https://jlt.iaet.ir/ Trends and Achievements in Learning Technology en daily 1 Mon, 22 Jun 2026 00:00:00 +0330 Mon, 22 Jun 2026 00:00:00 +0330 https://jlt.iaet.ir/article_735912.html This qualitative phenomenological study aimed to explore the challenges of the Farhangian University curriculum with an emphasis on the content component and to document the solutions experienced by pre-service teachers, Farhangian University faculty, and graduates in Ardabil Province. Methods: Purposive and convenience sampling were employed, with inclusion criteria: for faculty, at least five years of teaching at Farhangian University and teaching courses related to the elementary education curriculum content; for pre-service teachers (entry cohorts 2020 and 2021 in Elementary Education)—completion of practicum units and at least half of the program units; for graduates (entry cohorts 97 and 98 in Elementary Education)—at least one year of service in public and Shahed schools in Ardabil Province. Data were collected over three months (October–December 2023) through semi-structured interviews. In this qualitative study, interviews continued until theoretical saturation, ending with 11 interviews from pre-service teachers, 15 from faculty, and 20 from graduates. For trustworthiness, Lincoln & Guba (1985) criteria—credibility, transferability, dependability, and confirmability—were applied. Findings: The results indicate that the content of the Farhangian University curriculum faces several challenges. Participants proposed solutions to overcome these challenges, including: empowering faculty, conducting needs assessments, reflective approaches in content development, revisions to curricular resources, organizing conferences and calls for engagement to improve content, soliciting feedback from pre-service teachers, expanding university libraries, foresight in planning, addressing resource shortages, developing novel content, improving the quantity and quality of practicum units, delivering capacity-building workshops for pre-service teachers, bringing university training closer to school practice, and elevating the scholarly and research caliber of pre-service teachers. https://jlt.iaet.ir/article_735595.html Recent advances in artificial intelligence have transformed education; however, teachers’ readiness to effectively utilize this technology and its implications for the psychological dimensions of teaching have been less explored. The present study aimed to examine the effect of training in the use of AI tools in the design and implementation of instruction on teachers’ mindfulness in teaching and their readiness to adopt AI. This research employed a quasi-experimental design with a pretest–posttest control group. The statistical population consisted of 258 primary school teachers in Soltaniyeh, from whom 60 participants were selected through convenience sampling and randomly assigned to the experimental and control groups. The experimental group received eight training sessions on the use of Magic School, Fobizz, and Teach Better, while the control group used non-AI educational technologies such as Wordwall and Wayground during the same period. Data were collected using the Teachers’ Readiness for AI Applications Questionnaire (Ramazanoglu & Eklen, 2024) and the Mindfulness in Teaching Scale (Frank et al., 2016). The results of analysis of covariance indicated that the experimental group obtained significantly higher posttest scores than the control group on both mindfulness in teaching and readiness to use AI (p < 0.001). Moreover, AI-based training led to improvements in the experimental group’s technological self-efficacy, teacher–student interaction, and ethical awareness. The findings suggest that structured training in AI tools can enhance teachers’ professional readiness and, by reducing cognitive pressures associated with instructional design and implementation, facilitate the development of mindfulness in teaching. https://jlt.iaet.ir/article_735526.html This research aimed to investigate the effect of teachers' motivational strategies and supportive behavior in virtual education on the academic performance of monolingual and bilingual students in English language courses. The research method was descriptive-correlational, and the statistical population included high school teachers and students in Ahvaz. Multi-stage stratified cluster sampling was performed, and data were collected from 96 teachers and 192 students (96 monolingual and 96 bilingual) using standardized questionnaires for motivational strategies (TUMSS) and supportive behavior (TUSBS). Data analysis was conducted using SPSS version 24, employing Pearson correlation coefficient, multiple regression, independent t-test, and Fisher's z-test. The findings showed that teachers' motivational strategies and supportive behavior have a positive and significant relationship with academic performance in both groups, but the strength of this relationship for supportive behavior was significantly stronger in the bilingual group (z = −2.04, p < 0.05). Additionally, the regression model indicated that in the bilingual group, both predictor variables significantly predicted performance (R² = 0.37), while in the monolingual group, only supportive behavior had a significant effect (R² = 0.16). These