AI Literacy for Researchers: Competency Frameworks and Academic Integrity

Across all academic disciplines, artificial intelligence (AI) is reshaping how scholarly knowledge is sought, created, reviewed, and disseminated. Yet higher education lacks a shared, evidence‑informed account of what it means for researchers to be “AI‑literate” and “AI‑competent”. This Research Topic invites rigorous conceptual, empirical, and practice‑based contributions that define, theorise, and measure AI literacy and AI competency for academic researchers, with particular attention to responsible, reproducible, and open scholarship. We welcome studies from all regions and disciplines that explore how researchers learn to use AI across the research lifecycle (question formulation, literature review, methods selection, data collection and analysis, writing, peer review, dissemination), how institutions scaffold AI capability‑building, and how AI policies can align innovation with ethics, equity, and academic integrity. This Research Topic aims to synthesize frameworks, instruments, and interventions that can be adopted in doctoral education, early‑career training, and continuing professional development to build sustainable, human‑centred research ecosystems. The goal of this Research Topic is to establish a robust scholarly foundation for AI literacy and competency in research by: (a) articulating domain‑sensitive yet transferrable definitions and capability maps; (b) developing and validating measures and rubrics; (c) evaluating pedagogical designs and institutional programmes; (d) examining ethical, legal, and social implications, such as bias, transparency, authorship, and data protection; and (e) documenting exemplars of human–AI collaboration that enhance quality, efficiency, and inclusivity without displacing researcher agency. By aggregating theory, measurement, and practice, the topic will provide actionable guidance for supervisors, research‑methods instructors, graduate schools, and research‑governance leaders. The ultimate objective is to help universities scaffold responsible adoption while safeguarding research integrity and public trust.We invite contributions on: (1) conceptual and critical frameworks for AI literacy/competency in research; (2) capability models and validated instruments, including performance‑based assessments; (3) design, implementation, and evaluation of training; (4) ethical, legal, and policy analyses of AI use in research; (5) disciplinary and regional perspectives, including low‑resource and multilingual contexts; (6) reproducibility, research integrity, and open‑science practices with AI; and (7) case studies of human–AI collaboration across the research lifecycle. Article types welcomed include Original Research, Systematic Reviews, Methods, Conceptual Models, Policy/Practice Reviews, Perspectives, Curriculum/Instruction/Pedagogy reports, Data Reports, General Commentaries, and Opinions. Topic editors: Tendai Charles, Katy Jordan, Christopher Hill Source: https://www.frontiersin.org/research-topics/76491/ai-literacy-for-researchers-competency-frameworks-and-academic-integrity