The curriculum was implemented over one academic year and shared by residents from two specialties. Researchers used a qualitative descriptive design within an interpretivist paradigm, conducting 20 individual semi-structured interviews during the eight weeks following the first full curriculum cycle and its end-of-course objective structured clinical examination, or OSCE. The sample included eight residents—four in General Surgery and four in Orthopedic Surgery, with one resident representing each postgraduate year from PGY1 through PGY4—and 12 faculty members comprising four general surgeons, four orthopedic surgeons and four clinical anatomists. Interviews were conducted in Persian, recorded and transcribed verbatim; reported quotations were translated into English with independent back-translation. The transcripts underwent codebook thematic analysis using a hybrid inductive–deductive orientation. Dataset adequacy was evaluated through information power rather than saturation, and the researchers reported the study in line with the Consolidated Criteria for Reporting Qualitative Research, or COREQ.

Six themes emerged from the interviews. Across the three participant groups, anatomy was regarded as a perceived precondition for safe operative practice. Participants also reported an estimated five- to seven-year interval between their preclinical anatomy instruction and the period in which residents assumed operative responsibility. Access to the integrated curriculum was constrained by post-call fatigue, unprotected academic half-days and travel between hospitals. Residents described these conditions as barriers to participation, while faculty treated them as implementation or delivery problems. Faculty also emphasized the absence of a national reference curriculum for clinical anatomy. These accounts document perceived access and delivery at one institution, rather than the prevalence of similar barriers across other residency programs.

Specialty alignment produced a mixed response. General Surgery residents perceived the shared material as weighted toward orthopedics. Orthopedic residents did not report a reciprocal concern, and requests for deeper coverage of limbs and joints came from orthopedic faculty rather than from orthopedic residents. The most positively described components were cadaveric prosection, case-based Integrated Clinical Morning Report sessions and co-teaching in which surgeons and clinical anatomist worked together. Assessment feedback focused on the OSCE’s perceived clarity, timing and authenticity rather than on measured validity. Because the study did not quantify the examination’s reliability or validity, the favorable comments cannot establish that the OSCE was a valid measure of learning.

Residents and faculty also described hierarchy and embarrassment as barriers to asking elementary anatomy questions. Small-group and case-based settings were perceived as comparatively safer places to acknowledge uncertainty and expose knowledge gaps, giving the curriculum a psychological-safety dimension beyond subject coverage. The study’s defensible conclusion is that perceptions of relevance and acceptability were conditional: specialty balance, scheduling compatible with residency work, clear assessment standards and permission to ask foundational questions shaped how participants experienced the program. Because the qualitative sample was limited to 20 participants at a single center, the results do not demonstrate improved anatomical retention, examination performance, operative safety or patient outcomes. Those questions would require separate outcome-based evaluation, and the reported associations between curriculum conditions and participant perceptions should not be read as measured causal effects.

Subject of Research: Resident and faculty experiences of an integrated clinical anatomy curriculum in general and orthopedic surgery

Article Title: Resident and faculty experiences of an integrated anatomy curriculum in general and orthopedic surgery: a qualitative study of specialty alignment and psychological safety

Article References: Shahrezaee, M., Movahedinia, M., Keshtkari, S., Shahrezaee, M. H., Chamanara, M., Mosaed, R., Rezaee, S. P., Feizi, D., Shishegar, A., Sadeghi, S., Mirsaeid, S. G., & Naseri, A. (2026). Resident and faculty experiences of an integrated anatomy curriculum in general and orthopedic surgery: a qualitative study of specialty alignment and psychological safety. BMC Medical Education. https://doi.org/10.1186/s12909-026-10361-0

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10361-0

Keywords: clinical anatomy, surgical education, medical residents, curriculum integration, psychological safety, OSCE assessment, case-based learning, orthopedic surgery, general surgery, qualitative study, Resident, faculty

The study, published in the Journal of General Internal Medicine by Rachel Cox, MD, and colleagues, was built around a deceptively simple question: if you teach the people who give and receive feedback what high-quality feedback actually looks like, does the feedback itself get better? To find out, the team implemented targeted educational interventions in 2022 and then compared the narrative assessments written before and after those interventions using a validated scoring tool. The results showed a statistically significant improvement in feedback quality, with mean scores rising from 2.48 to 3.37 on a 12-point scale, a change the authors describe as meaningful even though it also reveals how much room remains for growth.

