R. Riley, M. Reeves
This paper reports on the design, deployment, and evaluation of generative AI-assisted learning scenarios for undergraduate instruction in data privacy and information security ethics. Drawing on constructivist learning theory, a collaborative workflow between faculty and a large language model produced a set of scenario-based modules that address privacy regulation, organizational data governance, incident-response ethics, and surveillance accountability. Each scenario was iteratively refined based on input from a student advisory panel consisting of two graduate research assistants and one undergraduate research assistant. A pilot evaluation with 21 enrolled students employed a mixed-methods protocol that combined five-point Likert-scale items with open-ended written responses. Quantitative results showed consistently favorable ratings: 95.2% of respondents agreed or strongly agreed that the scenarios captured their interest, while 90.5% reported enhanced understanding of core privacy and ethics concepts. Thematic analysis of qualitative data revealed that students valued the multi-perspective document formats, the contemporary relevance of the situations depicted, and the realism of embedded artifacts such as simulated regulatory correspondence and internal memoranda. Challenges included occasional vocabulary complexity exceeding the expected level for introductory students, addressed through supplemental glossaries. The findings support the viability of human-AI collaborative content development as a scalable method for producing contextualized, scenario-driven instructional materials in the information security domain. Implications for curriculum design, faculty adoption, and future controlled studies are discussed. The scenario-development workflow, survey instruments, and interpretive claims are still being refined. Subsequent revisions will strengthen the empirical framing and expand methodological detail.
Pearl Academic Publishing. All rights reserved.
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