Utilizing AI models to optimize blended teaching effectiveness in college-level English education

Abstract This paper proposes the adoption of AI technologies in higher education to support student learning. Using multi-modal blended learning theory and independent learning fundamental theory, the study explores the use of AI to evaluate and improve the effectiveness of blended teaching in college English courses. A new model of deep learning and a learning model of human job functions are proposed to explore the hybridization of college English education under the background of artificial intelligence. This study provides a road map for using AI in college-level English courses and offers valuable contributions to the field, including the proposed models of deep learning and human job functions which can be applied to other subjects and fields. By leveraging modern technologies such as cloud computing, big data, and AI. This study highlights the potential for educators to transform the way we teach and learn and improve the quality of education and support student success. Overall, this paper provides valuable insights for future research in the intersection of AI and education and emphasizes the importance of integrating technology in higher education to enhance the learning experience and meet the needs of modern students.


ABOUT THE AUTHORS
Lizhen Shi is a distinguished academic scholar whose career in education governance has left an indelible mark on the fields of English language instruction, education management, and international education.With a doctoral degree in Education Governance, her journey has been characterized by a profound commitment to advancing educational excellence.Her administrative experience, including her concurrent roles as Director of Foreign Affairs and Head of the Foreign Language Department at Inner Mongolia Honder College of arts and sceicens, attests to her depth of expertise and dedication.Her research contributions, showcased through an array of publications, underscore her exceptional scholarly achievements.Furthermore, Dr. Shi's significant impact on education has been duly recognized with multiple awards and honors, solidifying her status as a prominent figure in the realm of education governance.Arshad Muhammad Umer is working as an assistant professor at Inner Mongolia Honder college of arts and Sciences, Inner Mongolia, China.He has completed his PhD in Management from Inner Mongolia Agriculture University.Yanting Shi is working as lecture at Qingshuihe General High School.She has completed her Master from Inner Mongolia University and recently she is teaching English and her research focus the impact of artificial intelligence and its impact of teaching language.

