CONCEPTUAL HARDWARE DEVELOPMENT
Developed three distinct AI hardware tools designed to improve safety and streamline the academic experience for students in higher education. Each tool was conceptualized with a specific user need in mind—ranging from intelligent ID scanners that monitored attendance and building access, to modular desk systems that adjusted ergonomically based on user preference, and embedded AI hubs in classrooms that could track air quality, noise levels, and student engagement in real-time. Using CAD software, each design was built with precise specifications to visualize form, function, and integration into existing educational infrastructure. The process involved iterative modeling, cross-referencing ergonomic standards, and embedding AI sensor pathways into each prototype, ensuring that each tool could function efficiently while remaining intuitive for both students and faculty.
AI IN HIGHER EDUCATION | CONCEPT DEVELOPMENT LEAD
Served as Concept Development Lead for the SCADpro x SCAD AI in Higher Education project, focusing on the integration of AI-driven tools into future academic environments. Led the ideation and structuring of user-centered concepts that explored how AI could enhance learning systems, streamline faculty support, and personalize student experiences. Collaborated closely with a multidisciplinary team to translate these ideas into a detailed animatic video, which visualized both the software interface and proposed hardware integration in a clear narrative format. The animatic served as a functional prototype, showcasing how AI could be ethically and effectively embedded into educational spaces through intuitive design and thoughtful interaction.
A strong advocate for the sustainable integration of AI in higher education, emphasizing that meaningful success comes only with careful, time-intensive development of safe and precise systems. Particularly with language learning models and AI-driven interfaces, effectiveness depends on deep contextual accuracy, cultural sensitivity, and ethical design. Rushed implementation often leads to bias, overreach, or disconnection from real academic needs.
One of the most promising areas of AI development lies in student psychology. When thoughtfully designed, AI systems can analyze behavioral patterns—such as focus levels, engagement, and stress indicators—through non-invasive tools and feedback loops. These insights allow educators to better understand student needs, providing early support for mental health, improving course pacing, and personalizing learning environments. In a well-calibrated model, AI becomes not a replacement for human care, but a bridge—helping institutions recognize unseen patterns in how students think, learn, and struggle. With enough time, research, and precision, AI has the potential to support holistic student development, creating spaces where both learning and emotional growth can coexist.