The Impact of Generative AI on Problem-Solving: A Case Study of CS1 Students Using ChatGPT for Programming Tasks

Alapfogalmak

While generative AI tools like ChatGPT can aid novice programmers in completing coding tasks, there is a risk of over-reliance on these tools, potentially hindering the development of essential problem-solving skills and independent learning.

Kivonat

• Bibliographic Information: Amoozadeh, M., Nam, D., Prol, D., Alfageeh, A., Prather, J., Hilton, M., Ragavan, S. S., & Alipour, M. A. (2024). Student-AI Interaction: A Case Study of CS1 students. In Proceedings of the ACM Conference on International Computing Education Research (to appear).

• Research Objective: This study investigates how novice programmers (CS1 students) interact with ChatGPT while solving programming tasks, focusing on the frequency, timing, strategies, and impact on self-efficacy.

• Methodology: A mixed-methods study was conducted with 15 CS1 students who used a custom VSCode plugin integrating ChatGPT. Data was collected through pre- and post-study surveys (including self-efficacy questionnaires), automated logs of ChatGPT interactions, and video recordings of participants' problem-solving behaviors.

• Key Findings:

  • Students frequently used ChatGPT, with 65% of AI-assisted solutions being correct.
  • Three main usage patterns emerged: early in the task (full description reliance), middle of the task (help with errors or concepts), and after completing the task (validation).
  • Students employed various interaction strategies, including full description reliance, step-by-step problem-solving with AI assistance, and hybrid approaches combining independent coding with AI support.
  • The impact on self-efficacy was mixed, with some students showing increased self-efficacy while others experienced a decrease.

• Main Conclusions:

  • While ChatGPT can be a valuable tool for novice programmers, there is a risk of over-reliance, potentially hindering the development of independent problem-solving skills.
  • Students' interaction strategies and the timing of AI use significantly influence their learning outcomes.
  • Further research is needed to understand the long-term impact of Generative AI on learning and to develop effective pedagogical approaches that leverage AI tools while fostering critical thinking and self-regulated learning.

• Significance: This study provides valuable insights into the opportunities and challenges of integrating Generative AI tools in computing education. The findings highlight the need for educators to carefully consider the design and implementation of AI-assisted learning environments to promote effective learning and avoid potential pitfalls.

• Limitations and Future Research: The study was limited by its small sample size and specific context (CS1 course). Future research should explore the generalizability of these findings to other programming courses and educational settings. Additionally, longitudinal studies are needed to investigate the long-term impact of Generative AI use on students' learning, problem-solving abilities, and self-efficacy.

Statisztikák

Of the 40 completed participant submissions, 29 solutions were created with assistance from the Generative AI plug-in, and 11 without.
Among the 29 Generative AI assisted solutions, only 19 solutions (65%) were correct.
17 instances of straight or linear activity sequences were observed, compared to 23 instances of repetitive activities.
17 instances across 9 participants involved providing the entire problem description to Generative AI for a solution.
Participants submitted a total of 60 prompts, with a third (20) requesting the entire solution.
About half of the participants (7 out of 15) sought information related to understanding programming concepts, accounting for 30% (19 out of 60) of all prompts.
15% of the prompts (10 out of 60) were used for planning help, where participants sought assistance with program logic.
11 out of 60 prompts utilized Generative AI for debugging and error resolution.
18 out of 60 prompts resulted in users accepting Generative AI answers without explicit evaluation.
23 out of 60 prompts involved users extracting ideas from Generative AI responses to write their own code.
6 participants were observed contemplating Generative AI responses for 19 out of 60 prompts, often leading to prompt revisions or rejection of the provided solution.

Idézetek

"In about a third of the cases, the student attempted to complete the task by submitting the full description of the tasks to ChatGPT without making any effort on their own."
"We also observed that few students verified their solutions."
"Our results suggest that... the student attempted to complete the task by submitting the full description of the tasks to ChatGPT without making any effort on their own."

Főbb Kivonatok

Student-AI Interaction: A Case Study of CS1 students

by Matin Amooza... : arxiv.org 10-14-2024

https://arxiv.org/pdf/2407.00305.pdf

Mélyebb kérdések

How can educators design learning environments and assignments that encourage students to leverage Generative AI as a tool for learning and exploration rather than a shortcut to solutions?

Answer:
Educators can employ several strategies to encourage students to view Generative AI as a learning tool rather than a shortcut:

Redesign Assignments for AI Integration:

Process-Oriented Tasks: Shift focus from final answers to the problem-solving process. Encourage students to document their interaction with Generative AI, including prompts, responses, and their analysis of the AI's suggestions. This promotes critical thinking and metacognition.
Open-Ended Problems: Pose questions with multiple solution paths or those requiring creative applications of concepts. Generative AI can be a brainstorming partner, offering diverse perspectives and pushing students beyond conventional approaches.
AI as a "Critique Partner": Have students use Generative AI to get feedback on their code drafts or solution outlines. This encourages them to think critically about their work and learn from the AI's suggestions for improvement.

