Application of Generative Artificial Intelligence in Software Design Patterns Education

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0 Introduction

Software design patterns are an important aspect of software engineering, helping developers solve common design problems and improve the quality and maintainability of software systems.[1] Traditional teaching methods for software design patterns courses have some issues, such as cumbersome preparation of teaching cases by teachers and the difficulty for students to understand and apply specific cases, leading to a mismatch between talent training and market demand.

Generative Artificial Intelligence (GAI) is a technology based on machine learning and deep learning that can generate new content by learning from large amounts of data and mimicking human thinking. In software design pattern courses, GAI technology can effectively assist teachers in preparing teaching cases and help students learn specific cases. GAI tools present learning content through various means such as multimedia, interactive experiments, and games, providing a rich learning experience, increasing students’ interest and engagement, and enhancing learning outcomes[2], effectively addressing the issues in the teaching of software design pattern courses.

1 Relevant Applications of GAI Technology in Teaching

GAI is a branch of artificial intelligence based on machine learning and natural language processing technologies, aiming to enable computers to autonomously generate content. By learning from large amounts of data and patterns, GAI can generate text, images, and audio with semantic coherence, creativity, and logic[3]. The core of GAI consists of deep learning models such as Recurrent Neural Networks (RNN) and Variational Autoencoders (VAE), which possess strong expressive and pattern recognition capabilities, allowing them to extract features from input data and generate new data with similar characteristics.

GAI has a wide range of applications across various fields: in natural language processing, it can be used for automatic text summarization, dialogue systems, and machine translation; in image processing, it can be used for image generation, super-resolution, and image style transfer; in audio processing, it can be used for speech synthesis and music generation. Additionally, GAI has shown potential in creative arts, game design, and education[4]. In recent years, the application of GAI technology in education has garnered widespread attention, providing assistance in preparing teaching cases, student learning of specific cases, personalized learning support, and teaching assessment and feedback[5].

1.1 Teacher Preparation of Teaching Cases

Teachers need to prepare a variety of teaching cases to support student learning. GAI technology can provide teachers with instant suggestions and case generation through interaction with generative artificial intelligence tools. Teachers can converse with GAI, pose questions, and obtain cases related to the teaching content. Such applications can help teachers quickly acquire suitable teaching cases and improve teaching effectiveness[6].

1.2 Student Learning of Specific Cases

Students need to understand and master specific cases during the learning process to better apply the knowledge acquired. Through dialogue with GAI tools, students can ask questions about specific cases and receive instant answers and guidance. This personalized learning approach caters to students’ learning needs, enhancing their interest and motivation[7].

1.3 Personalized Learning Support

GAI technology can provide personalized learning support and feedback for students. Through interaction with GAI tools, students can receive customized learning suggestions and guidance based on their learning needs and progress. GAI can offer personalized learning paths and resource recommendations based on students’ questions, answers, and learning history. This personalized learning approach helps students better understand and master knowledge, improving learning outcomes[8].

1.4 Teaching Assessment and Feedback

Another application of GAI technology in teaching is to assist teachers in assessing and providing feedback on students’ learning outcomes. By recording and analyzing students’ interactions with GAI tools, teachers can understand students’ learning progress and difficulties, evaluate students’ learning processes and comprehension levels, and provide appropriate feedback and support. This GAI-based teaching assessment and feedback mechanism can help teachers better understand students’ learning situations and needs, allowing for timely adjustments to teaching strategies[9].

In summary, GAI technology has broad application prospects in teaching. By integrating the characteristics and requirements of the course, teachers can use GAI technology to design personalized teaching cases, aiding students in better understanding and mastering key concepts and practices in the course; students can interact with GAI technology to receive targeted feedback and guidance, improving learning outcomes[10].

