The distinction matters because a framework can define what professionals should know and be able to do without ensuring that they can perform those tasks in a real clinical environment. Capability describes the required knowledge, skills and behaviours. Competence concerns whether an individual has attained those capabilities at an appropriate standard, while performance is their demonstrated application in practice. Workforce readiness goes further: it includes the judgement, confidence, ethical reasoning and adaptability needed to use those capabilities in complex settings that change as technologies, regulations and patient needs evolve. The perspective also uses Miller’s pyramid as an interpretive lens, with readiness reflected in the ability to translate knowledge and competence into effective performance in authentic practice settings.

Australia’s coordinated policy work is described as a significant achievement. The National Digital Health Workforce and Education Roadmap established the case for a nationally consistent approach, while the National Nursing and Midwifery Digital Health Capability Framework, the Digital Health in Medicine Capability Framework and the Digital Health Capability Framework for Allied Health Professionals translated the broader policy direction into professional contexts. The cross-disciplinary Australian Digital Health Capability Framework then articulated shared expectations across the health and care workforce, including digital professionalism, leadership and advocacy, and the quality of data and information. Although the frameworks address different professional settings, the authors identify substantial common ground across digital professionalism, information and data literacy, technology-enabled care, human-centred practice and safe use of digital technologies.

These initiatives have helped move digital health beyond a narrow measure of technical proficiency. Their shared language covers not only the operation of digital systems, but also communication, human-centred care, collaboration, safe technology use and responsible handling of information. Australia has also begun building implementation support, notably through the Digital Health Train-the-Trainer Toolkit, developed by the Australian Digital Health Agency with the Australian Council of Senior Academic Leaders in Digital Health. It provides resources for university educators embedding eight digital health learning outcomes in pre-registration health degrees, recognising that educators themselves need support if curricula are to change. The initiative shifts attention from further refining capability statements toward the practical work of curriculum integration, educator preparation and implementation support.

The perspective does not present framework development as a completed solution. Many competency requirements have yet to be systematically embedded in health professional curricula, and evidence for sustained improvements in organisational and workforce outcomes remains limited. Reviews have found variation in the design of digital readiness interventions, their implementation and the measures used to assess success. Educational programs can strengthen digital competence and confidence, but the authors say that the evidence for long-term effects remains less developed. Their proposed response is not to discard capability frameworks, which will need to evolve with technology and regulation, but to connect existing expectations to training, assessment, simulation, workplace learning and continuing professional development. The success of the national agenda would then be judged by what becomes observable in education and practice rather than by the number of frameworks produced.

Authentic practice learning is central to that proposed transition. Students and health professionals must encounter situations in which they must judge whether information is reliable, protect privacy, navigate ethical dilemmas, support consumers with different levels of digital literacy and respond when a technology or process fails. A readiness indicator under the framework’s data and information quality domain, for example, would be the ability to critically evaluate data and AI-generated outputs before using them in clinical decisions. In technology domains, readiness would be demonstrated by recognising failures, seeking support and activating safe alternatives to maintain continuity and care. Under digital professionalism, the proposed indicator is the application of professional judgement when technologies create inequities, uncertainty or ethical dilemmas involving access, privacy, security, communication and AI-related issues. These are illustrative transformations of framework statements, not outcomes measured in the perspective.

Artificial intelligence makes this distinction increasingly consequential. The source argues that workforce preparation must extend beyond conventional technical competence to include assessment of AI-generated content, awareness of automation bias and hallucinated outputs, and recognition of the limitations of algorithmic recommendations. These are not simply additional software skills: they require professional judgement, data stewardship, privacy awareness, accountability, bias awareness and the ability to question an apparently authoritative output. The emerging position presented in the paper is that AI should augment rather than replace professional judgement, and that responsible innovation requires ethical governance, human-centred design and workforce leadership. Readiness therefore means being prepared for digitally mediated decision-making, not merely having access to a tool or knowing how to operate it.

Readiness also depends on the organisations in which professionals work. Leadership support, reliable digital infrastructure, interoperability, governance, workflow integration, technical assistance, workplace culture and protected learning opportunities all affect whether a person can translate training into everyday practice. The perspective therefore treats readiness as both an individual attribute and a product of a supportive organisational environment. Equity is an equally important concern because infrastructure, access to professional development and previous experience with digital tools may differ across metropolitan, regional, rural and remote settings, professional groups and career stages. Exposure to digital technology or access to a course consequently cannot be treated as evidence of readiness by itself. Planning must account for a person’s goals, motivations, literacy, aptitude and context as well as their formal access to training.

The proposed next phase calls for sustained, coordinated implementation and evaluation across universities, accreditation bodies, health services, professional organisations, policymakers and researchers. Questions include whether digital capabilities are consistently taught and validly assessed, whether educators are equipped to support learning, whether simulation and workplace placements provide authentic experience, and whether graduates and practising clinicians continue to demonstrate safe, responsible and adaptive digital practice over time. The Australian Council of Senior Academic Leaders in Digital Health supports a broader systems approach involving education, workforce development and digital health research, while DHWorx, an Australian and New Zealand digital health workforce research consortium, provides a mechanism for collaborative scholarship about capability, preparedness and emerging educational needs. Their value will depend on evidence showing that implementation is sustained rather than remaining aspirational.

The perspective is primarily Australian and draws on selected national initiatives and literature rather than a systematic review of international evidence. It also identifies substantial limitations in the empirical record: long-term effects of digital capability frameworks on the workforce remain limited, and readiness requirements will differ among professions, career stages, organisations and healthcare settings. Rapid technological change further means frameworks and readiness measures will need regular revision. The central message is nevertheless clear: Australia has made major progress in defining digitally capable practice, but producing a ready workforce now depends on measured implementation, supported learning, organisational conditions and continuous evaluation rather than the publication of more capability documents alone. The proposed transition is therefore from defining what digitally capable practice should look like to testing whether health professionals can enact it safely, responsibly and adaptably as healthcare changes.

Subject of Research: Australian digital health workforce capability and readiness

Article Title: Beyond capability frameworks: Australia’s next challenge in preparing the digital health workforce

Article References: Arabi, S., & Butler-Henderson, K. (2026). Beyond capability frameworks: Australia’s next challenge in preparing the digital health workforce. Frontiers in Health Services, 6, Article 1968499. https://doi.org/10.3389/frhs.2026.1968499

Image Credits: AI Generated

DOI: 10.3389/frhs.2026.1968499

Keywords: digital health, workforce readiness, capability frameworks, health education, artificial intelligence, digital literacy, organisational readiness, professional development, health policy, Beyond, capability, frameworks

One learner admitted, "I use Alexa every day but didn't know it was AI until you told me." Another described using ChatGPT to generate bedtime stories for a son without ever thinking of the chatbot as artificial intelligence. The adaptation reflects a growing recognition in the literature that for populations facing structural and social obstacles, technology acceptance is not purely a cognitive calculation but a socially mediated process shaped by instructors, peers, and institutional conditions. The analysis followed Moustakas' phenomenological procedure.

The researcher first practiced epoche, deliberately bracketing personal assumptions about AI in education to minimize bias, then applied horizontalization, treating every significant statement from the transcripts as equally important before condensing them into meaning units and clustering them into themes. From this process, four themes crystallized: understanding AI in everyday life, building confidence and a growth mindset around technology, personalized empowerment through AI, and willingness to learn amid structural barriers. Trustworthiness was reinforced through member checking, in which participants reviewed their transcripts, along with reflexive journaling to document analytic decisions.

