The research, conducted by Richa Indu and Atul Kulshrestha of the Department of Computer Science and Information Technology at the Himalayan Institute of Technology in Dehradun, Uttarakhand, focused on a cohort of 365 undergraduate students enrolled in Bachelor of Computer Applications and Bachelor of Science in Information Technology programs. Rather than relying on self-reported surveys or qualitative impressions, the authors constructed a numerical skill quotient for each student, computed from weighted combinations of attendance, assignment scores, and sessional test performance. The approach transforms an abstract policy goal into something that can be plotted, correlated, and regressed—a methodological choice that reflects a broader push in education research to ground reform debates in hard data.

The courses chosen for scrutiny were deliberately varied. Three Skill Enhancement Courses, a category introduced under the policy to embed practical competencies within degree programs, formed the analytical backbone: Mathematical Foundations of Computer Science, System Analysis and Design, and Graph Theory. Alongside these, the researchers examined a markup language, Hypertext Markup Language, and two programming languages, C and C#. This mix allowed the team to compare how skill attainment manifests across different kinds of intellectual demands—abstract mathematical reasoning on one end, applied systems thinking in the middle, and hands-on coding and markup fluency on the other.

The results reveal a striking heterogeneity in how skills develop across subjects. Among the Skill Enhancement Courses, System Analysis and Design recorded the highest mean skill score at 3.815, followed closely by Mathematical Foundations of Computer Science at 3.736. Graph Theory, however, lagged noticeably behind with a mean of 3.246. Among the programming and markup languages, HTML and C# posted nearly identical means of 3.620 and 3.619 respectively, while C trailed at 3.321. These gaps are not trivial. They suggest that even within a single department teaching a single cohort, the policy’s skill-development machinery produces uneven returns depending on the subject matter, with abstract theoretical content proving harder to translate into measurable skill gains than applied or structured content.

To probe these patterns, the researchers deployed a full statistical arsenal: descriptive statistics to characterize distributions, correlation analysis to detect associations, non-parametric tests to handle data that resisted normal-distribution assumptions, and regression models to disentangle the relative contributions of different academic indicators. The central question was deceptively simple—does showing up to class, completing assignments, and performing on sessional tests actually predict skill attainment as measured by the computed quotient? The answer, in short, is a qualified yes, with important caveats that carry real weight for how the policy should be evaluated going forward.

Perhaps the most consequential finding concerns the relative predictive power of different academic indicators. Regression analysis suggested that sessional test performance may be a stronger indicator of computed skill than attendance alone. In other words, what a student demonstrates on an in-course assessment tells you more about their skill development than the simple fact of their physical presence in a lecture hall. This finding cuts against a persistent institutional habit in Indian higher education, where attendance mandates are often treated as a proxy for learning. The data here imply that presence is a weak signal, while demonstrated performance carries more informational weight about whether skills are actually being absorbed.

Yet the study is equally clear about the limits of its own indicators. The analysis indicates that the relationship between attendance, academic performance, and skill attainment is subject-specific, meaning that no single statistical relationship holds uniformly across the curriculum. A correlation that appears in System Analysis and Design may not replicate in Graph Theory, and the strength of association between test scores and skill scores varies from course to course. This subject-specificity complicates any attempt to build a universal metric of skill development, and it suggests that administrators hoping to monitor the policy’s effectiveness with a single dashboard number may be oversimplifying a genuinely multidimensional phenomenon.

The authors are candid that attendance and academic performance, while useful, are insufficient to explain skill development independently. They point to a constellation of other factors that likely shape how competencies actually form: practical application of knowledge outside the classroom, active participation in learning activities, prior knowledge that students bring with them, individual learning strategies, and broader engagement with the material. This framing aligns with a substantial body of education research arguing that skill formation is a process of active construction rather than passive accumulation, and it implies that the policy’s emphasis on experiential learning is aimed at exactly the variables that traditional academic metrics cannot capture.

The study arrives at a moment when scrutiny of the National Education Policy 2020 is intensifying. A growing literature has examined the policy’s alignment with the United Nations Sustainable Development Goals, its implications for digital education, its relevance to lifelong learning, and its systematic review of higher education reforms. What distinguishes the Dehradun study is its granular, quantitative focus: rather than evaluating the policy at the level of national rhetoric or institutional design, it drills down to the level of individual students and individual courses, asking whether the day-to-day instruments of undergraduate assessment register the skill gains the policy envisions. The answer—that they register something, but far from everything—offers both reassurance and warning to policymakers.

