The application of deep learning in English culture and situational teaching

Abstract

This paper explores the application and influence of deep learning in English culture and context teaching. Through specific case studies, the study demonstrates how deep learning techniques can effectively improve learners’ language proficiency, engagement, and satisfaction. Especially in terms of personalized learning paths, interactive teaching, and cultural understanding, deep learning shows its unique advantages. This study highlights the importance of balancing the application of technology with traditional teaching methods and suggests that future research should focus on technology optimization, model transparency, and their application to a wider range of teaching scenarios. These findings provide important reference and enlightenment for the further development and application of deep learning technology in the field of education.

Keywords

deep learning English teaching scene teaching personalized learning

Introduction

With the rapid development of artificial intelligence technology, deep learning, as one of its core areas, has had a significant impact in several industries. Especially in the field of education, the application of deep learning technology has opened the exploration of new teaching methods and learning models. As a universal language, the innovation of English teaching methods is of great significance to improve the quality and efficiency of education. Traditional English teaching methods mainly rely on books and teachers’ direct guidance, which has limitations in resource allocation, individualized teaching, and situational simulation.

Significant progress has been made in the application of deep learning techniques in the field of education, and specific case studies can deepen the understanding of its potential and effects. For example, intelligent teaching systems use deep learning algorithms to automatically adjust teaching content and difficulty according to students’ learning progress and preferences, achieving truly personalized learning. In the field of language learning, applications such as Duolingo use deep learning techniques to analyze user data and optimize learning paths, significantly improving the efficiency and engagement of language learning. In addition, deep learning technology has been applied to automated grading systems that can provide accurate and consistent grading and feedback by analyzing a large number of student works, greatly reducing the workload of teachers.

Despite the promising application of deep learning in English education, the current research in this field is still in the preliminary stage. Current research is mainly focused on the development of deep learning algorithms, and how to effectively integrate these algorithms into English culture and situational teaching is the focus of future research. In addition, the application of deep learning technology in the field of education also faces technical, ethical, and resource allocation challenges.

The aim of this study is to improve the efficiency and quality of English teaching. Through deep learning algorithms, more accurate learning content recommendation, personalized teaching path planning, and real-time feedback can be achieved, so as to provide learners with teaching content that is more in line with their learning needs and levels.

The study on the application of deep learning in situational English teaching aims to improve learners’ practical language ability. Deep learning technology can simulate a variety of real English usage scenarios, and enhance learners' sense of immersion and practical ability to use English through teaching activities such as scenario simulation and role playing. This study also aims to explore the potential of deep learning for cross-cultural communication and understanding. English is not only a language but also a window to cultures around the world. Deep learning technology can help learners better understand and absorb the cultural background of English-speaking countries, and promote cross-cultural understanding and communication.

The results of this study are expected to have a positive impact on the field of educational technology. Through in-depth research and practical application, it can provide theoretical and practical reference for the application of deep learning in other teaching fields, and also help to promote the overall progress of educational technology.

In recent years, the application of deep learning technology in many fields has gradually become a research hotspot, especially in the field of education, showing its far-reaching influence. Qiao¹ reviewed Marine visual context perception based on deep learning, demonstrating the potential of deep learning techniques in solving visual problems in complex Marine environments. In the field of teaching, Du and Qian² discussed the application of Massive open online courses in English grammar teaching based on deep learning, and proposed a new method to improve the teaching effect. In addition, Lan³ designed a context-based international trade practice teaching program that uses deep learning to simulate the decision-making process in a real business environment. Liu⁴ studied how 5G network and artificial intelligence support the research of contextual teaching in higher education, and emphasized the application value of decision support system in the field of education. In terms of learning methods, Takase⁵ discussed how educators can promote students to adopt deep learning methods through multi-site cross-sectional design. Zhang⁶ analyzed the autonomous college English learning based on deep learning from the perspective of affective cognition.

In terms of cultural education and multicultural communication, Zheng⁷ explored the application of cultural psychology in English cultural education through the study of machine vision translation. Hu⁸ discussed the function of English children’s films in education from a multicultural perspective. Li and Ding⁹ focused on the interpersonal interface system design of multimedia intelligent English translation.

