The research team, led by Amisha Singh of the Department of Applied Psychology at the University of Delhi, together with Parag Rajkhowa and Bahniman Boruah, set out to examine how responsibility, resilience, and well-being interact in a real-world population. Rather than studying a narrow slice of society, they recruited 200 adults from four Indian states: Delhi, Haryana, Uttar Pradesh, and Assam. The sample was evenly split between 100 men and 100 women and deliberately spanned a wide occupational range, including educators, healthcare workers, corporate professionals, manual laborers, entrepreneurs, and homemakers. This diversity matters, because much of the existing resilience literature has focused on Western, educated, and predominantly professional populations, leaving open the question of whether the same psychological dynamics operate in other socio-cultural settings.

Methodologically, the study is a textbook example of mixed-methods design. On the quantitative side, participants completed a demographic questionnaire, a researcher-developed Responsibility Assessment Scale designed to capture the weight of personal and professional obligations, the Nicholson McBride Resilience Questionnaire, a widely used instrument that measures an individual’s capacity to cope with adversity and recover from setbacks, and the Satisfaction with Life Scale, the classic five-item measure of subjective well-being developed by Ed Diener and colleagues in 1985. On the qualitative side, the researchers conducted semi-structured interviews, allowing participants to describe in their own words how their responsibilities shaped their inner lives. The combination is important: scales can reveal statistical patterns, but interviews reveal the mechanisms and meanings behind them.

The headline quantitative result is striking in its simplicity. Balancing multiple personal and professional roles was positively associated with resilience, meaning that participants who reported greater responsibility also reported a stronger capacity to withstand and bounce back from stress. Perhaps more surprising, the researchers found no statistically significant gender differences in this relationship. In a society where caregiving and household duties often fall disproportionately on women, one might have expected men and women to experience the responsibility-resilience link differently. Instead, the data suggest that the psychological payoff of being needed, of holding roles that others depend on, operates similarly across genders, at least in this sample.

Delving deeper into the statistical structure, the team tested a mediation model, a form of analysis that asks whether the relationship between two variables flows through a third. The results indicated an indirect association between responsibilities and psychological well-being through resilience. In plain terms, taking on responsibilities was not directly linked to feeling satisfied with life so much as it was linked to building resilience, which in turn was associated with greater well-being. This pathway is technically significant because it reframes responsibility not as a direct source of happiness or misery, but as a training ground for coping capacity. The mediation model follows the regression-based approach popularized by Andrew Hayes, allowing the researchers to estimate the indirect effect while accounting for the intercorrelations among the measured variables.

The qualitative interviews added texture to these numbers. Participants consistently described family dependence, caregiving, and professional obligations as important aspects of their resilience and well-being rather than purely as sources of strain. A homemaker managing a household, a healthcare worker supporting patients through difficult shifts, and an entrepreneur carrying the weight of a small business all described how being relied upon gave their efforts structure and meaning. This resonates with a broader theoretical tradition in psychology that views resilience not as a fixed trait but as a dynamic process, one that is exercised and strengthened through engagement with real demands. It also echoes earlier findings on benefit finding, in which caregivers and people facing adversity report unexpected psychological gains, including a stronger sense of purpose and competence, alongside the acknowledged burdens.

The study’s socio-cultural grounding is one of its most distinctive contributions. Resilience research has increasingly recognized that the concept is not culturally neutral. What counts as an adversity, what resources are available for coping, and what roles confer status or meaning all vary across societies. In the Indian context, where joint family structures, filial obligations, and community interdependence remain influential, responsibilities are often woven into identity rather than experienced as external impositions. The authors argue that their findings provide a contextually grounded understanding of the relationships among responsibility, resilience, and psychological well-being within this setting, complementing a literature that has been dominated by individualistic frameworks in which personal autonomy is treated as the primary engine of well-being.