The second theme revealed how confidence actually forms. Participants described initial hesitation rooted in limited digital literacy and fear of making mistakes. "I was scared of it at first. But once I used it a few times, I realized it is not that bad," one learner reflected.

Another captured the social dimension of ease of use: "If someone shows me how to use it, I feel better. On my own, I get frustrated." This finding challenges the assumption that ease of use is a property of software design alone. For adult learners, perceived ease of use was socially constructed, built through instructor guidance, peer support, and low-stakes opportunities to experiment.

Confidence, in other words, is not downloaded; it is scaffolded. Once confidence took root, AI transformed from a novelty into a personalized learning partner. Participants praised tools like Grammarly and Google Translate for offering judgment-free practice, a benefit that proved especially powerful for English language learners. "I like that I can use Grammarly or Google Translate and not feel embarrassed if I make a mistake," one participant said.

Another compared ChatGPT to a tutor who adjusts to the learner: "ChatGPT explains things in a way I understand. It's like having a tutor who adjusts to me." The tools also extended learning beyond classroom hours. "It helps when I'm working at night or early in the morning. I do not have to wait for help," one learner explained, underscoring how AI's always-on availability fits the unpredictable schedules of adults juggling employment, caregiving, and coursework.

Yet enthusiasm collided with structural reality. Others felt overwhelmed by the pace of technological change layered on top of foundational computer skills they were still acquiring. Privacy concerns and uncertainty about how the systems work added further hesitation.

The result was a persistent gap between willingness and opportunity: learners consistently expressed a desire to engage with AI if someone would simply show them where to start. Without structured instruction, the study suggests, AI integration risks reinforcing existing inequities rather than closing them, particularly for learners from lower socioeconomic backgrounds or those with interrupted educational histories. The findings also carry implications for workforce preparedness, though the author is careful about the causal chain.

The study did not measure direct employment outcomes; instead, it found that AI use supported foundational academic skills, writing proficiency, and digital confidence, competencies that modern workplaces increasingly demand. Michel frames this as a developmental pathway: AI first builds educational readiness, which then becomes the foundation for workforce participation in technology-driven labor markets. Within the adapted TAM framework, trust emerged as a prerequisite for perceived usefulness, meaning learners were unlikely to view AI tools as beneficial at all unless they first felt confident in their accuracy and fairness.

The study's limitations are explicit. Eight participants drawn from a single region of Southern California cannot statistically represent the world's adult learners, and recruitment through educator referrals may have selected for learners already somewhat interested in technology. The snapshot design also captures perceptions at a single moment, early in what may be a long evolution of awareness and trust.

Subject of Research: Adult learners' perceptions, trust, and barriers regarding artificial intelligence use in adult education

Article Title: Investigating adult learners’ perceptions of artificial intelligence in relation to knowledge, trust, learning outcomes, and workforce preparedness

Article References: Michel, F. (2026). Investigating adult learners’ perceptions of artificial intelligence in relation to knowledge, trust, learning outcomes, and workforce preparedness. Discover Education, 5(1), Article 913. https://doi.org/10.1007/s44217-026-02134-0

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02134-0

Keywords: artificial intelligence, adult education, technology acceptance model, digital literacy, AI trust, workforce readiness, ESL learners, qualitative research, educational technology, lifelong learning, AI literacy, Southern California

Generative AI use was associated with stronger flow experiences and lower workplace objectification. Each factor partially mediated the relationship between generative AI use and proactive behavior, indicating that the survey analysis identified both an association with the quality of employees’ work experience and an association with reduced objectification. These results are consistent with the study’s dual-pathway framework, but the abstract does not provide enough methodological detail to determine how each variable was measured or how robust the mediation results were.

Digital literacy showed an asymmetrical moderating effect. It strengthened the negative relationship between generative AI use and workplace objectification, as well as the corresponding indirect relationship with proactive behavior. Its moderating effect on the relationship between generative AI use and flow experience was not statistically significant. In other words, the reported statistical models supported a moderation result for the objectification pathway but not for the flow pathway.

The evidence comes from a survey of full-time knowledge workers analyzed with mediation and moderation models, not from an experiment or another design capable of establishing causal effects. The supplied abstract does not specify recruitment procedures, the organizational or national setting, the measures used for proactive behavior, or possible unmeasured confounding. It also does not report effect sizes, confidence intervals, attrition across the three waves, robustness checks, or details relevant to generalizability. Because only the abstract was supplied, this report summarizes the accepted research as described there and does not independently assess the full study, its instruments or statistical analyses. The findings should therefore be interpreted as associations observed among the surveyed knowledge workers rather than demonstrated causal effects of generative AI use.

Subject of Research: Survey analysis of how generative AI use is associated with employee proactive behavior, flow, workplace objectification, and digital literacy.

Article Title: More flow and less objectification: Dual influence mechanisms of generative AI use on employees’ proactive behaviors

Article References: Xu, S., Mei, Y., Yao, K., Wang, R., & Zhong, J. (2026). More flow and less objectification: Dual influence mechanisms of generative AI use on employees’ proactive behaviors. BMC Psychology. https://doi.org/10.1186/s40359-026-05643-7

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05643-7

Keywords: generative artificial intelligence, employee proactive behavior, flow experience, workplace objectification, digital literacy, knowledge workers, time-lagged survey, mediation analysis, moderation analysis, flow, less, objectification

Computer and information literacy, or CIL, measures the ability to use computers to investigate, create and communicate effectively in different settings. Computational thinking, or CT, assesses the ability to recognize real-world problems that can be expressed computationally and to develop algorithmic solutions. In the study’s within-school comparisons, strict limits were negatively associated with CIL scores at B = −14.94 and with CT scores at B = −17.31, with both p-values below .001. Flexible limits that made exceptions for homework showed the opposite pattern: their associations with CIL and CT were positive at B = 2.17 and B = 8.34, respectively, also with p-values below .001.

Similar contrasts appeared when researchers compared schools. Schools with more students reporting strict parental limits had lower average performance in CIL and CT, with coefficients of −48.12 and −47.22, both statistically significant at p < .001. Schools in which flexible rules were more common instead had higher average CIL, at B = 9.74 with p < .05, and substantially higher CT, at B = 33.67 with p < .001. The models distinguished a student’s household rule from the prevalence of similar rules among classmates, allowing the analysis to separate within-school individual differences from broader school-level patterns.

The study used three-level linear models reflecting the structure of ICILS, which places students within schools and schools within countries. In null models, 34% of the variance in CIL and 48% in CT was attributable to differences between schools, while 50% and 22%, respectively, was associated with between-country variation. The researchers adjusted for socioeconomic status, gender, immigration status, computer experience, information and communications technology self-efficacy and internet access at home. Continuous variables were standardized, school-average predictors were included alongside group-mean-centered individual variables, and models were estimated with restricted maximum likelihood.

Because ICILS assessment outcomes were reported as five plausible values per domain, the researchers fitted separate models for each set and combined the estimates using Rubin’s rules. They also incorporated rescaled student sampling weights to reflect the study’s complex survey design, with weights adjusted within each country. The CIL analysis included students in 4,609 schools across 29 countries, while the CT analysis covered 2,722 schools in 19 countries. This difference in coverage reflects the fact that CIL was a core domain in more participating systems, whereas CT was administered as an optional domain.

