In the era of Artificial Intelligence (AI), where technology is transforming industries, education stands at a pivotal juncture. With an increasing emphasis on critical thinking and problem-solving, there is a growing need for innovative tools that can foster these essential skills among students. Traditional education methods need help making personalized scalable and interesting experiences for students at this task type which this research aims to solve. The research uses AI and deep learning tools to build an effective framework that enables better riddle solving for students by proposing state of the art deep features including sentence embeddings and ULMfit to be applied as input to deep learning models. In contrast, this study examines different traditional machine learning and deep learning models including ensemble learning models, used as baseline models for comparing the performance of the proposed transformer architectures based on RoBERTa-Large to determine which approach works best, achieving highest accuracy of 96% to effectively handle riddle complexity. The research studies used text data patterns using TF-IDF, Count Vectorization, and word embedding techniques which apply in the form of Roberta. Our research findings help educators, technology experts and scientific teams design educational tools with an easy-to-deploy AI solution. 

Based on this gap in the literature, the problem situation identified was deemed worth investigating in terms of contributing to the accumulation of knowledge on the subject area. In addition, it is thought that this study will contribute to future studies on artificial intelligence in primary school education. The aim of this study is to create a Likert-type attitude scale that can be used to determine primary school students’ attitudes towards artificial intelligence. In this study, exploratory sequential design, one of the mixed research method types, was used. A 32-item draft scale form was prepared in the light of the literature review, student opinions collected through a structured interview form and data obtained from field experts. In order to examine the validity of the scale, exploratory and confirmatory factor analyses, item-factor total correlations and item discriminations were evaluated. The goodness of fit values obtained in confirmatory factor analysis were [CMIN=245,020, df=159 (CMIN/df= 1.541), RMSEA= 0.45, RMR= 0.035, GFI= 0.916, AGFI= 0.889, CFI= 0.903, NFI= 0.773, IFI= 0.906]. To evaluate the reliability of the scale, internal consistency coefficient was calculated, and test-retest analysis was performed. Cronbach’s Alpha reliability coefficient for the overall scale was 0.807 and McDonald’s Omega coefficient was 0.816. As a result, it was determined that the Artificial Intelligence Attitude Scale, which consists of 4 factors and 20 items, is an appropriate, valid and reliable tool for evaluating primary school students’ attitudes towards artificial intelligence.

Climate literacy is crucial to increasing public understanding and engagement with the global climate catastrophe. However, current climate education approaches often fail to effectively raise concern and action, particularly across diverse age groups. This study makes a modest attempt to detail the design and development of a novel multilevel interactive digital climate education platform for early learners, adolescents, and adults using adaptive learning pathways, personalized content delivery, multimedia interactivity, and gamification features to promote learner engagement as well as learning outcomes across different age levels. A mixed-methods research design was used involving pre and post-survey quantitative measures as well as qualitative user experience testing. Post-survey results indicated significant improvement in climate literacy knowledge, attitudes towards the environment, and self-efficacy beliefs regarding individual efforts to mitigate future climate impacts (response efficacy), regardless of learner age group. The comparative analysis thus revealed certain content preferences by age as well as interaction patterns among functionalities and learning gains between groups based on user perspectives that point towards differentiated preference areas linked with diverse ages. The resulting platform exemplifies interactive digital technologies’ potential for achieving sustainable behavior change through optimised synergies with large-scale educational interventions for inducing positive spillover effects in terms of broader widespread climate change engagement impact over generational transition pragma.

The integration of artificial intelligence (AI) in education is a promising transformation. Drawing on advanced technologies, AI enriches the learning experience through intelligent systems capable of analyzing, adapting and personalizing teaching. Despite a growing volume of scientific publications, there remains a lack of critical synthesis on the real impact of AI on the role of teachers, student learning and the transmission of knowledge. To fill this gap, this article proposes a systematic literature review, conducted using the PRISMA method, to identify the opportunities and limitations of AI in educational environments. From 1,248 publications extracted from the Scopus database between 2018 and 2024, 20 relevant studies were selected and analyzed after applying inclusion and exclusion criteria. The results show significant growth in research in this field, and demonstrate that AI enables teachers to automate certain tasks, personalize teaching and better meet learners' individual needs. However, significant obstacles remain, including lack of digital skills, resistance to change, and ethical concerns. The study also points out that AI enhances learners' skills, promoting the personalization of pathways, the identification of struggling students, the adaptation of materials, as well as real-time engagement and monitoring. It also makes it possible to model and transmit knowledge through the creation and adaptation of digital educational resources. However, AI also presents certain limitations in the educational context, such as excessive dependence on technology, inequalities of access, automatic generation of answers without real learning, as well as issues relating to the confidentiality of personal data. AI is a powerful but complex lever in the field of education. Its effective integration requires targeted training for teachers, critical reflection on its uses, and a rigorous ethical framework. This review thus provides a solid basis for guiding future research towards complementary empirical studies, while accompanying practitioners in a reasoned and beneficial adoption of AI in educational contexts.

