The study applied the Theory of Planned Behavior to examine smartphone-related attitudes, subjective norms, perceived behavioral control, usage intention, actual use and scientific literacy. In this context, subjective norms referred to perceived social pressure surrounding smartphone use for science learning, while perceived behavioral control reflected students’ perceived ease or difficulty of learning science with a smartphone.

Researchers first conducted semi-structured interviews with 20 university students. Their accounts consistently represented attitudes, subjective norms, perceived behavioral control, usage intention and actual use, which informed the quantitative phase. Participants described both possible benefits and drawbacks: astronomy applications could broaden access to scientific information, while smartphones could also distract from learning.

The quantitative phase used an online questionnaire distributed through university and class WeChat groups at four universities in Shandong, Jiangxi and Guangxi provinces. Of 406 questionnaires received, 36 were excluded as rushed, careless or identical, leaving 370 valid responses. Participants were recruited through non-probability convenience sampling and voluntary participation, so the sample was not a random representation of Chinese university students. The final group included 101 men and 269 women across four undergraduate years.

The investigators analyzed the survey with partial least squares structural equation modeling. Reliability coefficients for the measured constructs ranged from 0.822 to 0.922, and the model met the reported checks for convergent and discriminant validity. Scientific literacy was assessed with a self-report scale rather than an objective test of scientific knowledge or performance. After measurement-model refinement, nine of the original 38 scientific-literacy indicators were retained.

Subjective norms had the largest direct positive association with self-reported scientific literacy, with a standardized path coefficient of 0.305. Actual use of smartphones for learning scientific knowledge followed at 0.286, while attitudes toward smartphone-based science learning had a coefficient of 0.179; all three associations were statistically significant. Together, the modeled predictors explained 49.1% of the variance in self-reported scientific literacy.

Subjective norms and attitudes also had significant indirect associations with scientific literacy through pathways involving usage intention and actual smartphone use. The estimated indirect coefficient was 0.126 for subjective norms and 0.116 for attitudes, indicating partial mediation in the specified model. These results describe statistical associations rather than causal boosts. Perceived behavioral control was positively associated with usage intention, with a coefficient of 0.134, but its indirect association with scientific literacy was not statistically significant. No direct path from perceived behavioral control to scientific literacy was estimated.

An Importance-Performance Matrix Analysis ranked subjective norms first in predictive importance, followed by attitudes, actual smartphone use, usage intention and perceived behavioral control. Subjective norms and actual use were placed in a quadrant combining relatively high importance with lower performance, leading the authors to identify them as areas for attention. Based on the results, the authors recommend supportive social environments, purposeful smartphone-based learning strategies, richer digital science resources and instructional designs that encourage support for smartphone learning from peers, teachers and parents.

The findings require several cautions. All major constructs were measured by the same self-report questionnaire at one time point, so temporal ordering cannot be established and common-method bias cannot be completely ruled out. Convenience sampling and self-selection limit generalizability, while the small fourth-year subgroup prevented reliable analysis of differences by academic year. The reduced scientific-literacy scale also leaned more heavily toward the nature-of-science dimension than the science, technology and society dimension, making it a narrower representation of the original concept. The authors say broader representative samples, multi-source or temporally separated data, further validation of the scale’s dimensional structure, and longitudinal or experimental designs could address these limitations.

Subject of Research: Associations between smartphone-use factors and self-reported scientific literacy among Chinese university students

Article Title: Impact of smartphone usage on university students' scientific literacy: a mixed-methods approach with importance-performance analysis

Article References: Luan, J., & Xin, S. (2026). Impact of smartphone usage on university students' scientific literacy: a mixed-methods approach with importance-performance analysis. Frontiers in Psychology, 17, Article 1888350. https://doi.org/10.3389/fpsyg.2026.1888350

Image Credits: AI Generated

DOI: 10.3389/fpsyg.2026.1888350

Keywords: scientific literacy, smartphone learning, university students, subjective norms, student attitudes, mixed-methods research, structural equation modeling, cross-sectional study, China, mobile learning, Impact, smartphone

The researchers examined whether personality development mediated relationships between four features of the boarding environment and students’ attitudes. The four features were peer interaction, independence, decision-making and separation from parental care. The analysis used social identity theory and Identity Process Theory, with the latter focusing on continuity, distinctiveness, self-esteem and efficacy as principles relevant to students’ adaptation to a changed social environment.

