Referential coherence was evaluated through coreference resolution techniques, which helped reduce semantic gaps and ambiguous references. Finally, scores from all dimensions were integrated into a unified scoring vector. The results were standardized to form a comprehensive evaluation system, enabling reliable comparison across different writing samples.

In the manual evaluation phase, this study developed a three-dimensional scoring system for writing transfer training, encompassing the obviousness of transfer behavior, naturalness of expression, and cultural adaptability, to enhance the relevance of teacher evaluation and the effectiveness of teaching feedback. The system combined its auxiliary functions to achieve an organic combination of quantitative scoring and qualitative comments. In the transfer behavior dimension, teachers judged students' use of transfer strategies in rewriting texts based on preset task objectives. The system provided a side-by-side comparison view of the original text and the generated version to assist teachers in identifying whether operations such as sentence adjustment and structural reconstruction effectively reflected the required transfer type.

The evaluation of natural expression focused on the fluency and authenticity of language output. Teachers used the language indicators generated by the system to evaluate whether the sentences conformed to the expression habits of native English speakers and to judge whether the grammatical structure, word collocation, and sentence rhythm were natural and appropriate. They also pointed out problems and suggested alternative expressions through annotations. The cultural adaptability dimension focused on the appropriateness of cross-cultural pragmatics. Teachers needed to identify whether the text contains inappropriate expressions resulting from interference from the source-language culture, such as semantic ambiguity, rhetorical conflicts, or vague value judgments. The system provided common cultural deviation word examples and supported teachers to explain the basis for modification and cultural context requirements in the comments, guiding students to improve cultural transfer awareness and expression accuracy. Each dimension was scored on a 5-point scale and included free annotations and suggested fill-in modules. The final evaluation results were presented as a radar chart.

To improve evaluation efficiency and support practical classroom use, this study developed a collaborative assessment mechanism that combined AI-based scoring with teacher evaluation. The system first performed a multidimensional assessment of the text generated by DeepSeek. It evaluated key aspects such as syntactic complexity, paragraph organization, and textual coherence through the automatic scoring module. The scoring results were then displayed in real time on the teacher interface. Teachers reviewed the detailed scores for each dimension and adjusted them when necessary based on their professional judgment and understanding of students’ transfer performance. They also added targeted comments directly to the text to provide feedback and instructional suggestions that might not be captured by automatic scoring. Each evaluation dimension adopted a five-point rating scale. The interface also included free-text annotation and recommendation modules to facilitate personalized feedback. The evaluation results were visualized through a radar chart, allowing teachers to quickly identify students’ strengths and weaknesses across different transfer dimensions.

In addition, the system allowed teachers to adjust the weighting of specific dimensions or the overall scores to align with instructional goals. This feature ensured that the final evaluation more accurately reflected students’ actual performance and learning needs. After manual review was completed, the system automatically integrated machine-generated and teacher-generated assessment data. It then produced a visual feedback report. The report included a transfer-performance radar chart and a version-tracking chart. These visualizations clearly illustrated changes in students’ transfer abilities across multiple rounds of text generation and revision, enabling continuous monitoring of learning progress.

Transfer training process

This study designed a complete transfer training process. First, the teacher assigned specific English writing tasks based on the teaching objectives and students' actual level, and clarified the transfer type and focus of the training. Then, the system automatically generated corresponding prompt instructions based on the tasks set by the teacher and called the DeepSeek model to generate a first draft of the writing, serving as the basic material for students' rewriting and transfer training. After receiving the first draft, students needed to actively identify the transfer features reflected in the text and rewrite the content in a targeted manner, using the transfer strategies they have learned to improve the appropriateness of syntactic structure, paragraph organization, and cultural expression. After completing the rewriting, the teacher provided a detailed review of the student's submitted text, highlighting the effectiveness of the transfer strategy and offering specific suggestions for improvement and writing guidance. Based on the teacher's feedback, the DeepSeek model was reinvoked to generate an optimized version, further supporting students in refining their texts and deepening their practice of transfer strategies. Finally, the student completed the submission of the final draft, filled in the transfer cognition table, self-evaluated, and summarized the transfer methods and their effects, and promoted the improvement of metacognitive ability and the internalization of writing transfer skills. The transfer cognition table designed for this study is shown in Table 2.

All items adopted a 1–5 rating scale ranging from 1 (no mastery) to 5 (full mastery). Self-assessment data were collected after three rounds of sequential training; scoring benchmarks refer to pre-defined transfer operation standards in the five-dimensional task library.

Results

Teaching experiment design

This study used the basic open-source version of DeepSeek, relying on the platform's open API interface for program calls. The complete set of five prompt template texts, four levels of task example entries, transfer annotation category tags, and writing error-template matching mapping rules were compiled in the supplementary appendix. The system had fixed calling parameters: a temperature coefficient of 0.7, and the generated length was dynamically ...

Discussion

This research constructs a multi-level instruction optimization framework grounded on DeepSeek’s MoE architecture for cross-cultural English writing transfer teaching. Centered on four-tier progressive tasks ranging from sentence to cultural dimension and a five-category standardized task library, the research realizes automatic prompt generation based on real student writing samples and establishes three-stage nested prompt chains to decompose complicated cross-cultural adaptive tasks. Different from conventional ...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
BERT Pre-trained Language Model	Google	https://github.com/google-research/bert	Text representation, paragraph structure recognition, semantic similarity calculation, and text classification support automated scoring of writing content.
Coreferee	Explosion AI	https://github.com/explosion/coreferee	Text referential relationships are identified, and discourse referential coherence is assessed to assist in the automatic scoring of writing quality.
DeepSeek Large Language Model	DeepSeek AI	https://chat.deepseek.com/	Based on the MoE model architecture, instruction optimization and prompt word chain design were carried out. The model uses fixed parameters: a temperature coefficient of 0.7, and the generated length is dynamically limited according to the question type.
spaCy	Explosion AI	https://github.com/explosion/spaCy	The dependency syntax structure of English text is analyzed, and indicators such as average sentence length and the number of nested clauses are extracted to quantify syntactic complexity.
SPSS 26.0	IBM	https://www.ibm.com/products/spss-statistics	Statistical analyses such as Repeated Measures ANOVA (RM-ANOVA), independent samples t-test, Pearson correlation analysis, effect size (Cohen’s d) calculation, and reliability (ICC) test were conducted.