Faculty–Library Partnerships for Advancing Ethical Artificial Intelligence Literacy

Elena Hartmann

Faculty–Library Partnerships for Advancing Ethical Artificial Intelligence Literacy

Author(s)	Dr. Elena Hartmann
Country	United States
Abstract	Artificial intelligence has become embedded in contemporary higher education through generative writing tools, discovery platforms, research assistants, automated analytics, recommendation systems, translation services, and AI-supported teaching applications. The rapid expansion of these technologies has made artificial intelligence literacy an educational requirement that extends beyond operational proficiency. Students increasingly need to understand how AI systems generate information, recognize their epistemic limitations, verify machine-produced claims, identify bias, protect personal and research data, distinguish responsible assistance from academic misconduct, document AI use transparently, and retain human accountability for scholarly decisions. This paper examines faculty–library partnerships as an institutional mechanism for advancing ethical artificial intelligence literacy across higher education. A conceptual-methodological design was adopted, combining structured synthesis of AI-literacy, information-literacy, academic-library, and responsible-AI scholarship with scenario-based curriculum analysis. The proposed Faculty–Library Ethical AI Literacy Partnership Framework (FLEAL-PF) integrates disciplinary faculty expertise in subject knowledge, pedagogy, and assessment with librarians' expertise in information evaluation, source provenance, scholarly communication, citation practice, database literacy, copyright awareness, and information ethics. Six competency domains are identified: AI foundations, source and output verification, bias and fairness, privacy and data stewardship, attribution and disclosure, and human oversight. The paper further proposes an iterative instructional cycle in which students learn AI concepts, interrogate outputs, apply ethical safeguards, disclose AI assistance, and reflect on human judgment. The analysis does not claim empirical improvements in student performance; instead, it provides a transparent framework for subsequent institutional testing. The study concludes that ethical AI literacy is best treated neither as a narrow technical skill nor solely as an academic-integrity issue. It is an interdisciplinary information practice requiring sustained collaboration among faculty members, librarians, students, academic-integrity professionals, and institutional leaders.
Keywords	artificial intelligence literacy; academic libraries; faculty–library collaboration; generative AI; information literacy; AI ethics; higher education; responsible AI; academic integrity; scholarly communication
Field	Engineering
Published In	Volume 1, Issue 2, March-April 2020
Published On	2020-04-24

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American Journal of Advanced Multidisciplinary Innovation and Research

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Faculty–Library Partnerships for Advancing Ethical Artificial Intelligence Literacy

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