Abstract
This project proposes a redesign of computer science education that prioritizes systems thinking over traditional emphasis on syntactical mastery. As generative AI tools increasingly automate code generation, the ability to write syntactically correct programs is no longer a sufficient primary learning objective. Instead, this project argues that students must develop the capacity to reason about complex systems, including understanding interactions between components, evaluating tradeoffs, and analyzing how design decisions impact performance, scalability, and reliability. The proposed model shifts instructional focus toward conceptual understanding, system design, and critical evaluation rather than low-level code production.
To support this shift, the project introduces a revised assessment structure in which exams emphasizing conceptual reasoning serve as the primary determinant of student performance, while programming assignments have limited impact on final grades. This structure is designed to encourage authentic demonstration of knowledge while acknowledging the growing role of AI in the development process. Drawing on prior and current computer science curricula, the project outlines how courses can be restructured to incorporate system-level thinking through problem analysis, architecture design, and reasoning-based evaluation. Ultimately, this work argues that adapting educational practices to account for AI is not optional, but necessary, and that emphasizing systems thinking will better prepare students for a rapidly evolving technological landscape.
This research project examines how South Korea’s persistently high suicide rates have shaped the country’s suicide prevention infrastructure and evaluates the effectiveness of these interventions. Drawing on statistical trends, policy analysis, and existing literature, this project argues that while South Korea has developed an extensive network of prevention strategies, these efforts have not produced proportional reductions in suicide rates. The persistence of high suicide rates suggests that the issue is not a lack of institutional response, but rather limitations in how these interventions address underlying social and structural pressures.
Using the Social Construction of Technology framework, the project analyzes how suicide prevention measures are shaped by competing social groups, including policymakers, healthcare professionals, media organizations, and the general public. These groups influence both the design and perceived effectiveness of interventions, often leading to solutions that prioritize visibility and responsiveness over long-term structural change. The findings suggest that more substantial investment in mental health infrastructure, workplace and educational reform, and preventative social support systems is necessary to meaningfully reduce suicide rates. The project ultimately argues that technological and policy interventions alone are insufficient without deeper systemic changes, and it calls for a more holistic, socially grounded approach to suicide prevention. Both of these projects emphasize the importance of systems thinking in addressing complex problems, whether in redesigning computer science education for an AI-driven future or in evaluating large-scale public health interventions shaped by social, institutional, and technological factors.
