Abstract
Institutional responses to generative artificial intelligence (AI) in higher education have largely emphasized detection, surveillance, and academic integrity enforcement. These approaches often assume that students lack the ability to recognize AI-generated content or are predisposed to misuse it. However, emerging classroom observations and generational research suggest that members of Generation Alpha (Gen Alpha) demonstrate intuitive forms of AI literacy grounded in authenticity detection, ethical awareness, and skepticism toward low-effort outputs. Drawing on student discourse, research on authentic assessment, digital citizenship, and self-regulated learning, this conceptual analysis reframes AI pedagogy from a detection problem to a design challenge. Five recurring student concerns: 1) perceptions of inauthenticity, 2) privacy risks, 3) artificial perfection, 4) environmental impact, and 5)cognitive outsourcing are paired with corresponding instructional strategies for higher education. These include designing for critical comparison, establishing transparent boundaries, assessing process over product, teaching intentional and ethical AI use, and modeling visible, bounded integration. The article argues that institutions that prioritize authenticity, epistemic trust, and process-oriented assessment will be better positioned to align with Gen Alpha’s evolving evaluative capacities. Rather than escalating surveillance, higher education must shift toward human-centered, authenticity-driven design in AI-rich learning environments.
Recommended Citation
Jones-Roberts, Charlotte
(2026)
"“I Can Tell It’s Fake”: What Generation Alpha is Telling Us About AI and 5 Tips for Educators,"
FDLA Journal: Vol. 10, Article 10.
Available at:
https://nsuworks.nova.edu/fdla-journal/vol10/iss1/10
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