The research team, led by Emre Yilmaz of the Department of Family Medicine together with biostatistician Mevlüde Yasemin Akşehirli Seyfeli and medical education specialist Zeynep Baykan, recruited from the full population of third-year medical students at the faculty. All 376 students in the preclinical year were invited to participate, and the response was remarkably strong: 341 students completed the face-to-face questionnaire, a participation rate of 90.7 percent. After exclusions, 319 students entered the final analyses. That near-census coverage matters, because chronotype research in medical education has often relied on small, self-selected samples in which the most sleep-deprived or disengaged students may be underrepresented.

To classify each participant, the researchers used the Morningness–Eveningness Questionnaire, or MEQ, a widely applied psychometric instrument that scores individuals along a continuum from pronounced morning preference to pronounced evening preference. The team analyzed chronotype in three categorical groups, evening, intermediate, and morning, and additionally treated the MEQ total score as a continuous variable, a dual approach that guards against the loss of statistical power and information that can occur when a continuous trait is artificially binned. Alongside the MEQ, students completed the Absenteeism Attitude Scale, a validated measure of how students think and feel about missing classes, and reported their own academic performance and attendance-related information.

The distribution of chronotypes in the cohort tells its own story about the population entering medical school. Intermediate types, those whose biological clocks fall between the extremes, dominated the sample at 72.1 percent. Evening types accounted for 17.9 percent, while confirmed morning larks made up just 10.0 percent. This skew toward the middle of the chronotype spectrum is consistent with the general adult population, in which extreme morningness and extreme eveningness are both relatively rare, and it suggests that medical students as a group are not systematically skewed toward either pole of the sleep-timing continuum.

Then came the central test. When the researchers compared grade point averages across the three chronotype groups, they found no statistically significant difference. The Kruskal–Wallis test, a non-parametric method appropriate for comparing three independent groups on a skewed outcome such as GPA, yielded H(2) = 1.750 with p = .417 and an effect size of ε² < 0.001, meaning the chronotype groups explained essentially none of the variance in academic performance. Final committee examination scores told the same story: H(2) = 0.078, p = .962, again with a negligible effect size. Whether a student was a lark, an owl, or somewhere in between, their examination results were statistically indistinguishable.

The continuous analysis reinforced the null result. Spearman correlation between the MEQ total score and GPA was ρ = 0.103 with p = .066, a weak positive trend that failed to clear the conventional significance threshold. In other words, there was at most a faint hint that slightly more morning-oriented students had slightly higher grades, but the data did not support even that modest claim with confidence. For a field in which chronotype has frequently been implicated in academic outcomes, the near-zero effect sizes here are notable, and they suggest that within a demanding, uniformly structured preclinical curriculum, individual differences in biological timing may simply be absorbed rather than amplified.

Where chronotype did show a signal was in the attitudinal domain. The MEQ total score correlated weakly but significantly with the Absenteeism Attitude Scale score (ρ = 0.152, p = .006), and the scale scores themselves differed across chronotype groups (F(2,316) = 6.997, p = .001, η² = 0.042). Intermediate-type students scored higher than evening-type students on the scale. The effect size, an eta-squared of about four percent, is small, but the pattern hints that evening-oriented students may hold somewhat more permissive or tolerant attitudes toward skipping class than their intermediate peers, a difference that could matter in attendance-structured programs.

Crucially, absenteeism attitudes were the variable most consistently tied to achievement. The Absenteeism Attitude Scale score showed a weak but significant positive association with GPA (ρ = 0.194, p < .001), a stronger and more reliable relationship than anything chronotype produced. This raises an intriguing possibility: what students believe about the acceptability of missing class may be a more meaningful psychological marker of academic engagement than when their internal clocks prefer to run. Attitudes toward attendance, unlike chronotype, are potentially modifiable, which makes them a more actionable target for educators concerned about student disengagement.