Household circumstances remained important even after these controls. Socioeconomic status was positively associated with both outcomes at the individual and school levels, as were general ICT self-efficacy and home internet access. Computer experience was also positively associated with performance within schools, at B = 8.51 for CIL and B = 9.74 for CT, both with p < .001. The socioeconomic measure combined parental education, parental occupation and the number of books available at home. These relationships mean that the screen-time estimates should not be interpreted as isolated effects of a single parental choice.

The clearest evidence of socioeconomic moderation concerned flexible rules and computational thinking. The interaction between socioeconomic status and flexible limits was positive at B = 3.82, with p < .01, indicating that the association between homework exceptions and CT was stronger among students from more advantaged socioeconomic backgrounds. No statistically significant moderation by socioeconomic status was detected for CIL. The interaction between socioeconomic status and strict limits was also not significant for CT, meaning the evidence does not show that socioeconomic background altered the negative association observed for rigid restrictions.

The results add large-scale evidence to a body of research in which screen restrictions have produced inconsistent findings. Previous work has linked restrictions to fewer opportunities to develop digital skills, while other studies have found benefits from moderate technology use or structured parental guidance. The new analysis suggests that the relevant distinction may be purpose as well as duration: a rule that ends a student’s access before educational work is completed differs from one that places recreational use in a structured boundary while allowing school tasks. The analysis is consistent with, but cannot prove, the possibility that flexible rules preserve opportunities for learning by doing while strict limits remove access at moments when digital tools may be needed.

Several limitations constrain what can be concluded. The design is cross-sectional and relies on students reporting whether parental limits apply during school days, so it cannot show whether rules preceded changes in digital competence or whether more digitally capable families selected particular strategies. The analysis cannot determine whether the associations operate through academic engagement, self-regulation, parental digital knowledge or other mechanisms. CT was assessed in fewer countries than CIL, and only student-level weights were used, as the authors noted limitations in available multilevel weighting methods. The relatively broad category of screen regulation also leaves the frequency, consistency and quality of parental enforcement unresolved.

Future research could test the proposed mechanisms with longitudinal and mixed-method studies, while also measuring negotiation, parental digital competence and co-learning more precisely. Comparisons across cultures could examine whether expectations about obedience, independence and technology use change the relationship between rules and digital performance. For now, the study’s clearest result is that “less screen time” and “more structured screen use” are not interchangeable: in large international Grade 8 samples, no-exception restrictions were associated with weaker digital performance, while flexible homework exceptions were associated with stronger performance, especially in computational thinking and among students with higher socioeconomic status.

Subject of Research: Parental screen-time regulation and adolescents’ computer literacy and computational thinking

Article Title: Parental screentime limits and child computer and information literacy and computational thinking: a multilevel analysis of 2023 ICILS data

Article References: Tlessov, A., Courtney, M. G. R., Vassilova, M., & Kambatyrova, A. (2026). Parental screentime limits and child computer and information literacy and computational thinking: a multilevel analysis of 2023 ICILS data. Large-scale Assessments in Education, 14(1), Article 56. https://doi.org/10.1186/s40536-026-00328-4

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00328-4

Keywords: screen time, adolescents, digital literacy, computational thinking, parental mediation, ICILS 2023, socioeconomic status, computer skills, Parental, screentime, limits, child

The study matters far beyond Botswana. Governments across Sub-Saharan Africa, from Rwanda to Kenya to South Africa, have poured resources into digital education initiatives in pursuit of the United Nations’ Sustainable Development Goal 4, which calls for inclusive, equitable, quality education for all. The Botswana findings add to a growing body of evidence that handing out hardware is the easiest part of educational transformation and, by itself, among the least effective. What determines success, the researchers argue, is a far messier combination of infrastructure, competence, pedagogy, and governance.

To capture that complexity, the team adopted a pragmatic, mixed-methods design rooted in two complementary theoretical traditions: the Technology Acceptance Model, which explains why people embrace or reject technology, and Social Constructivist Theory, which frames learning as a collaborative, socially mediated process. Quantitative data came from 252 final-year students selected through stratified random sampling, while qualitative insights were drawn from 86 participants, including students, teachers, school managers, information technology officers, and a Ministry of Education and Skills Development representative. The researchers triangulated surveys, semi-structured interviews, focus group discussions, open-ended questionnaires, and direct classroom observations across five schools in four districts chosen to span different levels of urbanization and connectivity.

The infrastructure findings were blunt. Among surveyed students, 69 percent rated internet connectivity as poor or very poor, and only 22 percent reported having operational projectors in their classrooms. Computer laboratories were frequently booked for formal ICT lessons, leaving other classes locked out. One student captured the frustration in a quote that has become emblematic of the study: we have laptops, but without reliable internet they are like typewriters; we can only type assignments, not really learn online. Through the lens of the Technology Acceptance Model, such conditions directly erode what researchers call perceived usefulness and perceived ease of use, the two perceptions that Davis’s classic model identifies as the primary drivers of technology adoption.

Digital competence told a similarly nuanced story. Roughly 60 percent of students expressed confidence using digital applications, but 44 percent said they had received inadequate digital training, and many of their skills were informal ones geared toward entertainment rather than academic work. Among teachers, the study found a generational divide: younger educators adapted quickly to new applications, while some older colleagues reported anxiety and struggled with basic functions. Yet the regression analyses were striking. Application competence was only a weak positive predictor of mLearning integration, with a standardized coefficient of 0.12 and a p-value of 0.085, and perceived training sufficiency showed no significant relationship at all. Together, training and competence explained a mere 2.4 percent of the variance in how thoroughly mobile learning was integrated into classrooms.

That statistical result cuts against a common assumption, namely that better-trained users automatically integrate technology more deeply. The researchers interpret it as evidence that training has focused on operating devices rather than transforming teaching. Classroom observations confirmed that instruction remained overwhelmingly teacher-centred, with laptop use largely confined to dedicated ICT lessons. Students reported using their laptops more at home than at school because lessons still relied, in one student’s words, on chalk and talk. The study situates this pattern within what the literature calls a digital facade: technology is present, but pedagogy has not changed, so devices serve as expensive substitutes for notes and presentations rather than as tools for the collaborative knowledge construction that social constructivism prizes.

The weakest dimension of all was policy awareness. A remarkable 93.7 percent of students held neutral views about whether mLearning policies even existed, and only 6.7 percent agreed or disagreed that such policies were in place. Teachers and school managers were barely better informed. We were briefed on the laptop initiative, but no policy was shared with us, one teacher reported. A school manager explained that ministry circulars addressed distribution, maintenance, and reporting of laptops, but offered no guidance on how the devices should actually be used in daily instruction. Inferential tests found no significant differences in policy awareness across gender, age, or school, indicating the gap is system-wide rather than localized.

From these findings the researchers built their central contribution: the Strategic mLearning Implementation Framework, or SmLIF. Unlike the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, or the FRAME model, which each emphasize single slices of the problem, SmLIF layers technological, pedagogical, institutional, and governance dimensions into one iterative design. Its foundation layer covers policy, governance, and infrastructure readiness. An operational layer addresses teacher and student preparation, digital pedagogy, and technical support. A strategic enabler layer institutionalizes monitoring and evaluation, feeding a continuous Plan, Act, Review, Improve cycle that treats implementation as an ongoing process rather than a one-time rollout.

