Jul 2026· Scientific Works· Vol 93, pp. 80-85· 0 citations
TL;DR
Overall, it can be concluded that schools can realize gains if they couple AI with clear learning goals, teacher capacity-building, and robust measurement plans.
Abstract
Across the world, secondary schools are experimenting with artificial intelligence (AI) to personalize instruction, automate feedback, and augment teachers’ capacity. Early evidence suggests AI can boost certain forms of engagement and achievement especially through intelligent tutoring systems (ITS), adaptive practice, and teacher-facing assistants. Yet risks remain around shallow learning, inequity, and student data protection. This article synthesizes research on the impact of AI on secondary students’ engagement (behavioral, emotional, cognitive) and achievement (course grades, standardized tests, mastery), and also contrasts traditional ITS findings with new evidence on generative AI. I propose a practical implementation blueprint covering pedagogy, staffing, procurement, safety, and evaluation, along with a responsible use and governance checklist aligned to recent policy guidance (e.g., UNESCO) [1] and regulation (e.g., EU AI Act)[2]. Overall, we can conclude that schools can realize gains if they couple AI with clear learning goals, teacher capacity-building, and robust measurement plans.
The rapid integration of artificial intelligence (AI) into education has introduced new possibilities for personalized, real-time instructional support, particularly through AI-driven scaffolded feedback. This study examines the impact of such feedback on students’ cognitive load and problem-solving efficiency, addressing a notable gap in existing literature, which has largely focused on academic achievement and technology acceptance in higher education settings rather than on cognitive mechanisms among younger learners. Grounded in Cognitive Load Theory, Vygotsky’s Sociocultural Theory of scaffolding, and Self-Regulated Learning Theory, the study explores how AI-generated feedback that is timely, individualized, and responsive to student performance can reduce extraneous cognitive load while promoting deeper engagement, self-regulation, and more efficient problem-solving. Employing a data-mining approach, the research draws on quantitative data collected through surveys, assessments, classroom observations, and records generated by AI-assisted learning platforms to identify patterns linking scaffolded feedback, cognitive performance, and problem-solving outcomes. Findings indicate that well-structured AI feedback systems help organize instructional content, minimize unnecessary cognitive strain, and support faster, more effective problem-solving, though challenges remain regarding technology access, teacher readiness, data privacy, and the need for continued oversight of AI-based instruction. The study concludes that while AI-driven scaffolded feedback holds significant promise for enhancing learning in K-8 classrooms, further research is needed to explore its long-term effects and to develop evidence-based strategies for effective implementation, particularly at the elementary school level.
Connie Ngujo, Precious Albao, Regina P. Galigao· International journal of hum...· 0 citations
Findings reveal that the teacher's engagement is non-linear and front-loaded: while Behavioral Engagement peaked during the Design and Development phases through active material generation, it significantly declined during the Implementation phase due to infrastructural anxiety and instability.
Nasai Danzeng· Region - Educational Researc...· 0 citations
As Generative Artificial Intelligence (GenAI) tools collect data from both reliable and unreliable sources across the internet, concerns have been raised about the credibility of the output content learners are exposed to when seeking these tools for assistance. To address this problematic issue, our study tests the development of a pedagogical assistant trained on a personalized course framework; Besides its equipment with content that aligns with classroom lectures, the virtual assistant was given a comprehensive set of instructions guiding its behavior; Restricting its responses to the provided course material, forbidding the provision of information from any external sources as an initial action to battle against information credibility issues, and limiting its interactions to course-related discussions to promote engagement and mitigate distractions. The study explores the perceived credibility of the tested agent alongside the perceived impact on students’ learning engagement. This study is significant in informing the design of credible, curriculum-aligned AI assistants for EFL learning contexts. To achieve the required results, our study adopts DeLone & McLean’s theoretical framework alongside a quantitative research design with a structured questionnaire as a data-gathering tool. The sample of this study consists of N = 63 students of the Higher School of Teachers, Moulay Ismail University. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 25. Students exhibited positive perceptions towards the custom agent, which they perceived as an engaging and credible source of information that also aligns with the course content they are exposed to during formal lectures. Our findings also revealed a strong correlation between Perceived Impact on Learning Engagement (PILE) and Perceived Credibility (PC), with r (61) = .780. The study acknowledges some limitations and offers recommendations for future studies.
