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The Influence of Digital Transformation on well-being – analysis of life stages and business sectors
No full textThe accelerating pace of digital transformation (DT) is profoundly reshaping the world of work, placing new demands on employees and affecting their well-being. As employee well-being is closely linked to engagement and performance, this PhD project investigates how organizations can engage employees during DT, with particular consideration of their well-being. The Self-Determination Theory (SDT) serves as the kernel theory in this research for understanding well-being, expanded to include physical health. Furthermore, both different working conditions and various life stages of employees are incorporated in order to capture the dynamic nature of well-being. However, promoting well-being requires a comprehensive understanding of its multifaceted effects, both positive and negative, on employees, a challenge further intensified by the ongoing DT. While many companies recognize the benefits of DT, they often struggle with its implementation and the associated impacts on the workforce. Maturity models are a common tool to provide guidance during DT by serving as frameworks for assessing and developing organizational capabilities. In practice, maturity models are often too strategic, inflexible, and insufficiently user-centered. Furthermore, social aspects such as employee well-being have so far been largely neglected. To close this gap, an adaptable human-centered maturity model focusing on well-being was designed and empirically validated within the framework of this cumulative dissertation consisting of six papers, following the Design Science Research (DSR) approach. The model uniquely integrates basic psychological needs, physical health, and life stage perspectives, dimensions largely absent in existing DT maturity models. Overall, this PhD project advances the human-centered discourse on well-being by providing a practice-oriented maturity model that supports organizations in identifying the effects of DT on well-being and deriving appropriate courses of action
Personalized learning based on AI and gamification: comparing the experience of Germany and Kazakhstan
No full textArtificial intelligence (AI) and gamification are becoming important tools in modern education. Gamification uses elements such as points, rewards, and challenges to increase student motivation and engagement. However, its effectiveness depends on learners’ interests, abilities, and the quality of game design.
Personalized learning aims to adapt content and tasks to individual student needs, but teachers often struggle to do this in traditional classrooms due to limited time and large class sizes. AI can support personalization by analyzing student performance, participation, and learning difficulties. Based on this data, AI systems can recommend appropriate materials, adjust task difficulty, and create individual learning paths.
This article compares educational platforms in Germany and Kazakhstan. Germany is well prepared to integrate AI into education and widely uses AI-based personalization and gamification. In contrast, many Kazakhstani platforms rely mainly on video lessons with limited interactive features.
The article also presents the educational chatbot “Help YOU!” for schools in Kazakhstan. By combining AI and gamification, the chatbot provides personalized student support and reduces teachers’ workload, demonstrating the potential of these technologies to improve education
AI tutors: replacing or supporting human teachers
No full textThis essay examines the role of artificial intelligence tutors in Kazakhstan's education system, addressing whether AI-based tools replace teachers or serve as supportive instruments that enhance teaching practice. Against the backdrop of ongoing digital transformation in education, the central thesis argues that AI tutors do not replace teachers but function as complementary tools that assist with specific tasks such as assignment evaluation, progress monitoring, and personalized feedback. While AI systems offer significant advantages — including personalized learning pathways, expanded access to educational resources in underserved rural areas, and reduced administrative burden for teachers — they cannot substitute essential pedagogical elements such as human interaction, emotional intelligence, cultural awareness, and moral guidance.