The implications for policymakers are pointed. National digital education programs, the authors conclude, must be accompanied by reliable internet access, adequate physical infrastructure, clear school-level policies, and continuous evaluation systems. Teacher training needs to move beyond basic device skills toward pedagogical methods, collaboration, and differentiated support that meets educators at varying skill levels. Without those complements, the study warns, even the most ambitious laptop programs risk producing symbolic rather than substantive change, leaving devices idle in bags while instruction proceeds exactly as it did before.

The researchers call for follow-up work to validate and refine SmLIF in other educational settings, including longitudinal studies tracking how technology use and teaching practices evolve over time and comparative investigations across countries. They also flag emerging frontiers such as the impact of mobile learning on digital equity and safety, learning analytics, and sustainable digital pedagogies integrated with artificial intelligence. For now, the message from Botswana’s classrooms is clear and increasingly resonant across the developing world: the digital transformation of education begins, but does not end, with a laptop on every desk.

Subject of Research: Mobile learning implementation in Botswana's public senior secondary schools following a national laptop initiative

Article Title: Investigation of mobile learning implementation in Botswana secondary schools for improved academic and digital literacy outcomes

Article References: Mabalane, D. K., & Batane, T. (2026). Investigation of mobile learning implementation in Botswana secondary schools for improved academic and digital literacy outcomes. Discover Education, 5(1), Article 802. https://doi.org/10.1007/s44217-026-02010-x

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02010-x

Keywords: mobile learning, Botswana, digital literacy, secondary education, Technology Acceptance Model, social constructivism, educational technology, laptop initiative, education policy, digital divide, teacher training, Sub-Saharan Africa

The study, led by Yusuf Keskin, Sevgi Coşkun Keskin, and Esma Budak of Sakarya University, involved 19 preschool children aged 48 to 72 months and their mothers, all recruited from a private school in the Maltepe district of Istanbul. Over four weeks, the children participated in a series of classroom activities designed around play, drama, guided discussion, and visual materials, each lasting roughly 20 to 30 minutes and woven into their regular preschool routine. The activities addressed digital boundaries, personal data awareness, content evaluation, and responsible digital decision-making. Alongside the classroom component, mothers received a family participation activity and an informational letter offering practical guidance on digital sharing, content selection, and personal data protection. All 19 children completed every classroom activity, and all 19 mothers completed the family component and both rounds of interviews, with no participants withdrawing.

Before and immediately after the intervention, researchers conducted semi-structured interviews with both children and mothers. Child interviews lasted about 10 to 15 minutes each, conducted individually in a quiet room familiar to the children, with questions phrased in age-appropriate language. Because of the children’s developmental stage, these interviews were not audio-recorded; instead, the researcher documented responses in writing using structured forms, reviewing them immediately afterward to verify completeness and minimize recall bias. Mother interviews, lasting 20 to 30 minutes, were audio-recorded and transcribed verbatim. The research team then applied qualitative content analysis, coding the transcripts inductively before organizing categories into broader themes aligned with the study’s research questions. Roughly one-quarter of the transcripts were independently coded by a second researcher, and the team refined the coding framework through discussion until consensus was reached, using MAXQDA Analytics Pro 2022 to manage the coded segments.

The technical findings reveal a shift in how children conceptualized digital privacy itself. Before the intervention, children’s responses were dominated by a lack of knowledge—digital privacy was largely an unfamiliar abstraction. Afterward, references to privacy violations through digital devices became considerably more frequent, while expressions of limited knowledge declined markedly. One child, identified as C11, initially described digital privacy as very difficult but later explained that the computer and phone camera can see our footprints. Though phrased in child-appropriate language, the statement suggests an emerging recognition that digital activities leave traces and that connected technologies can collect or reveal personal information. References to screen addiction also increased slightly, indicating that some children began associating digital privacy with broader ideas about self-regulation and responsible behavior.

Children’s strategies for evaluating digital content became more structured as well. Before the intervention, responses frequently reflected uncertainty and limited recognition of digital risks, with high frequencies for ignoring content risks and expressions of surprise, fear, or confusion. Afterward, children more often mentioned checking age ratings, recognizing violent or explicit material, and closing or avoiding unsuitable content. Participant C5, who initially reported simply asking her mother when encountering inappropriate material, later described a more self-directed response, explaining that she closes it and referring to age-appropriateness indicators encountered while selecting content. The researchers note that the reduced frequency of seeking family support should not be read as a diminished role for mothers; rather, some children appeared to be applying strategies previously discussed or modeled within family interactions when making their own decisions.

Perhaps the most striking technical development concerned personal data awareness. Before the intervention, most children’s responses reflected limited familiarity with the concept. Afterward, references demonstrating awareness of personal information became considerably more frequent. Participant C3, who initially reported no familiarity with personal data, later identified examples including address, ID number, credit card number, and password. The researchers interpret these responses as reflecting an emerging recognition of personal information within everyday digital experiences rather than a sophisticated understanding of digital surveillance or data processing. Importantly, several children continued to express uncertainty even after the program, suggesting that understanding of personal data protection develops gradually during early childhood and may require continued educational reinforcement rather than arriving as a single lesson.

Mothers’ accounts revealed a parallel but distinct pattern of reported change. Before the intervention, their descriptions of digital privacy primarily emphasized limiting children’s exposure to inappropriate content. Afterward, their accounts increasingly incorporated considerations of digital sharing, responsible technology use, and everyday family practices. Participant M13 initially acknowledged limited supervision, saying she could not say she paid attention to every video and was not able to restrict it. Following the intervention, she described a more active evaluative approach, noting that she pays attention to the content they watch and that sometimes they close it even before she warns them. References to careful or restricted sharing of children’s digital content became substantially more frequent, while reliance on trust-based sharing within close social circles and authority-based decision-making appeared less often. Several mothers also reported greater attention to password use, protecting personal information, and avoiding unnecessary storage of personal data on devices.

One mother, M14, described how her children questioned her social media habits after the program. She used to post their photos on Instagram, she explained, and both of her sons said together, Mom, don’t post them—it’s not good, because there are bad people. Another mother, M16, reported that she did not know she needed to check certain criteria when downloading games. The researchers emphasize that these accounts do not demonstrate that children consistently influenced parental behavior across all participating families; rather, they illustrate interactional patterns observed in a subset of the qualitative data, in which conversations initiated during the educational activities were reflected in everyday family life. In several families, children’s emerging awareness appeared to encourage mothers to reconsider aspects of their own digital privacy practices, suggesting patterns consistent with reciprocal learning within family contexts.

Building on these findings, the study proposes the Reciprocal Digital Privacy Learning Model as a tentative interpretive framework grounded in participants’ reported experiences. The model illustrates a reciprocal process in which developmentally appropriate digital privacy education encourages children’s emerging awareness of digital privacy, personal data, content evaluation, and responsible digital behavior, while simultaneously creating opportunities for reflection within family interactions. As children begin to apply and discuss these understandings in everyday situations, mothers may reconsider aspects of their own digital sharing, content mediation, and personal data protection. These evolving interactions may, in turn, reinforce children’s developing understandings through ongoing family communication. Rather than representing a linear sequence of effects, the model conceptualizes digital privacy learning as a dynamic and reciprocal process in which children and mothers jointly contribute to the continuing negotiation of digital privacy practices within family life. The researchers stress that the model complements rather than replaces established theories of parental mediation, extending the traditional parent-to-child view of digital socialization by recognizing young children as active participants who may contribute to family discussions.

