Iranian Journal of Learning and Memory

Abstract Objective: This study investigated the effect of an artificial intelligence-based digital learning environment on the digital skills and digital flourishing of lower secondary school teachers in Naqadeh. Method: In this quasi-experimental study with a pre-test-post-test control group design, 30 teachers (15 experimental, 15 control) were selected using convenience sampling. The experimental group participated in an 8-session AI-based digital learning environment intervention. Data were collected using the standard Digital Skills Questionnaire and Digital Flourishing Questionnaire and analyzed using MANCOVA and ANCOVA. Results: Descriptive findings showed increased mean scores for digital skills, digital flourishing, and all their components in the experimental group post-intervention. MANCOVA results indicated a significant effect of the AI environment on the linear combination of teachers' digital skills and flourishing (Wilks' Lambda = .181, F(2,25) = 56.59, P < .05). ANCOVA confirmed significant effects on digital skills (F = 130.95, P < .001, η² = .829) and all six components (P < .05), as well as on digital flourishing (F = 216.98, P < .001, η² = .889) and all four components (P < .05). Effect sizes ranged from moderate to very strong (η² = .273 to .910). Conclusions: The findings indicate that an AI-based digital learning environment can effectively enhance teachers' digital skills and flourishing. The intervention improved both technical competencies and psychological well-being in digital contexts. These results have important implications for designing AI-integrated professional development programs to prepare teachers for the digital age.

Abstract Objective: English, as an international language, is one of the most widely used languages ​​used in most countries, and language learning is a complex product of personality, cognitive, cultural, and social factors. Thus, the present study aimed to investigate the relationship of cultural intelligence, personality traits, and emotional creativity with English language learning among students. Method: This study adopted a correlational research design. The statistical population consisted of all secondary school students and first- and second grade high school students in Gorgan in 2024, from whom 126 participants were selected using GPower software and through convenience sampling. The research instruments included Averill’s Emotional Creativity Questionnaire (1991), Ang et al.’s Cultural Intelligence Questionnaire (2004), and Goldberg’s Short Form of Personality Traits (1991). Students’ final English course grades were used to measure the variable of English language learning. The data were analyzed using Pearson’s correlation coefficients and regression analysis by SPSS 18 software. Results: The results showed that cultural intelligence (.29), emotional creativity (.21), and the personality traits of extraversion (.20), agreeableness (.16), openness to new experiences (.20), and conscientiousness (.18) had a positive and significant relationship with English language learning. In contrast, neuroticism had a negative and significant relationship with English language learning (-.18) at P=.05. Regression analysis indicated that cultural intelligence, with a beta coefficient (β=.21), could predict English language learning. Conclusion: Based on the findings of the study, it is recommended that educational authorities and practitioners pay special attention to the role of cultural intelligence, personality traits, and emotional creativity in teaching foreign languages so that students’ motivation and ability to learn English may be enhanced.

Abstract Objective: This study sought to examine the relationships between self-regulated learning, AI literacy, and entrepreneurial intention among Nigerian postgraduate students studying in Nigeria and Iran. Method: A cross-sectional survey design was employed, involving 530 postgraduate students selected from universities in both countries. Data were collected using Liñán and Chen's Entrepreneurial Intention Questionnaire (EIQ) (2009), Barnard et al.'s Online Self-regulated Learning Questionnaire (OSLQ) (2009), and Wang et al.'s (2023) Artificial Intelligence Literacy Scale (AILS). Data were analyzed using descriptive statistics, multivariate analysis of variance, and hierarchical multiple regression. Results: The findings revealed that both self-regulated learning and AI literacy significantly and positively predicted entrepreneurial intention, with self-regulated learning demonstrating the stronger unique contribution. In addition, Nigerian postgraduate students studying in Iran reported significantly higher levels of entrepreneurial intention, self-regulated learning, and AI literacy than those studying in Nigeria. These findings highlight the importance of learning-related competencies in fostering entrepreneurial intention and suggest that differences in educational contexts were associated with variations in entrepreneurial intention, self-regulated learning, and AI literacy. Conclusion: The study establishes self-regulated learning and AI literacy as key digital human capital drivers of entrepreneurial intention, with implications for context-sensitive curriculum design.