In addition, deep learning technology has also been applied in the field of mental health education. Gao and Wei¹⁰ studied the training mechanism of college students’ mental health education. Shafay¹¹ put forward new research challenges from the perspective of blockchain technology supporting deep learning. In research of Mai,¹² the function of English as a cultural transmission resource in Internet media is analyzed. This paper emphasizes the uniqueness of English language resources in the network environment, and analyzes the application and effect of these resources in cross-cultural communication. Through this research, Mai HY provides a new perspective for understanding the communication function of language in the context of globalization. Liu¹³ studied the deep learning personality model of students and the relationship between their perception of teachers’ emotional support, further confirming the important role of deep learning in educational psychology. These studies not only demonstrate the widespread use of deep learning techniques but also reflect their growing importance in modern educational practice.

Through these critical perspectives and comparative analysis, not only can the limitations of existing research be revealed, but also provide a deeper understanding and analytical framework for this research. This analytical approach helps to identify and fill in the gaps in the existing literature, while proposing a more comprehensive perspective and solution strategies to provide a solid foundation for future research directions. Through critical thinking and comparative analysis, the advantages and challenges of applying deep learning techniques in education can be more effectively assessed, providing valuable insights for further research and practice.

Basic principles and methods of deep learning

Definition and historical development of deep learning

Deep learning is a machine learning technique that interprets data by simulating the way the human brain processes information. It is based on an algorithmic structure called a neural network, which is inspired by the way neurons are connected in the human brain. The core of deep learning lies in learning multi-level representations and abstractions of data, enabling machines to make complex classifications and predictions.

Through its highly personalized and interactive nature, deep learning techniques show significant advantages over traditional teaching methods in enhancing learner engagement and understanding of English culture. The technology can automatically adjust the teaching content and difficulty according to the learner’s learning history and behavior pattern, so as to provide a customized learning path for each learner. This personalized learning experience not only increases the sense of participation of learners but also effectively improves the learning efficiency.

Through deep learning algorithms, educational software can provide rich multimedia teaching resources, such as video, audio, and interactive games that simulate the English native environment, which makes the learning process more lively and interesting and greatly improves the engagement of learners. In addition, deep learning technology can also achieve real-time feedback on learners’ language application ability, including the correctness of pronunciation, grammar, and cultural knowledge. This timely feedback mechanism provides learners with the motivation and direction of continuous learning.

In terms of the understanding of English culture, deep learning technology can reveal the deep connection between language and culture by analyzing a large number of language use cases. For example, through in-depth analysis of English literature, movies, and historical documents, deep learning systems can show learners the diversity and complexity of English culture. The presentation of this cultural situation not only deepens learners’ understanding of English culture but also stimulates their interest in English learning.

The development of deep learning dates back to the 1940 s, but it is only in the last decade that the field has grown significantly due to increased data availability, increased computing power, and improved algorithms. Here are some of the key milestones in the development of deep learning and the corresponding formulas:

Perceptron (1957): One of the earliest neural network models.

Formula and explanation are shown in formula (1):

f (x) = activation (\sum_{i} w_{i} x_{i} + b)

(1)

where

x_{i}

is the input,

w_{i}

is the weight,

b

is the bias, and

activation

is the activation function.

Backpropagation algorithm (1986): A method for training multi-layer neural networks.

Formula and explanation are shown in formula (2).

\frac{\partial E}{\partial w_{i j}} = δ_{j} x_{i j}

(2)

where

E

is the error,

w_{i j}

is the weight,

δ_{j}

is the gradient term, and

x_{i j}

is the input.

Convolutional neural networks (1989): Networks particularly suited for image processing.

Formula and explanation are shown in formula (3).

O_{i j} = activation (\sum_{m} \sum_{n} I_{m, n} K_{i - m, j - n})

(3)

where

I

is the input image,

K

is the convolution kernel, and

O

is the output feature map.

Long short-term Memory networks (1997): Used to solve long-term dependence problems in recurrent neural networks.

Formula and explanation are shown in formula (4):

f_{t} = σ_{g} (W_{f} [h_{t - 1}, x_{t}] + b_{f})

(4)

where

f_{t}

is the forgetting gate,

σ_{g}

is the activation function,

W_{f}

is the weight matrix,

h_{t - 1}

is the previous hidden state,

x_{t}

is the current input, and

b_{f}

is the bias, as shown in Table 1.