The study’s limitations are significant and carefully acknowledged. The research was conducted with a small group from a single private preschool in Türkiye, and the findings should be interpreted within that specific educational and socio-cultural context rather than generalized to public educational settings or families with diverse socioeconomic characteristics. Because no longitudinal follow-up was conducted, it remains unclear whether the reported developments would persist over time. The single-group pre-post design also means the reported changes cannot be attributed to the intervention alone; children’s ongoing developmental maturation, repeated participation in interviews, increased familiarity with the interview process, social desirability, and demand characteristics may all have influenced responses. As no independent observational triangulation was conducted, the extent to which reported changes reflected participants’ experiences rather than expectations associated with the research context cannot be fully determined. Future research involving larger and more diverse samples, longitudinal designs, and different educational settings will be needed to examine the transferability and broader educational relevance of these interactional patterns. Still, the study suggests that digital privacy education can be meaningfully introduced during the preschool years through developmentally appropriate, play-based, and family-supported experiences—and that families, not just classrooms, may be essential partners in shaping how the youngest digital citizens learn to protect their boundaries online.

Subject of Research: Digital privacy socialization in early childhood through a qualitative intervention study in Türkiye

Article Title: Digital privacy socialization in early childhood through a qualitative intervention study in Türkiye

Article References: Keskin, Y., Keskin, S. C., & Budak, E. (2026). Digital privacy socialization in early childhood through a qualitative intervention study in Türkiye. Discover Education, 5(1), Article 829. https://doi.org/10.1007/s44217-026-02082-9

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02082-9

Keywords: digital privacy, early childhood education, parental mediation, digital literacy, educational technology, Türkiye, qualitative research, sharenting, Digital, privacy, socialization, early

The index arrives at a moment when the stakes of connectivity could hardly be higher. The COVID-19 pandemic turned internet access from a convenience into a lifeline, underpinning remote learning, telemedicine, and e-government. Yet scholars have long recognized that the digital divide is no longer a simple binary between the connected and the unconnected. It is a layered phenomenon shaped by skills, cost, infrastructure quality, and social context. In low- and middle-income countries, high data prices and scarce devices keep people offline even where networks technically exist. Women are consistently less likely than men to own phones or use digital services. Rural areas lag behind cities. More than a billion people worldwide face accessibility barriers in digital platforms. In fragile states such as Somalia, all of these pressures are compounded by weak institutions, security concerns, and political instability that deter investment in infrastructure.

To measure this layered reality, the researchers surveyed 80 respondents drawn from Somalia’s telecommunications sector and academia in Mogadishu, a purposive sample of the stakeholders most engaged with the country’s ICT ecosystem. The sample size was computed with a standard statistical formula for a 95 percent confidence level and a 5 percent margin of error, and the questionnaire’s reliability was confirmed with Cronbach’s alpha of 0.82, well above the conventional 0.70 benchmark for internal consistency in social science research.

The technical heart of the study is the construction of the index itself. Because the original survey scales varied in measurement units, responses were rescaled into a uniform 0-to-1 range using min-max normalization. Negative indicators, such as perceived service cost, were inverted so that larger values always represent better inclusion conditions—a higher score means more affordable, not more expensive. The three dimensions were then combined with weights assigned according to theoretical relevance and empirical reliability from the digital inclusion literature: digital literacy received the highest weight of 0.40, reflecting consistent evidence that skills are the strongest determinant of meaningful participation beyond simple access, while affordability and infrastructure adequacy each received 0.30 as complementary structural enablers. A respondent’s DII is therefore the weighted sum of their normalized literacy, affordability, and infrastructure scores.

The team stress-tested this weighting scheme with a sensitivity analysis comparing the weighted index against an equally weighted alternative of 0.33 for each dimension. Subgroup rankings and overall interpretations remained largely unchanged, indicating the findings are robust to reasonable variations in weighting assumptions. Exploratory factor analysis and principal component analysis were deliberately avoided because the small sample and composite dimensions would render them statistically unreliable; the authors flag these techniques as validation priorities for future research using larger datasets.

The results paint a portrait of Somalia’s most digitally active population. Sixty percent of respondents were male and 40 percent female. Fifty-one percent were aged 18 to 24 and 33 percent were 25 to 34, with only 16 percent older than 35. Eighty-four percent held university degrees. Smartphones dominated internet access at 55 percent of responses, followed by laptops at 35 percent and desktops at just 10 percent—a mobile-first pattern typical of low- and middle-income countries, where cost constraints, unreliable electricity, and portability advantages have pushed stationary computing aside. Communication was the most frequent internet use, followed by online learning and information search, while e-business, job searching, and e-government services showed minimal engagement, suggesting digital platforms remain poorly embedded in Somalia’s economic and governance structures.

The index itself revealed a mean DII of 0.61 with a standard deviation of 0.12—moderate but uneven inclusion. The subgroup differences were striking. Respondents aged 35 and above recorded the lowest average score at 0.51, highlighting generational skill gaps. Males scored slightly higher than females, 0.61 versus 0.57, primarily reflecting greater laptop ownership among men, though this gap was narrower than typically observed in many low- and middle-income countries. The most dramatic variation emerged along educational lines: respondents with a master’s degree or higher achieved the highest score at 0.67, while those with only secondary education scored substantially lower at 0.48—a gradient the authors identify as the most significant predictor of digital inequality in the sample.

Inferential statistics sharpened the picture. A chi-square test confirmed a statistically significant association between education level and digital service use. Binary logistic regression showed that respondents with a bachelor’s degree were 2.4 times more likely to use e-government services than those with secondary education, with an odds ratio of 2.4 and a 95 percent confidence interval of 1.1 to 5.5, while master’s degree holders were 3.6 times more likely, with a confidence interval of 1.4 to 9.1. Ordinary least squares regression showed that each one-level increase in educational attainment was associated with a 0.07-point increase in DII score, with a standard error of 0.02 and a p-value of 0.002, after controlling for age and gender. Males scored marginally higher on the index and younger respondents tended to score higher as well, but neither effect remained statistically significant in the adjusted models. Education, in short, is the variable that matters.

When asked directly about barriers, respondents identified lack of digital literacy as the most significant factor, cited by 37 respondents or 29 percent, followed closely by high cost of service at 28 percent, lack of infrastructure at 24 percent, and political instability at 19 percent. Perceptions of the digital landscape itself were revealing: 81 percent recognized a gap between rural and urban areas, 69 percent believed political instability worsens the divide, and 58 percent agreed the government promotes digital inclusion, though 28 percent remained uncertain. The authors are careful to note that these findings reflect the perspectives of digitally engaged stakeholders in Mogadishu rather than a nationally representative survey, and should not be generalized to rural communities or marginalized populations where infrastructure and access differ substantially.

Even with those caveats, the study maps each barrier to an actionable solution and offers an integrated policy roadmap. The recommendations include community-based digital literacy programs targeting people with lower educational attainment, women, older adults, and rural populations; public-private partnerships to subsidize internet costs and launch low-cost smartphone initiatives; expanded broadband infrastructure beyond urban centers; stronger governance frameworks to encourage private investment; and e-government and e-business platforms designed according to universal accessibility principles. The proposed index, the authors argue, offers a practical framework for assessing multidimensional digital inclusion in fragile-state settings—one that future research should validate against nationally representative datasets and internationally recognized indicators from bodies such as the International Telecommunication Union and GSMA. If the first number from Somalia’s digital ecosystem is any guide, the path to inclusion runs not through cables and towers alone, but through classrooms.