This research presents a novel approach to evaluating student academic performance at Nalla Narasimha Reddy Group of Institutions (NNRG) by implementing a Student Training Based Optimization (STBO) algorithm. The proposed method draws inspiration from the structured training and adaptive learning behavior of students, simulating their progression through knowledge acquisition, skill refinement, and performance enhancement phases. The STBO algorithm is applied to optimize academic performance assessment by identifying key parameters such as attendance, internal assessments, learning pace, participation, and project outcomes. By modelling student development as a dynamic optimization process, the algorithm effectively predicts academic outcomes and recommends personalized strategies for improvement. Experimental evaluation on real academic datasets from NNRG CSE, CSE (Data Science), and CSE (AIML) Students demonstrates that the STBO algorithm achieves higher prediction accuracy and adaptive feedback generation when compared to traditional statistical and machine learning techniques. This approach also facilitates early identification of at-risk students and promotes data-driven decision-making for faculty and administration. Overall, the STBO-based framework not only enhances performance assessment but also contributes to academic excellence by aligning learning strategies with individual student needs.

In the context of ongoing digitalization and the growing importance of non-formal education in Kazakhstan’s higher education system, there is an increasing demand for adaptive educational models that address students' individual learning needs and broaden the scope of academic engagement. This study examines the effects of an adaptive non-formal education model on students' learning activity and engagement, and identifies the model components with the most significant impact. A quantitative quasi-experimental design was employed, involving pre- and post-intervention assessments using validated questionnaires. Key indicators included participation in supplementary educational activities, online learning platforms, external courses, and project-based or volunteer initiatives. The results indicate a statistically significant improvement in students’ educational involvement in the experimental group, as demonstrated by increased participation in external learning events, greater self-directed learning, and the development of personalized educational trajectories. The study highlights the potential of adaptive non-formal education as a strategic tool to enhance institutional flexibility and student motivation. Its novelty lies in testing a context-sensitive adaptive non-formal education model tailored to Kazakhstan’s institutional realities. The findings contribute to the global discourse on flexible education strategies and suggest directions for scaling and integrating the model into digital academic ecosystems.

This study presents the development and evaluation of a local agent-based Retrieval-Augmented Generation (Agentic RAG) system designed for the intelligent analysis of GitHub repositories in computer science education and IT practice. The novelty of this work lies not in inventing a new RAG algorithm, but in orchestrating multiple existing components (LangChain, Redis, SentenceTransformer, and LLMs) into a multi-stage agent pipeline with integrated relevance evaluation, specifically adapted to offline repository mining. The proposed pipeline consists of four sequential stages: (1) query reformulation by a dedicated LLM agent, (2) semantic retrieval using SentenceTransformer embeddings stored in Redis, (3) response generation by a second LLM, and (4) relevance scoring through a verification agent with retry logic. Relevance is assessed via cosine similarity and LLM-based scoring, allowing iterative refinement of answers. Experimental testing compared the system against two baselines: keyword search and a non-agentic single-stage RAG pipeline. Results showed an average MRR@10 of 0.72, compared to 0.48 for keyword search and 0.61 for non-agentic RAG, representing a 33% relative improvement in retrieval quality. Human evaluators (n=15, computer science students) rated generated explanations on a 5-point Likert scale; the proposed system achieved an average 4.3/5 for clarity and correctness, compared to 3.6/5 for the baseline. Precision@5 for code retrieval improved from 0.54 (keyword) and 0.67 (non-agentic RAG) to 0.76 in the proposed system. Average query latency in the local environment was 3.8 seconds, indicating acceptable performance for educational and small-team IT use cases. The system demonstrates high autonomy by operating fully on-premises with only optional API access to LLMs, ensuring privacy and independence from cloud providers. Ease of use was measured through a System Usability Scale (SUS) questionnaire, yielding a score of 78/100, reflecting positive user perception of the Streamlit interface and minimal setup requirements. Nevertheless, several limitations were observed: the high computational cost of running embeddings and LLMs locally, potential hallucinations in generated explanations (particularly for complex or unfamiliar code), and the inability of vector search to fully capture code syntax and control flow structures. Furthermore, while the Analytic Hierarchy Process (AHP) was applied to select the system architecture, future work should complement this with benchmark-driven evaluations for greater objectivity. The contribution of this study is threefold: (1) introducing a multi-agent orchestration logic tailored to educational code repositories; (2) empirically demonstrating measurable gains in retrieval quality and explanation usefulness over baselines; and (3) highlighting both opportunities and limitations of deploying autonomous RAG systems locally. The proposed technology can benefit IT companies seeking secure in-house tools for repository analysis, universities aiming to integrate intelligent assistants into programming courses, and research institutions requiring reproducible, privacy-preserving environments for code exploration.