The technical core of the study lies in how feedback quality was measured. The researchers collected narrative assessments and written faculty-to-resident feedback from resident feedback forms across two periods: the 2021 to 2022 academic year, before the intervention, and the 2022 to 2024 academic years, after it. All assessments were de-identified and then evaluated by two independent scorers using the Narrative Evaluation Quality Instrument, or NEQI, a tool developed to assess the assessor rather than the trainee. The NEQI quantitatively scores feedback across three categories: the performance domains commented on, the specificity of the comments, and the usefulness of the feedback to the trainee. Each assessment receives a total score ranging from 0 to 12, with higher numbers indicating higher quality. In total, 1206 narrative assessments met the inclusion criteria, 316 from the pre-intervention period and 890 from the post-intervention period, giving the comparison substantial statistical power.

The numbers tell a consistent story. The mean NEQI score increased from 2.48 before the educational workshops to 3.37 afterward, an improvement that reached statistical significance at P less than 0.001. The effect size was 0.5, which the authors characterize as moderate, suggesting the intervention is impactful rather than marginal. Perhaps most striking is the intraclass correlation coefficient of 0.92, an indicator of excellent interrater reliability. In practical terms, the two independent scorers agreed closely on how to rate both the pre- and post-intervention datasets, which strengthens confidence that the observed rise in scores reflects genuine improvement in the feedback itself rather than inconsistency in how it was judged.

The intervention itself was deliberately lean. Faculty members participated in biannual 90-minute interactive workshops covering the core principles of high-quality feedback, the common barriers to delivering it, and techniques for producing action-oriented comments. Residents, meanwhile, attended a single 60-minute workshop focused on deliberate practice, goal creation, and building expertise through feedback, with an emphasis on learners taking ownership of their own learning. Neither arm involved new technology, additional staffing, or sweeping curricular reform. The authors argue that this cost-effectiveness and relatively straightforward integration make the approach a scalable solution for other residency programs across various specialties, even though implementing such programs requires time and commitment from program leadership.

What makes the improvement technically interesting is that it emerged from education aimed at both sides of the feedback exchange. Prior research cited in the study has described the highest quality narrative feedback as specific, actionable, and constructive, and has emphasized that feedback should incorporate and be targeted to learner goals. The faculty workshops aligned with those best-practice components, teaching clinicians how to deliver effective comments. The resident workshop tackled the other half of the equation, encouraging trainees to seek feedback deliberately, set explicit goals, and treat feedback as raw material for deliberate practice. The literature has long noted that residents often describe seeking feedback as burdensome and anxiety producing, even though residents and faculty tend to agree on what constitutes effective feedback. The authors suggest their intervention may reduce those perceived barriers to engagement, bridging the persistent gap between intention and execution.

The study’s framing within competency-based medical education gives the findings broader significance. Under the ACGME milestones framework, feedback is not an optional courtesy but a mandated mechanism for achieving clinical competence and progressing through training. Inconsistent feedback quality can potentially impede residents’ progression and development toward clinical independence, which is why addressing the gap in feedback quality is framed as crucial to ensuring residents reach their milestones on schedule. By demonstrating that structured education can enhance feedback processes within this framework, the study provides program directors with an evidence-based lever they can pull without waiting for system-wide reform of how feedback is standardized across graduate medical education.

Even so, the raw scores carry a sobering message. The authors acknowledge that the average scores of both the pre-intervention and post-intervention groups are low overall on the NEQI scale, given that the maximum score, representing the highest quality feedback, is 12. A rise from 2.48 to 3.37 is statistically robust and practically relevant, but it also means that even after targeted education, the typical narrative assessment in this program still scored far below what the instrument defines as excellent. The authors point to prior descriptions of best practices, including specific, actionable, and constructive comments targeted to learner goals, as the direction in which feedback should continue to move, and they suggest that providing targeted education on how to deliver effective feedback could improve quality further, given the effect size observed here.

The study’s limitations are candidly stated. It examined only a single residency program and only a single year of residents across two specialties, which constrains how far the results can be generalized. The authors also note that the datasets generated and analyzed during the study are available from the corresponding author on reasonable request, and that the authors received no funding related to the manuscript and declare no conflict of interest. The research team, spanning Wright Patterson Medical Center, Wright State University Boonshoft School of Medicine, and Uniformed Services University in Bethesda, Maryland, included Kelsey Wilson, DO, Marc Gutierrez, MD, John Arthur, DO, Bryan Harper, DO, and Kathryn M. Burtson, MD, with contributions ranging from conception and design to analysis, interpretation, and critical revision.

