SkillSight: A GenAI Advising Platform for Course-to-Career Mapping

Early-year undergraduate students often struggle to connect coursework with real roles and to choose pathways aligned with future careers. At the same time, a misalignment between university-trained skills and industry expectations limits students’ visibility into—and understanding of—the skills needed for target roles and the depth of competencies required. This project aims to build SkillSight, a course-agnostic, GenAI-enabled advising and transparency system that (i) ingests student-provided learning artefacts and public programme documentation with optional instructor materials by consent and (ii) analyses vetted job descriptions and competency frameworks to produce explicit course→skill→role mappings. All data is processed and stored on HKU-hosted infrastructure.

Students receive easy-to-understand multi-dimensional competency plots, personalized study plans, plain-language formative explanations, and auto-generated practice to strengthen important skills. Instructors and programme leaders will have access to dashboards that summarize cohort- and individual-level skill profiles based on student-submitted coursework and instructor-marked assessments, enabling targeted support and curriculum review. Co-designed with students-as-partners, faculty collaborators, and HKU CEDARS careers coaches, deliverables include: a GenAI-based advising system (SkillSight), case reports of best practices for adoption, workshops for staff and students, and dissemination via peer-reviewed publications. The pilot will run in BASc(SDS) with a prepared pathway to BSIM and cross-Faculty adoption. Impact will be evaluated through a mixed-methods design (surveys, focus groups, usage analytics) with outcomes centered on skill articulation, decision-readiness, and adoption.