results suggest that in a virtual environment, teacher support for bilingual students acts not only as a facilitator but also as a compensatory factor against linguistic-cultural gaps. Furthermore, a significant difference was observed between teachers' and students' perceptions of the use of these strategies (p < 0.001), indicating the presence of self-report bias in teachers and the necessity of using multi-source assessment. Overall, the findings emphasize that the success of virtual education in multilingual regions, beyond technological infrastructure, depends on the quality of the human relationship with the teacher, especially through empathetic and responsive supportive behaviors. https://jlt.iaet.ir/article_736936.html his study examines the role of modern technology in education through Martin Heidegger’s philosophy of technology, with particular emphasis on the concept of Gestell. Drawing on a qualitative, analytical–inferential approach, the study challenges instrumental and neutral conceptions of educational technology and argues that technology functions as a mode of revealing that shapes human understanding, learning, and educational practices. From this perspective, uncritical adoption of technological rationality risks reducing education to efficiency, standardization, and consumption, thereby weakening reflective thinking and existential self-understanding. Methodologically, the study employs conceptual and hermeneutic analysis of Heidegger’s key philosophical texts on technology and integrates the Frankena inferential model to derive educational implications. This model enables a systematic transition from philosophical descriptions to normative educational principles. The analysis reveals that while technology may contribute to learning, its dominance can reframe learners as standing-reserve and marginalize dialogical, ethical, and reflective dimensions of education.The findings suggest that technology should not be rejected in educational contexts but critically reoriented. When embedded within curricula that prioritize human values, reflective thinking, and meaningful engagement, technology can serve as a cognitive companion rather than a controlling framework. Accordingly, the study articulates several educational principles, including the humanization of curricula, the promotion of critical reflection, and the redefinition of the teacher’s role as a designer of learning experiences. Ultimately, the study argues that the central challenge of educational technology lies in cultivating reflective and ethically grounded modes of engagement that preserve the human core of education in the technological age. https://jlt.iaet.ir/article_737789.html Objective: This study aimed to assess the gap between theoretical knowledge and practical application of computer literacy among first-grade elementary school teachers in Famenin County and to examine its relationship with individual factors.Method: The present study employed a descriptive-survey design. The statistical population consisted of 384 teachers. Using Tabachnick and Fidell’s (2007) formula and stratified sampling, 112 individuals were selected as the sample. Data collection instruments included the Computer Literacy Questionnaire by Son, Robb, and Charismadjí (2011) and a researcher-constructed 15-item questionnaire (7-point Likert scale) measuring the practical application of technology in the teaching process. Content validity was confirmed through expert judgment and the Content Validity Index (CVI). Reliability was calculated using Cronbach’s alpha, yielding coefficients of 0.86 and 0.92, respectively.Findings: Teachers’ computer literacy knowledge was rated as satisfactory (mean 34.5 out of 46), while its practical application was evaluated at a moderate level (mean 45 out of 75). A significant inverse correlation was observed between the two variables (*r*(110) = −0.789, *p* < .001). Additionally, age showed a significant inverse relationship, and educational level a significant direct relationship, with computer literacy.Conclusion: The findings confirm a significant knowledge-practice gap and underscore the need to revise in-service teacher training programs with an emphasis on practical technology integration. https://jlt.iaet.ir/article_737822.html In the twenty-first century, teachers’ roles extend beyond the mere transmission of knowledge; they act as facilitators of learning, cultivators of critical thinking, and promoters of life skills. Recognizing and enhancing teachers’ professional competencies is essential for improving the quality of education. This study aimed to examine and analyze twenty-first-century teaching competencies among primary school teachers in Ahvaz. A mixed-methods explanatory design was employed. In the quantitative phase, 351 primary school teachers from four districts of Ahvaz were selected using multistage stratified sampling based on the Morgan table. In the qualitative phase, 20 school principals and teacher evaluators were interviewed purposively until theoretical saturation was achieved. Data collection involved a researcher-developed questionnaire with validated content (Lawshe technique) and acceptable reliability (Cronbach’s alpha > 0.70) for the quantitative phase, and semi-structured interviews for the qualitative