Subject of Research: Science

Article Title: Development and validation of a multidimensional digital inequality index for assessing digital literacy, affordability, infrastructure, and socio-demographic determinants of digital inclusion

Article References: Ishaq, O. M., Islam, F., Alam, M. J., Mumin, A. R. O., Hossain, S. M., Karim, H. A., & Sarker, M. T. (2026). Development and validation of a multidimensional digital inequality index for assessing digital literacy, affordability, infrastructure, and socio-demographic determinants of digital inclusion. Discover Education, 5(1), Article 842. https://doi.org/10.1007/s44217-026-02077-6

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02077-6

Keywords: digital divide, digital inequality, Somalia, digital literacy, digital inclusion, ICT policy, education, fragile states, Development, validation, multidimensional, digital

The research team, led by Fabio Tortella of the Azienda Ospedaliera Universitaria Integrata di Verona and Silvia Gianola of the IRCCS Istituto Ortopedico Galeazzi in Milan, with senior authors Giacomo Rossettini and Alvisa Palese, surveyed students enrolled in nursing programs at three Italian universities. The questionnaire was developed by a panel of experts based on the available literature and pilot-tested before deployment, and the survey itself was reported following three established methodological checklists: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES), the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), and the Consensus-Based Checklist for Reporting of Survey Studies (CROSS). That triple-reporting discipline matters, because online e-surveys are notoriously vulnerable to inflated response rates and self-selection bias, and the authors anchored their findings in a framework designed to keep those distortions in check.

In total, 812 nursing students participated, a response rate of 58.4 percent — a respectable figure for an online academic survey. The headline finding is one of near-universal awareness paired with cautious ambivalence. Most participants were aware of AI chatbots, with 700 of the 812 respondents, or 86.2 percent, reporting that they had heard of them. Yet when asked to rate the usefulness of these tools, the median response was only 2 out of 4, with an interquartile range of 2 to 3 — a rating the researchers characterized as “Somewhat useful.” And when the survey turned to the introduction of AI chatbots in education specifically, 445 students, or 54.8 percent, said it was too early to have a clear opinion. In other words, the generation that grew up with smartphones is not rushing to endorse the technology that could reshape how they learn to care for patients.

Beneath the aggregate numbers, the inferential statistics exposed demographic fault lines that the authors say carry real educational implications. Increasing age was associated with lower odds of having heard of AI chatbots, with an odds ratio of 0.96 (95 percent CI 0.93 to 0.99; p = 0.021), and with lower odds of having used them for academic purposes, with an odds ratio of 0.97 (95 percent CI 0.94 to 0.99; p = 0.046). The effect sizes are small per year of age, but across a cohort spanning multiple years they compound into measurable awareness gaps. Male students had higher odds of being aware of AI chatbots than female students, with an odds ratio of 2.06 (95 percent CI 1.19 to 3.55; p = 0.009), and among those with previous academic use, male students also had higher odds of reporting more frequent use, with an odds ratio of 2.15 (95 percent CI 1.28 to 3.63; p = 0.004). The gender gap in both awareness and intensity of use suggests that the benefits of AI tools may not be distributed evenly across the future nursing workforce, which is predominantly female.

Perhaps the most counterintuitive finding concerns academic progression. Students with regular academic progression had lower odds of previous academic use of AI chatbots than those experiencing difficulties in their academic progression, with an odds ratio of 0.71 (95 percent CI 0.51 to 0.99; p = 0.044). The authors interpret this pattern as evidence that students facing academic challenges may be turning to chatbots as compensatory aids — tools that help them keep pace when conventional study strategies fall short. That interpretation raises a double-edged question for educators: are struggling students using AI to genuinely support their learning, or are they leaning on a technology that masks underlying gaps in knowledge and competence? The study cannot answer that question directly, but it flags the population where educational vigilance may be most needed.

Digital self-confidence emerged as another powerful predictor. Students who rated their own digital competence positively had higher odds of reporting a greater likelihood of future AI chatbot use, with an odds ratio of 1.97 (95 percent CI 1.08 to 3.59; p = 0.026). This finding cuts to the heart of the digital literacy debate: perceived competence, not just actual competence, shapes whether students will engage with emerging technologies. If a student believes they are digitally capable, they are roughly twice as likely to say they will use AI chatbots going forward. The authors argue that this self-perception is precisely the kind of variable that educational interventions can target — building both genuine skills and the confidence to apply them responsibly.

Technically, the study’s design reflects the current state of AI chatbot research in health professions education. The term “AI chatbot” in this context refers to conversational systems built on large language models — the technology behind generative pre-trained transformers (GPT) — which produce fluent, human-like text in response to natural language prompts. These systems can summarize clinical guidelines, explain pharmacological mechanisms, draft care plans, and answer study questions, but they also carry well-documented limitations: they can generate plausible-sounding but incorrect information, they lack clinical judgment, and they cannot replace the relational core of nursing practice. The authors’ expert-panel questionnaire was designed to capture students’ knowledge of these tools, their patterns of academic use, and their perceived benefits and limitations, providing a structured evidence base rather than anecdote.

The methodological rigor of the survey deserves emphasis in an era when AI adoption studies often rely on convenience samples. By recruiting across three universities and reporting against CHERRIES, STROBE, and CROSS simultaneously, the team addressed the reproducibility crisis that has plagued e-survey research. The response rate of 58.4 percent, while not perfect, is substantially higher than the single-digit rates common in unsolicited online surveys, and the multicentre design reduces the risk that findings reflect the culture of a single institution. The use of descriptive statistics such as the interquartile range alongside inferential measures such as odds ratios with 95 percent confidence intervals gives readers a transparent view of both central tendency and uncertainty.

What do these findings mean for the future of nursing education? The authors conclude that the evidence supports the inclusion of digital literacy education and ethical guidance in nursing programs to promote informed and responsible use of AI chatbots. The near-universal awareness combined with the majority’s hesitation about educational integration suggests that students are not resisting the technology — they are waiting for frameworks that tell them when, how, and why to use it. The gender and age gaps identified in the study point to populations where targeted interventions may be most valuable, and the association between academic difficulty and AI use highlights the need for educators to understand what struggling students are actually doing with these tools.

The study also arrives at a moment when health systems worldwide are grappling with how to prepare the next generation of clinicians for an AI-augmented workplace. Nursing is the largest profession in health care, and the students surveyed here will spend their careers alongside systems that increasingly embed conversational AI — from triage support to patient education platforms. The Italian findings suggest that the pipeline from awareness to competent, ethical use is not automatic. It requires deliberate curriculum design, explicit ethical guidance, and continued research into the implications of AI for learning outcomes. The authors call for further research to evaluate the implications of AI chatbots for nursing education and to identify appropriate strategies for their integration into the curriculum. Until those strategies exist, the students’ own verdict stands: the technology is here, everyone knows about it, but it is too early to say what it will do to the way nurses learn to care.