For educators and administrators, the practical implications are concrete. If sessional tests outperform attendance as skill indicators, then assessment design deserves more investment than attendance enforcement. If skill attainment varies systematically across subjects, then Skill Enhancement Courses may need subject-specific pedagogical strategies rather than a one-size-fits-all template. And if the unmeasured factors—engagement, prior knowledge, practical application—do much of the heavy lifting, then institutions should consider supplementing numerical skill quotients with richer forms of evaluation, from project portfolios to peer assessment. The study does not declare the National Education Policy 2020 a success or a failure. Instead, it does something arguably more valuable: it demonstrates that the policy’s central promise can be measured, identifies where current measurement falls short, and hands researchers a replicable template for tracking, course by course and cohort by cohort, whether India’s great educational experiment is actually delivering the skills it promised.

Subject of Research: Quantitative assessment of skill development among Indian undergraduate students under the National Education Policy 2020

Article Title: Efficiency of the Indian national education policy 2020 in developing skills in undergraduate students

Article References: Efficiency of the Indian national education policy 2020 in developing skills in undergraduate students. (n.d.). https://doi.org/10.1007/s43545-026-01843-5

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01843-5

Keywords: National Education Policy 2020, skill development, undergraduate students, attendance, academic performance, skill score, experiential learning, computer applications, programming languages, regression analysis, India, higher education

The two goals, the authors argue, are not merely adjacent items on the United Nations agenda; they are structurally interlocked. Gender equality is a precondition for broad-based economic growth, and decent work is one of the most reliable vehicles through which women convert new capabilities into real autonomy. Yet the evidence base that could guide policy has grown in fragments. Development economists have measured income and employment effects, gender studies scholars have traced shifts in agency and decision-making power, and agricultural researchers have documented outcomes for rural women, with each discipline addressing one slice of the problem and none addressing the whole. The review was designed to close exactly that gap, bringing the pieces together into a single, critically appraised synthesis.

Methodologically, the team followed the PRISMA 2020 reporting guidance, conducting the search on Scopus, a choice the authors themselves flag as a limitation because it restricts the sweep to a single database. The initial search returned 3,841 records. After deduplication and language filtering, 2,614 records entered title and abstract screening, and 1,161 survived to full-text assessment. Sixty-three studies met every pre-specified inclusion criterion, but six were removed during re-screening, leaving a final corpus of 57 peer-reviewed journal articles published between 2005 and 2022. That funnel, from nearly four thousand records to fifty-seven eligible studies, illustrates both the volume of interest in the field and the strictness of the evidence bar the reviewers applied.

Quality appraisal was carried out with the Mixed Methods Appraisal Tool, a widely used instrument for judging the methodological rigor of qualitative, quantitative, and mixed-methods studies. Because some of the included designs fell outside the tool’s empirical scope, the team supplemented it with five adapted criteria tailored to those non-standard designs. This dual approach matters for readers of the synthesis: it means the thematic conclusions rest on studies whose strengths and weaknesses were explicitly assessed rather than assumed, and it gives future researchers a transparent template for evaluating the often heterogeneous literature on gender and livelihoods.

From the 57 studies, the review distilled ten thematic clusters, and together they read like a map of the entire intervention landscape. The first cluster covers vocational training and its labour market outcomes, the classic territory of skills programmes aimed at wage employment. The second addresses digital skills and technology-mediated empowerment, an area that has expanded rapidly as mobile phones and internet access spread across low- and middle-income countries. The third examines microfinance, financial inclusion, and women’s agency, probing whether access to credit and savings genuinely shifts household power dynamics or merely adds financial obligations. The fourth and fifth clusters turn to entrepreneurship and self-employment, and to self-help group and cooperative models, the latter being especially prominent in South Asian contexts where collective savings and lending structures have become a signature vehicle for women’s economic mobilization.

The remaining clusters extend the map beyond individual-level interventions. Rural livelihoods and agriculture form the sixth theme, reflecting the reality that a large share of women’s work in many economies is agricultural and often unpaid or informal. Gender-responsive policy frameworks constitute the seventh, asking how governments design and implement programmes that acknowledge, rather than ignore, the structural constraints women face. The eighth theme, decent work quality and SDG 8 alignment, is a corrective to a common blind spot: counting jobs created is not the same as measuring whether those jobs are safe, fairly paid, and secure. The ninth theme addresses SDG 5 and SDG 8 interlinkages and synergies directly, and the tenth confronts structural barriers and constraints, the persistent social norms, mobility restrictions, and caregiving burdens that can hollow out even well-designed training programmes.