phase. Findings revealed that competencies in creativity, problem solving, and collaborative activities were above the average level, communication skills were at an average level, while digital literacy and digital citizenship were below the average level. Qualitative findings largely supported the quantitative results, although no alignment was observed regarding collaborative activities due to differing teacher expectations and experiences. The study highlights that developing teachers’ competencies requires an integrated and multidimensional approach, emphasizing the interplay between communication, creativity, problem solving, collaboration, and digital literacy. Such an approach can strengthen teachers’ professional effectiveness and enhance the overall quality of education in primary schools. https://jlt.iaet.ir/article_735515.html This study examined the effectiveness of an artificial intelligence (AI)–based instructional design course on academic self-efficacy, achievement motivation, and self-regulated learning among students at Islamic Azad University, Azadshahr Branch. The research was applied in purpose and employed a quasi-experimental pretest–posttest design with a control group. The population comprised undergraduate students in Educational Sciences and related majors enrolled in the instructional design course. Sixty students were selected through multistage cluster sampling and randomly assigned to an experimental group (AI-based instructional design) or a control group (conventional instructional design). Measures included an Academic Self-Efficacy Questionnaire, Hermans’ Achievement Motivation Questionnaire, and the Self-Regulated Learning Questionnaire by Bouffard and colleagues. Data were analyzed using descriptive statistics and analysis of covariance (ANCOVA). After controlling for pretest scores, the experimental group achieved significantly higher posttest means in academic self-efficacy, achievement motivation, and self-regulated learning than the control group, and the instructional approach accounted for a substantial share of variance in all three outcomes. Accordingly, completing the AI-based course strengthened students’ beliefs in their academic capabilities, increased their motivation to strive for success, and enhanced key self-regulation processes such as planning, monitoring, and self-evaluation. Limitations include sampling from a single university branch, a limited time frame, and reliance on self-report data, which warrants cautious generalization. Practically, teacher education and educational science programs are encouraged to redesign instructional design courses by integrating AI tools in a structured manner across syllabi, assignments, and assessment. Emphasizing responsible AI use to build academic human capital, the study aligns with SDG 4 (Quality Education) and SDG 9 (Industry, Innovation and Infrastructure). https://jlt.iaet.ir/article_735822.html The present study aimed to examine the role of religious orientation in predicting AI ethical reflection among student teachers. The statistical population consisted of undergraduate student teachers in the Faculty of Educational Sciences (fourth semester and above) during the 2024–25 academic year, all of whom used AI tools for at least one hour daily. A sample of 150 participants was selected using a random sampling method. Research instruments included the Religious Commitment Questionnaire (intrinsic and extrinsic) and the AI Ethical Reflection Scale, which comprises three components: ethical awareness, critical evaluation, and the use of AI for social good. Data were analyzed using Pearson correlation and standard regression. The findings indicated that both intrinsic and extrinsic religious commitment had positive and significant correlations with all dimensions of AI ethical reflection (r = 0.28 to 0.47, p < 0.01). Intrinsic religious commitment showed the strongest relationships with critical evaluation and the use of AI for social good. Regression analysis also demonstrated that both dimensions of religious commitment significantly predicted AI ethical reflection; however, intrinsic religious commitment was the stronger predictor (B = 0.59, t = 5.12, p < 0.01), while extrinsic commitment had a weaker predictive role (B = 0.31, t = 2.05, p < 0.05). The overall regression model was significant (F = 15.5, p < 0.01) and accounted for approximately 36% of the variance in AI ethical reflection. Based on the results, intrinsic adherence to religious beliefs and values plays a more influential role in shaping ethical sensitivity, critical thinking, and responsible use of AI among student teachers. These findings highlight the importance of integrating religious and ethical education within academic programs related to emerging technologies. https://jlt.iaet.ir/article_736959.html The present study aimed to design and validate a comprehensive model for artificial intelligence (AI) innovation adoption in education oriented organizations. Despite the growing capacity of AI to enhance instructional and managerial processes, existing research lacks a multilevel and context specific framework tailored to the complexities of educational settings. To address this gap, the study employed an applied mixed methods approach consisting of qualitative exploration followed by quantitative validation. In the qualitative phase, a systematic literature review and semi structured interviews with experts were carried out, resulting in the identification of thirteen key components influencing AI innovation adoption. These components were subsequently classified into five analytical levels: individual, technological, organizational, environmental, and social. In the quantitative phase, data were collected from 360 questionnaires distributed among faculty members of Farhangian University, educational specialists, and administrative managers in 2025. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) in SmartPLS V4. The results demonstrated adequate construct reliability and convergent validity (CR > 0.70; AVE > 0.50), and all major structural relationships were confirmed at a significance level of p < 0.001. The coefficients of determination also indicated strong explanatory power for the proposed model. Findings further showed that technology interaction protocols, organizational alignment mechanisms, and technological trust and transparency had the greatest impact on AI innovation adoption. Consequently, the proposed model provides a practical analytical framework that can guide policymakers and educational leaders in designing and implementing effective strategies for the development and deployment of intelligent technologies in education oriented organizations. https://jlt.iaet.ir/article_737633.html This study aimed to explore the cognitive mechanics in AI-based games and ‎provide a preliminary evaluation of their impact on the components of creativity ‎‎(fluency, originality, flexibility, and elaboration) within an exploratory framework. ‎The present mixed-methods research utilized an exploratory single-group quasi-‎experimental design (pre-test/post-test). In the qualitative phase, through a ‎systematic content analysis of six selected games equipped with adaptive and ‎generative algorithms, key mechanics such as "interactive co-creation" and ‎‎"dynamic challenge adaptation" were identified. In the exploratory quantitative ‎phase, 12 children aged 4 to 7 in Tehran participated in a 4-week intervention ‎program (three 45-minute supervised sessions per week), selected through ‎purposive sampling with informed parental consent. The data collection tool was ‎the pictorial version of the Torrance Test of Creative Thinking (Form A), for which ‎inter-rater reliability was calculated at 0.87. Quantitative findings indicated a ‎significant increase in the total mean creativity scores from pre-test (M=85.50, ‎SD=5.20) to post-test (M=112.40, SD=6.10). The results of the paired t-test ‎revealed a statistically significant difference (t(11)=21.42, p<0.0001). The ‎calculated effect size (Cohen’s d=6.18) was very large. However, given the ‎exploratory nature of the study, the lack of a control group, and the small sample ‎size, these findings should be considered as preliminary evidence of the potential ‎of intelligent games to facilitate creativity, rather than conclusive proof of a causal ‎relationship. Qualitative analysis suggests that AI, by providing a platform for "safe ‎trial and error" and personalizing challenges, may act as a cognitive facilitator.‎ https://jlt.iaet.ir/article_738179.html The present study aimed to examine the relationship between digital leadership style and teachers’ innovative work behaviors, with psychological empowerment as a mediating variable. In terms of purpose, the study was applied, and in terms of method, it employed a descriptive–correlational design. The statistical population consisted of all elementary school teachers in Qazvin, totaling 1,220 individuals. Based on the Krejcie and Morgan table, a sample of 291 teachers was selected through stratified random sampling. Data were collected using the Digital Leadership Scale developed by Yülatas and Arslan (2017), Spreitzer’s (1995) Psychological Empowerment Questionnaire, and Janssen’s (2000) Innovative Work Behavior Scale. The validity of the instruments was confirmed through confirmatory factor analysis, and reliability was assessed using Cronbach’s alpha coefficients, which were 0.95, 0.84, and 0.95, respectively. The collected data were analyzed using descriptive and inferential statistics. The findings indicated that digital leadership style had a direct and significant effect on teachers’ innovative work behaviors (β = 0.41, T = 7.48) and their psychological empowerment (β = 0.80, T = 16.66). Moreover, psychological empowerment had a positive and significant effect on teachers’ innovative work behaviors (β = 0.50, T = 6.36) and played a mediating role in the relationship between digital leadership style and innovative work behaviors. The results suggest that digital leadership fosters teachers’ innovative work behaviors by enhancing their sense of meaning, competence, self-determination, and impact. Overall, psychological empowerment functions as a key mechanism through which digital leadership influences teachers’ innovative work behaviors and supports schools’ digital transformation. https://jlt.iaet.ir/article_739053.html Novice elementary school teachers often experience deficiencies in pedagogical content knowledge (PCK) during their early years of professional practice. This study investigated the effectiveness