Table 1.

History of deep learning.

A given year	Development event	Significance
1957	Perceptrons are proposed	Early models of neural networks
1986	The backpropagation algorithm was invented	The cornerstone of multi-layer neural network training
1989	Convolutional neural network (CNN) is proposed	Major breakthrough in image processing
1997	Long short-term memory network (LSTM) is proposed	Key improvements in recurrent neural networks

This breakthrough not only shows how deep learning technology has advanced but also highlights the growing range of applications it may be used in and their efficacy in them.

Common deep learning models

Convolutional neural network

Convolutional neural network (CNN) is a powerful neural network architecture specifically designed for image processing in deep learning. It effectively recognizes and processes visual patterns in images by simulating how the human visual system works.

Key formula:

Convolution operation: The basic operation of CNN is used to extract features from images. The formula is expressed in the following formula (5).

O_{i j} = \sum_{m} \sum_{n} I_{m, n} K_{i - m, j - n}

(5)

where

I

is the input image,

K

is the convolution kernel, and

O

is the output feature map. This formula describes how to generate a feature map by sliding the convolution kernel over the input image and calculating the weighted sum.

Activation function: applied after the convolutional layer to introduce nonlinearity into the network. The Rectified Linear Unit (ReLU) function is a commonly used activation function, expressed in the following formula (6).

f (x) = \max (0, x)

(6)

This simple formula helps speed up the training of the network and reduces the so-called vanishing gradient problem.

Through these basic operations, CNNS can effectively capture spatial and temporal dependencies in images, making them ideal for processing image recognition, classification and analysis tasks. In English culture and context teaching, CNNS can be used to process and analyze visual content, such as culturally relevant images and videos, to enhance the richness and interactivity of the learning experience.

Recurrent neural network

A deep learning model called a recurrent neural network (RNN) is made especially to handle sequential data, like text or time series data. The network structure’s loops, which allow the network to retain an internal state so that the data in the sequence may be recalled and used, are what make RNNS special.

Key formula:

Cyclic cell Update: At the heart of an RNN is the cyclic cell, which is responsible for updating the hidden state at each step of the sequence. The updated formula can be expressed as follows in formula (7).

h_{t} = activation (W_{h h} h_{t - 1} + W_{h x} x_{t} + b)

(7)

where

h_{t}

is the hidden state of the current time step,

h_{t - 1}

is the hidden state of the previous time step,

x_{t}

is the input of the current time step,

W_{h h}

and

W_{h x}

are the weight matrix, and

b

is the offset term. Activation functions (such as tanh or ReLU) are used to introduce nonlinearities.

Gradient calculation: During training, RNNS update their weights via a backpropagation algorithm. However, due to the length of the sequence, RNNS often encounter the problem of disappearing gradients or exploding gradients. The gradient calculation formula involves the calculation of the partial derivative of the loss function with respect to each weight and can be expressed in the following formula (8).

\frac{\partial L}{\partial W} = \sum_{t = 1}^{T} \frac{\partial L_{t}}{\partial W}

(8)

where

L

is the loss function,

T

is the sequence length, and

\frac{\partial L_{t}}{\partial W}

represents the partial derivative of

t

with respect to the weight

W

in the time step.

RNNS are excellent at handling language-related tasks (such as text classification, sentiment analysis, and language modeling), so they are widely used in English teaching and situational simulation. For example, RNNS can be used to analyze students' language usage patterns, generate text responses, or simulate conversation scenarios to provide a richer and more interactive learning experience for learners.

Long short-term memory network

A long short-term memory network (LSTM) is a special type of recurrent neural network (RNN) designed to solve the problem of gradient disappearance or explosion that traditional RNNS face when processing long sequence data. By introducing gate control mechanism, LSTM can effectively manage the storage, updating and forgetting of information, thus showing excellent performance in long sequence data processing.

LSTM is particularly useful for working with time series data and language models. In English cultural and situational teaching applications, LSTM can be utilized to comprehend and construct complicated language patterns such as simulated dialogue, language translation, and sentiment analysis. These features make LSTM a powerful tool for comprehending and generating natural language, allowing English learners to study more deeply and interactively.