To probe whether chronotype might influence grades indirectly through attendance-related attitudes, the team turned to structural equation modeling, fitting a path model within that framework. The model confirmed the direct picture: chronotype showed no statistically significant direct association with GPA (β = 0.062, p = .256). A small total indirect association was statistically supported (β = 0.048), but when the researchers examined the specific indirect pathways, only one survived scrutiny, a serial pathway running through absenteeism tendency and then absenteeism attitude, and even that was tiny (β = 0.007). The authors are careful to note that because the design is cross-sectional, measuring all variables at a single point in time, these indirect associations must not be read as evidence of causal mediation. A snapshot cannot establish that chronotype shapes attitudes which then shape grades; the arrows in the model represent hypothesized structure, not demonstrated causation.

The study, approved by the Erciyes University Health Sciences Research Ethics Committee and conducted in accordance with the Declaration of Helsinki, carries practical implications for medical schools wrestling with scheduling and attendance policy. Its message is not that sleep timing is irrelevant to student life, but that in this large, high-response preclinical cohort, the biological clock was not the academic differentiator that folklore might predict. Instead, the psychological stance students take toward attendance showed the more consistent connection to performance. For the substantial minority of evening types, roughly one in five students in this sample, the findings offer evidence-based comfort: their grades need not be hostage to their circadian preferences. And for educators, the results suggest that interventions targeting attitudes toward absenteeism, rather than attempts to re-engineer students’ sleep schedules, may be the more promising route to supporting achievement. As with all cross-sectional work, replication in other institutions and with longitudinal designs will be needed to confirm and extend these conclusions, but for now, the night owls of the preclinical years can attend their morning lectures with somewhat less dread.

Subject of Research: Associations between chronotype, academic achievement, and absenteeism attitudes among preclinical medical students

Article Title: Chronotypic characteristics, academic achievement, and absenteeism attitudes among preclinical medical students: a cross-sectional study

Article References: Yilmaz, E., Seyfeli̇, M. Y. A., & Baykan, Z. (2026). Chronotypic characteristics, academic achievement, and absenteeism attitudes among preclinical medical students: a cross-sectional study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10563-6

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10563-6

Keywords: chronotype, circadian rhythm, medical students, academic achievement, absenteeism, medical education, Morningness-Eveningness Questionnaire, structural equation modeling, cross-sectional study, GPA, sleep science, Erciyes University

The research team employed a mixed-methods approach, a design philosophy that deliberately combines numerical measurement with narrative depth. Specifically, they used what methodologists call a sequential explanatory design, in which a quantitative phase is conducted first and its results then inform and are interpreted through a subsequent qualitative phase. In this study, the quantitative component took the form of a survey, while the qualitative component was structured as a case study. This sequencing matters: by first establishing how much anxiety exists across a broad group, the researchers could then use interviews to explore why those levels exist and what they mean to the individuals concerned, giving the numbers a human voice.

The quantitative phase drew on a population of 208 pre-service teachers enrolled in the science education department of the faculty of education at a state university in Turkey. From that population, the researchers selected a sample of 162 pre-service science teachers using simple random sampling, a technique in which every member of the population has an equal chance of being included. Random sampling of this kind is a cornerstone of survey methodology because it reduces the risk of systematic bias in who ends up answering the questionnaire, making the resulting descriptive and inferential statistics more defensible as a portrait of the wider group. The instrument administered to these 162 students was a scale designed to gauge levels of anxiety specifically about artificial intelligence applications.

When the scale data were analyzed using both descriptive statistics, which summarize the distribution of responses, and inferential statistics, which test whether observed patterns are likely to hold beyond the sample, the central finding emerged clearly: the pre-service science teachers reported a moderate level of artificial intelligence anxiety. That middle-of-the-road result is itself informative. It suggests that these future educators are not experiencing the acute alarm sometimes portrayed in popular discussions of AI, but neither are they indifferent or fully at ease. A moderate reading points to a population that is aware of the technology, uncertain about its implications, and receptive—potentially—to training that could tip the balance toward confidence.