of structured online mentoring, facilitated by subject-group leaders, on the perceived and enacted dimensions of novice elementary teachers’ PCK. The study employed a quasi-experimental pretest–posttest control-group design. A total of 148 novice teachers from public elementary schools in Urmia were selected through stratified cluster sampling and randomly assigned to experimental and control groups. Over a 10-week period, the experimental group participated in weekly 90-minute online sessions via the Skyroom platform, facilitated by 12 trained subject-group leaders. The instruments included a perceived PCK questionnaire, situational vignettes, and a rubric for assessing PCK in lesson plans. Descriptive statistics, including means and standard deviations, were used to summarize the data. Analysis of covariance and one-sample t-tests were used to examine the effects of the intervention, while correlation and regression analyses were conducted to explore the relationships between perceived and performance-based PCK indicators. The findings indicated that the intervention significantly improved perceived PCK, performance on situational vignettes, and lesson-plan quality. In addition, perceived PCK was positively, though moderately, associated with performance-based indicators, and teaching experience played a moderating role. The results suggest that online mentoring can serve as an innovative and effective approach to supporting the professional development of novice teachers in Urmia. https://jlt.iaet.ir/article_739056.html The accelerating evolution of digital technologies necessitates a fundamental rethinking of pedagogical processes. This systematic review aims to identify the educational capabilities of the metaverse, implementation challenges, supporting learning theories, and transformation requirements within educational systems. Following the PRISMA 2020 protocol, a systematic search was conducted in domestic (Noormags, Magiran, Civilica) and international (IEEE Xplore, ScienceDirect, Scopus, Web of Science, ERIC) databases from January 2020 to May 2025. Through criterion-based selection prioritizing quality and direct relevance, 28 peer-reviewed articles were selected from 242 initial records. Inter-coder reliability demonstrated substantial agreement (kappa = 0.88). Data were analyzed using directed qualitative content analysis alongside frequency analysis. The findings revealed four main educational capabilities: facilitating cognitive immersion (86%, n=24), enabling co-creation in authentic experiential learning, developing avatar-based social interactions and scaffolding (18%, n=5), and supporting AI-driven personalized learning (43%, n=12). Five key challenges were identified: privacy and data security (50%, n=14), high infrastructure costs (46%, n=13), digital divide (39%, n=11), lack of standardized content (29%, n=8), and insufficient teachers' digital competencies (25%, n=7). Constructivism, Kolb's experiential learning cycle, and Vygotsky's social constructivism were identified as the primary theoretical foundations. Transformation requirements were categorized into five dimensions: policy-making, institutional, professional, technical, and ethical. The integrated conceptual framework was developed inductively based on qualitative content analysis of the 28 selected articles. The value added of this study lies in presenting an integrated conceptual framework (Figure 2) demonstrating that effective metaverse utilization requires a systemic shift toward blended and complementary learning ecosystems — positioning the metaverse as a reinforcing adjunct rather than a replacement for face-to-face instruction. https://jlt.iaet.ir/article_740706.html The present study aimed to identify the components of AI-based instructional design involving collaboration between humans and artificial intelligence. The research comprised two phases: a human-assisted phase and an AI-assisted phase. In the human-assisted phase, a systematic review was conducted. Using the specified keywords, a total of 299 studies were identified.After reviewing the titles of the articles, 71 articles were deleted due to duplication, and 228 articles were identified for title and abstract review. In this review, 123 were excluded by applying the criteria, and after review, 20 studies were finally selected for in-depth analysis. In the artificial intelligence assistance section Copilot, Gemini, ChatGPT, and Cloud software were used with prompts titled "Components of AI-supported instructional design ", "Human and Artificial Intelligence Collaboration in Instruction design", "Instructional AI-supported instructional design and Its Components" and "Components of Instruction design with Artificial Intelligence Assistance" for 5 time periods. The content analysis method was used to analyze the data. The research results showed that AI-based instructional design includes 14 main components, which are: needs analysis, content, instructional partner, feedback, interaction, evaluation, adaptation, ethics, information and data, teaching method, personalization, goals and outcomes, automation, support. The research results also show that the human search section on AI-supported instructional design components in the literature focuses on assessment, teaching method, content, instructional