Introduction
The advent of technology has brought about significant changes and opportunities in various sectors, including education (Alam, 2022).Learning management systems, student information systems, and other applications are also used for educational purpose, such as assignment distribution, schedule management, communications, and track student progress (Watson & Watson, 2007).In present ear, technological progress has changed the trend of knowledge renewal, which is turning faster and making people under tremendous pressure (Wajcman, 2008).To relieve this pressure and to adapt these social changes people must learn adapt these changes actively.Higher education institutions have been particularly impacted by technological advancements, leading to educational reforms aimed at enhancing teaching and learning experiences (Johnson et al., 2016).One such technological development that has gained prominence in recent years is artificial intelligence (AI).AI has the potential to revolutionize the field of education by offering novel ways to support and enhance student learning.One of the key trends driving educational technologies in China is the shift towards deep learning, which emphasizes the development of learners' highest-level competencies and problem-solving skills in real-life situations (Liu & Wang, 2009;Yau et al., 2023).As a result, the requirements for learner competencies in the information age are aligned with the core principles of deep learning.
Use and popularity of big data, the Internet of Things, cloud technology, artificial intelligence, and symbolic technicality continue to rise, a new technological revolution is inevitably on the horizon (Sezer et al., 2017).Through analyzing large amounts of data and information, inherent regularities, relationships, and development trends can be identified and applied to various sectors and industries (Glazer, 1991).However, in addition to the demand for trained data analytics talent in the Internet era, there is also a growing need for students to acquire knowledge and skills related to data analysis, as well as a demand for the development of data analytics course content across various disciplines.The overall goal of deep learning is to enhance the higher learning training ability of learners (Aminanto & Kim, 2016).Deep learning review takes the overall goal of deep learning as the starting point, focusing on the value orientation of the process results of deep learning, and reflecting on and adjusting the overall goal of deep learning.Figure 1 illustrates the deep learning route.
The field of deep learning in education has garnered significant attention from researchers and educators alike, aiming to enhance learning outcomes and instructional practices through the integration of advanced technologies (Zhu et al., 2016).Numerous studies have explored the effectiveness of deep learning approaches in classroom instruction.Belland et al. (2017) conducted a comprehensive meta-analysis of empirical studies and found that incorporating deep learning strategies.Similarly, Annetta et al. (2009) investigated the impact of deep learning pedagogies on student engagement and reported increased motivation and active participation among learners.Artificial intelligence (AI) technologies have been increasingly integrated into educational practices, opening new avenues for personalized and adaptive learning experiences.Recent research by Wang et al. (2023) examined the use of AI-driven adaptive learning platforms and found that personalized instruction based on students' individual needs and learning styles improves learning outcomes.Moreover, AI-based tutoring systems, as studied by Hsiao et al. (2010), have demonstrated the ability to provide personalized guidance and support to students.The integration of AI technologies in tutoring systems has shown promising results in improving students' problemsolving skills, conceptual understanding, and overall academic performance (Beal et al., 2010).
Despite the promising outcomes of deep learning approaches, there are challenges to their widespread adoption in educational settings.Bingimlas (2009) identified barriers such as limited teacher training in deep learning pedagogies, resistance to change, and a lack of suitable technological infrastructure.DeMatthews and Mawhinney (2014) highlighted the opportunities for deep learning to address educational inequities and promote inclusive practices.Assessment methods have also been influenced by deep learning approaches, revolutionizing traditional forms of evaluation.Research by Ariely et al. (2023) explored the use of machine learning algorithms in automated assessment and reported high accuracy and efficiency in grading student assignments.These automated assessment systems can analyze large amounts of data, providing timely feedback to students, and reducing the time burden on teachers.Furthermore, Huang et al. (2022) investigated the potential of deep learning models in assessing higher-order thinking skills, such as problem-solving and critical analysis.The integration of deep learning techniques in assessment strategies holds promise for more authentic and comprehensive evaluations of students' learning outcomes.
The literature highlights the positive impact of deep learning approaches in education, particularly in enhancing student engagement, promoting personalized learning experiences, and revolutionizing assessment strategies.While challenges and considerations exist in the adoption of deep learning practices, the opportunities for improving educational outcomes and fostering inclusive and equitable learning environments are significant.This indicates that there is opportunity for future research to explore experimental teaching methods grounded in practical contexts.This paper holds significant implications for the field of higher education.By exploring the adoption of AI technologies in college-level English courses, this study tries to address the growing demand for innovative teaching methods that effectively integrate technology and enhance the learning experience.This research seeks to bridge the gap between traditional teaching approaches and the potential of AI in education, with a specific focus on deep learning and the use of multimedia multi-modal blended learning models.By examining the effectiveness of these approaches, we aim to contribute to the development of evidence-based practices that can inform curriculum design, instructional strategies, and educational policies.Furthermore, by leveraging AI technology, we can personalize and adapt instruction to cater to the diverse learning needs of students, ultimately improving learning outcomes and fostering a more engaging and effective educational environment.The findings of this research have the potential to not only enhance English language education but also pave the way for the integration of AI technologies in other disciplines, revolutionizing teaching and learning practices in higher education institutions.

Multimedia multi modal learning in higher education
The multi-model hybrid college independent learning model refers to the use of multimedia to facilitate interactive learning based on multiple senses such as visual and auditory perception (Lin et al., 2020).Teachers can take advantage of multimedia-assisted teaching by selecting and preparing colorful content from college texts and visual raw materials to present their teaching mode to students, thus greatly increasing students' motivation to learn (Wang & Jan, 2022).This approach, known as the "multimedia multi model blended learning" model, plays a crucial role in shaping students' overall literacy and creative thinking skills (Salim Keezhatta, 2020).
The value of the multimedia multi-model independent learning model in higher education institutions lies in its ability to serve as a comprehensive service platform for learning and training, enabling students to actively build professional skills through various modes (Mathivanan et al., 2021).Figure 2 illustrates the model of multi modal classroom teaching entities in the field of Internet multimedia.
Multi-modal teaching is an instructional approach that incorporates a range of teaching methods, such as networking, group collaboration, association, role-playing, and more.The goal is to engage learners actively in the learning process and promote interaction (Richard et al., 2006).This approach combines listening, speaking, and writing practice in foreign language education, fostering a genuine interest in language learning.
In multi-modal teaching, teachers are encouraged to select one or more teaching methods based on factors like the curriculum, learning environment, and educational objectives.These methods can include listening exercises, communication activities, systemic reactions, suggestions, direct instruction, situational teaching, grammar translation, and more.The selection of methods should be thoughtful and tailored to the specific language-learning context.
In a multi-modal classroom, multimedia tools and resources are used to create immersive, reallife language environments.These tools stimulate learners through various senses, including auditory, visual, and tactile experiences.This approach helps learners better understand and use the language, enhancing their vocabulary and language output skills.Overall, multi-modal teaching aims to make language learning more engaging and effective by incorporating a variety of teaching techniques and multimedia resources.