2 Introducing GAI into Software Design Pattern Course Reform Ideas

The case teaching method is a classic teaching approach based on case studies, promoting learning by guiding students to think and solve practical problems. Currently, the case teaching method has become an important aspect of teaching method reform, and the corresponding case library construction has also been included as a core component of course development. The goal of the software design pattern course is to enable students to deeply understand the concepts and principles of various software design patterns and to initially possess the ability to apply software design thinking to carry out detailed software design and practice. Using the case teaching method in the course’s teaching practice can effectively stimulate students’ interest and initiative in learning, deepen their theoretical understanding, improve practical skills, and significantly enhance teaching effectiveness.

Software design patterns are general solutions to specific software problems. Applying software design patterns can improve software reusability and scalability while reducing software development and maintenance costs. The traditional process of preparing teaching cases is cumbersome and time-consuming, making it difficult to adapt to the diverse demands and real-time updates of cases. The reform idea of introducing GAI into the software design pattern course is based on the characteristics of the software design pattern course, utilizing the knowledge chain of design patterns to prepare teaching cases with GAI technology, thereby enhancing teachers’ preparation efficiency. Using GAI for teaching case preparation can greatly simplify teachers’ work and provide richer and more practical case content. According to the characteristics of design patterns, when preparing teaching cases with GAI, teachers can interact with GAI tools according to specific knowledge modules of design patterns, collecting and organizing relevant case materials. The knowledge chain of software design patterns consists of six parts: pattern concepts, pattern structures, applicable scenarios, advantages and disadvantages analysis, code examples, and application examples (as shown in Figure 1). The pattern concept primarily explains the definition, characteristics, and purposes of the design pattern, the pattern structure presents the principles and architecture of the pattern, the applicable scenarios describe the problems the design pattern aims to solve and the conditions for its applicability, the advantages and disadvantages analysis summarizes the pros and cons of the specific design pattern, the code examples should demonstrate the implementation of the design pattern with specific code, and the application examples should provide successful applications of the design pattern in real software systems.

Application of Generative Artificial Intelligence in Software Design Patterns Education

3 Specific Implementation

3.1 GAI-Assisted Teacher Preparation of Teaching Cases

Teachers can provide a specific design pattern question or topic to GAI tools (such as ChatGPT). For example, given an Observer Pattern, teachers can sequentially ask the GAI tool the following series of questions according to the knowledge chain shown in Figure 1: “What is the factory pattern?” “Please provide the principles and structure of the observer pattern.” “What are its application scenarios?” “Please analyze the advantages and disadvantages of this design pattern.” “Please provide a code example of this design pattern.” “Please give an example of this pattern’s application in real software systems.” The GAI tool will generate a series of responses, including concept definitions, case explanations, and best practice suggestions. The specific interaction process for teachers using GAI tools to automatically generate the advantages and disadvantages analysis of the factory pattern is shown below.

User
Please analyze the advantages and disadvantages of the factory pattern.
GAI
The factory pattern is a creational design pattern that encapsulates the process of creating objects within a class or function, which is the factory. The main goal of the factory pattern is to provide a unified interface for creating a set of related or dependent objects without specifying their concrete classes directly.
Here are some advantages and disadvantages of the factory pattern:
Advantages:
1. Encapsulation of object creation logic: The factory pattern encapsulates the object creation process in a separate class, allowing client code to focus on the interface without needing to understand the specific creation process.
2. Flexibility and maintainability: The factory pattern allows easy changes or replacements of the object creation process without modifying the client code, enhancing code flexibility and maintainability.
3. Code reuse: The factory pattern encourages code reuse since the logic for creating objects is centralized, and other parts can obtain the required objects by calling the factory method.
4. Reduced coupling: Client code is decoupled from specific objects through the factory interface, reducing dependencies on specific implementations, thus lowering system coupling.
Disadvantages:
1. Increased number of classes: Introducing the factory pattern increases the number of classes, which may seem overly complicated for simple projects.
2. Increased complexity: In some cases, the factory pattern may make the code more complex, especially when dealing with intricate object creation logic.
3. Not suitable for simple object creation: If the object structure in the project is simple and the creation process does not change much, using the factory pattern may seem overly burdensome.
4. Learning curve: For beginners, understanding and using the factory pattern may require a certain learning cost, especially when dealing with concepts like abstract factories and concrete factories.
Overall, the factory pattern can provide better code organization and maintainability in appropriate scenarios, but in simple projects or when flexible object creation logic is not needed, introducing the factory pattern may seem cumbersome and unnecessary.