The results also speak to a long-running debate about the nature of resilience itself. Some researchers treat resilience as a stable trait, something a person either has or lacks, while others conceptualize it as a state or process that fluctuates with circumstances. A third position, sometimes called transactional, holds that resilience emerges from ongoing transactions between individuals and their environments. The present findings lean toward the process view: if responsibilities, which are environmental and relational demands, are associated with higher resilience, then resilience is at least partly shaped by the roles people occupy and the demands those roles entail. This aligns with work by scholars such as Michael Ungar, who has argued that resilience must be understood across cultures as a function of the resources and obligations that communities provide, and with strengths-based approaches that emphasize what sustains people rather than what damages them.

There are, of course, important caveats. The study is cross-sectional, capturing a single moment in time, which means the direction of the associations cannot be definitively established. It is possible that resilient people are simply more willing and able to take on responsibilities, rather than responsibilities building resilience, or that some third factor, such as social support or personality, drives both. The sample of 200 adults, while diverse, was drawn from four states and may not generalize to India as a whole or to other countries. The Responsibility Assessment Scale was developed by the researchers themselves and has not yet accumulated the validation history of the older instruments used alongside it. The authors are candid about these limitations and explicitly recommend that future research examine these associations using longitudinal and culturally validated approaches to better understand how responsibility, resilience, and well-being develop and influence one another over time.

Even with those caveats, the implications are provocative. If the daily balancing of roles can foster the very psychological resources that protect against stress, then workplaces, families, and policymakers might reconsider how they frame the demands placed on adults. Rather than treating every obligation as a risk factor to be engineered away, the findings hint that meaningful responsibilities, when matched with adequate support, could be leveraged as assets in mental health promotion. For the educators, nurses, laborers, entrepreneurs, and homemakers of Delhi, Haryana, Uttar Pradesh, and Assam who took part in the study, the message is quietly empowering: the weight they carry each day may be doing more than testing their endurance. It may be building the very strength that keeps them standing.

Subject of Research: The relationship between personal and professional responsibilities, psychological resilience, and adult well-being in India

Article Title: Resilience in action: the influence of personal and professional responsibilities on adult well-being

Article References: Singh, A., Rajkhowa, P., & Boruah, B. (2026). Resilience in action: the influence of personal and professional responsibilities on adult well-being. SN Social Sciences, 6(9), Article 393. https://doi.org/10.1007/s43545-026-01687-z

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01687-z

Keywords: resilience, psychological well-being, responsibilities, work-life balance, India, mixed methods, life satisfaction, caregiving, gender differences, mediation analysis, SN Social Sciences, mental health

The core question the researchers set out to answer is deceptively simple: can a person’s risk of smartphone addiction be predicted accurately from measurable patterns of behavior, and can the model explain exactly which behaviors drive that prediction? This second half of the question matters enormously in practice. Black-box predictive models have long been viewed with suspicion in health and social science contexts, because clinicians, educators, and policymakers are reluctant to act on a risk score they cannot interrogate. The study’s answer is that high accuracy and interpretability need not be traded against each other. After preprocessing and feature transformation of the behavioral data, the team evaluated multiple classification models, and logistic regression emerged as the strongest performer, achieving an accuracy of 0.978 and a ROC-AUC of 0.999, figures that indicate near-ceiling discrimination between addicted and non-addicted usage profiles.

The choice of logistic regression as the winning model is itself instructive. In an era when deep neural networks dominate headlines, the study demonstrates that a linear classifier, whose coefficients can be read directly as the direction and magnitude of each feature’s influence, can outperform more complex alternatives when the underlying signal in the data is strong. Logistic regression estimates the probability that a given usage profile belongs to the addicted class by fitting a weighted sum of features through a logistic function. Its transparency means that every prediction can be traced back to specific behavioral inputs, which is precisely the property that regulatory frameworks and clinical ethics increasingly demand of automated decision systems.

Statistical analysis reinforced the machine learning results in striking fashion. The researchers report a p-value of approximately 8.71 times 10 to the power of negative 93 for the effect of daily usage on addiction, an astronomically small number that indicates the relationship between daily usage and addictive outcomes is overwhelmingly significant. In practical terms, a p-value of this magnitude means that the observed association would essentially never arise by chance if no true relationship existed. Daily usage emerged as the dominant behavioral signal, a finding that aligns with a substantial body of prior literature, including concurrent studies linking usage intensity to mobile phone addiction risk and meta-analyses documenting the effects of smartphone addiction on learning outcomes.