For the wider world of medical education, the takeaway is both encouraging and demanding. The study demonstrates that targeted education on feedback significantly improves the quality of narrative feedback in an internal medicine residency program, making feedback more focused, specific, and valuable, and thereby contributing to optimal resident progression through training. A few workshops, delivered consistently to faculty and residents alike, shifted the measurable quality of more than a thousand written assessments in a statistically significant way. But the same data show that feedback across graduate medical education remains, by validated standards, far from excellent. The intervention offers a scalable starting point; the distance to the top of the NEQI scale defines the work that remains.

Subject of Research: Impact of Faculty and Resident Education on the Quality of Narrative Feedback in Residency: An Interventional Study

Article Title: Impact of Faculty and Resident Education on the Quality of Narrative Feedback in Residency: An Interventional Study

Article References: Cox, R., Wilson, K., Gutierrez, M., Arthur, J., Harper, B., & Burtson, K. M. (2026). Impact of Faculty and Resident Education on the Quality of Narrative Feedback in Residency: An Interventional Study. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10447-x

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10447-x

Keywords: Impact, Faculty, Resident, Education, Quality, Narrative, Feedback, Residency, Interventional, scientific research

The research team, led by Saud Alkathiri and Ahmad Abdul-Wahhab Shahba from King Saud University’s Center for Excellence in Learning and Teaching, analyzed 157 completed grants drawn from 477 total submissions received over eight cycles. Their multi-source evaluation combined longitudinal grant-database records with faculty surveys (108 respondents) and student surveys (413 respondents), alongside a descriptive benchmarking exercise against 11 internationally visible universities including Harvard, Stanford, Cambridge, Princeton, and Toronto. The study was approved by the university’s Standing Committee for Scientific Research Ethics and conducted using Python-based statistical analysis with non-parametric Mann–Whitney U-tests for comparative faculty ratings.

One of the most striking patterns the evaluation uncovered was the program’s strategic shift in selectivity. The inaugural cycle attracted 112 submissions and accepted 63 percent of them, awarding 71 grants in a single year. By contrast, acceptance rates dropped sharply to 25 percent in Cycle 2 and then stabilized between 23 and 28 percent through Cycle 7, before rising again to 37 percent in Cycle 8. Annual grant counts fell from that initial high of 71 to a more selective 6–11 grants per year in recent cycles. This trajectory mirrors a similar pattern observed at Harvard, suggesting common underlying factors such as quality-focused investment, resource consolidation, and natural faculty capacity limits following initial demand surges.

Despite the tightening of acceptance rates, the program maintained an exceptional 95.7 percent completion rate across all 157 funded projects. The researchers interpret this figure as evidence that the multi-stage review process effectively selects viable, well-conceived initiatives and that grantees receive adequate support to carry their projects through to fruition. PELT’s review structure provides guidance at conception, mid-implementation checkpoints, and final evaluation, facilitating early identification of implementation risks. This structured oversight, the authors argue, reflects a mature and rigorous program management approach that distinguishes prospective teaching grants from retrospective recognition awards.

Disciplinary participation remained remarkably balanced throughout the program’s history. Completed grants showed near parity among three core clusters: Humanities (34.4 percent), Science (30.6 percent), and Health (28.0 percent), with a smaller 7 percent originating from administrative and niche entities such as the Common First Year Deanship and the Deanship of E-Learning. College-level analysis revealed three tiers of engagement, with the College of Education leading at 26 grants across multiple cycles, followed by Arts (16), Engineering (14), and Applied Medical Sciences (13). Even colleges with limited participation demonstrated the program’s distributed reach across the university’s diverse academic landscape.

Thematic analysis of the 157 completed grants revealed three persistent pillars: Teaching and Assessment Strategies dominated at 51 percent, Curriculum and Course Content Development accounted for 30 percent, and Student Professional Development represented 16 percent, with 3 percent addressing niche needs. However, the program demonstrated notable strategic agility across cycles. Cycle 4 pivoted toward job-market skills, Cycle 5 emphasized e-learning and reusable electronic resources, and Cycle 6 focused exclusively on self-learning with learning resources and self-assessment tests, resulting in 100 percent allocation to curriculum development. Cycles 7 and 8 reverted to comprehensive innovation, incorporating generative AI, student partnership in learning, universal design for learning, and digital education.

