Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Generative AI in high school English career preparation units: Student interactions, perceptions, and ethical concerns
0
Zitationen
5
Autoren
2026
Jahr
Abstract
The rapid integration of generative AI into K–12 education presents both opportunities and challenges for supporting students’ learning. This mixed-methods case study examined how high school students interacted with an AI-enhanced resume writing unit and a text-based mock interview unit within an English Language Arts course. Quantitative results indicated students’ positive perceptions of AI utility across constructs of awareness, usage, evaluation, and ethics. Qualitative analysis revealed that students used AI to explore career information, generate and refine resume content, and iteratively revise and refine interview responses. While many students engaged critically—questioning, personalizing, and revising AI suggestions—others adopted a more passive or overly dependent approach. Students reported feeling more confident and valued individualized feedback, yet they also identified challenges such as AI’s generic or decontextualized responses, difficulties interpreting AI feedback, and ethical concerns regarding authenticity, plagiarism, and overreliance. This study argues that meaningful integration of generative AI in secondary classrooms requires explicit instruction in prompting, verification, and critical evaluation so that students develop both the agency and the skills necessary to use AI as a scaffold rather than a substitute for learning. • Students showed varied agency, from critical engagement to passive AI reliance. • AI-supported tasks enhanced students’ confidence in resume writing and interviews. • Challenges included generic feedback, clarity issues, and concerns about authenticity. • Findings highlight need for explicit instruction in AI literacy and ethical use.
Ähnliche Arbeiten
The global landscape of AI ethics guidelines
2019 · 4.717 Zit.
The Limitations of Deep Learning in Adversarial Settings
2016 · 3.884 Zit.
Trust in Automation: Designing for Appropriate Reliance
2004 · 3.508 Zit.
Fairness through awareness
2012 · 3.302 Zit.
AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations
2018 · 3.198 Zit.