The headline finding of the synthesis is that integrated, multi-component programmes are associated with stronger empowerment and livelihood outcomes than standalone skills training. In other words, teaching a woman a skill in isolation rarely transforms her economic position. The studies suggest that training works best when it is bundled with complementary supports: access to finance, market linkages, mentorship, childcare, legal literacy, and engagement with the household and community norms that determine whether a woman can actually act on her new capabilities. This resonates with a broader lesson from development research, which has repeatedly found that constraints interact, so relieving one bottleneck while leaving others untouched often produces disappointing returns.

Beyond the thematic synthesis, the review makes a conceptual contribution that should prove useful to programme designers and researchers alike. The authors propose an integrated four-level framework that maps skill development inputs, empowerment pathways, livelihood outcomes, and policy moderators explicitly onto specified SDG 5 and SDG 8 targets. The framework’s value lies in forcing explicitness. Instead of vague claims that a programme empowers women, designers must specify which inputs are delivered, through which pathways they are expected to translate into agency and income, what outcomes are measured and at what time horizon, and which policy conditions moderate the chain. That structure also makes the SDG linkage operational, since each level can be aligned with concrete targets under the two goals rather than invoked rhetorically.

The review is candid about its own boundaries. The restriction to Scopus means relevant studies indexed elsewhere may have been missed, and the corpus is limited to peer-reviewed journal articles published between 2005 and 2022, which excludes grey literature from implementation agencies where much practical learning resides. The authors also note that the published record was still being finalized at the time of release, with the article shared early as a citable, peer-reviewed accepted version subject to further edits. These caveats do not undermine the central synthesis, but they do remind readers that thematic reviews are maps of the available evidence, not exhaustive inventories of everything known.

For policymakers, the practical implications are hard to ignore. As countries report on SDG 5 and SDG 8, the review suggests that progress metrics should track not only how many women complete training, but whether programmes are multi-component, whether they measure empowerment outcomes such as decision-making and control over income, and whether they account for job quality rather than job counts alone. For researchers, the ten thematic clusters and the four-level framework offer a shared vocabulary that can connect previously siloed literatures. And for the millions of women enrolled in skill programmes worldwide, the message is one of cautious optimism: skills matter, but skills embedded in supportive financial, social, and policy environments matter far more. The full open-access article is available under a Creative Commons Attribution 4.0 licence, and the research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors, with the authors declaring no competing interests.

Subject of Research: Skill development programmes for women's empowerment and sustainable livelihoods under SDG 5 and SDG 8

Article Title: A thematic review of skill development for women’s empowerment and sustainable livelihoods under SDG 5 and SDG 8

Article References: Srivastava, P., Noval, S., Nagar, P., Shekhawat, K., & Chopra, E. (2026). A thematic review of skill development for women’s empowerment and sustainable livelihoods under SDG 5 and SDG 8. Discover Sustainability. https://doi.org/10.1007/s43621-026-04676-7

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04676-7

Keywords: women's empowerment, skill development, SDG 5, SDG 8, sustainable livelihoods, decent work, gender equality, microfinance, vocational training, self-help groups, gender-responsive policy, thematic review

The research team approached the problem not by trying to pin down the causes of declining recruitment, but by examining how the roles of junior developers and the process of building professional expertise are actually changing on the ground. They chose South Korea as a case study in which these shifts appear in an early and highly visible form. According to data cited in the paper, AI is involved in 51.8 percent of work-related activities in Korea, nearly twice the comparable figure for the United States, while conventional entry-level hiring has contracted sharply. Entry-level job postings at the fifteen largest U.S. technology companies fell by 25 percent between 2023 and 2024, and IT job postings in Korea declined by 43 percent over the same period, with positions targeting entry-level candidates accounting for just 4.4 percent of all postings. The researchers caution that these declines cannot be attributed solely to generative AI, since post-pandemic workforce adjustments and the economic slowdown have also played a role, but the structural transformation of junior work was unmistakable in their data.

