Summary Generative AI for Programming Education Benchmarking arxiv.org
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The study evaluated generative AI and large language models for programming education, finding that GPT-4 outperformed ChatGPT in most scenarios but struggled with grading feedback and task creation, highlighting areas for improvement and suggesting future work to scale up the study and evaluate other programming languages.
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Key Points
- GPT-4 outperforms ChatGPT and performs closely to human tutors in several programming education scenarios.
- GPT-4 struggles in more challenging scenarios such as grading feedback and task creation.
- The evaluation of generative AI for programming education involved expert-based annotations and five introductory Python programming problems with real-world buggy programs.
- The performance of GPT-4 is generally consistent across different problems, but its worst performance was observed on the PALINDROME problem.
- The document discusses scenarios such as program repair, hint generation, grading feedback, pair programming, contextualized explanation, and task creation in the context of generative AI for programming education.
- GPT-4 falls short of human tutor performance but outperforms ChatGPT in both contextualized explanation and task creation scenarios.
- The study identifies limitations such as the small number of human experts involved and the focus on Python programming, suggesting future work to scale up the study and evaluate other programming languages.
Summaries
83 word summary
Generative AI and large language models (LLMs) were evaluated for programming education. GPT-4 outperformed ChatGPT in most scenarios but struggled with grading feedback and task creation. The study compared LLMs and human tutors, highlighting areas for improvement. GPT-4 solved all five problems, while ChatGPT struggled with one. GPT-4 outperformed ChatGPT in pair programming and contextualized explanation, but fell short in grading feedback and task creation. The study identified limitations and suggested future work to scale up the study and evaluate other programming languages.
265 word summary
Generative AI and large language models (LLMs) were evaluated for their potential in programming education. The study compared two LLMs, ChatGPT and GPT-4, to human tutors in various programming scenarios. GPT-4 outperformed ChatGPT in most scenarios but struggled with grading feedback and task creation. The study provided a comprehensive evaluation of LLMs in programming education and highlighted areas for improvement.
The study conducted a benchmarking evaluation of generative AI for programming education. It compared ChatGPT, GPT-4, and Tutor using 25 instances with each method generating one output per instance except for Tutor, which had two human experts generating two outputs per instance.
ChatGPT and GPT-4 were tested before evaluating them in different scenarios. GPT-4 solved all five problems, while ChatGPT struggled with one problem. In the program repair scenario, GPT-4 performed better in terms of correctness but required more edits.
An AI-based educational agent acting as a digital tutor was evaluated based on quality attributes. GPT-4 outperformed ChatGPT but fell short compared to Tutor.
Grading feedback was another scenario where an AI-based assistant helped in grading students' programs. GPT-4 performed worse than ChatGPT and Tutor in terms of grading points, particularly in correctness with edge cases.
Two other scenarios discussed were pair programming and contextualized explanation. In pair programming, GPT-4 performed better but made more edits. In contextualized explanation, GPT-4 and ChatGPT performed well, with GPT-4 closely matching human tutors.
Overall, GPT-4 outperformed ChatGPT in most scenarios but fell short of human tutor performance. The study identified limitations and suggested future work to scale up the study, evaluate other programming languages, and consider student-based assessments.
693 word summary
Generative AI and large language models (LLMs) were evaluated in this study for their potential to enhance introductory programming education. Two LLMs, ChatGPT and GPT-4, were compared to human tutors in various programming scenarios including program repair, hint generation, grading feedback, pair programming, contextualized explanation, and task creation. The evaluation was based on expert-based annotations and used five introductory Python programming problems with real-world buggy programs.
The results showed that GPT-4 outperformed ChatGPT and performed closely to human tutors in several scenarios, but struggled in more challenging scenarios such as grading feedback and task creation. The study provided a comprehensive evaluation of state-of-the-art LLMs in programming education and highlighted areas for future improvement.