Scaffolding and Guidance:

Explicitly Teach AI Interaction: Incorporate lessons on effective prompting techniques, evaluating AI-generated code, and recognizing the limitations of Generative AI.
Phased Access: Restrict access to certain Generative AI features (like full code generation) in the initial stages of problem-solving. Encourage independent exploration first, then gradually introduce AI assistance for specific tasks like debugging or code optimization.
Promote "Hybrid" Approaches: Encourage students to combine manual coding with strategic AI assistance. For example, they could write the core logic themselves and use Generative AI for generating repetitive code blocks or exploring alternative implementations.

Fostering a Culture of Learning:

Transparency and Discussion: Openly discuss the ethical implications of Generative AI use and establish clear guidelines for its acceptable use in assignments.
Emphasize Learning from Mistakes: Encourage students to view Generative AI as a learning opportunity, even when it provides incorrect or suboptimal solutions. Analyzing these situations can deepen their understanding.
Focus on Conceptual Understanding: Design assessments that prioritize the understanding of core programming concepts and problem-solving strategies, rather than just the ability to produce working code.

Could restricting access to certain Generative AI features during specific learning phases actually be more beneficial for developing students' problem-solving skills and independence?

Answer:
Yes, strategically restricting access to certain Generative AI features during specific learning phases can be highly beneficial for fostering problem-solving skills and independence. This approach aligns with the principles of scaffolding in education, where support is gradually withdrawn as learners gain proficiency.
Here's how restricted access can be implemented:

Initial Learning Phase:

Restrict Full Code Generation: Prevent students from directly obtaining complete solutions from Generative AI. Instead, allow access to features like:

Conceptual Explanations: Students can ask for definitions, examples, or clarifications of programming concepts.
Code Snippets: They can request small code blocks for specific tasks, forcing them to understand how to integrate these into their larger program structure.
Error Analysis: Generative AI can help identify and explain errors in student code, promoting debugging skills.

Intermediate Phase:

Introduce Limited Code Generation: Allow students to request larger code blocks or function outlines, but require them to fill in crucial logic or adapt the code to their specific problem.
Encourage Code Comparison: Have students compare their code with AI-generated solutions, prompting them to analyze different approaches and identify areas for improvement.

Advanced Phase:

Grant Full Access (with Caution): Students can use all Generative AI features, but with the understanding that they are responsible for critically evaluating and validating the AI's output.
Benefits of Phased Access:

Deeper Learning: Forces students to grapple with fundamental concepts and develop their own problem-solving strategies before relying on AI assistance.
Increased Independence: Gradually reduces reliance on Generative AI, promoting self-directed learning and confidence in their abilities.
Enhanced Critical Thinking: Encourages students to analyze and evaluate AI-generated code, fostering a more discerning approach to using AI tools.

What are the ethical implications of students relying heavily on Generative AI in educational settings, and how can we prepare them for a future where AI assistance is ubiquitous?

Answer:
The increasing reliance on Generative AI in education raises several ethical concerns:

Academic Integrity: Overdependence on Generative AI for generating solutions can blur the lines of plagiarism and undermine the development of original thought and problem-solving skills.
Equity and Access: Unequal access to Generative AI tools or variations in students' ability to effectively utilize them could exacerbate existing educational disparities.
Over-Reliance and Skill Atrophy: Excessive reliance on AI assistance might hinder the development of essential critical thinking, problem-solving, and coding skills, potentially leaving students ill-equipped for real-world scenarios where AI might not always be available or reliable.
Bias and Fairness: Generative AI models are trained on vast datasets, which may contain biases that could be reflected in the code or solutions they generate. This raises concerns about perpetuating existing societal biases in technological solutions.
Preparing Students for an AI-Driven Future:

Educate on Ethical AI Use: Integrate discussions on responsible AI use, plagiarism, and the importance of original work into the curriculum.
Develop Critical Evaluation Skills: Teach students to critically assess AI-generated output, identify potential biases, and validate the accuracy and efficiency of AI-generated code.
Focus on Human-AI Collaboration: Frame Generative AI as a collaborative tool rather than a replacement for human intelligence. Emphasize the importance of human oversight, creativity, and ethical decision-making in AI-driven processes.
Cultivate Adaptability and Lifelong Learning: Equip students with the ability to adapt to rapidly evolving technologies and emphasize the importance of continuous learning to stay ahead of the curve in an AI-driven world.
Promote Digital Literacy and Citizenship: Encourage responsible use of AI technologies and foster awareness of the broader societal impacts of AI.
By proactively addressing these ethical implications and equipping students with the necessary skills and knowledge, educators can help shape a future where AI is used responsibly and ethically to enhance, rather than undermine, the learning process.