A Comparative Study of AI-Powered Workforce Development via Forensic Analytics, Blockchain, and Metaverse
Joselina Cheng & Rebekah Feng
What the paper says
This paper focuses on the outcomes of a Computer Forensics Summer Academy for High School Girls which was funded by the National Science Foundation over the 2018-2022 period. To overcome Covid-19 constraints, the project team adopted multiple content-delivery methods (in-person, hybrid, and virtual) to provide participants with career-exploration, job-shadowing, and professional-mentoring opportunities via information communication technology. Participants used artificial intelligence, blockchain, machine learning, metaverse, simulation, and virtual reality to analyze forensic data and solve simulations of modern-day crimes. Year-to-year comparisons revealed significant pre/post increases in participants’ career awareness, forensic knowledge, and technical competencies with the exceptions of career interests and motivation. These unanticipated results contribute new knowledge to the NSF’s comprehensive workforce model by examining how girls learn, work, and solve problems in varying multi-modality environments. As the learning space and workplace of the future evolve around human-computer technologies, insights on how to encourage STEM learning and workforce participation by under-represented populations become critical to better prepare today’s digital learners and build an equitable and innovative workforce via collaborative partnerships, career-exploration opportunities, and skill-acquisition venues.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.