What elevates the study beyond a standard classification exercise is its systematic application of explainable artificial intelligence, or XAI. The authors deployed four complementary techniques: SHAP, LIME, Partial Dependence Plots, and Individual Conditional Expectation curves. SHAP, which stands for SHapley Additive exPlanations, borrows a concept from cooperative game theory to distribute the credit for each prediction fairly among the input features, providing a principled account of how much each behavior pushed a given user’s risk score up or down. LIME, or Local Interpretable Model-agnostic Explanations, takes the opposite approach, fitting a simple, interpretable model in the immediate neighborhood of a single prediction so that the reasoning behind that individual classification can be inspected directly.

Partial Dependence Plots and Individual Conditional Expectation curves address the global and local structure of the model’s behavior across the feature space. A Partial Dependence Plot shows how the model’s predicted probability of addiction changes on average as one feature, such as daily screen time, varies while other features are held in check, revealing thresholds and saturation effects that a single coefficient cannot capture. Individual Conditional Expectation curves disaggregate that average picture, tracing the response for individual users so that heterogeneity in the population becomes visible. Together, these four methods give researchers, clinicians, and app designers a layered view of the model: global importance rankings, local case-level explanations, and dose-response curves that show where behavioral risk begins to climb steeply.

The significance of this methodological package extends well beyond the specific dataset. Screening instruments for smartphone addiction, such as the Smartphone Addiction Scale and the Smartphone Overuse Screening Questionnaire developed in earlier research, rely on self-reported questionnaires that are vulnerable to response bias and require willing participation. A behavioral analytics pipeline that infers risk from objective usage features could, in principle, be integrated into digital wellbeing tools, allowing at-risk users to be flagged earlier and more consistently. The authors argue that the combination of high accuracy and interpretability makes their approach appropriate for practical deployment, a claim that rests on the model’s ability to justify its predictions to the humans who would act on them.

The study also situates itself within a rapidly growing research conversation. Earlier work has applied machine learning to problematic smartphone use among children and adolescents in South Korea with explainable methods, and integrative models have incorporated personality traits alongside usage data to predict addiction in teenagers. The present study contributes to this literature by demonstrating that a deliberately simple, interpretable classifier can match or exceed the performance one might expect from more elaborate architectures, provided the behavioral features are well constructed. It also underscores a recurring theme in the field: daily usage quantity is not merely correlated with addiction but appears to be its most powerful statistical signature, dwarfing the contribution of other measured behaviors.

Cautionary notes remain. The authors state that no new datasets were generated or analyzed during the study beyond the behavioral data used, and the underlying data source is a publicly available dataset on teen smartphone usage and addiction, which means the findings are bounded by how that dataset was collected and labeled. Self-reported addiction labels, even when paired with objective usage features, carry inherent measurement uncertainty, and generalization across age groups, cultures, and device ecosystems will require further validation. The authors also note that the research involved no studies with human participants, meaning the model operates on pre-existing behavioral records rather than prospective clinical follow-up. These limitations do not diminish the central result, but they frame it: this is a methodological demonstration of what explainable behavioral analytics can achieve, not yet a validated clinical screening instrument.

Nevertheless, the implications are considerable. As smartphone manufacturers expand digital wellbeing dashboards that already log screen time, app launches, and notification counts, the raw material for addiction-risk analytics is being generated on billions of devices every day. The study’s demonstration that a transparent logistic regression model, explained through SHAP, LIME, Partial Dependence Plots, and Individual Conditional Expectation curves, can predict addiction with an accuracy of 0.978 and a ROC-AUC of 0.999 suggests that early-warning systems for problematic use are technically feasible with methods simple enough to audit. The open question, which the authors’ work now sharpens, is not whether such predictions can be made, but how societies will choose to act on them, balancing the promise of early intervention against the privacy and autonomy concerns that always accompany behavioral surveillance at scale.