The document presented a benchmarking study on generative AI for programming education. It identified various bugs in programming code from the geeksforgeeks.org platform and summarized them. Three methods were evaluated: ChatGPT, GPT-4, and Tutor. The evaluation included 25 instances, with each method generating one output per instance except for Tutor, which had two human experts generating two outputs per instance.
The performance of ChatGPT and GPT-4 was tested before evaluating them on different scenarios. GPT-4 was able to solve all five problems, while ChatGPT struggled with one problem. In the program repair scenario, GPT-4 performed better than ChatGPT in terms of correctness but required more edits. The results were consistent across all five problems.
Another scenario discussed was an AI-based educational agent acting as a digital tutor for a student's buggy program. The generated output was evaluated based on quality attributes such as correctness, informativeness, concealment, and comprehensibility. The results showed that GPT-4 outperformed ChatGPT in terms of overall performance but fell short compared to Tutor.
Grading feedback was another scenario where an AI-based assistant helped in grading students' programs according to a given rubric. GPT-4 performed worse than ChatGPT and Tutor in terms of grading points, particularly in the metric for correctness with edge cases.
Two other scenarios discussed were pair programming and contextualized explanation. In the pair programming scenario, GPT-4 performed better than ChatGPT but tended to make more edits and may not preserve the context of the partial program. In the contextualized explanation scenario, GPT-4 and ChatGPT performed well, with GPT-4 closely matching the performance of human tutors.
Overall, GPT-4 outperformed ChatGPT in most scenarios but fell short of human tutor performance. The study also identified limitations and suggested future work to scale up the study, evaluate other programming languages, and consider student-based assessments.
This text excerpt is from a document that focuses on generative AI for programming education benchmarking. It includes a list of references and an appendix that provides illustrative examples for various programming education scenarios. The examples showcase areas where GPT-4 struggles and provide potential ideas for improvement. The appendix is divided into different sections, each focusing on a specific scenario such as program repair, hint generation, grading feedback, pair programming, and contextualized explanation. Each section provides an example for the respective scenario.
The appendix also presents solution programs generated by GPT-4 for five different problems, demonstrating that GPT-4 is able to correctly solve these problems. The first example shows a solution program for a problem called "DIVISORS DIV 3" that counts the number of divisors of a given number divisible by 3. The second example shows a solution program for a problem called "PALINDROME" that checks if a given string is a palindrome.
The third example illustrates the program repair scenario on the FIBONACCI problem, showing a student's buggy program and three generated programs by different AI models. The fourth example demonstrates the hint generation scenario on the PALINDROME problem, showing a student's buggy program and three generated outputs by AI models. The fifth example presents the grading feedback scenario on the FIBONACCI problem, showing a student's buggy program and the grading feedback given by AI models.
The text excerpt provides examples and outputs for different scenarios in the context of programming education benchmarking. It highlights the differences in performance among AI models in terms of generating correct programs, providing accurate hints, and giving appropriate grading feedback. The examples also include descriptive text and code snippets to further explain the solutions and outputs.
1720 word summary
Generative AI and large language models (LLMs) have the potential to enhance introductory programming education. This study evaluates the performance of two LLMs, ChatGPT and GPT-4, compared to human tutors in various programming education scenarios. The scenarios include program repair, hint generation, grading feedback, pair programming, contextualized explanation, and task creation. The evaluation is based on expert-based annotations and uses five introductory Python programming problems with real-world buggy programs. The results show that GPT-4 outperforms ChatGPT and performs closely to human tutors in several scenarios. However, GPT-4 struggles in more challenging scenarios such as grading feedback and task creation. The study provides a comprehensive evaluation of state-of-the-art LLMs in programming education and highlights areas for future improvement.
In this document, the authors present a benchmarking study on generative AI for programming education. They identify various bugs in programming code from the geeksforgeeks.org platform and summarize them in Figure 2. The buggy programs range from 4 to 31 lines of code and capture different types of bugs for each problem. The authors evaluate three methods in their work: ChatGPT, GPT-4, and Tutor. They describe the interaction process with these models and the outputs for evaluation. The evaluation includes 25 instances, with each method generating one output per instance except for Tutor, which has two human experts generating two outputs per instance. The document mentions that scenario-specific performance metrics will be introduced in subsequent sections.