Students were recruited through stratified random sampling from secondary schools in Osun, Lagos, Ogun and Oyo states. The target population was reported as approximately 130,000 boarding students in the region, and 398 participants were invited using a sample-size calculation. The final analyzed sample comprised 310 students: 167 male students, reported as 53.0%, and 143 female students, reported as 46.1%. The article contains inconsistent age ranges, but it states that 127 respondents were aged 7 to 15 and 183 were aged 16 to 24.

Data were collected face to face with a structured questionnaire containing demographic questions and five-point Likert-scale items. The instrument was reviewed by lecturers at the authors’ institution and piloted with about 50 respondents. Exploratory and confirmatory factor analyses supported the study’s validity checks, with average variances extracted above 0.5 and composite-reliability coefficients above 0.7. However, the authors reported a Kaiser-Meyer-Olkin measure of 0.55, which they considered close to the borderline and a sign that the correlation patterns were relatively weak and the sample may not have been fully adequate for robust results.

The researchers used descriptive statistics, one-sample t-tests and structural equation modelling, including path diagrams for direct and indirect effects. The reported equation-level goodness-of-fit measure was 0.9787, while the root mean square error was described as sufficiently close to zero. Wald and likelihood-ratio tests indicated that the fitted model was consistent with the data. These statistics assess model fit, but they do not demonstrate that boarding routines caused changes in personality or attitudes.

In the detailed path analysis, peer interaction, independence and decision-making had positive, statistically significant associations with personality development. Separation from parental care had a negative but statistically insignificant association with personality development. The article’s abstract separately reported that peer interaction, independence, decision-making and separation from parental care each had positive, statistically significant associations with students’ attitudes. The model also reported a negative, statistically significant association between personality development and attitudes, as well as a negative direct association between peer interaction and attitudes.

The mediation analysis produced mixed results rather than a single uniform pattern. Peer interaction, independence and decision-making had negative indirect-effect coefficients that the authors described as statistically significant, whereas the positive indirect coefficient for separation from parental care was not significant. The authors stated that personality development partially mediated the peer-interaction relationship with attitudes. They also described changes in the independence and decision-making paths after mediation as moderation, although the cross-sectional path model does not demonstrate causal moderation or a temporal sequence.

Based on the fitted model, the authors proposed that peer interaction, independence, decision-making and separation from parental care had direct associations with students’ attitudes, while peer interaction, independence and decision-making also had indirect associations through personality development. They interpreted the results in relation to identity disruption and the potentially influential role of peer groups during adolescence. These interpretations remain theoretical because the survey collected students’ perceptions at one point in time and did not measure personality development before the boarding experience or follow changes in the same students over time.

The study’s geographic scope was limited to boarding students in Southwest Nigeria, and its relatively small sample relative to the number invited limits broader generalization. The authors also called for studies in other regions, data triangulation through participant observation and further testing of additional moderators. Their practical proposals included providing professional guidance counsellors, monitoring peer interactions and developing extracurricular opportunities intended to strengthen peer learning, independence and decision-making. These were policy and management implications proposed by the authors, not interventions evaluated by the study.

Subject of Research: Cross-sectional study of boarding-school routines, personality development and student attitudes in Nigeria

Article Title: Boarding system and the attitudes of secondary school students: the mediation of personality development

Article References: Inegbedion, E., & Inegbedion, H. (2026). Boarding system and the attitudes of secondary school students: the mediation of personality development. Discover Education, 5(1), Article 932. https://doi.org/10.1007/s44217-026-01614-7

Image Credits: AI Generated

DOI: 10.1007/s44217-026-01614-7

Keywords: boarding education, secondary students, personality development, student attitudes, peer interaction, social identity, structural equation modelling, cross-sectional survey, Nigeria, Boarding, system, attitudes

The study, published in the journal Appetite, combined a large-scale survey with in-depth qualitative discussions, a mixed-methods design that allowed the researchers to measure attitudes across a broad population while also probing the reasoning behind them. In the autumn of 2021, the team surveyed students in grades 4 through 9 at six schools in Lund Municipality, with the main statistical analyses including 1,268 students. To complement the survey data, the researchers held qualitative sessions with 20 students at two of the schools, giving participants space to articulate in their own words what they thought about meals designed with the climate in mind.

