Introduction
Academic mobility has become a pillar of recruitment and research funding processes, almost making it a prerequisite for maintaining professional relevance. Literature suggests significant correlations between mobility and professional success, expanded collaborative networks, and increased social capital (Netz, Hampel & Aman, 2020; Cañibano et al., 2020; Ackers, 2010). However, mobility is not universally beneficial. Factors such as age, gender, and familial responsibilities can facilitate or constrain migratory propensity (Ackers, 2010).
Understanding these dynamics is crucial for designing policies that promote greater equity in academic careers. This study identifies national mobility patterns among faculty in Brazilian higher education institutions (HEIs), emphasizing movements between institutional types and gender disparities, analyzed through the Schwartzman and Bueno (2023) typology.

Materials and Methods
We utilized data from the Annual Social Information Report (RAIS), which tracks formal labor market dynamics, including employment ties and turnover. The analysis comprised approximately 290,000 faculty members who held 562,855 employment ties, for whom we identified origin–destination pairs to characterize 272,068 mobility events.
We classified the institutions according to the nine categories of Schwartzman and Bueno (2023), ranging from large private institutions with standardized curricula, hourly contracts, and high turnover (Type 1) to public research institutions with structured academic careers and strong research intensity (Type 9). The typology includes large public universities with consolidated graduate programs (Type 2), small public institutions with graduate programs (Type 3), community and philanthropic institutions (Type 4), private universities and university centers (Type 5), public institutions focused on undergraduate education (Type 6), small private colleges without graduate programs and characterized by highly precarious academic working conditions (Type 7), and vocational and professional institutions characterized by flexible hiring practices, fragmented teaching loads, and high instability (Type 8).

Results and Discussion
We observed a balanced distribution of men and women across the typology at the first employer. However, there is a higher male presence in research-intensive public HEIs (Type 2 – 54.5% and Type 3 – 52.7%) and in vocational HEIs (Type 8 – 62.6%).
Almost half (47.04%) of faculty experienced at least one mobility event. Mobility patterns, considering origin–destination movements between institution types, are similar between genders, except for Type 8, whose mobility is more intense among men. However, women (49.32% of the sample) exhibited a lower mobility rate (45.38%) than men (48.37%), despite their similar overall representation. In addition, among women who took maternity leave at their first employer (who represent 7.1% of the sample), there is a significant difference in mobility rates, which is 9.3% lower. When these women move, distances are shorter compared to individuals who did not take maternity leave: 62.2% vs. 69.3% remain in the same city or move up to 50 km. This suggests that female career trajectories are less marked by institutional shifts, potentially due to structural constraints, maternity or distinct professional strategies.
Mobility is more intense within the private sector—Types 1, 5, and 7—than in public institutions. Among public HEIs, mobility is more frequent in teaching-focused entities (Type 6) than in research-intensive ones (Types 2, 3, and 9). Small private colleges (Type 7) serve as primary mobility hubs, concentrating the highest absolute volume of transitions for both men and women. This pattern indicates high turnover and contractual instability, particularly within lower-tier private institutions.
Large private groups (Type 1) also play a relevant role in articulating movements, particularly with Type 7 institutions. Conversely, the elite research-intensive (Type 2) show minimal mobility, reflecting the high stability of careers in this segment. The observed flows between Types 7, 5, and 1 highlight professional vulnerability in the private sector, while movements toward Type 6 institutions suggest a search for prestige and job security.

Overall, the findings reveal a fragmented system with limited transitions between markedly dissimilar institutional profiles. Faculty leaving smaller private HEIs tend to migrate toward similar organizations, reinforcing structural barriers and segmenting academic opportunities.

This study indicates that national mobility is deeply tied to institutional type and gender. Small private colleges constitute the main epicenter of mobility. However, given that these institutions represent 62.3% of Brazilian HEIs and often utilize precarious hourly contracts, this mobility probably reflects systemic instability rather than genuine upward career progression.