Subject of Research: Explainable machine learning prediction of smartphone addiction from behavioral usage data

Article Title: Behavioral analytics for smartphone addiction prediction: an explainable and interpretable machine learning approach

Article References: Urkude, V. R., Vairachilai, S., Urkude, S. V., & Bharaneedharan, S. V. B. (2026). Behavioral analytics for smartphone addiction prediction: an explainable and interpretable machine learning approach. SN Social Sciences, 6(9), Article 410. https://doi.org/10.1007/s43545-026-01697-x

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01697-x

Keywords: smartphone addiction, machine learning, explainable AI, SHAP, LIME, logistic regression, behavioral analytics, digital wellbeing, problematic smartphone use, statistical analysis, prediction models, SN Social Sciences

The concept of e-health readiness sits at the intersection of technology acceptance and health psychology. It captures the degree to which a person possesses the psychological preparedness, confidence, and willingness to adopt electronic health services such as telemedicine platforms, personal health record systems, and online appointment tools. Readiness is not simply a matter of owning a smartphone or having an internet connection. Drawing on theoretical foundations from Bandura’s work on self-efficacy and Ajzen’s theory of planned behavior, researchers have long argued that perceived usefulness, perceived ease of use, and confidence in one’s own abilities shape whether people actually embrace digital health tools. A scale that measures this latent construct reliably allows health systems to identify populations that need support before rolling out expensive digital infrastructure.

Adapting a measurement instrument from one language and culture to another is far more involved than translation. Guidelines from cross-cultural psychometrics, including the widely cited work of van de Vijver and Hambleton, warn that literal translations can distort item meaning, shift response patterns, and ultimately invalidate scores. The researchers followed standard adaptation procedures to ensure that the Turkish version preserved the conceptual content of the original scale while reading naturally for Turkish respondents. This process matters because constructs like readiness are embedded in cultural context: attitudes toward technology, trust in health institutions, and familiarity with digital services differ substantially between countries, and a poorly adapted scale could systematically overstate or understate a population’s preparedness.

A distinctive strength of the study lies in its statistical treatment of the data. Because responses on Likert-type scales are ordinal rather than continuous, treating them as interval data can bias factor analytic results. The researchers therefore used polychoric correlations combined with minimal residual extraction in their exploratory factor analysis, an approach recommended for ordinal variables. To determine how many factors underlie the scale, they did not rely on the often-criticized Kaiser criterion alone but also applied Horn’s parallel analysis and Velicer’s minimum average partial procedure, two of the most defensible factor retention methods available. Both exploratory and confirmatory approaches were used, giving the findings a level of robustness that exceeds typical scale adaptation studies.

The results were strikingly clear. Exploratory factor analysis revealed a single-factor structure, with that one factor accounting for 58.453 percent of the total variance, a substantial proportion for a unidimensional attitude scale. Confirmatory factor analysis, conducted with weighted least squares mean and variance adjusted estimation and theta parameterization, verified that the single-factor structure of the original version held in the Turkish sample. In other words, the scale measures one coherent construct, e-health readiness, and it does so in Turkish respondents just as it was designed to do in its original context. The consistency between the exploratory and confirmatory results, and their agreement with the parallel analysis and MAP procedures, strengthens confidence that the one-factor solution is not a statistical artifact.

Reliability, the degree to which a scale produces consistent scores, was assessed from multiple angles. Internal consistency was excellent: Cronbach’s alpha reached 0.938, and McDonald’s omega, an increasingly preferred coefficient because it relaxes the assumption of equal item loadings, matched it at 0.938. Reporting both coefficients is considered best practice in contemporary psychometrics, since alpha can underestimate reliability when items load unequally on the factor. Stability over time was examined with a test-retest design, yielding a correlation coefficient of 0.907, which indicates that the scale captures a stable attribute rather than a fleeting mood. Together, these figures place the Turkish adaptation among the most reliable instruments in the e-health measurement literature.

The study also tackled a question that many adaptation efforts neglect: does the scale function equivalently for men and women? Using multi-group confirmatory factor analysis, the researchers compared configural, metric, scalar, and strict invariance models, testing whether the scale measures the same construct with the same precision across gender groups. The changes in fit indices met established thresholds, with changes in the comparative fit index at or below 0.010 and changes in root mean square error of approximation at or below 0.015, criteria drawn from the influential work of Cheung and Rensvold. Achieving measurement invariance up to the strict level means that observed score differences between men and women, if they appear in future studies, can be interpreted as genuine differences in readiness rather than artifacts of the measurement instrument itself.

