Journal of Research in Educational Systems

Abstract The aim of the study is to design Nexus curriculum model for the major group of humanities. For higher education, it is of great importance to understand how research can be integrated into the curriculum to enhance student learning and enhance capabilities. The curriculum can act as a bridge between the macro-level of decision-making and its micro-level. In the present study, a qualitative approach and a research synthesis method were used to explain the curriculum elements. The statistical population included upstream documents, books, articles, dissertations, and related theses. After content analysis and axial and selective coding of the data obtained from document review, the initial characteristics of the nexus curriculum elements were extracted and then interviews were conducted with relevant experts and professors, and their opinions were used to modify the proposed model,. For reliability, two evaluators recoded the findings. In order to confirm the reliability, In this model, 52 axial codes and 11 selective codes were identified. Such as accepting the temporary nature of knowledge, promoting a national research culture, selecting interdisciplinary contents, participatory and project-based teaching methods, student-based activities, employing research groups, critiquing or conducting real research, culminating research in final semesters, and the need for rich and supportive environment, were identified. In summary, it can be said that research enhances teaching by keeping the content up-to-date and engaging, and the research-teaching linkage curriculum enriches education by making research an integral part of learning.

Abstract Objective: Universities drive innovation by nurturing talent and generating beneficial knowledge. In today's technological landscape, open innovation enhances competitive advantage through internal and external knowledge flows. This study addresses Iran's higher education challenges by designing a paradigmatic model of essential skills for advancing open innovation.
Method: A qualitative grounded theory approach was employed. Data were collected via in-depth semi-structured interviews with 11 experts in open innovation and higher education, selected through purposive and snowball sampling until theoretical saturation. Validity was ensured through content validity assessment and member checking. Transcripts were analyzed using Strauss and Corbin's (1990) systematic coding with MAXQDA 2022.
Results: Findings identified two skill categories: hard skills (specialized knowledge, knowledge/project management, R&D, financial literacy, market awareness, technology) and soft skills (self-awareness, communication, negotiation, creativity, flexibility, risk-taking, critical thinking, networking, ambiguity tolerance). The paradigmatic model comprises 86 basic concepts and 30 axial components organized into three categories: conditions (contextual, causal, intervening), interactions (individual actions, interpersonal collaborations), and consequences (scientific-technological innovation, social value creation, organizational growth).
Conclusions: Advancing open innovation requires a multidimensional approach integrating both skill sets. Universities should implement targeted faculty training, foster innovation culture with risk-taking emphasis, expand industry-research collaborations, allocate dedicated resources, and revise promotion criteria to value innovation competencies alongside publications. This context-specific model provides a practical framework for transitioning Iranian universities toward entrepreneurial, ecosystem-oriented generations aligned with global higher education evolution.

Abstract This study examined the quality of paratextual elements and their relationship with the cognitive level of reading‑comprehension questions in the fourth‑grade Persian textbook.

A descriptive‑analytical content analysis based on the operational PMF‑7 framework assessed seven paratextual dimensions: organization, reading guidance, image – text alignment, punctuation and tone, interactivity/questioning, multimedia, and cognitive load/layout. The sample comprised eight representative lessons from different chapters, each independently coded by two raters using a 28‑item checklist. Quantitative analyses included descriptive statistics, Spearman’s rank correlation to examine the association between overall paratext scores and mean cognitive intensity of questions, Cohen’s kappa for interrater reliability, and Cronbach’s alpha for subscale internal consistency
Results: Results showed an average total paratext score of 37.25 out of 56 (≈66.5%); interrater reliability was acceptable (Cohen’s Kappa ≈ 0.79), and Cronbach’s alpha for subscales ranged from 0.71 to 0.86. Correlational analysis indicated a positive and significant relationship between overall paratext quality and the cognitive intensity of questions. Dimension‑level findings revealed that organization and image–text alignment most strongly supported higher‑order, analytical questions, whereas reading guidance and multimedia alignment require strengthening.
Conclusions:The study concludes that targeted revisions—such as structured before‑during‑after reading templates, clear instructions for audio clips, and technical editing—could effectively raise the cognitive level of questions and improve reading learning; these redesigns should be tested in a classroom pilot intervention.