To understand what lay behind that moderate score, the researchers turned to the qualitative phase of the sequential explanatory design. They conducted semi-structured interviews with eight pre-service science teachers, selected for maximum diversity, a purposive sampling strategy that deliberately seeks participants who differ from one another in relevant characteristics so that the qualitative findings are not confined to a single type of respondent. Semi-structured interviews follow a prepared guide of questions but allow the conversation to develop naturally, letting participants raise concerns the researchers might not have anticipated. The interview transcripts were then subjected to content analysis, a systematic method of coding textual data to identify recurring themes and patterns of meaning.

One of the most striking outcomes of the interview phase was the degree of convergence between participants with high anxiety and those with low anxiety. Despite sitting at opposite ends of the anxiety scale, the two groups gave strikingly similar answers in conversation. Both groups agreed that artificial intelligence cannot replace the teaching profession. That shared conviction, held across the anxiety spectrum, is a significant insight: it suggests that anxiety about AI among future science teachers is not simply a proxy for fear of professional obsolescence. Even the most anxious participants did not believe their chosen career was about to be automated away, and even the least anxious recognized that the technology raises real questions for educators.

What the interviews add, then, is a picture of anxiety that is more textured than a single scale score can convey. Moderate anxiety coexisting with confidence in the irreplaceability of teachers implies that the concerns of these pre-service educators likely center on practical and pedagogical uncertainties—how to use AI applications appropriately, what such tools mean for the integrity of learning, and how their own preparation has left them under-equipped to judge. The study’s design cannot assign precise weights to those individual concerns, but the pattern of a shared belief in the durability of the teaching profession, held by both high- and low-anxiety participants, anchors the interpretation of the quantitative result in the participants’ own words.

Based on these findings, the research team put forward two recommendations. The first is to provide pre-service teachers with the necessary training on artificial intelligence, so that the moderate anxiety observed in the survey can be addressed directly through competence-building rather than left to resolve itself. The second is to offer an artificial intelligence course to all pre-service teachers, embedding AI literacy into teacher education as a standard component rather than an elective add-on. Taken together, the recommendations treat AI anxiety not as an individual failing of nervous students but as a structural gap in teacher preparation—one that faculties of education can close by making the technology a routine, examined part of the curriculum.

The study was conducted with formal ethical oversight. All protocols and methods involving human participants were reviewed and approved by the Erciyes University Social and Human Sciences Ethics Committee under approval ID 596, and the research was carried out in accordance with relevant institutional and ethical guidelines, including the principles of voluntary participation and confidentiality. Informed consent was obtained from all participants before data collection began, and participant consent forms were required for the collection of participants’ opinions. These safeguards are standard for research involving human subjects, but they are worth noting because they underline that the anxiety being measured is self-reported, personal, and offered voluntarily by students describing their own professional futures.

For the wider conversation about artificial intelligence in schools, the study offers a timely data point. Debates about AI in education often swing between utopian predictions of personalized learning at scale and dystopian warnings about deskilled classrooms, and both extremes tend to be articulated by adults already established in their careers. This study instead listens to the people who will actually inherit the AI-infused classroom: students now training to teach science. Their measured verdict—moderate anxiety, coupled with a firm belief that no algorithm can replace a teacher—suggests that the most productive path forward is neither alarm nor complacency, but deliberate preparation. If the authors’ recommendations are heeded, the next generation of science teachers may enter the profession not anxious about artificial intelligence, but equipped to use it, question it, and keep the human core of teaching intact.

Subject of Research: Anxiety levels of pre-service science teachers regarding artificial intelligence applications in education

Article Title: Pre-service science teachers’ anxiety levels about artificial intelligence applications

Article References: Çakir, C., Gökçe, H., & Kariper, İ. A. (2026). Pre-service science teachers’ anxiety levels about artificial intelligence applications. Discover Psychology, 6(1), Article 269. https://doi.org/10.1007/s44202-026-00909-y

Image Credits: AI Generated

DOI: 10.1007/s44202-026-00909-y

Keywords: artificial intelligence, pre-service teachers, science education, teacher anxiety, mixed methods, sequential explanatory design, survey research, semi-structured interviews, content analysis, teacher training, educational psychology, Erciyes University