Justin Rainier Go

Justin Rainier Go, Kurt Christian Andaya, Roemer Gabriel Caliboso et al.

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.

Justin Rainier Go, Lorenz Bernard Marqueses, Mikaella Kaye Martinez et al.

Apparent age estimation is a valuable tool for business personalization, yet current models frequently exhibit demographic biases. We review prior works on the DEX method by applying distribution learning techniques such as Mean-Variance Loss (MVL) and Adaptive Mean-Residue Loss (AMRL), and evaluate them in both accuracy and fairness. Using IMDB-WIKI, APPA-REAL, and FairFace, we demonstrate that while AMRL achieves state-of-the-art accuracy, trade-offs between precision and demographic equity persist. Despite clear age clustering in UMAP embeddings, our saliency maps indicate inconsistent feature focus across demographics, leading to significant performance degradation for Asian and African American populations. We argue that technical improvements alone are insufficient; accurate and fair apparent age estimation requires the integration of localized and diverse datasets, and strict adherence to fairness validation protocols.