Categorization of development stages
Using "year" as a keyword, the research employed Bicomb software to sort out the references pertaining to "deep learning scientific research in China" from different years.This data compilation was then visualized in a data map, represented as Figure 3, showcasing the annual distribution of the number of references.By closely examining the development trend illustrated in Figure 3, the deep learning scientific research in China can be categorized into three distinct stages: the early stage, the development stage, and the current developing trend stage.These categorizations are based on the patterns and shifts observed in the annual distribution of references.This analysis provides valuable insights into the progression and evolution of deep learning research within the Chinese context.
In addition to the keyword network structure, Figure 4 also presents the analysis results from UCIENT, an entertainment network analysis software.Specifically, it highlights the "point degree," "light to medium degree," and "proximity degree" generated by UCIENT, further enhancing the understanding of the network structure and its significance within the context of deep learning scientific research in China.For reference and comprehensive analysis, Table 1 provides the "proximity" keywords generated by UCIENT.These keywords offer additional insights into the key themes and topics that are closely related to the field of deep learning research in China.For reference and comprehensive analysis, Table 1 provides the "proximity" keywords generated by UCIENT.These keywords offer additional insights into the key themes and topics that are closely related to the field of deep learning research in China.By leveraging these visualization and analysis techniques, the research not only provides a comprehensive overview of the annual distribution of references but also offers a detailed understanding of the keyword network structure and its significance within the research domain.This analysis serves as a valuable resource for categorizing the development stages of deep learning scientific research in China.

Educational applications of artificial intelligence technology
The role of student awareness and motivation of interest in learning, highlighting how students often recognize their own and others' academic research abilities but may not reflect on their own limitations.It emphasizes that motivation driven by interest plays a crucial role in maintaining learning behavior with specific academic goals.Personal interests and development, along with cognitive provisions and external factors, further contribute to students' engagement in learning (Guo et al., 2019).In the era of artificial intelligence, students are increasingly adopting two learning strategies: seeking help, managing time and environment, and choosing to learn with a small partner, particularly among students at lower school levels.The AI era has led to biased online learning that focuses on learning innovation, offering more avenues for seeking help and facilitating students' management of their time and environment.Compared to simple response reporting, the AI era stimulates students to select higher quality learning strategies in online learning, as depicted in Figure 5.
The research focus in the field of deep learning is primarily on educational science research, with a strong emphasis on instructional design.Some scholars argue that deep learning involves guiding students through a meaningful and engaging cognitive learning process during specialized sessions with their teacher.Consequently, teachers' lesson plans play a crucial role in achieving deep learning objectives (Gao et al., 2020).Curriculum design is a key component in guiding students towards deeper learning, and classroom activities are an essential element in implementing instructional design.Our educational research focuses on innovative classroom teaching models, such as flipped lessons and blended teaching, to achieve deep learning.Through the reinvention of curriculum design and using the new AI technology educational application, deep learning can be accomplished in the classroom.
The integration of science, technology, and education has greatly influenced productive activities, becoming a key driver for advancement in these fields.Artificial intelligence technology plays a critical role in deep learning by fostering learner interest and motivation, facilitating the integration of new and existing knowledge, and promoting interdisciplinary and contextual understanding (Liu et al., 2022).It enables the transfer of internal relationships and external expansion, while mitigating learners' reliance on cognitive strategies.However, current curricula often prioritize surface-level learning, overlooking the potential of deep learning.To address this, deep learning aims to surpass superficial knowledge and encourage students to explore research topics, develop a comprehensive understanding, and engage in critical thinking.
Deep learning involves guiding students through a meaningful and engaging cognitive learning process during specialized sessions with their teacher.Consequently, teachers' lesson plans play a crucial role in achieving deep learning objectives (Gao et al., 2020).Curriculum design is a key component in guiding students towards deeper learning, and classroom activities are an essential element in implementing instructional design.Artificial intelligence technology plays a critical role in deep learning by fostering learner interest and motivation, facilitating the integration of new and existing knowledge, and promoting interdisciplinary and contextual understanding (Liu et al., 2022).It enables the transfer of internal relationships and external expansion, while mitigating learners' reliance on cognitive strategies.However, current curricula often prioritize surface-level learning, overlooking the potential of deep learning.To address this, deep learning aims to surpass superficial knowledge and encourage students to explore research topics, develop a comprehensive understanding, and engage in critical thinking (Sun et al., 2021).While artificial intelligence and deep learning techniques have been incorporated into university classrooms, their application is still in the early stages and lacks a well-defined framework.The following sections of this paper will delve into the methodology.