Subject of Research: Medical Education

Article Title: What are the knowledge, use, and perceived benefits and limitations of artificial intelligence chatbots among Italian nursing students? A cross-sectional multicentre study

Article References: Tortella, F., Gianola, S., Castellini, G., Turolla, A., Landuzzi, M. G., Cook, C., Corradi, F., Bulfone, G., Canzan, F., Chiappinotto, S., Sist, L., Rossettini, G., & Palese, A. (2026). What are the knowledge, use, and perceived benefits and limitations of artificial intelligence chatbots among Italian nursing students? A cross-sectional multicentre study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10439-9

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10439-9

Keywords: artificial intelligence, AI chatbots, nursing education, nursing students, digital literacy, medical education, Italy, survey research, knowledge, perceived, benefits, limitations

The study, published in Discover Education, is notable for how it defines its target. Rather than treating cybersecurity awareness as a single quality a person either has or lacks, the researchers grounded their survey in two behavioral models widely used in information security research: Protection Motivation Theory and the Health Belief Model. Protection Motivation Theory holds that people perform two appraisals before acting on a warning: a threat appraisal, weighing how serious and likely a danger is, and a coping appraisal, weighing whether the recommended behavior actually works and whether they can perform it. The Health Belief Model, originally developed to explain preventive health behavior, adds that people act when they believe they are susceptible, that the consequences are serious, that a behavior is beneficial, that barriers can be overcome, and that an external trigger, a cue to action, sets the process in motion. A structured training session, in this framing, is a deliberate cue to action aimed at shifting specific beliefs rather than raising one undifferentiated awareness level.

To measure those beliefs, the team administered a five-point Likert-type survey online to pilot high schools drawn from four school types: an Anatolian High School, a Girls’ Anatolian Imam Hatip High School, a Vocational and Technical Anatolian High School, and a Science High School. The first survey ran between November and December 2024. Instructors from the Department of Management Information Systems at Gebze Technical University, including research assistants and associate professors with expertise in information systems and cybersecurity, then delivered the same one-hour session in each school’s conference hall between February and March 2025. The training assumed no prior knowledge and covered basic cybersecurity concepts and terminology, personal security measures, and practical guidance on identifying fraudulent websites and misleading content, supported by examples, visual demonstrations and selected videos. The same survey was sent again in April 2025.

Of 1029 student responses before the training and 870 after, only students who completed both surveys and could be matched by school and student number were kept, producing a final paired sample of 601. The researchers acknowledge that this attrition was not random: chi-square comparisons between matched and unmatched students showed differences on gender, school and grade, and matching rates ranged from 15.6 percent to 86.7 percent across the four schools. The findings therefore describe the matched participants rather than the full baseline population.

The survey’s structure emerged from a principal component analysis on the pre-training responses, which produced a five-dimensional scale with acceptable overall internal consistency, Cronbach’s alpha of 0.86. The Kaiser-Meyer-Olkin measure of 0.905 and a significant Bartlett sphericity test confirmed the data were suitable for factor analysis, and the five factors, with initial eigenvalues of 6.24, 2.07, 1.30, 1.15 and 1.08, accounted for 56.42 percent of the total variance. The dimensions were conceptual awareness of cybersecurity, covering knowledge of malware, firewalls and social engineering; action-oriented behaviors, covering device authentication, email checking and password security; awareness of advanced protection mechanisms such as VPNs and two-factor authentication; security in online interactions and transactions, combining cyberbullying and card-security items; and digital risk tendency and privacy management, covering unsecured networks and credential sharing in gaming or streaming. Two-item subscales showed lower internal consistency and were interpreted with caution.

Paired-samples t-tests on the 601 matched students revealed a pattern the authors describe as mixed. Scores on action-oriented protective behaviors rose significantly, which the researchers interpret as a strengthening of coping appraisal in Protection Motivation Theory terms: students increasingly believed that protective measures were useful and within their reach. In contrast, self-reported scores on the conceptual-knowledge dimensions declined significantly, with a similar and more pronounced decrease on the advanced protection mechanisms factor. One plausible reading, the authors suggest, draws on the Dunning-Kruger effect: students who had overestimated their familiarity with cybersecurity terminology may have judged themselves more realistically after the training. Because the study measured only self-assessment and not actual knowledge, the same decline could equally reflect response-shift bias, the instability of a short self-report measure, or a real drop in reported confidence, so the interpretation is offered tentatively rather than as proven fact.

The most concrete evidence came from a practical task embedded at the end of the survey. Participants judged ten website screenshots, eight fraudulent and two genuine, designed from real-world attack cases and deliberately containing cues such as urgent requests, mismatches between displayed URLs and embedded hyperlinks, grammatical errors and abnormal visual design. McNemar tests showed that correct identification improved significantly on four of the fraudulent screens, for example rising from 50.4 percent to 63.9 percent on one item. The improvement was not uniform: no significant change appeared on two fraudulent screens, one of which 87.2 percent of students already identified correctly before the training, and correct identification decreased on two fraudulent screens that already had high pre-training rates. For the two genuine screens, accuracy did not improve and declined significantly on one, from 94.0 percent to 89.7 percent, meaning students became somewhat more likely to label legitimate pages as fraudulent. The authors suggest future training should give more attention to distinguishing fraudulent from genuine pages rather than treating every page as suspicious.

Demographic comparisons revealed differences that shifted in complex ways. Before the training, male students scored significantly higher on the knowledge-oriented dimensions, with the largest gap on conceptual awareness, where male students averaged 3.46 against 2.87 for female students, an effect size of d = 0.68. Female students scored higher on the online interaction and privacy dimensions. After the training, the gap on conceptual awareness disappeared, with female students averaging 2.47 and male students 2.39, while the difference on advanced protection mechanisms reversed in direction, reflecting a larger decline among male students rather than a gain among female students. Across schools, the Girls’ Anatolian Imam Hatip High School scored significantly lower than the other three schools on conceptual awareness before the training, yet after the training the school means converged, ranging from 2.29 to 2.47, and the between-school difference was no longer statistically significant. The authors caution that because the study included no control group and no time-by-school or time-by-gender interaction was tested, these are described as observed differences at each time point rather than as evidence that the training equalized the groups. Grade level, from 9th through 12th, was not a meaningful source of variation on any dimension.

The authors are careful about what the design can and cannot show. Because the same students were measured before and after without a comparison group, the results are interpreted as changes associated with the training rather than definitive proof of cause, and part of the measured change may reflect growing familiarity with the questions. The four pilot schools were purposively selected from a single district, each school type was represented by a single institution, and one all-girls school means the gender findings are partly confounded with school composition. Many statistical tests were conducted without correction for multiple comparisons, so individual significant results, especially the smaller between-group differences and the item-level screen comparisons, are treated as tentative, with more weight placed on the overall pattern.

Within those limits, the study supports integrating standardized cybersecurity training into secondary education. The training appears to have worked mainly as a cue to action in Health Belief Model terms, prompting students to apply protective measures, while strengthening the coping appraisal described by Protection Motivation Theory. The authors suggest that a single program spanning all high school grades may be a practical approach, given that grade-level means were similar before and after the training, and that standardized school-wide training may be more practical than curricula tailored to individual schools. They propose extending the research to pilot schools from different districts to examine the influence of school location, and to middle schools, since children are starting to use the internet at increasingly younger ages and the benefits of introducing cybersecurity education earlier remain to be examined.

