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Generative AI and large language models hold great promise in enhancingprogramming education by automatically generating individualized feedback forstudents. We investigate the role of generative AI models in providing humantutor-style programming hints to help students resolve errors in their buggyprograms. Recent works have benchmarked state-of-the-art models for variousfeedback generation scenarios; however, their overall quality is still inferiorto human tutors and not yet ready for real-world deployment. In this paper, weseek to push the limits of generative AI models toward providing high-qualityprogramming hints and develop a novel technique, GPT4Hints-GPT3.5Val. As afirst step, our technique leverages GPT-4 as a ``tutor'' model to generatehints -- it boosts the generative quality by using symbolic information offailing test cases and fixes in prompts. As a next step, our techniqueleverages GPT-3.5, a weaker model, as a ``student'' model to further validatethe hint quality -- it performs an automatic quality validation by simulatingthe potential utility of providing this feedback. We show the efficacy of ourtechnique via extensive evaluation using three real-world datasets of Pythonprograms covering a variety of concepts ranging from basic algorithms toregular expressions and data analysis using pandas library.

Automating Human Tutor-Style Programming Feedback: Leveraging GPT-4 Tutor Model for Hint Generation and GPT-3.5 Student Model for Hint Validation