At the heart of the study are semi-structured interviews, each lasting approximately fifty minutes, with fourteen participants: six senior developers currently working in the industry and eight junior participants preparing to enter software development careers. The senior participants were required to have at least six years of professional experience and to have entered the workforce before the emergence of generative AI, while the junior participants were undergraduates whose university years spanned both the pre-AI and post-AI eras. The team analyzed the transcripts using reflexive thematic analysis, a qualitative method in which researchers iteratively examine their own interpretive role while identifying recurring patterns across interviews. Four major themes emerged, and together they sketch a system in which the loss of junior-level work is inseparable from the loss of junior-level learning.

The first theme was the shift of junior-level tasks toward what the researchers describe as “senior plus AI” workflows. Senior developers increasingly use AI to directly handle basic implementation, debugging, and documentation that would previously have been assigned to junior staff, and in doing so they absorb the very tasks through which novices once gained hands-on experience. One startup founder interviewed for the study said the company had substantially reduced its junior workforce without any measurable impact on productivity, asking pointedly, “What makes a junior developer better than a KRW 100,000-per-month AI subscription?” The economic logic of that question is difficult to dispute in the short term, and it explains why individual firms have strong incentives to make the substitution. The study’s point is that the costs of this rational choice do not disappear; they are displaced onto a different group of people at a different point in time.

That displacement is captured in the second theme: what is disappearing is not simply work, but the “opportunity to fail.” The researchers frame this through the concept of “productive struggle,” the well-documented principle in learning science that when learners make mistakes and correct them on their own, they develop not only knowledge but also the metacognitive ability to recognize what they do not know and to detect errors in the results they produce. Junior participants reported that they could now achieve good results with far less effort than before, yet many described a troubling sense of “not knowing what I don’t know.” One participant who took two courses in the same field received the same grade in both, but said that one course left them with actual knowledge while the other left them only with “the ability to use AI.” The single difference between the courses was whether the assignments could be completed using AI. The researchers found that current educational assessment methods do not adequately capture this distinction, meaning grades can mask profound differences in what students have actually learned.

Senior developers voiced the same concern from the other side of the career ladder. One participant explained, “The reason you become a senior is that experience teaches you what not to do. Now juniors can no longer learn from bad examples.” This observation connects directly to the study’s theoretical grounding in “situated cognition,” the idea that the problems a person can perceive depend on the position they occupy. Senior developers and the organizations with the power to address the problem may find it difficult to fully appreciate the circumstances facing juniors, while juniors who directly experience the problem lack the power to change educational and hiring structures. The asymmetry is captured starkly in the words of another senior participant: “We have 20 years of accumulated experience, so we can judge whether AI outputs are right or wrong. The next generation will not be able to reach that position. So we are fine.”

The third and fourth themes underscore that these dynamics are structural rather than matters of individual choice, and that they will not correct themselves. When every one of a student’s peers uses generative AI, grading on a curve effectively becomes a mechanism that compels AI use, since opting out means accepting a competitive disadvantage. In the courses taken by the interview participants, AI use was rarely restricted, so the loss of failure-based learning was built into university classrooms just as surely as into workplaces. Meanwhile, because seniors and juniors perceive the same situation so differently, no natural feedback loop exists to trigger correction. The researchers also found that the problem did not originate with generative AI alone: senior developers themselves largely built their expertise through hands-on work and trial and error rather than through formal training systems. In this sense, generative AI has not eliminated a newly created development pathway; it has accelerated the erosion of a pathway that was never institutionally protected in the first place.

The study argues that the solution lies in deliberate institutional protection rather than individual effort, and it draws on examples from other high-risk fields where automation has already forced this reckoning. In aviation, the Federal Aviation Administration’s SAFO 13002 advises pilots to maintain opportunities for manual flying so that automation does not erode their manual flying skills, and the nuclear industry requires regular simulator retraining to keep operators’ response capabilities sharp. These are examples of industries deliberately preserving the experiences necessary for developing and maintaining expertise even when machines can perform the underlying tasks. The research team proposes analogous reforms across three areas of software engineering. At universities, courses in which AI cannot achieve the learning objectives on behalf of students should be designated as required, and “reducing AI dependence” should serve as a criterion for evaluating educational quality. In hiring, employers should evaluate not only candidates’ ability to use AI to produce results quickly, but also their ability to recognize gaps in their own knowledge and detect errors while working alongside AI. Within companies, learning opportunities should be deliberately created for entry-level developers, for example by assigning them small modifications to actual products and letting them experience the full process from commits and code review through deployment.

