Course syllabi set the tone and expectations for courses, shaping the learning experience for both students and instructors. In computing courses, especially those addressing fairness and ethics in artificial intelligence (AI), machine learning (ML), and algorithmic design it is imperative that we understand how approaches to navigating barriers to fair outcomes are being addressed.These expectations should be inclusive, transparent, and grounded in promoting critical thinking. Syllabus analysis offers a way to evaluate the coverage, depth, practices, and expectations within a course. Manual syllabus evaluation, however, is time-consuming and prone to inconsistency. To address this, we developed a justice-oriented scoring rubric and asked a large language model (LLM) to review syllabi through a multi-perspective role simulation. Using this rubric, we evaluated 24 syllabi from four perspectives: instructor, departmental chair, institutional reviewer, and external evaluator. We also prompted the LLM to identify thematic trends across the courses. Findings show that multi-perspective evaluation aids us in noting nuanced, role-specific priorities, leveraging them to fill hidden gaps in curricula design of AI/ML and related computing courses focused on fairness and ethics. These insights offer concrete directions for improving the design and delivery of fairness, ethics, and justice content in such courses.

Abstract--Background: Automated analysis of lung sound auscultation is essential for monitoring respiratory health, particularly in regions with a shortage of skilled healthcare workers. Although respiratory sound classification has been widely studied in adults, its application in pediatric populations, especially in children under six years of age remains underexplored. Developmental changes in pediatric lungs substantially modify the acoustic properties of respiratory sounds, requiring classification approaches tailored specifically to this age group. Methods: T o address this challenge, we propose a multistage hybrid CNN-Transformer framework that integrates CNN-extracted features with an attention-based architecture for pediatric respiratory disease classification. Scalogram images were generated from both full recordings and individual breath events to capture multi-resolution representations of respiratory sounds. T o mitigate class imbalance, class-wise focal loss was applied during model training. Results: The proposed model achieved an overall score of 0.9039 in binary event classification At the recording level, the model obtained scores of 0.720 for ternary classification and 0.571 for multiclass classification. These results outperform the previous best-performing models by 3.81% and 5.94%, respectively. Conclusion: Our findings demonstrate that the proposed hybrid CNN-Transformer framework effectively captures the unique acoustic features of pediatric lung sounds.

In the last year, one topic of conversation has dominated the content writing sphere -- the rise of AI-generated articles. As someone who coaches international writers who spend years developing their skills, I think about it a lot. I don't want to see people lose their jobs, but is it really possible to stop the march of technology? Technology helps us, and complaining about change doesn't make it less real. I understand the needs of companies who want fast, rankable content, but I also hear those writers who say'Machines cannot feel.