The student survey provided compelling contextual evidence that aligns closely with PELT’s thematic evolution. Among 413 respondents, 71.9 percent prioritized professional skills development for job-market readiness, 52 percent favored problem-based learning, and 57 percent preferred project-based assessment over traditional written exams. Communication skills emerged as the paramount professional competency at 75.8 percent, followed by problem-solving (67.1 percent) and technical competencies (58 percent). The researchers note that these preferences correspond directly with the strategic focus of Cycle 4 and the current Cycle 9’s emphasis on professional certification integration, demonstrating potential alignment between institutional initiatives and learner needs.

The faculty survey revealed critical participation dynamics that the program must address going forward. Among 66 non-applicants, awareness gaps emerged as the primary barrier, with 54.5 percent reporting they were unaware of grant announcements and 45.5 percent lacking application guidance. Workload constraints affected 33.3 percent of non-applicants, while 25.8 percent cited insufficient financial support. A statistically significant perceptual divide between accepted and rejected applicants emerged on application clarity and ease (p ≤ 0.001), with large effect sizes (rank-biserial r = 0.72 for ease and 0.67 for clarity). The researchers caution that the small rejected-applicant subgroup (n = 11) limits the stability of these estimates, but suggest that outcome bias may color retrospective evaluations of the application process itself.

Successful grantees reported uniformly high satisfaction, with means ranging from 4.1 to 4.5 across all evaluated dimensions on a five-point scale. Overall satisfaction reached 4.5, and perceived impact on teaching practices scored 4.5 as well. Among rejected applicants, 45.5 percent had no intention to reapply, while those willing to resubmit planned improvements in innovation clarity, budget justifications, methodology, and alignment with strategic objectives. The authors propose formalized resubmission pathways and actionable feedback mechanisms to convert initial rejection into constructive learning steps rather than terminal outcomes.

The international benchmarking positioned PELT in alignment with corresponding teaching-grant schemes at leading universities. Annual grant allocations across benchmarked institutions ranged from 5 to 10 grants (Cambridge’s former TLIF) to peaks of 91 grants (University of Michigan), with PELT’s current 6–11 annual grants falling within the lower-to-moderate volume range. PELT’s 25,000 SAR funding cap aligns with 27 percent of peer institutions, though 64 percent employ tiered models extending above that threshold. The authors recommend tiered funding, targeted outreach, and expansion into service-learning grants—already initiated through a dedicated CELT program—as strategies to advance pedagogical innovation. They also suggest developing a PELT Ambassador network in each college and streamlined exemplar-based application guides to demystify the process for newcomers, positioning programs like PELT as a promising template for the design and evolution of university teaching-grant schemes worldwide.

Subject of Research: Education

Article Title: A longitudinal evaluation of a university-wide faculty development grant program: pedagogical innovation, student priorities, and programmatic evolution

Article References: Alkathiri, S., Shahba, A. A.-W., Alsadoon, E., Alharbi, A. A., Alrusaiyes, R., Ain, G., & Alnowibet, K. (2026). A longitudinal evaluation of a university-wide faculty development grant program: pedagogical innovation, student priorities, and programmatic evolution. Frontiers in Education, 11, Article 1862014. https://doi.org/10.3389/feduc.2026.1862014

Image Credits: AI Generated

DOI: 10.3389/feduc.2026.1862014

Keywords: faculty development, pedagogical innovation, higher education, teaching grants, King Saud University, PELT, student priorities, longitudinal evaluation, longitudinal, evaluation, university-wide, faculty

The study followed a descriptive qualitative design and adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ), a standardized checklist that improves the transparency and rigor of interview-based work. Between December 2024 and April 2025, the researchers conducted semi-structured interviews with 28 participants drawn from two accredited universities: 16 nursing students and 12 faculty members. Rather than testing hypotheses with numbers, the team used deductive and inductive content analysis, a dual approach that allowed themes to emerge both from the pre-established framework of the Technology Acceptance Model and from the raw, unexpected details participants volunteered in conversation.

The Technology Acceptance Model, developed originally to explain why people adopt new information systems, rests on two psychological levers: perceived ease of use and perceived usefulness. If a technology feels simple to operate and demonstrably improves performance, users form a behavioral intention to use it, which then translates into actual system use. In this study, all four TAM constructs surfaced clearly in the interview data. Both students and faculty described AI tools as accessible and valuable, meaning the classic adoption pathway was fully engaged. But the researchers went further, documenting dimensions of AI use that the original model never anticipated.