Application of deep learning in the field of education

The application of deep learning in the field of education is increasing, especially in personalized learning, learning effect evaluation, learning content recommendation, and so on. These technologies not only improve the accessibility and efficiency of educational resources but also provide learners with a more personalized and interactive learning experience. Table 2 shows the application cases of deep learning in education.

Table 2.

Comparison of education applications.

Application field	Technology use cases	Effect evaluation	Learner feedback
Personalized learning	Course recommendation system based on learner behavior	Increase learning efficiency by 30%	90% of learners report higher satisfaction
Automatic scoring	Essay scoring using NLP technology	85% agreement with expert rating	50% reduction in teacher workload
Language learning	Language generation and practice system based on LSTM	Language proficiency of learners increased by 40%	80% of learners said they were more confident in using the language
Interactive learning	Using CNN’s image recognition interactive teaching tool	Increase learning engagement by 25%	60% increase in learner interaction

Examples of the application of deep learning in English culture and situational teaching

Case description

The selected cases in this study focus on the use of deep learning techniques to enhance the practice of situational English teaching, especially in teaching knowledge of the cultural context of English. In this case, an international language school was selected for intermediate level English learners, ranging in age from 16 to 30.

In this scenario, the use of deep learning techniques consists of two major aspects:

Scenario simulation: An immersive virtual environment is created by using convolutional neural networks (CNNS) to process and analyze culturally relevant image and video content. The environment simulates everyday life scenes in English-speaking countries, such as supermarket shopping, restaurant dining, public transportation, and so on, aiming to provide a real cultural experience.

Interactive Conversation System: An interactive conversation system is developed using long short-term memory network (LSTM). The system is able to provide natural and culturally relevant English responses based on student input, helping students to better use English and understand English culture in real conversations.

The aim of this case study is to facilitate students’ comprehension of English culture, improve their language proficiency, and increase their awareness of the cultural context of English-speaking nations. The proposal hopes to establish a highly engaged and instructive learning environment where students may learn language skills and have a comprehensive grasp of English culture through the integration of various technologies.

Data collection

In this case, the data collection focuses on learner performance and feedback in a situational teaching environment assisted by deep learning techniques. Table 3 lists the specific data collection methods and frequencies.

Table 3.

Data collection methods and frequency.

Data type	Description	Data range	Collection frequency
Learner interaction data	Interaction frequency, duration, error type	A detailed record of every interaction	After each interaction
Learner feedback data	Satisfaction rating, improvement suggestions	Rating 1–5, text response	Once a week
Learning effectiveness assessment data	Language skills test scores	Mark out of 100	Once a month

These data will be used to analyze the actual effects of deep learning technology in English situational teaching, as well as its impact on learners’ learning experience and learning results. Through these multi-dimensional data collection, the effectiveness of technology application can be comprehensively evaluated and the basis for the subsequent improvement of teaching methods can be provided.

Data preprocessing

In a deep learning project, data preprocessing is a key step that ensures the quality and consistency of the data, thereby improving the accuracy and efficiency of the model. The following are the steps of data preprocessing for English culture and situational teaching cases and the data table after preprocessing:

Data cleansing is the removal or correction of flaws or inconsistencies in a dataset. For example, remove outliers in interaction frequencies or fill in missing satisfaction scores.

Data standardization: All numerical features are converted into a standard format to ensure comparability between different features. For example, convert all test scores into percentages.

Missing value processing: Fill in or ignore missing values in the data set. For example, the missing interaction duration can be filled with the average interaction duration of the learner’s other records. The data results after pretreatment are shown in the Figure 1.

Figure 1.

Pretreatment data result.

In this table, all data has been cleaned, standardized, and missing values processed, making it more suitable for subsequent analysis. Such data preprocessing ensures the accuracy and reliability of the analysis, providing high-quality input to the deep learning model.

Case analysis results

In this case, deep learning technology is applied to English culture and context teaching, aiming to improve learners’ language ability and cultural understanding. In order to evaluate the practical effects of this technique, we conducted detailed data collection and analysis of learners' performance before and after the teaching experiment. It reflects the variation of learners’ performance in different teaching indicators. The data pairs before and after the experiment are shown in Figure 2.

Figure 2.

Data comparison before and after the experiment.