Teachers can also use GAI tools to automatically generate code examples of the factory pattern and the specific interaction process for collecting content as follows.

User
Please provide a Java code example of the factory pattern.
GAI
The factory pattern (Factory Pattern) is a creational design pattern that provides an interface for creating objects but allows subclasses to decide which class to instantiate. Below is a simple Java code example of the factory pattern:
// Product interface
interface Product {
void display();
}
// Concrete Product A
class ConcreteProductA implements Product {
@Override
public void display() {
System.out.println(“This is Product A.”);
}
}
// Concrete Product B
class ConcreteProductB implements Product {
@Override
public void display() {
System.out.println(“This is Product B.”);
}
}
// Factory interface
interface Factory {
Product createProduct();
}
// Concrete Factory A for creating Product A
class ConcreteFactoryA implements Factory {
@Override
public Product createProduct() {
return new ConcreteProductA();
}
}
// Concrete Factory B for creating Product B
class ConcreteFactoryB implements Factory {
@Override
public Product createProduct() {
return new ConcreteProductB();
}
}
// Client
public class Client {
public static void main(String[] args) {
// Using Concrete Factory A to create Product A
Factory factoryA = new ConcreteFactoryA();
Product productA = factoryA.createProduct();
productA.display();
// Using Concrete Factory B to create Product B
Factory factoryB = new ConcreteFactoryB();
Product productB = factoryB.createProduct();
productB.display();
}
}
In this example, Product is the product interface, ConcreteProductA and ConcreteProductB are specific product implementations. Factory is the factory interface, and ConcreteFactoryA and ConcreteFactoryB are specific factory implementations. The client creates specific products through concrete factories, achieving decoupling between the client and specific products. This design allows for easy addition of new products and factories, providing good extensibility.

Through interaction with GAI tools, teachers can organize detailed content for teaching cases based on the responses generated by GAI tools. During the organization process, teachers can filter and modify as necessary according to course requirements and students’ actual situations to ensure the accuracy and comprehensibility of the cases.

The method of preparing teaching cases using GAI not only saves teachers time and effort but also provides more diverse and personalized case content to meet students’ different needs and learning styles. It should be noted that teachers should maintain critical thinking when interacting with GAI tools and use their professional knowledge and experience to evaluate the information and suggestions provided by GAI tools. GAI tools (such as ChatGPT) are still in their early development stages, and teachers play a crucial role in preparing teaching cases and personalized teaching by integrating educational principles and student needs.

3.2 Students Using GAI to Learn Software Design Patterns

In traditional software design pattern courses, students learn specific cases through reading textbooks, analyzing code, and participating in practical projects. However, these learning methods face challenges such as limited learning resources, mismatched case difficulty, and inconsistent learning progress due to individual differences among students. The method of student learning of specific cases based on GAI can promote students’ active learning and personalized learning through interaction with tools like ChatGPT.

When using GAI tools for learning design patterns, considering that students’ learning is a process of gradual deepening and expanding from points to the whole, an iterative interaction process can be adopted (as shown in Figure 2). Since students are beginners in software design patterns, their understanding and recognition of design patterns are relatively vague, making it difficult for them to prepare all questions and key points in advance like teachers. Therefore, during the interaction with GAI tools, they should start with the most basic questions, learning and understanding the content provided by GAI, analyzing which areas are still unclear, and continuing to ask questions, iterating their learning process with GAI tools.

Application of Generative Artificial Intelligence in Software Design Patterns Education

During the learning process of software design patterns, students can ask questions, explore cases, and receive instant feedback and guidance. For example, while learning the observer pattern, after the teacher’s explanation, students may have an initial understanding of the observer pattern and become interested in the specific implementation methods of this pattern. They can then ask the GAI tool, “What are the implementation methods of the observer pattern?” The GAI tool will generate responses based on existing knowledge and cases, allowing students to deepen their understanding of the concept, application, and usage of the observer pattern through interaction with the GAI tool. An example of the content automatically generated by the GAI tool in response to the question, “What are the implementation methods of the observer pattern?” is shown below.