Those dimensions turned out to be strikingly different across the two groups. Nursing students reported using AI primarily for presentations, visual content creation, and case analysis, tasks that align with the visual and applied nature of clinical training. Faculty members, by contrast, leaned on AI for course material development, academic writing, and administrative work, effectively outsourcing the bureaucratic overhead that competes with teaching time. This division of labor suggests that AI is not a single monolithic tool in nursing education but a flexible instrument whose function shifts depending on where the user sits in the academic hierarchy.

Perhaps the most unexpected finding was the emotional layer. Participants described AI as providing cognitive, instructional, and psychosocial support, with some students characterizing it as a companion during academic challenges and periods of stress. In a discipline where students face high-stakes clinical placements, examinations, and the emotional weight of patient care, an always-available conversational agent appears to fill a support gap that traditional resources do not. The researchers conclude that AI in nursing education extends beyond cognitive and instructional functions into emotional and psychosocial territory, a finding that challenges purely technical models of technology adoption.

Enthusiasm, however, came with clear-eyed caution. Participants voiced concerns about reduced critical thinking, the spread of misinformation, plagiarism, and unequal access to AI tools across student populations. These worries are not trivial in nursing, a field where clinical judgment can mean the difference between life and death. If students habitually offload reasoning to a chatbot, the very skill that defines safe practice may atrophy. Misinformation carries particular danger in healthcare contexts, where plausible-sounding but incorrect answers can propagate into clinical reasoning if learners lack the foundational knowledge to catch them.

The study’s authors argue that these risks point directly toward structured AI literacy programs, ethical frameworks, and interdisciplinary institutional support. AI literacy, in this framing, is not simply knowing how to prompt a model but understanding its limitations, verifying its outputs against authoritative sources, and recognizing when its use is appropriate in an academic or clinical context. Ethical frameworks would address plagiarism and academic integrity, while institutional support would ensure that access disparities do not create a two-tier system in which some students benefit from cutting-edge tools and others are left behind.

The researchers also emphasize the importance of balancing AI with traditional pedagogical approaches. Rather than replacing lectures, textbooks, or hands-on clinical simulation, AI appears most effective when layered on top of them, accelerating routine tasks while human instructors retain responsibility for mentorship, ethical reasoning, and the nuanced judgment that defines nursing practice. This hybrid vision aligns with broader trends in health professions education, where technology is increasingly seen as an amplifier of human expertise rather than a substitute for it.

Methodologically, the study’s strength lies in its dual perspective. By interviewing both students and faculty within the same theoretical framework, the researchers captured the full ecosystem of nursing education rather than a single vantage point. The COREQ adherence and the use of both deductive and inductive analysis strengthen confidence that the four TAM-aligned categories, perceived ease of use, perceived usefulness, behavioral intention to use, and actual system use, genuinely reflect participant experience rather than researcher expectation. Ethical approval was obtained from Suleyman Demirel University’s Ethics Committee, and all participants provided written and verbal informed consent in line with the Declaration of Helsinki.

As AI tools continue to proliferate across higher education, this study offers a timely warning and a roadmap. The technology is already woven into how nursing students study and how faculty teach, and its emotional and psychosocial dimensions are real and measurable. The question is no longer whether AI belongs in nursing education but how institutions will govern it, teach it, and balance it against the human skills that remain irreplaceable at the bedside. For a profession built on trust, precision, and compassion, getting that balance right may be the most important lesson of all.

Subject of Research: Experiences of nursing students and faculty with artificial intelligence in education using the Technology Acceptance Model

Article Title: Nursing students’ and faculty experiences with artificial intelligence in education: a qualitative study using the technology acceptance model

Article References: Akbaba, A., & Kus, A. C. (2026). Nursing students’ and faculty experiences with artificial intelligence in education: a qualitative study using the technology acceptance model. BMC Nursing. https://doi.org/10.1186/s12912-026-04986-1

Image Credits: AI Generated

DOI: 10.1186/s12912-026-04986-1

Keywords: artificial intelligence, nursing education, technology acceptance model, qualitative research, AI literacy, healthcare education, Nursing, students, faculty, experiences, artificial, intelligence

The research team, led by Ayesha Khatun of VIT-AP University in Amaravati, India, together with colleagues from Lovely Professional University, Symbiosis Law School Nagpur, Villa College in the Maldives, and Azimpur Government Girls College in Bangladesh, set out to answer a deceptively simple question: does micromanagement predict quiet quitting? To do so, they adopted a quantitative approach, distributing a pretested questionnaire to full-time academics and analyzing the responses with structural equation modeling conducted in SmartPLS 4, a statistical package widely used in management and organizational research for testing relationships between latent constructs such as perceived supervision and behavioral intentions.

