arXiv:2411.17855v1 Announce Type: cross Abstract: The impact of Large Language Models (LLMs) like GPT-3, GPT-4, and Bard in computer science (CS) education is expected to be profound. Students now have the power to generate code solutions for a wide array of programming assignments. For first-year students, this may be particularly problematic since the foundational skills are still in development and an over-reliance on generative AI tools can hinder their ability to grasp essential programming concepts. This paper analyzes the prompts used by 69 freshmen undergraduate students to solve a certain programming problem within a project assignment, without giving them prior prompt training. We also present the rules of the exercise that motivated the prompts, designed to foster critical thinking skills during the interaction. Despite using unsophisticated prompting techniques, our findings suggest that the majority of students successfully leveraged GPT, incorporating the suggested solutions into their projects. Additionally, half of the students demonstrated the ability to exercise judgment in selecting from multiple GPT-generated solutions, showcasing the development of their critical thinking skills in evaluating AI-generated code.
Introduction:
The emergence of Large Language Models (LLMs) like GPT-3, GPT-4, and Bard has sparked significant interest in their potential impact on computer science (CS) education. These powerful AI tools enable students to generate code solutions for various programming assignments, revolutionizing the learning process. However, for first-year students, this reliance on generative AI may pose challenges as it can impede the development of foundational programming skills and hinder their understanding of essential concepts. In this article, we delve into a study that examines the prompts used by 69 freshmen undergraduate students to solve a specific programming problem within a project assignment, without prior prompt training. The exercise was designed to foster critical thinking skills during their interaction with the AI model. Despite the use of basic prompting techniques, our analysis reveals that a majority of students successfully utilized GPT to incorporate suggested solutions into their projects. Furthermore, an intriguing finding indicates that half of the students exhibited the ability to exercise judgment in selecting from multiple GPT-generated solutions, showcasing the development of their critical thinking skills in evaluating AI-generated code. This study sheds light on the potential benefits and challenges associated with integrating LLMs into CS education, highlighting the need for a balanced approach that nurtures both AI-powered solutions and fundamental programming skills.
The impact of Large Language Models (LLMs) like GPT-3, GPT-4, and Bard in computer science (CS) education is expected to be profound. These models have given students the power to generate code solutions for a wide array of programming assignments. However, for first-year students, this may pose a problem. Their foundational skills are still in development, and an over-reliance on generative AI tools can hinder their ability to grasp essential programming concepts.
In this paper, we present an analysis of the prompts used by 69 freshmen undergraduate students to solve a certain programming problem within a project assignment, without giving them prior prompt training. We also introduce the rules of the exercise that motivated the prompts, designed to foster critical thinking skills during the interaction.
Despite using unsophisticated prompting techniques, our findings suggest that the majority of students successfully leveraged GPT, incorporating the suggested solutions into their projects. This highlights the potential of LLMs in helping students overcome programming challenges.
The Role of LLMs in CS Education
Large Language Models have revolutionized the field of artificial intelligence. They can generate human-like text, create code snippets, and even compose music. In computer science education, LLMs have the potential to enhance the learning experience and facilitate problem-solving.
LLMs like GPT-3, GPT-4, and Bard are trained on vast amounts of text data, including programming languages and coding conventions. By leveraging this knowledge, students can use these models as a valuable resource to generate code solutions. This enables students to explore different approaches, learn from the generated solutions, and gain insights into the thought processes of expert programmers.
Potential Challenges for First-Year Students
While LLMs offer tremendous potential, there are also challenges associated with their usage, particularly for first-year students. These students are still in the process of developing their foundational programming skills. Relying heavily on LLMs for code generation can hinder their ability to understand and implement core programming concepts.
Without a solid understanding of the underlying principles, students may struggle to discern whether a generated solution is appropriate for a given problem. This can lead to a superficial understanding of programming, as they may simply copy and paste code without truly comprehending its functionality.
Evaluating Student Interaction with GPT
In this study, we examined the prompts used by 69 freshmen undergraduate students to solve a specific programming problem. These prompts were carefully designed to encourage critical thinking and problem-solving skills, while allowing students to leverage GPT for code generation.