The results defied any simple narrative of either enthusiastic support or stubborn resistance. Students were neither clearly positive nor clearly negative about more climate-friendly school meals. Instead, attitudes were strikingly mixed: around four in ten students in the survey described themselves as neutral toward the idea, while roughly equal proportions were positive and negative. That distribution matters for policymakers, because it suggests that the success or failure of climate-oriented menu changes is not predetermined by a hostile student body but is instead contingent on how the changes are designed and communicated.

What did predict a positive attitude? The strongest associations appeared with factors rooted in students’ own food choices and habits. Students who said they considered climate important when choosing food expressed markedly more positive attitudes toward climate-friendly school meals, as did those who ate vegetarian food at school more often. Gender also played a role, with girls expressing more positive attitudes than boys across the sample. These patterns indicate that personal engagement with sustainability and repeated, everyday exposure to vegetarian dishes go hand in hand with acceptance, hinting that familiarity built through routine may be one of the most powerful tools available to school kitchens.

Perhaps the most sobering finding concerned knowledge. Awareness that red meat has a larger climate footprint than vegetarian food was high even among students who held more negative attitudes toward climate-friendly meals, and this awareness was only weakly associated with their attitudes. In other words, information alone does not change how young people feel about what lands on their plate. As Monica Hunsberger, associate professor of public health at Sahlgrenska Academy at the University of Gothenburg, put it: “Students are aware of the climate footprints of foods, but when it comes time for lunch, other things become more important. Above all, they think about whether the food tastes good, fills them up, and feels familiar.”

The qualitative sessions illuminated why those three qualities carry so much weight. In the discussions, meat emerged as the norm against which many students judged what counted as a proper, filling meal. Vegetarian dishes were not evaluated in a vacuum; they were measured against this meat-centered expectation and were received better when they resembled foods the students already knew and liked. This finding has practical implications for menu design: vegetarian options that mimic familiar formats and flavors are likely to face less resistance than dishes that depart radically from what students recognize as a complete lunch.

The study also compared two distinct strategies for reducing the carbon footprint of school meals, and the difference between them was telling. Students were somewhat more supportive of reducing the amount of meat within existing recipes than of adding more all-vegetarian days to the weekly schedule. In the discussions, students described recipe modification as easier to accept, a preference that aligns with the broader pattern of familiarity driving acceptance. At the time of the study, the participating schools already served one or two all-vegetarian days per week, so students were responding from lived experience rather than abstract speculation about what such days might mean.

The research was conducted in connection with a practical intervention reported in an earlier study, in which the carbon footprint of school meals was reduced by 20 percent without affecting how much students ate or how satisfied they were with the food. That earlier result demonstrated that meaningful emissions reductions are technically achievable in real school kitchens without compromising quantity or satisfaction. The new study adds the crucial social dimension, showing that the pathway to student acceptance runs through sensory and experiential qualities rather than through climate messaging alone.

Erik André, a doctoral student at Chalmers University of Technology and the City of Gothenburg, framed the findings as guidance for kitchen staff and administrators. “Many school kitchens already work on modifying recipes so that they contain smaller amounts of ingredients with a high climate footprint, while also serving a high proportion of vegetarian dishes. This study suggests that such recipe reformulation can be a useful complementary approach for reducing climate impact while ensuring that the food is still appreciated by students,” he said. The message is one of pragmatism: rather than framing the transition as a choice between meat-heavy tradition and an all-vegetarian future, schools can pursue incremental reformulation that preserves the sensory qualities students value.

Taken together, the findings arrive at a moment when school food systems across Europe and beyond are under pressure to align public procurement with climate goals. School meals reach millions of children daily, making them one of the most scalable levers for reducing food-related emissions, and Sweden has been among the countries most actively experimenting with lower-footprint menus. What this study contributes is a reminder that behavioral science and sensory science belong at the center of that transition. Students are not blank slates waiting to be informed; they are discerning eaters with established norms about what a meal should be. Meeting them where they are, with food that tastes good, satisfies hunger, and echoes familiar dishes, may accomplish what facts alone cannot: turning climate-friendly school meals from a policy mandate into a lunch that students genuinely want to eat.