Professor Moon, who supervised the research, framed the findings as a warning about a hidden ledger. “Some of the productivity we are gaining today may effectively be borrowed in advance from the expertise of the next generation,” he said. “Because the cost is borne by a different group and at a different point in time from those making the decisions, it is structurally difficult to recognize. This study is significant because it empirically reveals that blind spot.” He added that, as a department that develops AI technologies itself, SNU bears a particular responsibility to ask what those technologies are doing to the development of the next generation, and that the team plans follow-up research focused on university education and on incorporating the findings into the design of the undergraduate curriculum. The paper’s first author, Ph.D. student Sumin Yu of SNU’s M.IN.D Lab, conducts empirical research on how generative AI affects human learning, decision-making, and social institutions, and plans to continue studying the interaction between AI technologies and society across a range of fields.

The significance of the work extends beyond software engineering. AIES, jointly organized by the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery, is regarded alongside ACM FAccT as one of the two leading venues for research on the ethical and societal impacts of AI, and the ninth edition of the conference will be held in Malmö, Sweden, from October 12 to 14. The research was supported by the National Research Foundation of Korea, the Institute of Information & Communications Technology Planning & Evaluation, and the BK21 FOUR Education and Research Program at Seoul National University. What the study ultimately documents is a paradox at the center of the AI productivity boom: the judgment that makes senior engineers valuable is produced by exactly the kind of slow, error-prone, hands-on work that AI now performs so cheaply. An industry that optimizes away that work may find, a decade from now, that it has optimized away its own future experts, and the bill for today’s efficiency gains will come due in the form of a generation that never learned what it does not know.

Subject of Research: How generative AI erodes the professional development pathway of junior software developers

Article Title: SNU professor Taesup Moon’s team reveals how generative AI is eroding the development pathway for software developers

Article References: SNU professor Taesup Moon’s team reveals how generative AI is eroding the development pathway for software developers. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: generative AI, software engineering, junior developers, skill development, productive struggle, situated cognition, AIES 2026, Seoul National University, entry-level hiring, AI ethics, developer education, automation

The study rests on an integrated theoretical framework combining three established models. The Job Demands–Resources model explains how excessive workloads, environmental hazards, and psychological strain erode well-being when they are not balanced by adequate resources such as protective equipment, supervision, and social support. Reason’s Accident Causation Theory contributes the distinction between latent failures, which are systemic weaknesses embedded in organizational processes, and active failures, the unsafe acts committed by frontline workers. Human Capital Theory completes the picture by framing education, training, and skill acquisition as determinants of productivity and safety. Together, these lenses allowed the researchers to trace how structural conditions, managerial decisions, and worker capacities interacted to produce harm during the crisis.

On the physical health front, the interviews documented a striking rise in respiratory illnesses, including asthma, shortness of breath, and chest congestion, which participants linked to the absence of personal protective equipment, overcrowded sites, viral exposure, and environmental pollution. Workers with pre-existing conditions were hit hardest. One asthmatic worker described having to continue living in a tent on site to support his family, which worsened his condition. Skin diseases such as itching, eczema, and infections also surged, attributed to frequent contact with unclean tools and limited access to safe water. Notably, the study captured a sharp divergence in accountability: building owners held contractors responsible for worker protection, while contractors framed the illnesses as natural phenomena or the result of workers’ own poor hygiene practices, and a government official acknowledged that Bangladesh lacks a comprehensive occupational health framework tailored to construction, with no standardized injury reporting or emergency response protocols.

Mental health emerged as a second, less visible casualty. Fear of infection combined with income uncertainty imposed by movement restrictions created profound psychological strain. The researchers found that socioeconomic position shaped behavioral responses: some workers from relatively well-off families simply stopped working to protect themselves, while those without savings faced an agonizing choice between infection risk and family survival. One worker recounted being penniless during the pandemic and continuing to work despite fear and uncertainty because stopping meant no income for his family. This psychosocial tension, the authors argue, represents a persistent dilemma for informal laborers whose livelihoods depend on daily physical presence at hazardous sites.

Environmental conditions at the worksites compounded these risks. Participants described unsanitary sites lacking adequate water and soap, makeshift tents that were dirty and overcrowded, and waste scattered across working areas. Weak site organization and poor management amplified the hazards. Yet even here, narratives diverged: one contractor insisted that soap and water had been provided at every site under his contract, blaming workers’ lackadaisical attitudes for non-compliance, while workers pointed to the absence of basic facilities. The researchers interpret this gap between managerial claims and worker experience as evidence of both weak enforcement and uneven adherence, mediated by limited education and awareness.

