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Iran’s Konkur exam (national university entrance test) assesses EFL proficiency solely through multiple-choice items, neglecting writing/speaking despite their academic importance. This study compares Intelligent Computer-Assisted Language Assessment (ICALA) and traditional assessments to address this gap. This 12-week mixed-methods study examined how ICALA affected motivation, anxiety, and proficiency in 120 intermediate Iranian EFL learners (CEFR B1–B2). The experimental group ( n  = 60) used ICALA via DeepSeek, while the control group ( n  = 60) received traditional instructor-led assessments with identical tasks (250-word essays, 2-min oral responses). Quantitative data from standardized measures (motivation, anxiety, and proficiency scales) and qualitative data from interviews and reflective journals were analyzed. ICALA demonstrated stronger benefits for motivation, anxiety reduction, and proficiency gains compared to traditional assessments, particularly among upper-intermediate (B2) learners. Qualitative analysis revealed three dominant themes: (1) enhanced competence through specific feedback, (2) reduced evaluation pressure, and (3) systematic skill improvement. While B2 learners thrived with ICALA’s detailed feedback (e.g., cohesion suggestions), some B1 learners required simplified guidance due to cognitive load. Although Konkur omits productive skills, ICALA improves writing and speaking proficiency, bridging the gap between exam preparation and academic needs. Simplified feedback for B1 learners, along with balanced speaking tasks, could further enhance outcomes. These findings inform EFL instruction reform in Konkur-driven contexts and contribute to Asia–Pacific and global AI assessment research.

Background Given the serious consequences of depression and the lack of information about it during the crucially developmental period from the National College Entrance Exam (CEE, i.e., Chinese gaokao ) to college, this study aimed to estimate the cumulative incidence, prevalence, age of onset, correlates, and service use of depressive disorders (DDs) among youth who passed the CEE and were enrolled at Hunan Normal University in China. Methods A two-stage cross-sectional epidemiological survey of DDs was conducted from October to December, 2017 among 6,922 incoming college students (98.5% effective response, N = 6,818, 71.4% female, age range: 16–25 years, mean age = 18.6). Using a stratified sampling method based on the risk of depression, 926 participants (mean age = 18.5, 75.2% female) were selected and subsequently interviewed with the Kiddie–Schedule for Affective Disorders and Schizophrenia–Present and lifetime version (K-SADS-PL). Results The sex-adjusted 9-month (i.e., 3 months pre-CEE, 3 months after CEE, and 3 months post-matriculation) incidence of new-onset DDs was 2.3% (standard error [S.E.] 0.3%), and the sex-adjusted 1-month, 6-month and lifetime prevalence were 0.7 (S.E. 0.3%), 1.7 (S.E. 0.2%) and 7.5% (S.E. 1.3%), respectively. The median age of onset was 17 (interquartile range: 16–18) years. Critically, over one-third (36.5%, S.E. 0.6) of depressed youth had their new onset during the 9-month period. The risk factors for depression included having mothers with higher education, experiencing major life events, being female, and experiencing parental divorce or death. The adjusted lifetime treatment rate was 8.7%. Conclusion The 9-month incidence of new-onset depression from gaokao to college among the youth sample in China is similar to the global annual incidence (3.0%), but the 1-month and lifetime prevalence are significantly lower than the global point (7.2%) and lifetime prevalence (19%). These findings suggest a high proportion of new-onset depression during the CEE to college among the sample youth in China. The risk of depression is associated with familial and stress correlates. Low treatment is a serious concern. Emphasis on early prevention and available treatment for adolescent and young adult depression is a critical need in China.

There are two main exams in Turkish educational system. One is at the end of middle school for entrance to selective high schools and the other is after high school to enter universities. Both exams are compulsory, both have four subtests (mathematics, natural sciences, Turkish and social sciences) and all the questions have to be from the shared curriculum. In this study high school entrance exam (HSEE) and university entrance exam (UEE) results of 34,479 students in 570 schools of three selective school types (Anatolian High Schools, Science High Schools and Anatolian Teacher Training High Schools) were compared to test the effectiveness of the types of school concerned with respect to academic achievement as determined by four subtests of HSEE and UEE. The relationship (correlation) and the difference (mean) between achievement levels for school types, for subtests and for gender were investigated. Correlations between HSEE and UEE-1 scores were the highest in Anatolian high schools for three subtests. One-way Anova and post hoc Scheffé test results showed there is significant difference in mean scores among school types. As for mean differences, Anatolian High Schools scored better than the other two school types. School type and focus on a subtest are related, and exam achievement of girls in science high schools is higher than achievement of boys.