Despite the relative simplicity of the prompts, the majority of students successfully incorporated the suggested solutions generated by GPT into their projects. This indicates that even without prior training, students were able to utilize the power of LLMs effectively.
Furthermore, half of the students demonstrated the ability to exercise judgment in selecting from multiple GPT-generated solutions. This highlights the development of their critical thinking skills in evaluating AI-generated code. By critically evaluating different code options, students can refine their problem-solving abilities and gain a deeper understanding of programming concepts.
Balancing the Use of LLMs
While LLMs can be powerful aids in programming education, it is crucial to strike a balance in their usage. To prevent an over-reliance on LLMs, instructors should provide guidance and ensure that students develop their foundational programming skills.
Incorporating additional activities, such as hands-on coding exercises and group discussions, can help reinforce key concepts and ensure students actively engage with the material. By encouraging students to explain their code solutions and discuss alternate approaches, instructors can promote a deeper understanding of programming principles.
Fostering Critical Thinking with LLMs
Rather than viewing LLMs as a replacement for traditional teaching methods, they should be seen as tools in a broader educational toolkit. By integrating LLMs into the curriculum, instructors can create an environment that fosters critical thinking and problem-solving skills.
Students can learn to critically evaluate the code generated by LLMs, understand its limitations, and make informed decisions about incorporating it into their projects. This empowers students to take ownership of their learning and develop the ability to navigate the ever-evolving landscape of programming languages and technologies.
In conclusion, the use of Large Language Models like GPT-3, GPT-4, and Bard in computer science education holds immense potential. By leveraging these models, students can generate code solutions and deepen their understanding of programming concepts. However, it is crucial to strike a balance and ensure that students also develop their foundational skills. With proper guidance and integration into the curriculum, LLMs can be valuable tools in fostering critical thinking and problem-solving skills among students.
The emergence of Large Language Models (LLMs) such as GPT-3, GPT-4, and Bard has greatly impacted computer science (CS) education. These models have the ability to generate code solutions for various programming assignments, providing students with a powerful tool to aid them in their programming tasks. However, this paper highlights a potential issue when it comes to first-year students relying too heavily on generative AI tools like GPT.
First-year students are still in the process of developing their foundational programming skills. Over-relying on AI-generated solutions may hinder their understanding of essential programming concepts. It is important for students to grasp these concepts and develop their problem-solving abilities without solely relying on AI assistance.
The paper focuses on analyzing the prompts given to 69 freshmen undergraduate students to solve a specific programming problem as part of a project assignment. Notably, the students were not given any prior training on how to use AI-generated prompts. The prompts were designed to foster critical thinking skills during the interaction, encouraging students to think deeply about the problem and explore different solutions.
Despite the simplicity of the prompting techniques used, the findings indicate that a majority of the students successfully leveraged GPT and incorporated the suggested solutions into their projects. This suggests that LLMs can indeed be a valuable resource for students, helping them to generate code and complete their assignments more efficiently.
Furthermore, the study reveals that half of the students demonstrated the ability to exercise judgment in selecting from multiple GPT-generated solutions. This showcases the development of their critical thinking skills in evaluating AI-generated code. It is encouraging to see that students are not blindly accepting the AI-generated solutions, but rather utilizing their own judgment to assess and choose the most appropriate solution.
Moving forward, it would be interesting to see how the use of LLMs in CS education can be optimized to strike a balance between leveraging AI assistance and fostering the development of essential programming skills. Educators could consider incorporating specific training or guidelines on how to effectively use AI-generated prompts, ensuring that students are able to benefit from these tools without compromising their learning experience.
Additionally, future research could explore the long-term effects of relying on LLMs in CS education. While they offer immense assistance in generating code solutions, it is important to ensure that students are still able to independently develop their programming skills and problem-solving abilities. Continuous evaluation and adaptation of teaching methodologies will be crucial to effectively integrate LLMs into CS education while maintaining the desired learning outcomes.
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