Subject of Research: Students' perceptions of climate-friendly school meals in Swedish primary and secondary schools

Article Title: Taste crucial to students’ support for climate-friendly school meals

Article References: Taste crucial to students’ support for climate-friendly school meals. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: school meals, climate-friendly food, food sustainability, vegetarian diets, student attitudes, red meat footprint, recipe reformulation, University of Gothenburg, Chalmers University of Technology, Appetite journal, mixed-methods study, food acceptance

The study, led by Julio Cabero-Almenara, Julio Barroso-Osuna, and Antonio Palacios-Rodríguez of the University of Seville, together with María Isabel Loaiza Aguirre of the Private Technical University of Loja in Ecuador, took a deliberately granular approach. Rather than treating attitudes toward artificial intelligence as a single, undifferentiated feeling, the researchers drew on the classic ABC model of attitudes, which decomposes any attitude into cognitive, affective, and behavioral components. The cognitive component captures beliefs and conceptions about the technology, the affective component captures emotions of acceptance or rejection, and the behavioral component captures intentions and dispositions to act. To this triad the team added a fourth, less commonly measured dimension: the ethical component, covering students’ sensitivity to issues such as algorithmic bias, privacy, and the responsible handling of personal data.

The sample comprised 570 students enrolled in master’s programs related to educational technology at universities in Ecuador, Spain, and the Dominican Republic. Ecuador contributed 188 participants, or 33 percent of the sample; Spain contributed 110, or 19.3 percent; and the Dominican Republic contributed 272, or 47.7 percent. Women made up nearly two-thirds of the participants, and more than half were under thirty years old. Importantly, 44 percent of the students were also working as teachers at some level of the education system, a detail that would prove statistically decisive. The participants rated their own technical mastery of artificial intelligence and their knowledge of its didactic use on a scale from one to ten, and their self-assessments clustered in the moderate range, with slightly higher confidence in pedagogical application than in technical operation.

The measurement instrument was an adaptation of the SATAI, the Student Attitude Toward Artificial Intelligence scale originally developed by Suh and Ahn and later adapted for university contexts, expanded with nine newly constructed items probing ethical attitudes. The final questionnaire contained 32 items across the four components, each rated on a seven-point Likert scale. Reliability was exceptional: the total instrument achieved a Cronbach’s alpha of 0.96, a figure well above the 0.90 threshold conventionally regarded as indicating excellent internal consistency. The researchers also computed McDonald’s omega, and the dimension-level values confirmed that the instrument measured its four components coherently. Because the data were not normally distributed, the team relied on non-parametric statistics, applying the Mann-Whitney U test for two-group comparisons, the Kruskal-Wallis test for comparisons across three or more groups, and Spearman rank correlations to map the relationships among components.

The headline finding is that attitudes toward artificial intelligence among these students are broadly positive. On the seven-point scale, average scores ran roughly two points above the scale’s midpoint. The cognitive and behavioral components scored highest, indicating strong beliefs in the value of the technology and a firm willingness to engage with it, followed closely by the affective-emotional component, which signals genuine emotional acceptance rather than grudging tolerance. Behavioral intention, the students’ stated readiness to incorporate artificial intelligence into their academic and professional lives, was similarly robust. Yet the ethical component stood out for the wrong reasons: it received the lowest scores of any dimension. The authors interpret this as evidence of limited knowledge and training regarding the ethical challenges of artificial intelligence, including biases embedded in training models that can shape the responses users receive, and the privacy implications of how personal data are collected and processed.