Abstract Objective: The dynamics of learning and the cultivation of cognitive capacities in the process of identifying Scholastic Aptitude (SA) constitute a complex challenge within educational systems. Thus, the present study aimed to elucidate its psychometric nature, gene-environment interactions, and developmental catalysts (2000–2025). Methods: In this systematic review, a comprehensive analysis was conducted on 58 pivotal studies, which were screened and selected in accordance with the PRISMA guidelines spanning the 2000–2025 timeframe. Results: Scholastic aptitude is a multidimensional construct. Although the SAT remains the most widely administered assessment instrument, its inherent socioeconomic biases necessitate the implementation of composite evaluation methods. The trajectory of talent actualization is grounded in the gene-environment interaction model and Gagné’s Differentiated Model of Giftedness and Talent (DMGT). Consequently, innate intelligence and baseline cognitive capacities are insufficient in isolation; rather, four synergistic catalysts—intrinsic motivation, dynamic educational environments, multidimensional family support, and effort reinforcement—account for over 60% of the variance in academic success. Accordingly, a five-pillar roadmap was derived, encompassing multidimensional assessment, personalized learning trajectories, environmental enablement, continuous monitoring, and educational equity. Conclusion: Scholastic aptitude is not a unidimensional phenomenon reducible to cross-sectional test scores; rather, it emerges from the intricate interplay among working memory capacities, cognitive processing mechanisms, and an enriched learning ecosystem. Transitioning toward dynamic, composite assessments and making coordinated investments in developmental catalysts are imperative to optimize learning capacities and prevent the attrition of human capital.

Abstract Objective: The main objective of the present study was to design a behavioral model for cyberspace consumers based on the psychology of human behavior. Method: In this study, a mixed methods design was used. The statistical population included two groups: (a)10 experts in the field of media and communications sciences selected purposefully for the qualitative part; (b) 384 Digikala consumers selected based on the Morgan and Krejci table due to the uncertainty of the study population and on a cluster-sampling manner. The experts took part in semi-structured interviews while the 384 participants in the quantitative section completed a researcher-made questionnaire. In the qualitative section, three types of coding including open coding, axial coding, and selective coding were used via using the MAXQDA software while Smart PLS 3 software was used to examine the quantitative data due to the abnormality of the data, the exploratory nature of the research, the complexity of the model, and the purpose of the research, namely the design and test the proposed model. Results: The results of the research showed that the factors affecting cyberspace consumer’s behavior include: 1) causal factors (trust, purchase incentives, support, etc.); 2) contextual factors (individual and psychological factors, family factors, etc.); 3) intervention factors (security concerns, negative attitudes, etc.); 4) strategies (strengthening security, personalizing services, interaction-oriented, and producing educational content); and 5) consequences (psychological, social and cultural, economic, technological, security, and legal consequences). Conclusions: The results of the structural equation model showed that the model of cyberspace consumer behavior has a good fit, suggesting a practical model for assessing digital consumers’ behaviors.

Abstract Objective: Electric potential difference is a fundamental concept in physics, yet students frequently experience conceptual difficulties in understanding it. Although previous research has documented common misconceptions through different assessment frameworks, relatively little is known about the structural quality of students' understanding as described by the SOLO taxonomy. Therefore, this study aimed to assess high school students' conceptual understanding of electric potential difference using the SOLO taxonomy and to identify the common misconceptions reflected in their responses. Method: The study employed a descriptive study with a quantitative approach. The participants were 92 female high school students in grades 11 and 12. Data were collected using a researcher-developed open-ended diagnostic assessment ( ) aligned with the SOLO taxonomy. Written responses were analyzed to determine understanding levels and identify common misconceptions. Results: Most students demonstrated understanding at the unistructural and multistructural levels, with few reaching relational or extended abstract levels. Analysis revealed persistent conceptual difficulties in distinguishing electric potential difference from electric potential and electrical energy, highlighting students' fragmented knowledge.