Despite the voluminous literature on the potentials of single-sex schools, there is no consensus on the effects of single-sex schools because of student selection of school types. We exploit a unique feature of schooling in Seoul—the random assignment of students into single-sex versus coeducational high schools—to assess causal effects of single-sex schools on college entrance exam scores and college attendance. Our validation of the random assignment shows comparable socioeconomic backgrounds and prior academic achievement of students attending single-sex schools and coeducational schools, which increases the credibility of our causal estimates of single-sex school effects. The three-level hierarchical model shows that attending all-boys schools or all-girls schools, rather than coeducational schools, is significantly associated with higher average scores on Korean and English test scores. Applying the school district fixed-effects models, we find that single-sex schools produce a higher percentage of graduates who attended four-year colleges and a lower percentage of graduates who attended two-year junior colleges than do coeducational schools. The positive effects of single-sex schools remain substantial, even after we take into account various school-level variables, such as teacher quality, the student-teacher ratio, the proportion of students receiving lunch support, and whether the schools are public or private.

Objective To evaluate the performance of advanced large language models (LLMs)—OpenAI-ChatGPT 4, Google AI-Gemini 1.5 Pro, Cohere-Command R + and Meta AI-Llama 3 70B on questions from the Turkish Medical Specialty Training Entrance Exam (2021, 1st semester) and analyze their answers for user interpretability in languages other than English. Methods The study used questions from the Basic Medical Sciences and Clinical Medical Sciences exams of the Turkish Medical Specialty Training Entrance Exam held on March 21, 2021. The 240 questions were presented to the LLMs in Turkish, and their responses were evaluated based on the official answers published by the Student Selection and Placement Centre. Results ChatGPT 4 was the best-performing model with an overall accuracy of 88.75%. Llama 3 70B followed closely with 79.17% accuracy. Gemini 1.5 Pro achieved 78.13% accuracy, while Command R + lagged with 50% accuracy. ChatGPT 4 demonstrated strengths in both basic and clinical medical science questions. Performance varied across question difficulties, with ChatGPT 4 maintaining high accuracy even on the most challenging questions. Conclusions GPT-4 and Llama 3 70B achieved satisfactory results on the Turkish Medical Specialty Training Entrance Exam, demonstrating their potential as safe sources for basic medical sciences and clinical medical sciences knowledge in languages other than English. These LLMs could be valuable resources for medical education and clinical support in non-English speaking areas. However, Gemini 1.5 Pro and Command R + show potential but need significant improvement to compete with the best-performing models.

Students wishing to study engineering, mathematics and physics at Iranian universities are only graduates of high school mathematics- physics majors. Over the past two decades, there has been a significant reduction in the number of students choosing mathematics-physics major in high schools and, as a result, the number of applicants in the mathematics-physics major of the national university entrance exam has been reduced. This trend has led to a decrease in engineering applicants and a drop in their quality of academic level. Elite high school students do not show much desire to choose mathematics-physics major in high schools due to the numerous employment opportunities of graduates of experimental science major, especially medicine. This, in addition to reducing the number of applicants entering mathematics-physics major, but also has declined the quality of science of applicants entering engineering fields. The Society of Engineering Education of Iran organized a roundtable to investigate the reasons for this situation, and the material that I presented in this roundtable will be discussed in this article.

The use of college entrance exams for facilitating admission decisions become controversial, and the central argument is around the fairness of test scores. The Kuwait University English Aptitude Test (KUEAT) is a high-stakes test, but very few studies have examined the psychometric quality of the scores for this national-level assessment. This study illustrates how measurement approaches can be used to examine the fairness issues in educational testing. Through a modern view of fairness, we assess the internal and external bias of KUEAT scores using differential item functioning analysis and differential prediction analysis, respectively, and provide a comprehensive fairness argument for KUEAT scores. The analysis for examining the internal evidence of bias was based on 1790 examinees’ KUEAT scores in November 2018. KUEAT scores and first-year college GPAs of 4033 students enrolled in KU were used for assessing the external evidence of bias. Results revealed many items showing differential item functioning across student subpopulation groups (i.e., nationality, gender, high school majors, and high school types). Meanwhile, KUEAT scores also predicted college performance differentially by different student subgroups (i.e., nationality, high school majors, and high school types). Discussion and implications on the fairness issues of college entrance tests in Kuwait are provided.