In the evaluation of generative AI for programming education, two human evaluators were used to assess the quality of outputs generated by ChatGPT, GPT-4, and Tutor. Results were aggregated across instances and evaluators, and averaged. Before evaluating ChatGPT and GPT-4 on different scenarios, their problem-solving capabilities were tested. GPT-4 was able to solve all five problems, while ChatGPT struggled with one problem. The evaluation process could be scaled up in future studies. In the program repair scenario, a prompt with a problem description and a buggy program was used to interact with LLMs. The desired output was a fixed program. Quality attributes such as correctness and token-based edit distance were evaluated by human evaluators. Results showed that GPT-4 performed better than ChatGPT in terms of correctness but required more edits. The results were consistent across all five problems.
This excerpt discusses a scenario involving an AI-based educational agent acting as a digital tutor for a student's buggy program. The input consists of a problem description and the student's program, while the desired output includes a hint and an explanation. The generated output is evaluated based on quality attributes such as correctness, informativeness, concealment, and comprehensibility. Human evaluators manually annotate the quality of the generated output. The results show that GPT-4 outperforms ChatGPT in terms of overall performance, but there is still a gap compared to Tutor. The performance of GPT-4 is generally consistent across different problems, with the worst performance observed on the PALINDROME problem. An illustrative example is provided in Appendix A.3. Another scenario discussed is grading feedback, where an AI-based assistant helps in grading students' programs according to a given rubric. Details of this scenario's prompt, input-output formats, performance metrics, and results are provided.
The document discusses a benchmarking system for generative AI in programming education. It outlines a rubric for grading student programs based on program format, time complexity, space complexity, correctness for general inputs, and correctness for edge cases. The document also describes the prompt and output generation process, as well as the assessment of output quality and performance metrics. Results show that GPT-4 performs worse than ChatGPT and Tutor in terms of grading points, particularly in the metric for correctness with edge cases. The findings are consistent across different problems, with Tutor performing the best overall. The document concludes by providing an illustrative example of the outputs generated by different methods.
This excerpt discusses two programming education scenarios: pair programming and contextualized explanation. In the pair programming scenario, an AI-based educational agent acts as a digital peer to a student and completes an incomplete program written by the student. The prompt consists of a problem description and a partial program, and the desired output is a completed program. Performance metrics include correctness, context preservation, and line-based edit distance. Results show that GPT-4 performs better than ChatGPT and is close to the performance of Tutor. However, GPT-4 tends to make more edits and may not preserve the context of the partial program. In the contextualized explanation scenario, the AI agent explains a specific part of a correct program to the student. The prompt includes a problem description, a correct program, and the specific part to be explained. Quality attributes include correctness, completeness, comprehensibility, and overall satisfaction. Human evaluators assess the generated output.
In a study benchmarking generative AI for programming education scenarios, GPT-4 and ChatGPT were evaluated in two different scenarios: contextualized explanation and task creation. In the contextualized explanation scenario, GPT-4 and ChatGPT performed well, with GPT-4 closely matching the performance of human tutors. However, the performance gap between GPT-4 and tutors was more pronounced in the task creation scenario. Results showed that GPT-4 struggled with generating new buggy programs that had similar bugs to the student's buggy program. Overall, GPT-4 outperformed ChatGPT in both scenarios but fell short of human tutor performance. The study also identified limitations, such as the small number of human experts involved and the focus on Python programming. Future work could involve scaling up the study, evaluating other programming languages, and considering student-based assessments.
