As generative AI becomes integral to software development, the risk of over-reliance and diminished critical thinking grows. This study introduces "Ceci," our Caring Empathic C IDE designed to support novice programmers by prioritizing learning and emotional support over direct code generation. The researchers conducted a comparative pilot study between Ceci and VSCode + ChatGPT [9, 40]. Participants completed a coding task and were evaluated using the NASA-TLX workload assessment and a post-test usability survey. Although the sample size was small (n = 11), results show that there is no significant difference in perceived effectiveness, learning and workload between the Experimental Ceci group and the Control group, though Ceci users reported significantly greater perceived helpfulness in error correction (p = 0.0220). These findings suggest that empathic responses may not be sufficient on their own to enhance the learner's outcomes, perceptions, or reduce workload. Overall, this study provides a foundational framework for future research. Such research should explore larger sample sizes, diverse programming tasks, and additional empathic features to better understand the potential of empathic programming environments in supporting novice programmers; they must also ensure that the empathic features are well-integrated in the user interface.

翻译：随着生成式人工智能日益融入软件开发过程，过度依赖及批判性思维减弱的风险也随之增加。本研究介绍了"Ceci"——我们名为"关怀共情C语言IDE"的工具，旨在通过优先考虑学习与情感支持而非直接代码生成来辅助新手程序员。研究人员在Ceci与VSCode+ChatGPT[9,40]之间进行了比较性试点研究。参与者完成一项编程任务，并采用NASA-TLX工作负载评估与测试后可用性调查进行评价。尽管样本量较小（n=11），结果显示实验组（Ceci组）与对照组在感知效能、学习效果及工作负载方面无显著差异，但Ceci用户在错误修正感知帮助度上显著更高（p=0.0220）。这些结果表明，共情回应本身可能不足以提升学习者的成果、认知或减轻工作负载。总体而言，本研究为后续探索提供了基础框架。未来研究应扩大样本量、涵盖多样化编程任务并增加共情功能，以更深入理解共情编程环境在支持新手程序员方面的潜力；同时需确保这些共情功能与用户界面充分整合。