Applied Research in Consulting

Abstract The aim of this study was to investigate the effect of attitude towards the use of artificial intelligence in education on promoting digital literacy, digital competence and digital media literacy. The research method was correlation-analytic with a survey approach. The statistical population included teachers working in the primary education sector in Divandarreh city in the academic year 2024-2025. The distribution method was a convenient sample (228 people). The measurement tools included the questionnaires Attitude towards Artificial Intelligence in Education by Chatterjee (2020), Digital Literacy by Rodriguez et al. (2016), Digital Competence by Bettin et al. (2023), and Media Literacy by Yazdani (2012). The tests used in this study included correlation and regression statistics. The results showed that there is a significant relationship (P> 0.05) between the attitude towards the use of artificial intelligence in education and the promotion of digital literacy, digital competence and digital media literacy of male and female teachers. Focusing on effective learning, problem-solving, AI learning, digital literacy, and information management can produce significant results for strategic policymaking. Education and training should prioritize the integration of digital literacy, digital competence, and digital media literacy education to prepare teachers for the challenges and opportunities presented by AI and other advanced technologies.

Abstract The present study aimed to investigate psychopathology resulting from cancer diagnosis, emphasizing the mediating role of mentalization in the relationship between personality dimensions and clinical disorders, in the cancer patient community of Urmia city. The present study was descriptive-correlational and was conducted within the framework of a clinical research on 170 cancer patients referring to Urmia medical centers in 1403. Data collection tools included the Big Five Personality Factors Questionnaire (NEO-FFI), Mentalization Scale (RFQ), and Clinical Symptom Checklist (SCL-90-R). The data were analyzed using descriptive statistical methods, Pearson correlation, independent t-test, and structural equation modeling (SEM). The results showed that neuroticism had the highest positive correlation with mental disorders (r=0.64, p<0.001) and mentalization had a negative and significant correlation with psychopathology (r=-0.53, p<0.001). Also, patients with high mentalization experienced lower levels of psychopathology. In structural equation modeling, the mediating role of mentalization in the relationship between neuroticism and mental disorders was confirmed (indirect effect β=0.12, p<0.01) and the model fit indices (CFI=0.94, RMSEA=0.06) were assessed as appropriate. Overall, the findings of this study indicate that personality traits and patients' mentalization capacity have a significant effect on psychopathology resulting from a cancer diagnosis.

Abstract This study aimed to investigate the latent growth patterns of academic engagement and effort among secondary school students and the role of socioeconomic status and prior academic performance in individual differences in these patterns. The research was longitudinal, utilizing latent growth curve modeling. The population consisted of 10th to 12th-grade students in Darab city during the 2022-2023 academic year, with a sample of 600 students selected through multistage cluster sampling. Data were collected over four waves (October to January) using Fredricks’ (2004) Academic Engagement Questionnaire, Pintrich and De Groot’s (1990) Motivational Strategies for Learning Questionnaire, Ghodratnama’s (1392) Socioeconomic Status Questionnaire, and students’ GPA as an indicator of academic performance. Results indicated a linear, declining trend in both academic engagement and effort over the year. Significant variances in intercepts and slopes confirmed individual differences. A negative correlation (r=-0.27, p<0.05) was found between initial levels and decline rates. Socioeconomic status and prior academic performance positively and significantly influenced initial levels and growth rates. The model showed good statistical fit. The findings highlight the need for school-based interventions and support for disadvantaged students to sustain academic motivation and effort.