Experimental validation
At the First step, the experimental method used a combination of quantitative and qualitative analysis (Wu, 2020) to gain a comprehensive understanding of the relevance and effectiveness of the multimedia multi modal independent learning model design on students' comprehensive abilities in colleges and universities.(Based on the reliability analysis of the evaluation scale for the Artificial Intelligence Experimental Course using Cronbach's alpha coefficient (Table 5).
First, At the outset of our research, we formulated the following hypothesis.

H1:
The implementation of the multimedia multi-modal independent learning model in colleges and universities will significantly enhance students' comprehensive abilities.
Our hypothesis, H1 serves as the foundational premise driving our study, and we aim to empirically validate or refute this hypothesis.In the pursuit of empirical evidence to either support or refute H1, we employed a combination of quantitative and qualitative analysis techniques, including the application of a t-test, to rigorously assess the model's impact on student performance.
In this study, we employed a combination of quantitative and qualitative analysis techniques to gain a understanding of the impact of the multimedia multi-modal independent learning model on students' comprehensive abilities in colleges and universities.Quantitative analysis was utilized to assess the numerical data, while qualitative analysis was applied to gain insights from non-numeric data.Our quantitative analysis involved various data collection methods, including pre-and post-experiment assessments of student performance through standardized examinations and surveys.We used the t-test to compare the means of the experimental group, which employed the learning model, and the control group, which did not.In this study a T-test was employed to access the effectiveness of the multimedia multi-modal independent learning model in enhancing students' comprehensive abilities in colleges and universities.The t-test was chosen for its ability to determine whether there existed a statistically significant difference between the experimental group, which utilized the learning model, and the control group, which did not.By conducted t-tests both before and after the experiment, we aimed to evaluate the impact of the learning model.The results were analyzed to determine if any observed differences in the exam performance of these two groups were likely to be genuine and not attributable to random chance.The t-test is well-suited to such comparative analyses, offering ease of interpretation and versatility in handling small sample sizes, making it a valuable tool for our research methodology.
In addition to quantitative data, we collected qualitative data through interviews, open-ended survey questions, and observations.Qualitative analysis was used to extract valuable insights from the narratives, opinions, and experiences of the participants.We conducted thematic analysis to identify recurring themes and patterns in the qualitative data, which enriched our understanding of the factors contributing to the observed results.The specific results are presented in Tables 3  and 4 in the Results and Discussion sections.Table 3 show that there was no significant difference between the experimental and control groups before the experiment, indicating that the starting point of students in both groups was similar.After one year of using the multimedia multi modal university independent learning model in three classes of the experimental group, students in six classes took the university final exam.Statistical analysis was conducted on the exam results of the two sample groups, as presented in Tables 3 and 4. By employing both quantitative and qualitative analysis techniques, we aimed to provide a comprehensive assessment of the effectiveness of the multimedia multi-modal independent learning model in enhancing students' comprehensive abilities, not only by examining statistical differences but also by delving into the qualitative aspects of the learning experience and its impact on student outcomes.