Workforce management failures formed the study’s second major theme. Contractors, who often serve as the sole authority on Bangladeshi construction sites, were frequently absent during the pandemic. Their absence produced disorganization, communication gaps, and neglected site maintenance, with debris left scattered and workers receiving no clear guidelines or schedules. In the power vacuum, older workers, those with higher education, and relatives of contractors assumed informal supervisory roles, pressuring colleagues to complete tasks quickly to demonstrate performance to the contractors rather than to protect health and safety. Compliance with protective measures proved almost entirely externally driven: workers reported wearing masks and gloves only when the contractor or site owner visited, focusing otherwise on finishing quickly to secure their daily wage.

The pandemic also fractured schedules and supply chains. Lockdowns, transportation restrictions, and shop closures disrupted the delivery of construction materials, while contractors’ absences left workers uncertain about their next tasks and procedures. Irregular working hours, sometimes beginning before 9 a.m. or extending past 5 p.m. without consultation, spilled over into workers’ family lives and mental health. Building owners reported difficulties paying wages and meeting financial obligations to suppliers and subcontractors as cash flow constraints intensified. The study links these operational breakdowns directly to increased occupational risk, arguing that role ambiguity and disorganized activity translated managerial inefficiency into physical danger.

Perhaps the most culturally distinctive finding concerns religious belief. Many workers interpreted COVID-19 as a curse from Allah, a consequence of human sins that would affect only the guilty, and believed that ultimate protection came from God rather than human intervention. While such beliefs provided psychological comfort, they often reduced adherence to preventive measures. One building owner described advising a worker to refit a mask that had slipped beneath his lip; the worker refused, stating that infection happens according to Allah’s will. The authors emphasize that these fatalistic interpretations, particularly common among workers with limited literacy, underscore the need for health education that is culturally sensitive rather than dismissive of religious context.

Skill development, the third theme, revealed a structural failure long predating the pandemic. Formal training programs and vocational certification schemes for construction workers in Bangladesh remain limited, and most workers enter the trade without task-specific competencies in plastering, bricklaying, handling sharp tools, or working at heights. Skills are instead acquired through informal mechanisms: learning from mistakes, observation, peer mentoring, and occasional safety meetings held by contractors. One worker described progressing from material bearer to plasterer over nine years of gradual development, while noting that many newcomers lose their jobs before achieving proficiency. The pandemic severed these fragile learning pathways, as lockdowns and social distancing ended safety discussions and reduced contact with experienced workers, leaving novices confused, error-prone, and unsafe.

The study’s integrative contribution lies in showing how these three domains reinforce one another. Limited skills made workers unable to understand or apply pandemic protocols, such as physical distancing or sanitizing shared tools, while managerial absence removed the guidance that might have compensated. The result was slower task completion, repeated errors, interrupted workflows, and reduced productivity, alongside heightened infection and injury risk. The authors propose a multi-level response: strict enforcement of the Bangladesh National Building Code, institutionalized safety audits, mandatory contingency planning with risk financing and insurance, continuous site-level supervision with quality protective equipment and digital communication tools, structured competency-based vocational training, and community-based mental health services and awareness campaigns delivered by NGOs and labor organizations. They acknowledge limitations, including the regional focus on Sylhet, purposive sampling, and self-reported data, and call for mixed-methods and post-pandemic research. But their central conclusion stands: the pandemic exposed how structural, managerial, educational, and cultural factors interact to produce occupational vulnerability, and only coordinated, evidence-informed action across all of them can build a resilient construction workforce in Bangladesh.

Subject of Research: Impacts of the COVID-19 pandemic on occupational health, workforce management, and skill development among construction workers in Bangladesh

Article Title: Impacts of COVID-19 on occupational health, workforce management, and skill development in the construction sector in Bangladesh

Article References: Ahmad, I., & Hossain, M. I. (2026). Impacts of COVID-19 on occupational health, workforce management, and skill development in the construction sector in Bangladesh. Discover Social Science and Health, 6(1), Article 94. https://doi.org/10.1007/s44155-026-00426-2

Image Credits: AI Generated

DOI: 10.1007/s44155-026-00426-2

Keywords: COVID-19, construction industry, occupational health, Bangladesh, workforce management, skill development, informal workers, mental health, workplace safety, qualitative research, Sylhet, labor informality