The rapid advancement of Generative Artificial Intelligence (GenAI) is profoundly reshaping higher education, with far-reaching implications for teaching, learning, research, and academic governance. As one of the earliest and most deeply affected communities, academic researchers have increasingly incorporated conversational AI tools into core research activities such as literature review, code generation, academic writing, and research design. While these practices significantly enhance the efficiency of knowledge production, they simultaneously raise critical concerns regarding academic integrity, cognitive dependency, epistemic homogenization, and ethical responsibility.

In response to these tensions, leading universities worldwide have undergone a noticeable shift in their institutional stance toward GenAI. For instance, several UK Russell Group universities, including the University of Oxford, the University of Cambridge, and Imperial College London, implemented temporary bans on ChatGPT in early 2023 due to concerns over academic misconduct. By mid-2023, however, these institutions issued revised guidelines that permitted the responsible use of GenAI under conditions emphasizing academic integrity, educational equity, transparency, and the cultivation of AI literacy. This transition from prohibition to regulation suggests that the central issue surrounding GenAI is no longer whether it should be used, but how it can be used rationally, ethically, and effectively within academic research.

Against this backdrop, this study addresses a critical question in the era of digital intelligence: what capabilities become essential for researchers operating in an academic environment characterized by the widespread integration of GenAI? Adopting a qualitative research design, the study conducts a systematic text analysis of GenAI usage guidelines from QS Top 100 universities, with particular attention to provisions related to research practices. This analysis is complemented by in-depth semi-structured interviews with academic researchers of various disciplines, including university faculty members, doctoral candidates, and some master’s students. Through an integrated analysis of institutional discourses and researchers’ lived experiences, the study conceptualizes researchers’ GenAI literacy as a multidimensional construct composed of four core competencies, synthesized into the WISE framework: Wield, Integrate, Scrutinize, and Envision.

Within this framework, the ability to wield refers to researchers’ capacity to purposefully select GenAI tools aligned with specific research tasks, design effective prompts for human–AI interaction, and critically refine and integrate AI-generated outputs to enable efficient and responsible human–machine collaboration. The ability to integrate emphasizes not only an understanding of GenAI’s underlying principles, technical paradigms, language-generation logic, and developmental trajectories, but also the strategic use of GenAI to deepen disciplinary expertise, track frontier research developments, and enhance higher-order research capabilities. Importantly, GenAI substantially lowers the barriers to interdisciplinary learning, enabling researchers to rapidly acquire foundational concepts and research paradigms from adjacent fields, thereby expanding cognitive boundaries and supporting collaborative approaches to complex, cross-domain problems.

The ability to scrutinize highlights the necessity of maintaining critical judgment toward AI-generated content, including the evaluation of accuracy, bias, logical coherence, and risks of epistemic homogenization. This competency underscores the importance of clearly delineating appropriate boundaries of use, upholding academic ethics, safeguarding human agency, and avoiding overreliance on GenAI. Central to this dimension is the principle of human-in-the-loop, which ensures that researchers retain epistemic authority and moral responsibility throughout the research process. Finally, the ability to envision foregrounds a future-oriented and leadership-driven perspective, whereby researchers move beyond instrumental tool use to actively anticipate GenAI’s evolution, leverage it to drive research innovation, transcend traditional disciplinary boundaries, and address complex real-world challenges. This competency positions researchers not merely as passive users of AI, but as proactive shapers of AI-enabled research futures.

By articulating the WISE framework as an ability-oriented model of researchers’ GenAI literacy, this study offers a structured conceptualization of the competencies required in the digital intelligence era and provides a reflective lens on the evolving relationship between artificial intelligence and academic research. As GenAI accelerates the erosion of traditional metrics of intellectual labor and fundamentally reconfigures the logic of knowledge production, researchers’ long-term competitiveness will increasingly depend on distinctly human capacities that remain difficult for AI to replicate, such as critical judgment, ethical reasoning, integrative understanding of complex problems, sensitivity to values and meanings, aesthetic and humanistic insight, and foresight into emerging trends. In this sense, what ultimately distinguishes future researchers is not their technical proficiency with GenAI, but their ability to exercise critical judgment, epistemic responsibility, and intellectual agency within AI-mediated research practices.