To establish criterion-related validity, the researchers examined correlations between the adapted scale and established e-health literacy measurement tools, including instruments in the tradition of Norman and Skinner’s eHEALS. Positive associations with e-health literacy measures are exactly what theory predicts, since individuals who feel capable of finding and using health information online should also report greater readiness to adopt electronic health services. This convergent evidence ties the new Turkish instrument into a broader international research ecosystem, allowing findings from Turkish samples to be compared meaningfully with studies conducted elsewhere using related measures of digital health preparedness.

The timing of this work is significant for Türkiye specifically. National statistics from the Turkish Statistical Institute document rapidly rising household internet access and information and communication technology use, and the country’s e-Nabız personal health system has brought digital health records to millions of citizens. Demirci’s broader doctoral research, from which this study emerges, examines factors associated with the use of that very system, including e-health readiness, engagement, communication competence, and impact. A validated readiness scale gives Turkish health administrators an evidence-based instrument for segmenting populations, identifying groups that may need training or alternative service channels, and evaluating whether interventions designed to boost digital adoption are actually working.

The implications extend well beyond one country. Studies from Ethiopia, Iraq, Poland, and other settings have shown that e-health readiness assessment is a cornerstone of sustainable digital health implementation, particularly in developing health systems where resources are limited and failed rollouts are costly. Research on older adults has repeatedly found that psychological factors, not just access, determine whether digital health services are embraced, and randomized trials of e-health interventions increasingly measure readiness as a moderator of outcomes. By providing a rigorously validated Turkish instrument, the study fills a gap in a large and growing literature. The authors recommend that future research test the scale in more diverse population groups, a sensible next step given that psychometric properties can shift across age bands, educational levels, and clinical populations. For now, the Turkish adaptation stands as a model of careful cross-cultural measurement: ordinal-appropriate statistics, dual reliability coefficients, invariance testing, and criterion validation, all converging on a single, trustworthy measure of how ready people are to bring their health into the digital age.

Subject of Research: Turkish cultural adaptation and psychometric validation of the E-Health Readiness Scale

Article Title: Adaptatıon of the E-Health Readıness Scale to Turkısh culture

Article References: Demirci, H. F., & Yardan, E. D. (2026). Adaptatıon of the E-Health Readıness Scale to Turkısh culture. SN Social Sciences, 6(10), Article 520. https://doi.org/10.1007/s43545-026-01837-3

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01837-3

Keywords: e-health readiness, scale adaptation, psychometrics, validity, reliability, confirmatory factor analysis, exploratory factor analysis, measurement invariance, digital health, Turkey, e-health literacy, SN Social Sciences

The research, conducted by Fitria Fitria of UIN Maulana Malik Ibrahim in Malang, Faishol Mahmud Adam Ibrahim of the University of the Holy Quran and Islamic Sciences in Omdurman, Sudan, and Irham Irham of Universiti Sains Malaysia, examined how Indonesian pre-service Arabic teachers perceive the status and role of Arabic and how those perceptions shape their professional aspirations. The team employed a convergent parallel mixed-methods design, a strategy in which quantitative questionnaire data and qualitative semi-structured interview data are collected and analyzed in parallel before being merged into a single interpretive account. This design allowed the researchers to measure broad attitudinal patterns across the participant group while simultaneously probing the personal narratives and ideological commitments that underlie those patterns.

Two theoretical lenses guided the analysis. The first is language ideology, a framework in sociolinguistics concerned with the beliefs, often implicit, that speakers hold about what languages are, what they are for, and who has the authority to speak and teach them. The second is native speakerism, a concept introduced in applied linguistics to describe the assumption that native speakers of a language are, by virtue of birth alone, its ideal owners and therefore its ideal teachers. Native speakerism has been extensively documented in English language teaching, where it structures hiring practices, curriculum design, and student expectations worldwide. Its application to Arabic pedagogy in a non-Arab country adds an important comparative dimension, because Arabic carries a sacral weight that English does not.