partner, and learning goals and outcomes, and the AI response section emphasizes components such as interaction, evaluation, feedback, information and data, and support. Hence, it can be concluded that in order for AI to be used effectively in instructional design, attention should be paid to the aforementioned components. https://jlt.iaet.ir/article_740707.html The aim of this study was to identify the factors influencing the acceptance and use of artificial intelligence technology among higher education students, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, and to extend it by incorporating the construct of “perceived risk.” The research employed a descriptive–correlational design. The study population consisted of students at the University of Tehran; 335 participants were selected using convenient sampling. Data were collected through a researcher-made questionnaire based on the UTAUT model and relevant prior studies. The questionnaire’s reliability and validity were confirmed using Cronbach’s alpha (0.78 to 0.91) and confirmatory factor analysis with appropriate fit indices (CFI > 0.90, RMSEA < 0.08). Data were analyzed using SPSS-27 and AMOS-24, employing covariance-based structural equation modeling. The results showed that performance expectancy, effort expectancy, and social influence had positive and significant effects on behavioral intention, whereas facilitating conditions and perceived risk did not have significant effects. Moreover, behavioral intention had a strong and significant effect on actual technology-use behavior. The findings highlight the importance of strengthening infrastructure, simplifying AI tools, providing targeted training, and emphasizing the benefits of this technology to enhance student acceptance. These results can be used to design and improve AI-related educational programs in higher education. https://jlt.iaet.ir/article_741478.html The rapid digital transformation of higher education has highlighted the need to redesign teaching and learning processes. However, empirical evidence regarding the effectiveness of technology-enhanced flipped learning for teaching abstract scientific concepts, particularly in teacher education, remains limited. This study investigated the effectiveness of technology-enhanced flipped learning on the academic achievement and academic motivation of science student teachers in optics and modern physics. A quasi-experimental pretest–posttest control group design was employed. Eighty-three student teachers at Farhangian University were selected through convenience sampling and randomly assigned to experimental and control groups. Over eight instructional sessions, the experimental group received technology-enhanced flipped learning incorporating multimedia resources, interactive simulations, artificial intelligence (AI)-supported learning tools, and active learning activities, whereas the control group received conventional instruction. Data were collected using a researcher-developed Academic Achievement Test and the Harter Academic Motivation Questionnaire and analyzed using analysis of covariance (ANCOVA) and Pearson's correlation coefficient. The findings indicated that technology-enhanced flipped learning significantly improved both academic achievement and academic motivation compared with conventional instruction. A significant positive relationship was also found between academic achievement and academic motivation. The results suggest that the effectiveness of this approach stems not merely from shifting content delivery outside the classroom but from purposeful instructional design, the integration of technology with active learning, and meaningful learner engagement. These findings provide empirical support for developing innovative technology-enhanced science education and teacher education programs. https://jlt.iaet.ir/article_741584.html This qualitative study aimed to investigate the mechanisms, effects, and implications of Artificial Intelligence (AI) application in representing and facilitating the understanding of abstract phenomena and concepts in elementary science education. The research design followed a qualitative approach, employing a systematic review and thematic synthesis methodology, guided by the PRISMA statement. The scope of the research encompassed credible studies published between 1390-1404 Solar Hijri (2010-2025 AD). A purposive sampling strategy, based on predefined criteria, was utilized to select the study sample. The search strategy, adhering to PRISMA guidelines, involved searching, screening, and evaluating relevant articles from domestic databases including Magiran, IranDoc, Civilica, and Noormags, as well as international databases such as Web of Science, Scopus, ScienceDirect, and Google Scholar, using specific keywords. After excluding duplicates and applying inclusion/exclusion criteria, 10 articles were selected for final analysis. The data were analyzed using the thematic analysis method to extract the main themes. Findings revealed that AI, through mechanisms such as multimodal representations, interactive simulations, knowledge modeling, and personalized learning, significantly aids in better comprehension of abstract phenomena and concepts in elementary science. Specifically, AI reduces cognitive