Deep learning algorithms
At the second step, we have harnessed the power of deep learning algorithms to introduce innovative models for assessing the impact of the multimedia multi-modal independent learning model on students' comprehensive abilities in higher education.Deep learning's capacity to uncover complex data patterns and relationships, automatically extract relevant features, and its scalability in handling large educational datasets make it a vital tool.Additionally, its track record of achieving state-of-the-art performance in education, as well as the option for efficient transfer learning from pre-trained models, enhances our ability to introduce new models.The availability of extensive resources for deep learning simplifies their implementation, aligning with the specific demands of our research to enrich our understanding of educational dynamics.
The acceleration optimization method of deep learning is used to establish the forward propagation model of the convolutional neural network (Fu et al., 2018;Namaziandost et al., 2021;Wanore, 2022).Firstly, the convolutional layer is utilized for image processing.The input of the convolution kernel and the convolution layer is typically three-dimensional (Xie et al., 2021).By multiplying and accumulating multiple two-dimensional K × K convolution kernels with multiple two-dimensional input feature maps, two-dimensional output feature maps can be obtained (Dedeakayogullari & Burnak, 1999;Fu et al., 2018).The 2D output feature maps OUTj corresponding to multiple different sets of 3D convolution kernels can be obtained according to the convolution corresponding to the input feature maps.
In the equation, OUTj represents the jth output feature map; kij represents the convolution kernel corresponding to the output feature map; INi represents the ith input feature map; and bj represents the bias corresponding to the input feature map.For example, considering the height h of the input feature map, the dimensions between the input image and the output image are governed by the following equation (2): where r denotes the height corresponding to the output characteristic map; s denotes the sliding step of the convolutional box; and p denotes the filling cell.By multiplying m sets of K × K × N convolution kernels, m two-dimensional output profiles of size R × C can be obtained for n input profiles of size h × l.The number of convolution kernels is the same as the number of output profiles, and the number of channels can usually be expressed as n.
(2) Pool layer: The input size and output size are consistent with the following equation: where s represents the corresponding move step of the pooling frame, and k represents the corresponding size of the pooling frame.
(3) Activation function The activation function plays an important role in the hidden layer.The input and output have a linear weighted relationship in the convolution calculation process (Yu et al., 2019).Commonly used activation functions include the Sigmod function, which is expressed as follows: Like the Sigmoid function, the tanh function also compresses the results of the convolution calculation.The expression of the tanh function is as follows: The ReLU function is faster since there is no complex exponential calculation involved in the calculation process.Moreover, the expressions for the ReLU function are relatively simple.
Where Kt denotes the set of subscripts in di for the vocabulary t of the English segment-assisted review.x and y are two unknown segments, and the detection method is taken to obtain the statistical feature amount of the reliability detection for any x and y.The inter-class distributed feature clustering parameter is The random probability distributions of the two-dimensional feature components x and y are denoted as P(x) and P(y), respectively, while P(x ∩ y) represents the joint probability distribution function (Wu, 2020).