The study’s central finding is what the authors describe as a sacred–instrumental duality in participants’ motivations for learning Arabic. On one side of this duality, the future teachers view Arabic as the indispensable key to Islamic knowledge, the language in which the Quran, the hadith corpus, and centuries of religious scholarship are encoded. Learning Arabic is thus framed as an act of religious devotion and a prerequisite for authentic engagement with sacred texts. On the other side, participants treat Arabic as a strategic resource, a form of what the sociologist Pierre Bourdieu called linguistic capital: an asset that can be converted into educational opportunity, international mobility, and professional advantage. The same language that opens the gates of theology also opens doors to scholarships in the Arab world, employment in Islamic institutions, and participation in transnational Muslim networks.

This duality extends into the participants’ geographic imagination. The researchers identified a strong attachment to what they term sacred geography, particularly Saudi Arabia, which participants associated with both linguistic and religious authenticity. In the classical Islamic tradition, scholars developed elaborate mappings of the world organized around the Kaaba in Mecca, a phenomenon historians of science have described as the culmination of Islamic sacred geography. The study suggests that a modern descendant of this orientation persists in the way Indonesian trainee teachers imagine the Arab heartland as the place where Arabic is spoken in its purest and most legitimate form. Mecca and Medina function not merely as pilgrimage destinations but as symbolic centers of linguistic authority, against which local varieties of Arabic and local teaching practices are implicitly measured.

Perhaps the most nuanced finding concerns the participants’ stance toward native speakers. On the surface, the future teachers idealized the native speaker as the authoritative owner of Arabic, reproducing the classic native speakerist hierarchy in which birthplace confers pedagogical legitimacy. Yet when the researchers examined the participants’ actual pedagogical values, they found a pragmatic shift underway. The trainees prioritized academic qualification and professional training over nativeness alone, indicating that they regarded a well-trained non-native teacher with strong credentials as at least equal to, and potentially preferable to, an untrained native speaker. This divergence between ideological idealization and practical evaluation suggests that native speakerism in Arabic teaching operates less as a rigid doctrine than as a negotiable framework, one that yields to considerations of competence, certification, and classroom effectiveness.

The Indonesian context is essential for understanding why these findings matter. Indonesia is home to the world’s largest Muslim population, and Arabic has long been embedded in its educational infrastructure, from the pesantren, the traditional Islamic boarding schools where classical texts are studied, to state Islamic universities where Arabic serves as a medium of instruction in international programs. Previous scholarship has documented persistent methodological problems in teaching Arabic to non-native speakers in Indonesia, as well as debates over the language’s place in the national curriculum. At the same time, research on English in Indonesian Islamic higher education has shown that students routinely weigh the promises of global languages against local religious and cultural commitments. The new study extends this line of inquiry to Arabic, revealing that the same negotiation between global utility and local identity plays out in the religiously charged domain of Arabic pedagogy.

The theoretical implications reach beyond Indonesia. The findings suggest that for many learners in Muslim-majority settings, Arabic pedagogy functions as a site of negotiation between spiritual idealization and localized professional competence. Language ideology and native speakerism, the authors argue, intersect in ways that are specific to sacred languages: the authority of the native speaker is reinforced by religious associations with the language’s places of origin, yet it is simultaneously tempered by the practical recognition that teaching is a skill requiring training. This produces a hybrid professional identity in which the future teacher is both a devotee approaching a sacred tongue and a technician mastering a teachable subject. The duality is not experienced as contradiction by the participants themselves; rather, it appears to be a stable and productive feature of their motivation.

For teacher educators and policymakers, the study carries concrete lessons. If pre-service Arabic teachers are motivated by both religious attachment and career aspirations, then teacher preparation programs that address only one of these dimensions are likely to miss the mark. Curricula that acknowledge the spiritual significance of Arabic while simultaneously building rigorous pedagogical and linguistic competence may be better positioned to develop teachers who can serve Indonesia’s diverse Islamic educational institutions. The finding that participants value qualifications over nativeness also offers support for strengthening local teacher training rather than relying on imported native-speaker models, an approach that has been criticized in English language teaching for marginalizing local expertise.

