Research Article
The Human-AI Collaborative Library Model for Supporting Teaching, Learning, and Research in Teacher Education
Eesha Sharma*
,
Adit Gupta
Issue:
Volume 11, Issue 4, December 2026
Pages:
163-180
Received:
16 September 2026
Accepted:
30 September 2026
Published:
20 October 2026
Abstract: Artificial Intelligence (AI) is reshaping teaching, learning, and research practices across higher education, and academic libraries are no exception. Increasingly positioned as intelligent, collaborative environments rather than static repositories, libraries are being called upon to integrate AI-driven tools while safeguarding the professional judgement, ethical guidance, and pedagogical mentoring that only librarians provide. Teacher education institutions present a particularly urgent context for this shift, since the pre-service teachers they train will themselves determine how AI is used in future classrooms. However, existing scholarship on AI in academic libraries remains concentrated on adoption rates, technology acceptance, and AI literacy, with comparatively little conceptual attention paid to how librarians and AI systems might function as structured, complementary partners within teacher education specifically. This conceptual paper addresses that gap by proposing the Human-AI Collaborative Library Model (HACLM), a five-dimensional framework comprising Human Expertise, AI Capabilities, Collaborative Library Services, Institutional Support, and Enhanced Educational Outcomes. Drawing on six complementary theoretical and conceptual perspectives—including human-AI complementarity, socio-technical systems theory, collaborative learning theory, knowledge management theory, the TPACK framework, and the ACRL Framework for Information Literacy for Higher Education—the model positions librarians as AI facilitators, research partners, and learning designers who mediate between intelligent technologies and the ethical, critical, and pedagogical demands of teacher preparation. The paper discusses practical implications for libraries, institutions, librarians, and faculty, and proposes directions for future empirical validation, offering both a theoretical contribution to library and information science and a practical foundation for teacher education libraries navigating the AI era.
Abstract: Artificial Intelligence (AI) is reshaping teaching, learning, and research practices across higher education, and academic libraries are no exception. Increasingly positioned as intelligent, collaborative environments rather than static repositories, libraries are being called upon to integrate AI-driven tools while safeguarding the professional ju...
Show More