T test results of experiment
This study used the experimental method used a combination of quantitative and qualitative analysis and results are presented in the Table 2, and 4. Our qualitative analysis revealed valuable insights into the experiences and perceptions of students participating in the multimedia multimodal independent learning model.Through interviews, open-ended survey responses, and observations, we identified recurring themes and patterns in the qualitative data.Students consistently reported a heightened sense of engagement with the multi-modal approach, citing increased motivation and interactivity as key factors.Furthermore, the model facilitated personalized learning experiences, allowing students to tailor their approach to their individual preferences and needs.Qualitative findings suggest that the multimedia multi-modal independent learning model not only enhances academic performance but also fosters a deeper connection to the learning process, creating a more enriching and dynamic educational environment.
Table 2 shows the results of a paired samples t-test conducted to compare the English proficiency test scores between the experimental and control groups prior to the experiment.The mean score for the control group was 69.2756 with a standard deviation of 11.59119, while the mean score for the experimental group was 69.8718 with a standard deviation of 12.35482.The results indicate that there was no significant difference between the two groups in terms of English proficiency test scores prior to the experiment.
Table 3 shows the results of a paired samples t-test conducted to compare the English proficiency test scores between the experimental and control groups after the experiment.The mean score for the control group was 70.3846 with a standard deviation of 11.68316, while the mean score for the experimental group was 73.8269 with a standard deviation of 9.80728.The results indicate that there was a significant difference between the two groups in terms of English proficiency test scores after the experiment, with the experimental group scoring higher than the control group.This suggests that the multimedia multi-modal independent learning model design used in the experimental group was effective in improving students' English proficiency.Table 4 displays the results of the reliability analysis of the teaching evaluation scale for the artificial intelligence laboratory course using Cronbach's alpha index.
Our study demonstrates that the multimedia multi-modal independent learning model significantly enhances students' comprehensive abilities, particularly in terms of English proficiency.Quantitative data from Tables 2 and 3 show no initial difference in English proficiency, but after implementing the model, the experimental group outperformed the control group.This quantitative shift aligns with qualitative insights revealing increased student engagement and motivation in the experimental group.Together, these findings strongly support our hypothesis (H1) and emphasize the model's effectiveness in elevating English proficiency and fostering an engaging learning environment.
In addition to our quantitative and qualitative findings, the outcomes presented in Table 4 further substantiate the impact of the multimedia multi-modal independent learning model on students' comprehensive abilities.Table 4 presents the results of the reliability analysis for the Teaching Evaluation Scale of Artificial Intelligence Laboratory Course.The table shows three dimensions: Teaching Content Evaluation, Satisfaction with Teaching Aids, and Student Suggestions.Each dimension has a different number of items, and the Cronbach's alpha coefficient is used to measure the internal consistency reliability of each dimension.The Cronbach's alpha coefficient ranges from 0 to 1, with higher values Indicating greater Internal consistency.Our finding reveals that Teaching Content Evaluation dimension has five items and a Cronbach's alpha of 0.715, indicating a satisfactory level of internal consistency.Similarly, the Satisfaction with Teaching Aids dimension, featuring four items and a Cronbach's alpha of 0.665, indicating an acceptable level of internal consistency.The Student Suggestions dimension, consisting of three items and a Cronbach's alpha of 0.626.These results collectively underscore the good internal consistency.Overall, the teaching evaluation scale of the artificial intelligence laboratory course has good internal consistency reliability of the teaching evaluation scale, lending further support to our hypothesis (H1) that the multimedia multi-modal independent learning model enhances students' comprehensive abilities in the context of the Artificial Intelligence Laboratory Course.

Instructional design for deep learning
To determine the similarity of high-frequency keywords, we used SPSS to generate a similarity matrix (Table 5).The matrix indicates the relationships between different keywords and provides an initial understanding of the analysis perspective of the current deep learning research topic.To better demonstrate the relevance of other keywords to deep learning, we ranked the dissimilarity matrix from near to far (Wu & Zu, 2019).As shown in Table 2, the research topics related to deep learning are ranked in order from core literacy to artificial intelligence, reflecting the current domestic research focus on deep learning from the perspective of core literacy.Some researchers have also explored micro perspectives of deep learning, such as flipped classes, shallow learning, advanced thinking, learning assessment, and teaching strategies, which have some relevance to the literature.

Deep learning-based teaching model for college English smart classroom
Our study results explained the intelligent classroom teaching model for college English is constructed from the perspective of deep learning, as shown in Figure 6.This teaching model comprises three main modules: smart classroom teaching objectives, smart classroom teaching activities, and smart classroom teaching evaluation.
Figure 6 illustrates the relationship between the different components of the Smart Classroom Teaching Model.The model is designed to promote deep learning and ensure that students are fully engaged in the learning process.Using intelligent classroom teaching objectives, activities, and evaluation, the model provides a framework for effective teaching and learning in the modern classroom.The model has four main components and play a crucial role in achieving the objectives of the model.The smart classroom teaching model also encourages publishing resources and providing preview feedback.Overall, the Smart Classroom Teaching Model is a comprehensive approach that leverages artificial intelligence and deep learning techniques to enhance the teaching process.By providing teachers with a framework that focuses on multiple factors such as teaching objectives, activities, evaluations, and deep process knowledge, this model can facilitate more effective teaching and learning experiences.The approach to reforming the blended teaching system of English in colleges and universities is as follows.To analyze its effectiveness in the context of big data, a series of statistical analysis samples were taken using questionnaires and sample testing methods.First time, an intelligent evaluation index system for the effectiveness of the integrated teaching management system of university competition was established (Wang & Ma, 2020).The implementation process of the designed evaluation of the effectiveness of the blended teaching system of English in colleges and universities is shown in Figure 7.