The study, published on 15 September 2026 as volume 6, article 436 of SN Social Sciences, was conducted as independent research without external funding and followed established ethical protocols, with informed consent obtained from all participants and pseudonyms used throughout to protect privacy. The authors note that the underlying data are not publicly available for confidentiality reasons but can be requested from the first author. As debates over language policy in superdiverse societies continue, the Indonesian case offers a vivid illustration of how a single language can be many things at once: a prayer, a paycheck, a passport, and a profession. For the next generation of Arabic teachers in the world’s largest Muslim-majority country, the classroom is where all of these meanings converge.

Subject of Research: Language ideology and native speakerism among Indonesian pre-service Arabic teachers

Article Title: Intersecting perspectives of Indonesian pre-service Arabic teachers towards Arabic: from a language of communication to expression of religious attachment

Article References: Fitria, F., Ibrahim, F. M. A., & Irham, I. (2026). Intersecting perspectives of Indonesian pre-service Arabic teachers towards Arabic: from a language of communication to expression of religious attachment. SN Social Sciences, 6(9), Article 436. https://doi.org/10.1007/s43545-026-01735-8

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01735-8

Keywords: Arabic language, Indonesia, pre-service teachers, language ideology, native speakerism, linguistic capital, sacred geography, religious identity, Islamic education, mixed methods, language teaching, SN Social Sciences

The collaborative intelligence perspective that anchors the study draws on a body of theory in human-computer interaction and learning science which holds that the most productive human-AI partnerships are those in which the human retains epistemic authority while the machine supplies speed, breadth, and fluency. In writing tasks, this means the AI may suggest phrasing, generate outlines, explain grammar, or summarize sources, but the student must decide what counts as an argument, which claims are defensible, and how the text should be organized for a specific audience. When this division of labor works well, the student learns more efficiently because cognitive load is reduced on low-level mechanical tasks and redirected toward higher-order thinking. When it works poorly, the student risks accepting fluent but shallow or inaccurate output, and the writing becomes a patchwork of machine text that the author does not fully understand or endorse. The Indonesian context adds a distinctive layer to this dynamic, because most generative AI systems are trained predominantly on English-language data while Indonesian students often write in English as an additional language.

English-medium academic writing is a requirement in many Indonesian universities, particularly for students preparing journal articles, theses, and international conference papers. For these students, generative AI presents an alluring proposition: a tireless, instant, and free editor that can smooth grammar, suggest academic vocabulary, and restructure sentences. The study situates itself in this reality, recognizing that Indonesian students arrive at AI tools with genuine practical needs. At the same time, universities in Indonesia, as elsewhere, have scrambled to formulate policies on AI use, with some institutions prohibiting it outright and others encouraging disciplined experimentation. The absence of settled policy creates an environment in which students largely regulate their own AI use, guided by peer norms, instructor expectations, and their own judgments about academic integrity. Understanding what students actually do, rather than what policies assume they do, is therefore an essential empirical task, and it is precisely the gap this research sets out to fill.

Methodologically, the study adopts an assessment-oriented design, gathering evidence of how students interact with generative AI during authentic academic writing activities. Rather than relying solely on surveys about attitudes, the researchers examine the texture of the interaction itself: how students prompt the AI, how they evaluate its suggestions, when they accept, reject, or modify output, and how the final written products reflect the negotiation between human intent and machine generation. This interactional focus is what distinguishes a collaborative intelligence analysis from a simple usage study. The unit of analysis is not the tool and not the student in isolation, but the dyad, the evolving partnership through which a text comes into being. Assessment in this framework is not merely grading the final essay; it is diagnosing the quality of the collaboration, including the student’s critical engagement, revision behavior, and willingness to exercise authorship over machine-generated material.