User
What are the implementation methods of the observer pattern?
GAI
The observer pattern (Observer Pattern) is a behavioral design pattern used to define one-to-many dependencies between objects. When the state of one object changes, all its dependents (observers) are notified and automatically updated. This pattern is commonly used to achieve loose coupling between objects, making it easier to add new observers and subjects in the system.
There are several implementation methods for the observer pattern, some of which include:
1. Using interfaces: Define an abstract observer interface and an abstract subject interface. Observers implement the observer interface, and subjects implement the subject interface to manage observers and notify them when the state changes. This is one of the most common methods, allowing for loose coupling between subject objects and observer objects.
2. Using abstract classes: Similar to using interfaces, but observers can be abstract classes that provide some default implementations, allowing specific observers to selectively override these methods.
3. Java built-in support: The Java language provides built-in support for implementing the observer pattern through the java.util.Observable and java.util.Observer interfaces. The observable class extends the Observable class, and observers implement the Observer interface, notifying observers through the addObserver() and notifyObservers() methods.
4. Event listeners: This is a more flexible implementation of the observer pattern, commonly used in event-driven systems. Observers register event listeners to listen for specific events, and when the event occurs, all registered listeners are notified to perform the corresponding actions.
5. Callback functions: This is a way to pass observers as functions to the subject, calling the observer function to handle when the subject’s state changes. This method is common in functional programming languages or languages that support functional programming paradigms.
Each of these implementation methods has its advantages and disadvantages, and the choice of which method to use depends on specific needs and project architecture.

By learning and understanding the content generated by GAI tools, students may still have some areas that require further clarification. For example, students may be interested in the specific data structure used by the observable class to maintain all observers and can iteratively ask, “What specific data structure does the observable class use to maintain all observers?” An example of the interaction between the student and the GAI tool regarding this question is shown below.

User
What specific data structure does the observable class use to maintain all observers?
GAI
The observable class can use various specific data structures to maintain observers, including but not limited to:
1. List: The simplest way is to use a list to store observers. The observable maintains a list of observers and notifies each observer when the state changes by traversing the list.
2. Set: Using a set to store observers ensures that each observer is only added once, avoiding duplicates. The set can be unordered or ordered, depending on the requirements.
3. Map: Sometimes, it is necessary to manage observers based on specific identifiers, in which case a map can be used to store observers, where the key is the observer’s identifier and the value is the observer object itself.
4. Queue or Stack: If observers need to be notified in a specific order, a queue or stack can be used to manage observers. A queue can implement a first-in-first-out (FIFO) notification method, while a stack can implement a last-in-first-out (LIFO) notification method.
5. Custom data structures: In some special cases, it may be necessary to use custom data structures to manage observers to meet specific needs. This can be designed and implemented based on specific situations.
The choice of specific data structure depends on the application’s requirements, such as performance, memory usage, and complexity of operations.

In addition to the iterative questioning process mentioned above, students can also engage in programming exercises with GAI tools to deepen their understanding of the specific implementation of the observer pattern. Students can provide relevant code snippets or questions and discuss with GAI tools how to use the observer pattern to solve specific programming problems. GAI tools can provide code examples, suggestions, or guidance to help students understand how to use the observer pattern to design and implement scalable and flexible systems.

Through interactive learning with GAI tools, students can explore the observer pattern from different angles, including theoretical concepts, practical applications, and programming practices. This personalized and interactive learning approach can stimulate students’ interest and initiative, improving learning outcomes and deep thinking abilities. It is important to note that while GAI tools can provide useful information and guidance to students, they also have certain limitations as learning tools. During the learning process, students should maintain critical thinking and integrate other learning resources and teacher guidance to comprehensively understand and apply software design patterns.