Higher education is a powerful tool for migrant integration into destination countries. This paper presents an empirical comparison between native and migrant students in Chile, focusing on their trajectory through different transitions: high school graduation, performance on university entrance exams, and decision to enroll in technical or university higher education. Results show that, when controlling for school performance, natives are more likely to complete every transition compared to migrant students, except in the enrollment to technical higher education where migrants have a higher rate. However, we also show that the timing of arrival to the educational system matters and that the differences between groups completely disappear if migrant students arrive before age 10 in the educational system. Therefore, we expand the educational literature by empirically showing that when migrants enter the school system is crucial. If they arrive early in life, they can have a trajectory similar to that of native students.

The medical profession is a prestigious position that requires very extensive higher education, to which only a small proportion of applicants are accepted. Changes in selection criteria can profoundly impact applicants’ pre-educational choices, early medical studies, and the characteristics of future medical professionals. This study assesses the impact of changing the admission requirements of medical schools in Finland. We examined two cohorts of students admitted to the University of Oulu’s medical school: 2009–2011 (n = 316) and 2013–2015 (n = 339). The first cohort prepared for the entrance exam with a field-specific book, while the second cohort focused on secondary school subjects such as biology, chemistry, and physics. We analysed the effects of the changes on accepted students’ profiles and the relationship between their prior performance, entrance exam success, and performance in medical studies. Changing the entrance exam content did not significantly alter accepted students’ profiles or ease access for recent matriculants. However, minor changes in correlations between prior performance, entrance exam performance, and medical study success were observed. The entrance exam’s predictive power for academic success was weak in both admission periods. This comparative study found that changing the entrance exam material did not notably influence the characteristics of accepted students. The changes to the selection criteria appear to have a minor impact on the actual success of students studying medicine. Regardless of the selection criteria, those who are accepted typically demonstrate strong learning capabilities. Despite modifications in the required entry-level knowledge, students with strong skills are admitted.

Sleep disruptions among prospective university students are increasingly recognized for their potential ramifications on academic achievement and psychological well-being. But, information regarding sleep issues among students preparing for university entrance exams is unknown. Thus, this study aimed to investigate the prevalence and factors associated with sleep duration and insomnia among university entrance test-takers in Bangladesh, utilizing both traditional statistical analyses and advanced geographic information system and machine learning techniques for enhanced predictive capability. A cross-sectional study was conducted in June 2023 among 1496 entrance test-takers at Jahangirnagar University, Dhaka. Structured questionnaires collected data on demographics, academic information, and mental health assessments. Statistical analyses, including chi-square tests and logistic regression, were performed using SPSS, while machine learning models were applied using Python and Google Colab. Approximately 62.9% of participants reported abnormal sleep duration (<7 hours/night or >9 hours/night), with 25.5% experiencing insomnia. Females and those dissatisfied with mock tests were more likely to report abnormal sleep duration, while repeat test-takers, those with unsatisfactory mock test results, or anxiety symptoms had a higher risk of insomnia. Machine learning identified satisfaction with previous mock tests as the most significant predictor of sleep disturbances, while higher secondary school certificate GPA had the least influence. The CatBoost model achieved maximum accuracy rates of 61.27% and 73.46%, respectively, for predicting sleep duration and insomnia, with low log loss values indicating robust predictive performance. Geographic analysis revealed regional variations in sleep disturbances, with higher insomnia prevalence in some southern districts and abnormal sleep duration in northern and eastern districts. Sleep disturbances are prevalent among prospective university students and are associated with various factors including gender, test-taking status, mock test satisfaction, and anxiety. Targeted interventions, including sleep education and psychological support, hold promise in ameliorating sleep health and overall well-being among students, potentially enhancing entrance test performance.