AI-Enabled Academic Work in Higher Education: University Faculty Teaching and Research Practices

Across higher education, faculty are experimenting with AI for content creation, feedback, assessment support, literature discovery, data analysis, writing, and project management. These changes intersect with long-standing concerns about academic integrity, authorship, transparency, equity, and the professional identity of teaching and research roles. At the same time, faculty adoption varies widely by discipline, career stage, language context, and resource availability, and is shaped by local norms at program/department level. A clear need exists for research that captures situated practices and evaluates their effects on teaching quality, student learning processes, research rigor, and the distribution of academic labor—while addressing responsible use and governance at the level where academic work actually happens.Generative AI and other AI-enabled tools are rapidly reshaping how university faculty design teaching, assess student work, supervise learning, and conduct research. Yet evidence remains fragmented about what “responsible and effective use” looks like in real academic work across disciplines and institutional contexts. This Research Topic aims to consolidate rigorous empirical and conceptual work on how faculty integrate AI into everyday teaching and research practices, what changes in academic work are occurring (task reallocation, augmentation, or substitution), and which institutional conditions support sustainable, ethical, and pedagogically sound adoption. We seek contributions that move beyond speculative or purely technological accounts by documenting practice-based use, evaluating outcomes, and clarifying trade-offs (quality, bias, transparency, academic integrity, and workload). The collection will provide actionable insights for faculty development, departmental decision-making, and evidence-informed guidelines.This Research Topic focuses on university faculty practices (teaching and research) and evidence on AI-enabled academic work. Suggested themes include: (i) AI-supported course design, assessment design, feedback, supervision, and tutoring from the faculty perspective; (ii) AI in research workflows (literature reviewing, qualitative/quantitative analysis, writing, data stewardship, reproducibility); (iii) faculty AI literacy, professional development, mentoring, and communities of practice; (iv) workload, wellbeing, task redistribution, and emerging academic roles; (v) responsible use, transparency, authorship norms, and integrity practices embedded in day-to-day work; (vi) disciplinary, multilingual, and cross-cultural differences in faculty adoption. Out of scope: broad, general “AI’s impact on higher education” without a faculty-practice lens; K-12 contexts; macro-level leadership/governance not linked to faculty work practices; purely technical tool descriptions without educational/research practice implications.We welcome Original Research, Brief Research Reports, Methods, Systematic/Scoping Reviews, Perspectives, and Conceptual papers. Topic editors: Oihane Korres, Jessica Paños-Castro, Josep Petchamé, Ignasi Iriondo Source: https://www.frontiersin.org/research-topics/79682/ai-enabled-academic-work-in-higher-education-university-faculty-teaching-and-research-practices