The conceptual scaffolding of the study draws heavily on self-determination theory, the influential framework developed by psychologists Edward Deci and Richard Ryan, which holds that human motivation flourishes when three basic psychological needs are met: autonomy, competence, and relatedness. Micromanagement, by its very nature, attacks the first of these. When a supervisor dictates how, when, and in what order work must be done, employees experience a loss of autonomy that the theory predicts will shift them from intrinsic motivation, doing work because it is inherently satisfying, toward controlled or amotivated states. Quiet quitting, in this light, can be read as a behavioral symptom of that motivational collapse: employees remain in their posts but withdraw the discretionary effort, the extra hours, the voluntary mentoring, the creative curriculum experiments, that once distinguished engaged academics.

The study operationalized micromanagement through its characteristic behaviors, with close supervision emerging as the aspect most tightly bound to quiet quitting outcomes. Close supervision means exactly what it sounds like: managers monitoring subordinates’ work at a granular level, demanding frequent reports, scrutinizing minor details, and requiring approval for routine decisions. In a university setting, this might translate into department heads dictating slide-by-slide lecture content, requiring sign-off on research correspondence, or tracking attendance with an intensity usually reserved for factory floors. The researchers found that this supervisory style correlated most strongly with the detachment dimension of quiet quitting, in which employees psychologically disengage from their roles, and with the lack of initiative dimension, in which they stop volunteering for tasks beyond their formal job descriptions.

Why should this matter beyond the campus gates? Higher education institutions are knowledge organizations, and their output, teaching quality, research productivity, community engagement, depends disproportionately on discretionary effort. A professor who teaches exactly the assigned hours and refuses to supervise extra theses is not technically doing anything wrong; indeed, some commentators have argued that what is called quiet quitting is better described as calibrated contributing, a reasonable recalibration of effort in response to unreasonable demands. But for institutions competing for rankings, accreditation, and research funding, a workforce of calibrated contributors represents a substantial loss of human capital. The authors argue that their findings carry profound implications for practitioners, particularly those in leadership positions and human resource departments, who may be inadvertently manufacturing disengagement through the very oversight mechanisms intended to guarantee quality.

The South Asian context adds a distinctive layer to the story. India, Bangladesh, and the Maldives have all experienced rapid massification of higher education, with surging enrollments, expanding private colleges, and intensifying audit cultures in which institutional performance is measured, ranked, and published. Scholars of audit culture have noted that governing by numbers tends to breed precisely the kind of hierarchical surveillance that micromanagement embodies, as administrators respond to external accountability pressures by tightening internal controls. At the same time, cultural factors documented in cross-national leadership research, including the GLOBE study of 62 societies, suggest that power distance and collectivist norms in South Asian workplaces may shape how employees interpret and respond to controlling supervision. The new study does not claim that its results generalize to all cultures, but it does suggest that the micromanagement-quiet quitting link is potent enough to surface clearly even within these institutional settings.

Methodologically, the team took steps that lend the findings credibility. The questionnaire was pretested before deployment, a practice recommended in scale development research to catch ambiguous items and translation problems before they contaminate the data. The sample size of 465 comfortably exceeds the thresholds suggested by power analysis tools such as G*Power for detecting medium-sized effects in structural models. The analysis followed the reporting standards for partial least squares structural equation modeling, including assessment of measurement quality through criteria such as the Fornell-Larcker benchmark and the HTMT criterion for discriminant validity, both standard safeguards ensuring that the constructs being measured are statistically distinct from one another. The researchers also addressed common method bias, the inflation of correlations that can occur when all data come from self-report surveys, using a full collinearity assessment approach developed for PLS-SEM studies.

Still, the study has the limitations inherent to its design. It captures a snapshot in time, so it cannot definitively establish that micromanagement causes quiet quitting rather than merely preceding or coinciding with it; only longitudinal or experimental designs could settle that question. Self-reported data are vulnerable to social desirability bias, a well-documented phenomenon in which respondents, particularly in cultures where organizational loyalty is valued, underreport negative attitudes and behaviors. And the sample, drawn from three countries, cannot speak for higher education systems elsewhere in South Asia, let alone beyond the region. The authors themselves frame the work as a significant first investigation into the relationship rather than its final word, and the quiet quitting literature, which has expanded explosively since the term went viral around 2022, remains young and conceptually contested.