The correlational analysis added technical depth to this picture. All four components correlated positively and significantly with one another, but the strength of those relationships varied in revealing ways. The strongest association, at a Spearman coefficient of .774, linked the cognitive and behavioral components, suggesting that what students believe about artificial intelligence strongly shapes what they intend to do with it. The affective-emotional component correlated at .723 with behavioral intention, confirming that emotional comfort with the technology is a key driver of adoption. Most striking was the ethical component, which showed the weakest correlations in the model, reaching only .424 with behavioral intention. In other words, ethical awareness appears to be only loosely coupled to the decisions students make about using artificial intelligence, a disconnection that could allow enthusiastic adoption to proceed without ethical guardrails. Notably, in the Spanish subsample the ethical component’s relationships with other dimensions weakened further, in some cases to statistical insignificance.

The group comparisons told an equally nuanced story. Gender produced no significant differences in any component or in overall attitudes, a result consistent with a substantial body of international research but at odds with studies reporting male advantage. Country of study, by contrast, mattered: Ecuadorian students scored highest on most components, Dominican students led specifically on the ethical dimension, and Spanish students posted the lowest averages across the board, which the authors suggest may reflect a more moderate or critical stance toward artificial intelligence in education. Field of study also shaped attitudes significantly. Students from Arts and Humanities and from Engineering and Architecture recorded the highest overall scores, science students excelled on the cognitive component, and social science and law students led on intention to use. Health sciences students ranked lowest on every dimension analyzed, a gap the authors flag as urgent given the coming transformation of AI-enhanced healthcare environments.

Two personal variables proved especially powerful. Students who were themselves practicing teachers held significantly more favorable attitudes toward artificial intelligence than their non-teaching peers, with mean differences of roughly fifty points across all components, suggesting that hands-on professional experience makes the technology feel useful rather than threatening. Even more dramatic was the effect of self-assessed knowledge: students who rated their technical and didactic mastery of artificial intelligence at eight, nine, or ten out of ten scored more than one hundred points higher than those at the lower levels, across every component and in their intention to use the technology. The authors caution, however, that artificial intelligence often functions as a black box whose internal workings are opaque even to frequent users, so self-reported confidence may not always track genuine understanding, particularly of how the information these systems generate is actually produced.

The study arrives amid a broader regional reckoning. Recent reviews describe artificial intelligence as a disruptive agent in the digital transformation of Latin American universities, driving new forms of teaching, evaluation, and academic management, while flagging persistent obstacles: the digital divide, inadequate infrastructure, insufficient teacher training, and a shortage of long-term research. A World Bank report cited in the study found that while 65 percent of teachers consider artificial intelligence an opportunity, more than 80 percent say their institutions lack clear guidelines for its use. Flagship institutions such as the National Autonomous University of Mexico, the Monterrey Institute of Technology, and the Pontifical Catholic University of Chile have launched pilot programs, and UNESCO and the Development Bank of Latin America and the Caribbean have convened ministerial summits on AI ethics in the region.

The authors are candid about the limitations of their work. The sampling was one of convenience rather than probability, the instrument was a self-report questionnaire vulnerable to inflated self-perception, and the sample was drawn exclusively from educational technology programs, a population likely more tech-friendly than average. They frame the study as exploratory and call for replication with probabilistic samples, more diverse disciplines, and performance-based instruments that ask students to solve real cases rather than rate their own confidence. Still, the practical prescription is clear: universities should integrate digital ethics literacy programs and adopt clear institutional guidelines so that the region’s evident enthusiasm for artificial intelligence is matched by the critical judgment needed to use it responsibly. The students, the data suggest, are ready; the ethical framework must now catch up.

Subject of Research: Multidimensional attitudes of Latin American university students toward artificial intelligence in higher education

Article Title: Artificial Intelligence in higher education: cognitive, emotional, behavioral and ethical attitudes of Latin American students

Article References: Cabero-Almenara, J., Barroso-Osuna, J., Loaiza Aguirre, M. I., & Palacios-Rodríguez, A. (2026). Artificial Intelligence in higher education: cognitive, emotional, behavioral and ethical attitudes of Latin American students. Journal of New Approaches in Educational Research, 15(1), Article 13. https://doi.org/10.1007/s44322-026-00063-2

Image Credits: AI Generated

DOI: 10.1007/s44322-026-00063-2

Keywords: artificial intelligence, higher education, Latin America, student attitudes, digital ethics, SATAI scale, educational technology, behavioral intention, AI literacy, university students, ethics training, survey research