Houda Louatouate, Mehdi Karmouch, M. Zeriouh· Arab World English Journal· 0 citations
The rapid expansion of artificial intelligence (AI) in education has renewed debates about its pedagogical value, particularly in relation to learner-centered approaches and motivational processes. While a growing body of research has examined AI-supported learning in cognitively oriented subjects, empirical evidence remains scarce in practice-based disciplines such as physical education (PE), especially within secondary school contexts in developing countries. This study investigated the effects of integrating AI-supported instructional tools into PE lessons on students' motor learning, intrinsic motivation, and engagement in Tunisian secondary schools. A quasi-experimental pre-test/post-test design involving two intact classes was employed because individual random assignment was not feasible in the school setting (N = 56; age range 15–17 years, M = 16.4, SD = 0.9), including an experimental group receiving AI-supported instruction and a control group following traditional teaching methods over an 8-week intervention period. Motor learning was assessed using the TGMD-3, intrinsic motivation through the Intrinsic Motivation Inventory, and engagement via a multidimensional student engagement scale. Results revealed statistically significant and educationally meaningful improvements in motor skill acquisition, intrinsic motivation, and engagement among students exposed to AI-supported instruction compared with their peers in the control group. Effect sizes indicated strong practical relevance, suggesting that AI-supported visual feedback based on predefined movement indicators was interpreted and contextualized by the teacher to support motor skill acquisition. These findings suggest that AI-supported instruction may enhance motor learning, intrinsic motivation, and student engagement when integrated into teacher-mediated physical education. Given the quasi-experimental design involving intact classes, these findings should be interpreted as evidence of association rather than definitive causal effects. These findings contribute context-sensitive empirical evidence to the field of educational psychology by highlighting the potential of human-centered AI integration in secondary school physical education within a developing country context. The study underscores the importance of pedagogical mediation, learner autonomy, and ethical awareness in the design and implementation of AI-supported learning environments.
Amin Daly, Sofiene Mnedla, M. Chelly· Frontiers in Psychology· 0 citations
The findings show that AI is increasingly seen as a transformative academic tool, especially for research, language learning, writing support and problem‐solving, and despite widespread AI adoption, the study identifies significant gaps in institutional infrastructure and the absence of systematic training programmes.
M. Doğan, B. Kashkhynbay, Zhaniyat Baltabayeva· European Journal of Educatio...· 0 citations
Background Recent advances in artificial intelligence (AI) have accelerated the development of educational technologies intended to support students’ learning processes. However, classroom-based evidence on the educational impact of purpose-built AI applications remains limited, particularly across national contexts. Methods This study examined the effects of an AI conversational study assistant (Study Buddy) on secondary students’ academic performance, motivation, and engagement in authentic school settings. A quasi-experimental design was employed across two case studies conducted in Cyprus (Grade 10 Physics, N = 47) and Greece (Grade 7 History, N = 70). In each context, intact classes were assigned to experimental and control conditions. Academic performance was assessed using curriculum-aligned teacher-developed tests, while motivation and engagement were measured using the Motivation and Engagement Survey. In addition, system-generated usage logs were analyzed descriptively to document student access, interaction intensity, and patterns of tool use during the intervention. Statistical analyses included descriptive statistics and parametric or non-parametric group comparisons depending on distributional assumptions. Results Students who used Study Buddy demonstrated significantly higher learning gains in Physics and higher post-test scores in History compared to peers in control groups. In contrast, no statistically significant differences were observed for motivation or engagement in either case study. Usage log analysis indicated that students actively engaged with the application and that interaction patterns reflected the instructional design of each case study. Conclusions The findings suggest that purpose-built AI study assistants can support academic learning when integrated into regular classroom instruction. However, short-term exposure and predominantly task-focused interactions may limit their influence on motivational and engagement-related outcomes. The study contributes classroom-based, cross-national evidence on educational AI tools and highlights the importance of instructional design and teacher mediation in shaping both usage patterns and learning outcomes.
Theodoros Karafyllidis, Anna Vacalopoulou, S. Stamouli et al.· Open Research Europe· 0 citations
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