That contest is worth pausing on. Since the phrase quiet quitting entered the global vocabulary through social media, workplace commentators have debated whether it describes genuine withdrawal, a healthy boundary-setting exercise, or simply a new name for the old practice of working to rule. Gallup polling has questioned how much of the phenomenon is real versus a relabeling of long-standing disengagement, and management scholars have proposed frameworks ranging from self-determination accounts to exchange-based theories, in which employees reciprocate perceived unfair treatment by withholding effort, a logic rooted in Blau’s classic social exchange theory. The new study sidesteps none of this; instead, it grounds the debate in concrete data from a region and sector, South Asian academia, that had been largely absent from the conversation, which until now has been dominated by studies of hospitality workers, nurses, technology employees, and professors in East Asia, Europe, and the Philippines.

For university leaders, the practical message is stark: the cheapest intervention against quiet quitting may be to stop micromanaging. The broader literature on autonomy-supportive leadership, including meta-analytic evidence that leader autonomy support is consistently associated with better motivational outcomes, points toward delegation, trust, and need-supportive supervision as the antidotes to the surveillance spiral. The authors of the study direct their implications squarely at those in leadership positions and human resource departments, suggesting that leadership development programs in higher education should treat micromanagement not as a personality quirk but as an organizational risk factor with measurable costs. As higher education across South Asia continues to expand, and as global competition for academic talent intensifies, the institutions that learn to lead without hovering may find that the quietest employees are not the disengaged ones, but the ones too busy doing excellent work to complain.

Subject of Research: The relationship between micromanagement and quiet quitting among faculty in South Asian higher education institutions

Article Title: Micromanagement and quiet quitting: a study of HEIs in South Asian Countries

Article References: Khatun, A., Joshi, A., Afaq, A., Abdullah, A., & Uddin, S. (2026). Micromanagement and quiet quitting: a study of HEIs in South Asian Countries. SN Social Sciences, 6(10), Article 546. https://doi.org/10.1007/s43545-026-01829-3

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01829-3

Keywords: micromanagement, quiet quitting, higher education, South Asia, employee engagement, leadership, self-determination theory, structural equation modeling, faculty, human resource management, organizational psychology, employee detachment

The study, conducted between November 2024 and April 2025, recruited sixteen participants, eight faculty members and eight students, drawn from diverse medical disciplines at Shiraz University of Medical Sciences. All had prior experience using AI tools, and the researchers employed purposive and snowball sampling to reach participants with genuine, hands-on familiarity rather than secondhand impressions. Data came from semi-structured, in-depth interviews lasting fifty to sixty minutes each, supplemented by field notes, and the interviews continued until the team reached data saturation, the point at which new conversations stopped yielding new themes. Every transcript was analyzed verbatim using Graneheim and Lundman’s qualitative content analysis approach, supported by the MAXQDA 24 software package, and the team followed COREQ reporting guidelines to ensure methodological transparency and trustworthiness.

From this analysis, five overarching themes emerged, accompanied by sixteen subthemes: the functional and practical value of AI; human oversight, ethics and authenticity; perceived capacity building and institutional readiness; cognitive, emotional, and behavioral influence; and socio-cultural dynamics and future transformation. The architecture of these themes matters because it resists the simple binaries that dominate public discourse about AI in education. Participants did not describe the technology as either a miracle or a menace. Instead, they described a continuous negotiation, weighing efficiency gains against ethical risks, cognitive costs, and institutional constraints that vary from one classroom, one country, and one user to the next.

One of the study’s most striking findings is how differently the two groups used the same tools. Faculty members reported a strategic, almost managerial relationship with AI applications. They delegated routine, repetitive tasks, drafting administrative communications, summarizing material, handling the mechanical scaffolding of academic work, precisely so that their own cognitive effort could be preserved for complex judgment calls that demand expertise. Students, by contrast, exhibited what the researchers characterize as a more relational reliance on AI. Rather than treating the technology as a disposable assistant, students described a bond of habitual dependence, and that dependence appeared to heighten their vulnerability to automation bias, the well-documented tendency of human users to accept machine-generated output uncritically, even when it is wrong.