load, enhances causal reasoning, increases learner motivation and engagement, and elevates learning from rote memorization to deep conceptual and exploratory understanding. Consequently, AI can serve as an effective tool for transforming elementary science education. However, its successful implementation necessitates appropriate instructional design and readiness in terms of infrastructure and professional development for teachers. https://jlt.iaet.ir/article_741689.html The present study was conducted with the aim of performing a quantitative content analysis of the eighth grade Persian textbook in the first cycle of secondary education based on the principles of emotional design. This study was conducted using quantitative content analysis methods. The analytical framework of the research included three main components (textual, visual, and supplementary elements) along with related sub-indicators for each component. A total of 1,117 instances of emotional design elements were identified in the analyzed textbook. Among the textual elements component, the highest frequency corresponded to the indicator, variety and appropriateness of color in relation to the text, (with pink appearing 165 times), while the lowest frequency was related to the gray color and the indicator, humorous text, both of which appeared zero times. In the visual elements component, the indicator variety of colors in images and shapes (with cool-green appearing 136 times) had the highest frequency, whereas, humanization of images,(1 instance) showed the lowest frequency. In the supplementary elements component, nonverbal and sensory communications, (2 instances) received the greatest attention, while, multimedia and animation, recorded zero instances. . These findings indicate that although the eighth-grade Persian textbook is relatively rich in terms of visual elements and color schemes, and to some extent adheres to aesthetic and emotional principles, deeper aspects of emotional design such as humor, humanization, multimedia, animation, and nonverbal communication have been largely neglected. https://jlt.iaet.ir/article_741841.html Reading was recognized as a fundamental educational skill and an essential cognitive and communicative tool for participation in academic, social, and professional life. Difficulties in reading often caused serious educational, emotional, and social problems among students. The purpose of this study was to review and analyze previous research on the use of educational and therapeutic games for students with reading disorders to determine their role in diagnosis, prevention, intervention, and improvement. This systematic review was conducted in October 2024 following the PRISMA guidelines. The research design was a document-based analytical review, and the statistical population included national and international studies related to games and reading disorders. A total of 505 articles were identified through searches in six international and three national databases, and after applying inclusion and exclusion criteria, 17 studies were selected. Data were analyzed through descriptive synthesis and thematic comparison. Results showed that games had been applied in four main areas: diagnosis and screening, prevention and early intervention, treatment and remediation, and evaluation of students with reading disorders. Most studies emphasized therapeutic applications, while early detection and prevention received limited attention. It was concluded that educational games provided significant potential for improving reading abilities and motivation in students with reading disorders, though their effectiveness depended on appropriate design, platform compatibility, and educational objectives. https://jlt.iaet.ir/article_742204.html Learning algebraic concepts, particularly algebraic identities, is one of the major challenges in mathematics education at the lower secondary level. This study aimed to investigate the effectiveness of computer-supported collaborative learning with the aid of AI-based group chats on ninth-grade students’ understanding of algebraic identities, based on the APOS theoretical framework. The research employed a mixed‑methods (quantitative–qualitative) design. In the quantitative phase, a quasi-experimental pretest–posttest design with a control group was used. The sample consisted of 60 ninth-grade male students from Kerman city in the 2024–2025 academic year, selected through cluster random sampling and assigned to experimental and control groups. The experimental group received instruction over two weeks in four consecutive sessions using computer-supported collaborative learning with AI-assisted group chats, while the control group received traditional instruction. The research instrument was a researcher-made test based on APOS levels. The findings indicated a significant improvement in the experimental group’s mean scores, with a statistically significant advantage over the control group and a large effect size (p < 0.001). The qualitative analysis of interactions also revealed cognitive development from the Action level toward the Process, Object, and Schema levels. Overall, integrating artificial intelligence into collaborative learning contributed to deepening students’ conceptual understanding of algebraic identities.