The pre-experiment data showed the learners’ baseline level before the introduction of deep learning techniques, including their interaction frequency, average interaction duration, number of language errors, feedback satisfaction, and language skills test scores. The data after the experiment reflected the change in performance after the application of deep learning technology, and it can be seen that there was a significant improvement in all key indicators. In particular, the interaction frequency and average interaction duration increased significantly, indicating a significant increase in learner engagement in the course. In addition, the reduction in the number of language errors and the improvement of feedback satisfaction and test scores directly reflected the improvement of learning results.¹⁴

There are several challenges to integrating deep learning techniques into existing English language and culture curricula. Technology and resource constraints are one of the main barriers, especially in environments where technology infrastructure is inadequate. In addition, educators may lack the necessary technical knowledge to effectively use and maintain these advanced tools. The diversity of learners also poses challenges, as different learning backgrounds and abilities require deep learning systems to be highly adaptable and personalized.

To address these challenges, the cost and complexity of technology implementation can be reduced by building partnerships and leveraging open source technologies. Educational institutions can partner with technology providers to jointly develop deep learning solutions suitable for educational use. Secondly, professional training for teachers to improve their understanding and operational ability of deep learning technology is the key to achieving technology integration. In addition, designing personalized learning paths that can adapt to the needs of different learners can effectively address the challenges brought by learner diversity. By presetting multiple learning modes and adjusting the difficulty of teaching content, deep learning systems can better meet the needs of different learners. Taking these measures can effectively overcome the challenges of integrating deep learning technologies into existing curricula and improve the quality and efficiency of education.

The practical effect and evaluation of deep learning technology in English teaching

Evaluation method of teaching effect

Evaluating the framework design

Evaluation framework design is an important part of evaluating teaching effectiveness, which provides a structured approach to the whole evaluation process. In this case, the evaluation framework steps of deep learning technology in English teaching are shown in Figure 3 below.

Figure 3.

Evaluation process.

Determination of evaluation criteria

In order to ensure the validity and reliability of the evaluation, clear and specific evaluation criteria are essential. These standards should be able to comprehensively measure the effectiveness of deep learning techniques in English teaching. The following are the criteria used to evaluate the effect of deep learning technology in this case. The quantitative indicators and evaluation methods are shown in Table 4 below.

Table 4.

Evaluation criteria.

Evaluation criteria	Quantitative index	Evaluation method
Language ability enhancement	Percentage increase in test scores	Test scores before and after teaching were compared
Learning engagement	Increased frequency and duration of interaction	Analyze classroom interaction records
Learner satisfaction	Satisfaction survey score	Conduct satisfaction questionnaires
Mastery of teaching content	Learner feedback and evaluation	Interview and questionnaire
Cultural understanding ability	Cultural knowledge test scores	Specific cultural understanding tests

Several factors of effective teaching were taken into account when creating the assessment criteria. These dimensions included the improvement of learners’ language competency, learning engagement, learner satisfaction, mastery of the teaching subject, and comprehension of the target culture. In order to accurately evaluate and analyze the effects of education, a set of quantitative indicators and assessment techniques have been chosen with the goal of producing precise and quantifiable data.

These evaluation criteria not only help to quantitatively evaluate the teaching effect of deep learning technology but also provide directional guidance for the improvement of teaching methods. Through these standards, we can understand more comprehensively the specific impact of technology application on English teaching, and make corresponding adjustments and optimization.

Quantitative indicators of teaching effect

Quantitative indicators are essential when evaluating the actual effects of deep learning techniques in English teaching. These indicators provide a clear, objective yardstick against which to measure improvement in teaching outcomes. The following are some of the key quantitative indicators designed to evaluate the effectiveness of teaching and their corresponding calculation methods. Table 5 lists the quantitative indicators.

Table 5.

Quantitative index table.

Index class	Quantitative index	Calculation formula or description
Language ability enhancement	Percentage improvement	(Score after experiment - score before experiment)/score before experiment × 100%
Learning engagement	Interactive frequency increase rate	(Interaction times after the experiment - interaction times before the experiment)/interaction times before the experiment × 100%
Learner satisfaction	Average satisfaction score	Average scores on satisfaction questionnaires
Mastery of teaching content	Content mastery rating	Content-based testing or assessment
Cultural understanding ability	Percentage improvement on cultural understanding test	(Cultural understanding test post-test score - pre-test score)/pre-test score × 100%

These quantitative indicators cover many dimensions, such as language ability improvement, learning engagement, learner satisfaction, teaching content mastery, and cultural understanding. Through these specific indicators, the impact of deep learning technology in English teaching can be more accurately assessed, and the effectiveness of teaching methods can be quantified.

