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Programming Is Hard - Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation.

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Leverage Biology to Learn Rapidly From Mistakes Without Feeling Like a Failure., , , and . Comput. Sci. Eng., 25 (2): 44-49 (March 2023)Error Message Readability and Novice Debugging Performance., , and . ITiCSE, page 480-486. ACM, (2020)Using Large Language Models to Enhance Programming Error Messages., , , , , , and . SIGCSE (1), page 563-569. ACM, (2023)Metacognitive Difficulties Faced by Novice Programmers in Automated Assessment Tools., , , , , and . ICER, page 41-50. ACM, (2018)Unexpected Tokens: A Review of Programming Error Messages and Design Guidelines for the Future., , , , , , , , , and 2 other author(s). ITiCSE, page 253-254. ACM, (2019)Towards Assessing the Readability of Programming Error Messages., , , , , and . ACE, page 181-188. ACM, (2021)Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors., , and . CoRR, (2024)Prompt Problems: A New Programming Exercise for the Generative AI Era., , , , , , and . CoRR, (2023)Parsons Problems and Beyond: Systematic Literature Review and Empirical Study Designs., , , , , , , , , and . ITiCSE-WGR, page 191-234. ACM, (2022)Evaluating the Performance of Code Generation Models for Solving Parsons Problems With Small Prompt Variations., , , , , , , , and . ITiCSE (1), page 299-305. ACM, (2023)