Abstract Objective: This study aimed to design and validate a model of an ethical university in Iranian medical universities.
Method: The research was applied and mixed-method. In the qualitative phase, data were collected using the grounded theory approach (Strauss & Corbin, 1990) through semi-structured interviews with 26 experts in professional ethics, educational management, and higher education. Data were analyzed through open, axial, and selective coding. The qualitative sample included managers and administrators from medical universities in Zanjan, Qazvin, Qom, Saveh, Arak, and Alborz, faculty members, Ministry of Health experts, and specialists in ethics and medical education. In the quantitative phase, a 30-item researcher-made questionnaire based on qualitative findings was developed and distributed among 384 managers, deputies, experts, faculty members, and researchers. Data were analyzed using SPSS, AMOS, and Smart PLS.
Results: Second-order confirmatory factor analysis indicated satisfactory model fit: χ²/df = 2.081, RMSEA = 0.040, CFI = 0.948, GFI = 0.962, and TLI = 0.956. Key factors influencing the model included policies and structures, ethical education and research, ethical leadership, ethical artificial intelligence, ethical culture and behavior of actors, supervision, social responsibility, and ethical development. Thirty core indicators were identified, including a code of ethics, ethics committees, training courses, responsible research workshops, and ethical guidance for managers.
Conclusion: An ethical university is a multidimensional and dynamic phenomenon that integrates policymaking, culture, education, and ethical technology. It can serve as a framework for enhancing organizational health and professional ethics in medical higher education in Iran.

Abstract Objective: The present study aimed to examine differences in academic well-being, academic help-seeking, parent–child relationship, and emotional independence among elementary school students based on their mothers’ employment status (employed vs. stay-at-home)
Method: This applied study adopted a causal-comparative design using a quantitative approach. The statistical population consisted of fourth- to sixth-grade students in District 7 of Tehran during the 2024–2025 academic year. A total of 312 students were selected through cluster random sampling. Data were collected using validated questionnaires measuring academic well-being (Tuominen-Soini et al., 2012), academic help-seeking) Ryan & Pintrich ,1997(,emotional independence(Steinberg & Silverberg, 1986), and parent–child relationship (Fine, Moreland, & Schubel ,1983). Data analysis was conducted using multivariate analysis of variance (MANOVA) in SPSS version 27.
Results: The results indicated statistically significant differences (p < 0/05)between students with employed and stay-at-home mothers in emotional independence, parent–child relationship, academic satisfaction, and school engagement. Students with employed mothers demonstrated higher performance in academic dimensions, whereas students with stay-at-home mothers obtained higher scores in emotional independence and relational components. No significant differences were found between the two groups in overall academic well-being, school value, academic burnout, or academic help-seeking.
Conclusions: The findings suggest that mothers’ employment status is associated with particular academic, emotional, and relational outcomes in elementary school students. These differences can be explained through the frameworks of socio-emotional development, autonomy, attachment theory, and ecological systems theory. These findings emphasize the importance of focusing on the quality of parent–child interactions and the mother’s role in children’s academic and emotional development.

Abstract Objective: The emergence of Artificial Intelligence (AI) in education, despite its vast potential to enhance teaching and learning, has introduced critical challenges to maintaining academic integrity. This study aimed to systematically identify AI-related challenges to academic honesty, explore influencing factors, and review educational interventions addressing these issues.
Method: A systematic review was conducted in accordance with the PRISMA model. The Scopus database was searched using keywords related to “artificial intelligence,” “academic dishonesty,” “formal education,” and “intervention.” Out of 322 initially identified studies, 33 empirical papers met the inclusion criteria after screening and full-text evaluation.
Results: The findings revealed five major categories of AI-related academic integrity challenges: (1) ambiguity policy, (2) weakness of tools and difficulty of detection, (3) excessive use of AI, (4) threats to educational fairness and trust, and (5) declining learning quality. Conversely, effective interventions reported by teachers and instructors encompassed assessment and task redesign, ethical and skill-based empowerment, responsible AI integration, enhancement of human interaction, detection strategies, policy clarification, and prevention and punishment.
Conclusions: The study concludes that safeguarding academic integrity in the age of AI requires redefining the teacher’s role, developing techno-ethical literacy, and establishing coherent institutional policies that balance innovation with ethical responsibility.