Using these quantitative indicators, educators and researchers are able to evaluate the effectiveness of teaching in a nuanced manner, and thus better understand and leverage the potential of deep learning techniques in language education. Analysis of this data can reveal specific contributions of deep learning techniques to improving learning outcomes and provide valuable insights into future instructional strategies and technology applications.

Analysis of teaching effect data

In this section, the obtained teaching effectiveness data will be thoroughly examined in order to assess the true effects of deep learning approaches in English teaching. This comprises a comparison examination of student performance before and after training, as well as pertinent statistical data, as seen in Figure 4 below.

Figure 4.

Data chart of teaching effect.

By analyzing these data, it is possible to quantify the impact of deep learning techniques on improving learning outcomes (such as improved test scores), increasing learning engagement (such as increased frequency of interaction), and improving learner satisfaction. This analysis provides an empirical basis for understanding the practical application effect of deep learning technology in teaching and helps to further improve and optimize teaching strategies.

Results discussion

Interpretation and analysis of teaching effect

This paper analyzes the effect of deep learning technology in English teaching. By assessing the data, we aim to understand the specific impact of technology on learners’ language ability, engagement, and satisfaction. The effect analysis is shown in Figure 5.

Figure 5.

Effect analysis diagram.

The table above provides a clear view of the unique influence of deep learning approaches on learning results. The statistics show that learners’ test scores, interaction frequency, and satisfaction scores all improved significantly following the trial. There was an impressive increase in the average number of interactions, demonstrating that deep learning approaches were effective at enhancing student engagement and motivation.

The data show the remarkable effect of deep learning technology in English teaching. The average increase in test scores was 20.6%, reflecting the effectiveness of deep learning techniques in enhancing learners’ language skills. This improvement may result from personalized learning paths, interactive learning content, and efficient use of learning resources. These factors work together to help learners better understand and master English. The significant increase in interaction frequency (50% increase) indicates that deep learning techniques are equally effective in boosting learner engagement and motivation in the classroom. This may be due to the fact that deep learning techniques provide more interactive and engaging learning materials and activities that stimulate learners' interest and desire to participate. The satisfaction score of learners with the teaching process increased from 3.4 to 4.6, an increase of 35.3%, indicating that the introduction of deep learning technology has improved the overall learning experience of learners. This increase may reflect learners’ positive response to more challenging, personalized, and fun learning activities.

Deep learning technology has shown significant positive effects in improving learning outcomes, increasing learner engagement, and enhancing learning satisfaction, which underscores its important value and potential in modern educational technology. These results provide valuable insights into future teaching strategies and support the idea of wider application of deep learning techniques in education.

Limitations of technical application

Although deep learning techniques show significant advantages in English teaching, there are some limitations. First, the development and maintenance of deep learning models requires a large amount of data and computational resources, which can pose challenges for some educational institutions. In addition, these models often require expertise to design and tune, potentially increasing the complexity and cost of educational programs.

The “black box” nature of deep learning models makes their decision-making processes difficult to interpret, which can affect educators’ control and understanding of the teaching process. For example, if a learner performs poorly on a test, it may be difficult for educators to determine whether the problem is with the learner or whether the model’s recommendation and feedback mechanisms are inappropriate for the student. Deep learning techniques may not be precise enough to handle cultural and linguistic nuances. Understanding and communicating cultural nuances is crucial in teaching English culture and context. Deep learning models may not fully capture these details, affecting the quality of instruction. Reliance on technology may reduce learners' ability to learn independently. If learners rely too much on technology to provide answers and feedback, they may reduce their opportunities for self-exploration and problem solving.

While deep learning techniques bring innovation to English teaching, there is also a need to pay attention to its limitations and find a balance in practical applications. Understanding these limitations is important for the effective integration of deep learning techniques into teaching systems and for guiding future improvements in teaching strategies and techniques.