Automation bias is not a hypothetical concern in medicine. A medical student who accepts an AI-generated summary of a clinical guideline without checking the primary source, or who trusts a chatbot’s explanation of a drug interaction without verifying it, is practicing a habit that could later surface in patient care. The Shiraz study suggests that this risk is not evenly distributed: it concentrates among learners whose relationship with AI is intimate and habitual rather than instrumental and bounded. The researchers’ framing implies that the very population with the most to gain from AI’s speed and convenience, students still building their foundational knowledge, may also be the population most exposed to its cognitive costs.

Both faculty and students voiced a shared and deeply felt worry that the researchers label intellectual deskilling. The concern is that if AI systems routinely perform the cognitive heavy lifting of academic work, searching, synthesizing, drafting, and explaining, then the muscles those tasks once trained may atrophy. In medical education, where the goal is not merely to produce documents but to shape clinical reasoning, diagnostic intuition, and ethical judgment, deskilling is not an abstract worry. It strikes at the core purpose of the enterprise. The authors argue that mitigating this risk requires human-in-the-loop supervision, a design and pedagogical principle in which a human being remains the final arbiter of AI output, checking, correcting, and taking responsibility for what is ultimately used or taught.

The study also surfaces a dimension that is often missing from Western-centric AI-in-education literature: infrastructure and geopolitics. Participants described barriers to access that their counterparts in well-resourced institutions rarely confront, including the need for workarounds such as virtual private networks to reach AI services that are restricted or unavailable in their region. The researchers conceptualize this as a geopolitical algorithmic divide, a structural inequity in who can participate in the AI transformation of education and on what terms. This framing reframes the global conversation: the digital divide of the previous era concerned connectivity and hardware, while the new divide concerns access to the large language models and generative platforms that increasingly mediate academic work. Equitable integration of AI into medical education, the authors suggest, will require institutional support to bridge this gap, not merely individual initiative.

Underlying all of these findings is a methodological point worth appreciating. Most existing evidence on AI in medical education is quantitative and drawn from Western institutions, which limits the field’s ability to understand how socio-cultural and economic context shapes adoption. By choosing a qualitative descriptive design and grounding it in a constrained educational setting in Iran, Homayouni and Karimian demonstrate that the experience of AI is not universal. Attitudes toward authenticity, the ethics of outsourcing thinking, and the practical feasibility of using these tools are all filtered through local conditions. A student in Shiraz navigating service restrictions and VPN workarounds is having a categorically different experience from a student in a Silicon Valley-adjacent university with seamless, subsidized access, even if both are typing prompts into the same model.

The study’s conclusions point toward a pedagogical shift that many educators have begun to advocate: moving away from assessing the products of learning and toward cultivating evaluative judgment. If AI can generate an essay, a summary, or a study plan in seconds, then the scarce and valuable skill is no longer production but evaluation, the capacity to judge whether an AI output is accurate, appropriate, and safe. The authors argue that sustainable integration of AI into medical education depends on this shift, alongside institutional policies and support structures, rather than on ad hoc individual experimentation. Without such a framework, the gap between enthusiastic early adopters and cautious skeptics within a single institution can itself become a source of inequity and confusion.

Finally, the researchers are candid about the limits of a snapshot. Their study captures a single moment in a rapidly evolving landscape, and they recommend longitudinal research to track how perceptions, ethical concerns, and usage patterns evolve as AI becomes more deeply embedded in medical training. The question their work leaves open is one that every medical school now faces: how to harvest the genuine efficiency of these tools without hollowing out the very expertise they are meant to serve. The answer, this study suggests, will not be found in blanket bans or uncritical embrace, but in the deliberate, context-sensitive negotiation between human judgment and machine capability that the Shiraz participants are already, sometimes uneasily, performing every day.

Subject of Research: Experiences of medical sciences faculty and students using artificial intelligence applications in medical education

Article Title: Exploring the experiences of medical sciences faculty members and students in using artificial intelligence applications: a qualitative descriptive study

Article References: Homayouni, L., & Karimian, Z. (2026). Exploring the experiences of medical sciences faculty members and students in using artificial intelligence applications: a qualitative descriptive study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10331-6

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10331-6

Keywords: artificial intelligence, medical education, generative AI, qualitative research, automation bias, intellectual deskilling, faculty, students, large language models, digital divide, human-in-the-loop, Shiraz University of Medical Sciences