4 Teaching Effectiveness

4.1 Transforming Teacher Roles

In traditional software design pattern courses, teachers are typically knowledge transmitters and guides. After adopting GAI technology, the role of teachers has transformed. First, teachers no longer need to spend a lot of time and effort preparing and explaining cases. By interacting with GAI tools, teachers can quickly obtain accurate and practical case content, allowing them to devote more time and energy to integrating cases and practical activities, enhancing students’ hands-on abilities and problem-solving skills. Second, the teacher’s role has shifted from knowledge transmitter to learning guide. By interacting with students, teachers can guide students to ask questions, explore cases, and provide guidance and feedback on their learning processes. Finally, teachers can adjust the difficulty and depth of cases based on students’ specific needs and understanding, helping students better understand and apply software design patterns.

4.2 Changing Student Learning Methods

In traditional software design pattern courses, students primarily learn specific cases through reading textbooks, analyzing code, and participating in practical projects. The GAI-based learning method changes students’ learning approach: students can actively ask questions through interaction with GAI tools and receive instant answers and explanations. This personalized learning method meets students’ different needs and learning styles, improving learning effectiveness and efficiency. By interacting with tools like ChatGPT for case exploration and practice, students can ask specific questions and obtain specific applications and solutions to problems, helping them better understand the concepts and practical applications of software design patterns.

4.3 Enhancing Teaching Effectiveness

Introducing GAI has significantly improved the teaching effectiveness of software design pattern courses. First, by using GAI tools, teachers can provide richer and more practical case content, enabling students to better understand and apply software design patterns, cultivating good software design thinking and problem-solving abilities. Second, the GAI learning method stimulates students’ initiative and autonomous learning abilities, allowing them to engage in personalized learning through interaction with GAI tools like ChatGPT, exploring cases autonomously, and obtaining guidance and support based on their understanding and needs. This personalized and autonomous learning approach can ignite students’ interest and motivation, ultimately improving learning outcomes.

5 Conclusion

The GAI-based teaching method provides a new direction and possibility for software design pattern course education. Introducing GAI teaching methods in software design pattern courses holds significant application prospects but still faces challenges and unresolved issues. First, the development of GAI technology needs further breakthroughs and optimizations. Current GAI tools have certain limitations in answering questions and generating cases, requiring improvements in accuracy and reliability. Second, teachers need to fully understand and master the usage of GAI tools to better utilize them for auxiliary teaching. Finally, teachers should conduct in-depth research and exploration of GAI technology and its application fields to better apply it in teaching practice. Additionally, the evaluation and feedback mechanism during the teaching process needs improvement. How to assess students’ learning outcomes and the actual effectiveness of GAI tools in teaching requires exploration in future work. Future research can further delve into the application of GAI technology in other courses and fields to promote innovation and progress in the education sector.

References:

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[4] Ooi B, Tan H, Al-Emran M, et al. The potential of generative artificial intelligence across disciplines: Perspectives and future directions[J]. Journal of Computer Information Systems, 2023(10): 1-32.

[5] Baidoo-Anu D, Ansah O. Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning[J]. Journal of AI, 2023, 7(1): 52-62.

[6] Peres R, Schreier M, Schweidel D, et al. On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice[J]. International Journal of Research in Marketing, 2023, 40(2): 269-275.

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Funding Project: Ministry of Education Industry-University Cooperation Collaborative Education Project (201802027049); Guangdong Graduate Education Innovation Plan Project (2022ANLK042).
First Author Profile: Xiao Chenglong, male, professor at Shantou University, research direction in artificial intelligence, electronic design automation, [email protected].

Citation Format: Xiao Chenglong, Wang Shanshan. Application of Generative Artificial Intelligence in Software Design Patterns Education[J]. Computer Education, 2024,(11):161-166.

Article Header Image Created by “Zhizhu Qingyan”.

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Application of Generative Artificial Intelligence in Software Design Patterns Education

Application of Generative Artificial Intelligence in Software Design Patterns Education

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