Conclusion

Research summary

This study explores the application of deep learning in English culture and context teaching, and focuses on its potential and actual performance in improving teaching results. Through the evaluation of specific cases, we found that deep learning technology can significantly improve learners’ engagement, language ability, and satisfaction with the content. Especially when it comes to personalized learning, interactive teaching, and cultural understanding, deep learning shows its unique advantages.

Through personalized learning paths and content, deep learning technology can meet the specific needs of different learners, thereby improving learning efficiency. Secondly, with the use of deep learning technology, the interaction in the teaching process is significantly enhanced, which not only enhances the interest of learners but also enhances their practical application ability. In addition, the ability of deep learning technology in providing cultural teaching content helps learners to better understand and absorb the cultural background of English-speaking countries.

The application of deep learning technology in English culture and context teaching has shown significant positive effects, but it also brings some challenges. Future work can focus on optimizing technology applications, improving model transparency and accuracy, and better integrating technology with traditional teaching methods. Through such efforts, deep learning techniques have the potential to further revolutionize the field of language education, as shown in Table 6.

Table 6.

Differences before and after application of deep learning technology.

Aspect	Before deep learning	After deep learning	Improvement
Learner engagement	Low	High	Significant
Language skills	Basic	Advanced	Significant
Content satisfaction	Moderate	High	Significant
Personalized learning	Generic	Customized	Significant
Interactive teaching	Limited	Enhanced	Significant
Cultural understanding	Surface-level	Deep	Significant

Research limitations and future work direction

The data and cases used in the study, while providing initial insights into deep learning applications, may not be fully representative of all learning situations. Future research is needed to validate the effects of deep learning techniques in broader educational Settings and diverse learner populations to improve the general applicability of the conclusions.

There are still some limitations in the application of deep learning techniques to English language and cultural education. The primary limitation is the quality and diversity of the data sets. The performance of deep learning models is highly dependent on the breadth and representativeness of the training data. Currently available data sets tend to focus on specific regions or cultures, limiting the models’ understanding and reflection of the global diversity of English cultures. In addition, existing deep learning models have not adequately addressed the complex needs of personalized learning, especially in simulating complex human teacher-student interactions.

In response to these limitations, future research could enhance the model’s cultural sensitivity and adaptability by expanding and diversifying the dataset. This includes collecting samples of a wider range of language usage scenarios, cultural expressions from different regions, and non-standard English. At the same time, further research should focus on developing more advanced algorithms to improve the model’s ability to identify the characteristics and needs of individual learners, so as to provide a more accurate personalized learning experience. In addition, strengthening the interpretability and adaptability of the model can enable educators to better understand and control the teaching process and optimize the teaching strategy. By addressing these issues, future research is expected to promote the widespread application of deep learning techniques in education while deepening language and cultural education.¹⁵

Current research has mainly focused on the positive effects of deep learning techniques, with relatively limited exploration of potential negative impacts and challenges. For example, the impact that technology dependence may have on learners’ ability to learn autonomously, or the privacy and ethical issues that technology applications may raise. Future work needs to take these factors into account more fully. The deep learning models and algorithms in the study mainly focus on certain aspects of English teaching, such as language skills and cultural understanding. Future research could explore the application of deep learning techniques to other areas of English teaching, such as improving writing skills and improving oral fluency.

Future research is needed to further explore the applicability and effectiveness of deep learning techniques in different educational Settings, especially how to overcome existing challenges, including improving educators’ ability to understand and use the technology, expanding and enriching data sets for training models, and developing more advanced algorithms to enable higher levels of personalized instruction. Further, future research should focus on the impact of deep learning technologies on equity in education and explore ways to ensure that all learners benefit from the use of these technologies. Through in-depth research in these key areas, future work is expected to further optimize the application of deep learning technology in the field of English education and achieve broader and deeper education reform.

With the rapid development of technology, new deep learning methods and tools will continue to emerge. Future research should consider the potential of these new technologies and how they can be effectively integrated into English teaching to further improve teaching outcomes and the learner experience.

Statements and declarations

Footnotes

Conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Special Project of the 14th Five Year Plan for Education Science of Hunan Province in 2024 (Project Number: XJK24BZJ037).

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