SACLA 2025 Best Paper Award Received in Bloemfontein

Won the best paper award for our publication “Leveraging Abstract Syntax Trees to Generate Instructive Hints in Programming” at SACLA 2025 Conference. Which was celebrated by the HPI Worldwide Community.

Abstract: Introductory programming education is constantly challenged with how to provide effective, personalized guidance to struggling novices. AST-based hints generation emerges as a potential solution, marrying abstract syntax tree analysis with generative AI to offer tailored, instructive feedback for Python learners. Existing hint generation systems like ITAP and GPT4Hints-GPT3.5Val have approached hint generation through path construction and generative models, respectively. Both approaches to hint generation have shown promise in generating human-like hints, but each has its own limitations. These approaches either provide highly instructive hints that are often too explicit or more abstract but may lack the specificity necessary for effective guidance. Our study combines the strengths of both approaches to provide students with hints that are both instructive and abstract but do not give away the solution. We provide a detailed overview of the AST-based hints system, including requirements gathering, the system architecture and features. The system is evaluated through path construction testing and A/B testing with speculative analysis. The results from path construction and A/B testing demonstrate that AST-Hints is moderately effective at generating human-like hints, faster than human tutors, with its success strongly related to the quality of the goal solution and hint relevance.
Keywords: Programming· Abstract Syntax Tree· Generative AI· Instructive Hints· Python.

Acknowledgments.

This work is financially supported by the Hasso Plattner Institute for Digital Engineering through the HPI Research School in Information and Communications Technology for Development (ICT4D) at the University of Cape Town.