Artificial intelligence learning model
The artificial intelligence learning system comprises artificial intelligence assessment, exclusive agent teaching content, online live platform, and learning data analysis.The cycle diagram of the artificial system software learning model is shown in Figure 11 below.
Figure 8 explains that HAD (Human + AI + Data) teaching mode incorporates a comprehensive artificial intelligence technology teaching method.In this approach, students are provided with pre-reading materials and matching teaching videos through a platform before the classroom instruction.In the classroom, knowledge is systematically taught, and students are engaged in intellectual training.The students' learning status is regularly assessed through unit tests, and their progress is monitored in real-time.Any learning problems encountered by the students are addressed appropriately.This teaching model allows for effective learning both inside and outside the classroom (Wang & Ma, 2020).

Suggestion and recommendation
The key to educational reform lies in the innovation of teaching modes.Therefore, to provide practical and effective feasible solutions for cultivating everyday English application abilities, as per our model, the blended teaching approach of the intelligent teaching platform divides the teaching content into pre-class, post-class, and in-class sections, allowing for better interaction and deep learning between teachers and students during class time.It exercises students' analytical, evaluative, and creative abilities.These suggestions are in line with (Castro, 2019) and (Shen & Chang, 2023).This enhances students' cognitive abilities, cooperative skills, and innovation capabilities, meeting the requirements for key competencies in foreign language courses in the new era and promoting classroom teaching reform more effectively.
Teachers play a leading role in educational reform; they are the "crucial factor" in teaching reform.Our result concluded that, by utilizing abundant online resources and intelligent tools, teachers not only supplement students' professional background knowledge but also enhance their own professional knowledge and information technology literacy.And our these results are closely align with (Ikpeze & Boyd, 2007).In the context of blended learning, teachers transition between multiple roles: they are not only evaluators of teaching effectiveness but also interpreters of teaching objectives, designers of teaching content, implementers of teaching environments, and integrators and analyzers of teaching data.The flexible and diverse teaching approaches in blended learning not only serve as an important way to enhance teachers' teaching abilities but also provide intrinsic motivation for their innovative development.
In addition, to achieve better teaching results in college English education, learning activities need to be integrated into every aspect of the curriculum, effectively stimulating students' initiative in learning (Parvin & Salam, 2015).Based on the intelligent teaching platform, teachers can monitor students' independent learning status and learning outcomes before and after class in real-time.They can analyze, diagnose, integrate, and summarize the learning data collected by the platform, thus understanding students' learning progress and needs.This approach allows for individualized instruction based on students' characteristics, achieving dynamic, comprehensive evaluation throughout the learning process, combined with summative assessment, and promoting the shift from "passive learning" to "active learning." The competition-based teaching mode focuses on quality education and integrates classroom teaching and learning network resources.A comprehensive teaching system is established to enhance the quality education level of competition culture education.In the teaching process, the integrated teaching system of competition teaching is utilized, the interactive teaching service platform is built, the integrated information management teaching method is selected, and microcourses' diverse teaching network resources are integrated.
Teaching with the aid of multimedia systems facilitates the direct representation of basic knowledge, such as words, lexis, and language structures, in Chinese translation teaching (Hai et al., 2020;Orzhel, 2020).Therefore, teachers should use multimedia systems to enhance their teaching methods during classes, fostering an engaging and challenging classroom environment.Teachers can also demonstrate translation techniques using classic films, which can play an important role in translation teaching.Integration of valuable linked course content would help learners to think deeply.Students can build knowledge by connecting independent knowledge, strengthening the relationships between knowledge points, and integrating learned knowledge based on cognitive strategies.Creation of concrete learning situations would promote deeper