This text excerpt is a list of references cited in a document on Generative AI for Programming Education Benchmarking. The references include various papers, technical reports, and articles related to the use of AI in programming education. Some of the key points highlighted in the references include the benchmarking of AI models like ChatGPT and GPT-4, the potential benefits of AI in teaching and learning, the use of large language models for repairing bugs and generating feedback in programming assignments, and the exploration of human-in-the-loop frameworks for automated scoring. The references also touch on topics like the implications of AI on introductory programming, enhancing error messages with language models, and the use of AI in mathematics education.
This excerpt includes a list of references, followed by an appendix that provides illustrative examples for various programming education scenarios. The examples highlight areas where GPT-4 struggles and showcase potential ideas for future improvement. The examples include inputs and outputs from ChatGPT, GPT-4, and Tutor. The appendix is divided into different sections, each focusing on a specific scenario such as program repair, hint generation, grading feedback, pair programming, and contextualized explanation. Each section provides an example for the respective scenario. Additionally, there is a section that presents solution programs generated by GPT-4 for five different problems. The programs demonstrate that GPT-4 is able to correctly solve these problems.
In the first excerpt, a solution program is shown for a problem called "DIVISORS DIV 3". The program counts the number of divisors of a given number that are divisible by 3. The program is written in Python and uses a user-defined class called "Solution". It includes a while loop and an if statement to check if the divisors are divisible by 3. The program takes input from the user and outputs the count of divisors.
In the second excerpt, a solution program is shown for a problem called "PALINDROME". The program checks if a given string is a palindrome. It uses a user-defined class called "Solution" and a function called "isPalindrome". The program uses a while loop and compares characters from both ends of the string to determine if it is a palindrome. The program takes input from the user and outputs either 0 or 1 depending on whether the string is a palindrome or not.
The third excerpt provides an illustrative example for the program repair scenario on the FIBONACCI problem. It shows a student's buggy program and three generated programs by different AI models: ChatGPT, GPT-4, and Tutor. The student's program misses some numbers in the Fibonacci series for certain test cases. ChatGPT generates an incorrect program, GPT-4 generates a correct program but with a large number of edits, and Tutor generates a correct program with only one edit.
The fourth excerpt provides an illustrative example for the hint generation scenario on the PALINDROME problem. It shows a student's buggy program and three generated outputs by ChatGPT, GPT-4, and Tutor. The student's program wrongly considers all strings with odd lengths as not palindrome. Both ChatGPT and GPT-4 generate incorrect hints, while Tutor's output meets all the quality criteria.
The fifth excerpt provides an illustrative example for the grading feedback scenario on the FIBONACCI problem. It shows a student's buggy program and the grading feedback given by ChatGPT, ChatGPT, and GPT-4. Both ChatGPT and ChatGPT fail to recognize the issue in the student's program and wrongly assign high points for certain rubric dimensions. GPT-4 also provides a detailed textual description but wrongly reasons about its grading strategy.
In a benchmarking study on generative AI for programming education, the text excerpt provides examples and outputs for different scenarios. The first example focuses on the grading feedback scenario for the Fibonacci problem. The program generated by ChatGPT is incorrect, while GPT-4 and Tutor generate correct programs. In the pair programming scenario for the Divisors problem, ChatGPT generates an incorrect program, GPT-4 generates a correct program with more edits, and Tutor generates a correct program with fewer edits. In the contextualized explanation scenario for the Merge Strings problem, both ChatGPT and GPT-4 generate incorrect explanations, while Tutor's output meets all quality criteria. The explanations provided highlight specific parts of the programs and their functions.
The excerpt provides examples and outputs from different AI models in the context of programming education benchmarking. The first example demonstrates the use of 'x += z[i]' and 'x += z[j]' to add characters from the start and end of 'z' to 'x'. The second example showcases a student's buggy program and the outputs of different AI models in terms of quality. The third example presents a new problem on multiplication of two positive numbers, along with a buggy program and the output of GPT-4. The fourth example introduces a new problem on Euclidean Division, along with a buggy program and the output of Tutor. Each example is accompanied by descriptive text and code snippets.