The findings, as framed by the researchers, point toward a picture of students as active negotiators rather than passive consumers of AI text. Students in the study engaged with generative AI for a range of sub-tasks within writing, from brainstorming topics and generating thesis statements to checking grammar and rephrasing awkward passages. Crucially, the collaborative intelligence lens reveals that the value of these interactions depends heavily on the student’s own disciplinary knowledge and language confidence. Students with stronger command of their subject and of academic English were better positioned to interrogate AI output, spot errors, and reshape suggestions to fit their arguments. Students with weaker foundations were more likely to accept output uncritically, which the study treats as a pedagogical warning sign. This pattern reinforces a conclusion that is emerging across the international research literature: AI literacy cannot be separated from domain literacy, and tools that promise to compensate for weak knowledge may instead entrench it if critical evaluation is absent.

One of the most technically interesting dimensions of the study is its attention to prompting behavior, the set of instructions students type into generative AI systems to elicit output. Prompting is, in effect, a new form of academic skill, blending elements of information retrieval, task specification, and iterative dialogue. Effective prompters provide context, specify audience and register, request alternatives, and feed back corrections across multiple turns. Novice prompters issue vague commands and receive generic text. The study’s interactional data show that Indonesian students vary widely along this spectrum, and that prompting quality is itself learnable. This observation has direct implications for curriculum design. If prompting, evaluating, and integrating AI output are teachable skills, then universities can respond to generative AI not with bans but with instruction, embedding AI interaction training inside existing writing courses where students can practice under guidance and receive feedback on both their prose and their process.

The collaborative intelligence perspective also reframes the perennial anxiety about academic integrity. From this viewpoint, the central question is not whether a student used AI but whether the student can account for the text, defend its claims, and explain its reasoning. A student who uses AI to polish grammar while retaining full ownership of ideas and structure is engaged in a fundamentally different activity from one who submits generated text on a topic they have never researched. The study argues that assessment practices should be designed to make this distinction visible, through oral defenses, process portfolios, draft histories, and in-class writing components that reveal the student’s actual competence. Such approaches align assessment with the reality of professional writing in the AI era, where working academics and professionals already use these tools under norms of disclosure and accountability.

The broader significance of the research extends beyond Indonesia. The country hosts one of the largest higher education systems in Southeast Asia, with tens of millions of students, and its experience with generative AI in writing offers lessons for any education system serving multilingual learners. Generative AI models perform unevenly across languages and cultural contexts, and students writing in an additional language face both opportunities and hazards: the tool can bridge a linguistic gap, but it can also flatten the author’s voice, introduce subtle factual errors, and produce text that sounds native while lacking depth. The Indonesian case thus functions as a natural laboratory for studying how AI mediation reshapes academic literacy in the Global South, where adoption has been rapid but institutional guidance has lagged. The study’s insistence on empirical, interaction-level evidence provides a model for researchers in other national contexts who want to move beyond attitude surveys and policy commentary.

For educators, the practical takeaways are concrete. Writing instruction should explicitly teach critical evaluation of AI output, including fact-checking generated claims against real sources, recognizing hallucinated citations, and revising machine suggestions for argumentative fit. Assessment should reward visible process, not just polished product. Institutional policy should define acceptable use with clarity and nuance rather than blunt prohibition, which research consistently shows drives use underground without improving judgment. And instructors themselves need professional development, because students’ AI practices will only be as sound as the feedback culture surrounding them. The study concludes that generative AI is not an external threat to academic writing but a new participant in it, and that the task of universities is to cultivate collaborative intelligence: partnerships in which human writers remain the authors of their ideas while machines serve as capable, critically supervised assistants.

Subject of Research: Indonesian university students' interaction with generative AI in academic writing from a collaborative intelligence perspective.

Article Title: Assessing Indonesian university students’ interaction with generative AI in academic writing activities: a collaborative intelligence perspective

Article References: Mulyono, H., Suryoputro, G., Azizah, U. S. A., & Falah, Z. A. (2026). Assessing Indonesian university students’ interaction with generative AI in academic writing activities: a collaborative intelligence perspective. SN Social Sciences, 6(9), Article 425. https://doi.org/10.1007/s43545-026-01716-x

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01716-x

Keywords: generative AI, academic writing, collaborative intelligence, Indonesian university students, human-AI interaction, higher education, AI literacy, assessment, prompting, academic integrity, SN Social Sciences, Assessing