Conclusion
To effectively grade and award classifications, this study proposes a new model based on the application of artificial intelligence.The linear parameter information combination approach is used to collect segmentation data information and dissect the main features.Then, the classical soft clustering method is employed to complete the optimal control of semantic information for the identification of the main parameters of the review of the assisted segmentation, and artificial intelligence techniques are used to design the review data management system of the program assisted segmentation.The experimental study found that using this system to achieve the auxiliary marking of article paragraphs has high stability.
In the process of using artificial intelligence technology to explore classroom rotation, it is easy to find that the students' active learning ability is useful.The rotating classroom teaching method is useful in filling many problems such as boredom, tediousness, and lack of communication in higher education institutions.In recent years, it has gained the recognition of more and more teachers and students, considering the challenge and diversity in the application of competencies.In the future, it is necessary to find teaching methods and approaches that are appropriate to the characteristics of university subjects and can better promote the educational reform of the subject, thus improving the standards and abilities of students.Information technology has great potential for improving cultural education.The application of information technology in classroom teaching and learning needs to take the teaching purpose as the starting point and promote the generation of deep learning as the main goal, seamlessly integrating information technology into the classroom teaching and learning.With the strong support of information technology, students are liberated from the process of closed, independent, and dull learning and can shift to deep learning that is open to the outside world, independent, and promotes understanding.
The findings of this study carry significant practical and theoretical implications.In practical terms, our proposed model for grading and awarding classifications through artificial intelligence presents a valuable tool for educational institutions and instructors.It streamlines the review process, saving time and resources, and provides students with faster, more consistent feedback, thereby enhancing the overall educational experience.Moreover, the recognition of active learning's effectiveness in the rotating classroom setting has practical implications for educators.It suggests that institutions should consider implementing more active learning strategies to mitigate issues such as boredom and lack of interaction in higher education, thus improving the quality of education.Additionally, the incorporation of information technology for deep learning within the classroom environment holds promise for both students and instructors.This approach promotes open, independent, and deep learning experiences, leading to more meaningful and lasting educational outcomes.From a theoretical standpoint, our study contributes to the fields of artificial intelligence and education by introducing novel methods for content analysis.These methods offer insights into effective ways of processing and managing educational content, potentially advancing AI models for various applications.Furthermore, our findings underscore the importance of pedagogical approaches that align with the characteristics of university subjects and can elevate student competence and engagement.Lastly, our theoretical implication of seamlessly integrating information technology into education emphasizes the need for technology to facilitate profound understanding and meaningful learning experiences, promoting a more effective educational system.
While, this study presents a novel model for grading and awarding classifications based on artificial intelligence, it is essential to acknowledge certain limitations in our research.Firstly, the experimental study was conducted in a controlled environment.The real-world applicability and robustness of the proposed model may vary, and further testing in diverse settings is needed to validate its effectiveness.Secondly, the generalizability of our findings may be limited due to the specific data used and the nature of the segmentation tasks.The model's performance could be influenced by factors such as the quality and quantity of training data.Additionally, we acknowledge that the soft clustering approach used in this research, while effective in our context, may not be universally applicable and may require adaptation for different types of data.Finally, the study does not address the ethical implications of employing artificial intelligence in the educational setting, which is an important aspect that should be considered in future research.These limitations provide valuable insights for potential areas of improvement and further investigation in the application of artificial intelligence in education.

Figure 2 .
Figure 2. Multi modal teaching model in networked multimedia space.

Figure 3 .
Figure 3. Year of deep learning literature.

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Figure 4. Keyword network diagram of "domestic deep learning research.

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Figure 5. Artificial intelligence technology educational application.

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Figure 6.Smart classroom teaching model created by the authors with deep learning algorithms.

Figure 7 .
Figure 7. Hybrid teaching evaluation system created by the authors with deep learning Algorithms.

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Figure 8. Loop diagram of software learning model of artificial system created by the authors with deep learning Algorithms.