Subject of Research: Cybersecurity

Article Title: Evaluation of cybersecurity awareness among high school students in Turkey before and after training

Article References: Cubukcu Cerasi, C., Erat, S., Huseynov, F., Kilic, A., Ozdenizci Kose, B., & Saritoprak, B. (2026). Evaluation of cybersecurity awareness among high school students in Turkey before and after training. Discover Education, 5(1), Article 1194. https://doi.org/10.1007/s44217-026-02338-4

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02338-4

Keywords: cybersecurity awareness, high school students, Turkey, phishing, cybersecurity education, Protection Motivation Theory, Health Belief Model, digital literacy, Evaluation, cybersecurity, awareness, high

The study’s most striking empirical finding is the near-total absence of perceived school-based instruction in foundational digital citizenship and online safety. Across all four student groups surveyed in Hungary and the Netherlands, every substantive response indicated that children had not encountered structured teaching on these topics at school, even though device access among the same students was close to universal. This mismatch between connectivity and curricular coverage suggests that the problem is not one of hardware or infrastructure but of institutional priorities. Children arrive at school already immersed in digital environments, yet the institutions responsible for cultivating their competences have largely left the interpretive and safety dimensions of that immersion to chance.

The survey evidence also reveals how early this informal learning begins. Parents reported that children form digital skills and habits before they ever enter formal schooling, which means that by the time teachers encounter students, patterns of use, expectations, and even risk exposure are already established. This developmental timing has consequences for policy design. Interventions that begin in primary school may arrive years after the most formative habits have taken shape, and the families who shaped those habits rarely receive guidance on how to fulfill the educational role they have effectively assumed.

The parent data from the DRONE project, which gathered responses from 135 participants across six European countries, quantifies this shift of responsibility. Families consistently identified themselves as the primary teachers of digital literacy, a role they occupy not by choice alone but because structured school programs are scarce and often implemented late. The researchers link this to a broader pattern in which formal education systems have changed far more slowly than the digital practices governing children’s everyday lives. The result is a widening gap between what children actually do with technology and what institutions formally teach about it.

This redistribution of educational labor is not neutral. The study emphasizes that community-based learning both mitigates and reproduces inequality. Families differ enormously in their own digital skills, confidence, and resources, and these differences mirror broader socio-economic and cultural stratifications. When a child’s digital education depends primarily on household circumstances, children from families with greater knowledge and capacity accumulate advantages that schools would at least theoretically be positioned to equalize. The authors argue that informal provision, left unexamined, can reinforce the very disparities that public education is meant to reduce.

The conceptual framework the researchers employ helps explain why this fragmentation has proven so persistent. Building on the ecological model of child development, they describe how digitalization has blurred the traditional boundaries between the micro-level settings of family and peer life, the meso-level connections among institutions, the exo-level structures of policy and platform design, and the macro-level cultural norms. New actors, particularly technology companies, now shape the conditions of children’s digital participation through regulation, infrastructure investment, and platform design, often without coordination with schools or community organizations. The learning ecosystem, in their terms, becomes broken when its components fail to integrate, producing fragmented and poorly coordinated arrangements that cannot sustain equitable learning outcomes.

To analyze this brokenness, the study combines three complementary lenses. The ecological model identifies where learning actually occurs, which the survey data show is overwhelmingly outside school. The rights-based framework, grounded in the United Nations Convention on the Rights of the Child and its General Comment No. 25, establishes what children and the adults and institutions responsible for their welfare are entitled to and required to do. Multi-stakeholder governance, the third lens, addresses how distributed actors with overlapping authority can coordinate their responsibilities. The authors argue that each framework answers a distinct question, and that only together do they capture the full complexity of digital literacy as a policy domain.

The rights-based dimension deserves particular attention because it reframes the debate in political rather than purely pedagogical terms. The SAILS project, which contributed the legal research component of the study, examines the balance between children’s rights to protection and their rights to participation in digital environments. These two principles are frequently treated as opposing forces, with protection arguments favoring restriction and participation arguments favoring access. The study suggests instead that both must be understood as entitlements that public institutions have a duty to support, and that current arrangements, in which families shoulder the burden without systematic support, fail on both counts.

The three EU-funded projects underlying the analysis each illuminate a different layer of the problem. DRONE focuses on digital literacy and the combat of disinformation among vulnerable groups of adolescents, drawing attention to how misinformation risks fall disproportionately on young people already facing social, linguistic, or educational disadvantage. EFFEct, funded under the Horizon Europe programme, investigates how students, teachers, school leaders, and parents engage with digital technology for educational purposes, providing the comparative survey evidence from Hungary and the Netherlands. SAILS contributes the legal analysis of children’s digital rights. Together they allow the researchers to integrate levels of analysis that are usually studied in isolation.

The authors’ argument about governance follows directly from this integration. If digital literacy is produced by families, peers, community organizations, schools, regulators, and platform designers simultaneously, then no single actor can be held accountable for outcomes, and no single reform can fix the system. They call for coordinated multi-stakeholder governance, curricular reform that addresses the identified gap in digital citizenship and online safety instruction, empowerment of teachers who currently lack the training and mandate to deliver such instruction, and inclusive community-based approaches that explicitly recognize the central role families already play. The alternative, they imply, is a continued drift in which responsibility is distributed by default rather than by design.

Positioning digital literacy as a political question rather than a technical one is perhaps the study’s most consequential contribution. The distribution of educational responsibility among states, schools, families, communities, and technology companies is a matter of power and accountability, not merely curriculum. When public institutions fail to recognize and support the actors who currently perform core educational functions outside formal schooling, they effectively privatize a public good while leaving its provision to those least equipped to carry it consistently. The researchers argue that reconstructing broken learning ecosystems requires policy frameworks that acknowledge community-based learning as a fundamental component of children’s digital development, supported rather than displaced by formal education.

For practitioners, the findings suggest several immediate implications. Schools seeking to close the curricular gap identified in the survey data would need to move beyond purely technical instruction toward sustained teaching of digital citizenship and online safety, areas where students reported no perceived provision at all. Teacher education programs would need to prepare educators for this expanded role, since the study notes that current instruction is often irregular, outdated, or narrowly technical. Parent support initiatives would need to recognize that families are already the primary educators in this domain and provide them with accessible guidance rather than assuming the school will take over. Community organizations, libraries, and non-formal learning settings would need to be integrated into policy frameworks as legitimate educational actors rather than peripheral supplements.

The comparative dimension of the evidence also matters. Because the survey data come from two European countries with different educational traditions, and the parent data from six countries, the finding that families bear primary responsibility appears to be robust across contexts rather than an artifact of any single national system. This consistency strengthens the argument that the fragmentation of digital literacy education is a structural feature of contemporary education systems rather than a local failure, and that remedies must therefore operate at the level of governance and policy design rather than through isolated institutional fixes.

Ultimately, the study reframes a familiar anxiety about children and screens into a question about institutional design. The issue is not simply that children use technology extensively, but that the institutions charged with preparing them for digital life have not adapted to the reality that most of this preparation already happens elsewhere. Recognizing families and communities as the primary site of digital literacy development, and building governance structures that support them, the authors contend, is the necessary first step toward an education ecosystem capable of serving all children equitably in a digital society.

Subject of Research: Community-based digital literacy development among European school-aged children

Article Title: Community learning drives children’s digital literacy development: evidence for reconstructing broken education ecosystems

Article References: Horgas, J., Salamon, E., & Protopsaltis, A. (2026). Community learning drives children’s digital literacy development: evidence for reconstructing broken education ecosystems. Frontiers in Political Science, 8, Article 1845968. https://doi.org/10.3389/fpos.2026.1845968

Image Credits: AI Generated

DOI: 10.3389/fpos.2026.1845968

Keywords: digital literacy, children, community learning, schools, parents, digital rights, education policy, Europe, digital divide, teacher training, online safety, family engagement