The analysis draws on theoretical frameworks from UNESCO and OECD, examining both the potential and limitations of AI integration within Kazakhstan's educational context, where regional disparities in infrastructure and digital literacy create uneven implementation. Through comparative insights from Germany's education system, the essay demonstrates how AI tutors can be effectively integrated as supportive tools while preserving the teacher's central role in pedagogical decision-making and student development. The findings emphasize that successful AI integration requires maintaining human oversight, addressing the digital divide, and ensuring that teachers retain professional autonomy in shaping educational outcomes
Mathematical Modeling and Process Optimization of Composite Polymer Stabilizers
No full textThis paper examines current mathematical modeling methods used for the development and optimization of composite polymer stabilizers. A review of key challenges related to the stability of polymer materials is provided, along with a discussion of modern computational approaches, including molecular dynamics, finite element analysis, thermodynamic modeling, and machine learning. The necessity of an interdisciplinary approach integrating chemistry, materials science, and computational technologies is justified. Perspectives on the further development of modeling methods to enhance the efficiency and stability of polymer stabilizers are presented
Integrating EdTech and Artificial Intelligence into School Education through the Lens of Strategic Management: The Experience of Developed Countries and Kazakhstan
No full textThis longitudinal research paper investigates digital transformation of the traditional education sector of Kazakhstan as it pertains to Edtech (educational technologies) and progressive use of artificial intelligence (AI) in classrooms by developing a strategic approach to the creation of an eco-system of education on a national level. The focus of the study is to investigate the barriers that need to be addressed to enable Kazakhstan to achieve its goal of transitioning to an eco-system of education. Research Problem: The current situation in Kazakhstan is that billions of dollars have been invested in the creation of a digital infrastructure to support the implementation of digital initiatives throughout the country. However, the country has been unable to bring its diverse digital initiatives together into a cohesive programme due to the fact that there is still a gap between the ability of the country to implement technology and the improvement of educational outcomes of its students. The primary hindrance to bringing together the successful use of all of the technologies being implemented is due to a lack of a comprehensive strategy to integrate and govern the use of technology in education. Methodology: To develop the recommendations for transitioning Kazakhstan to the creation of an eco-system of education, the researchers utilised a comparative analysis of three strategic models of Edtech governance from leading educational systems of Singapore, Finland, and Germany. The rationale for choosing these three countries is based on the differences in their methods of implementation in education
Integration of Artificial Intelligence in Education: Opportunities and Challenges
No full textThis paper examines the integration of artificial intelligence (AI) into the education system, focusing on its key opportunities and challenges in the context of the Fourth Industrial Revolution. Artificial intelligence has become an important tool for modernizing education, enhancing learning quality, supporting personalized instruction, and improving educational management processes.
The study is based on a comparative analysis of the experiences of Germany and Kazakhstan in implementing AI in education. The German model emphasizes strategic planning, teacher training, and ethical and legal regulation, while Kazakhstan’s approach focuses on accessibility and rapid implementation through widely used EdTech platforms.
The findings indicate that the effective use of artificial intelligence depends not only on technological infrastructure but also on teacher readiness, data security, and ethical responsibility. The paper highlights the potential of AI to transform education and identifies the key conditions for its sustainable and balanced integration
Internal Governance and Performance of Universities in the Context of New Public Management and Stratification of Higher Education
No full textIn this thesis, I examine the relationship between internal governance and university performance within the context of Russian higher education from 2012 to 2020, a period marked by the prominent application of New Public Management (NPM) instruments. This study investigates several dimensions of internal governance and its connection to university performance. First, how do internal governance characteristics - such as centralization, stakeholder involvement, external communication, and strategic orientation - relate to university performance? Second, is there a relationship between institutional strategy adoption and university performance from the perspective of university department heads? Additionally, I explore the institutional structures and governance arrangements in Russian higher education, with particular attention to two interrelated developments: the adoption of NPM instruments and system stratification. The study draws on data from a national-level survey of university leaders and administrators, complemented by statistical information. Depending on the data structure and variable characteristics, various quantitative methods - from simple difference tests to conditional efficiency estimations - will be applied to address the research questions
The Human Factor in Digital Transformation: An Employee-Centered Change Management Maturity Model for the AI Era
No full textDigital transformation (DT) fundamentally reshapes organizational structures and work processes. Despite its strategic importance, up to 70% of DT initiatives fail, primarily due to insufficient consideration of human factors. This cumulative dissertation addresses this gap by developing and validating a human-centered Change Management Maturity Model that systematically integrates employee needs into digital transformation processes, with particular emphasis on the AI-driven third phase of DT.
Existing DT maturity models predominantly focus on technological, strategic, and organizational aspects while neglecting human-centered dimensions such as employee motivation, psychological well-being, and change readiness. Likewise, established change management frameworks tend to operate either at the organizational level (e.g., McKinsey 7S) or the individual level (e.g., ADKAR), without systematically integrating both perspectives. To address this limitation, this dissertation proposes a comprehensive maturity model comprising nine dimensions across three categories: Motivation & Leadership Behavior, Dealing with Change, and Well-being & Health.
The research follows an echeloned Design Science Research (eDSR) approach and is structured as a cumulative dissertation consisting of six research papers. The model is grounded in multiple kernel theories, including Self-Determination Theory, Herzberg’s Two-Factor Theory, Maslow’s Hierarchy of Needs, the Dynamic Capabilities Framework, and established change management models. Empirical validation was conducted in the skilled trades sector and across industries in the retail sector, demonstrating the model’s applicability across organizational contexts and its practical relevance for managing AI-driven transformation initiatives
Automation of error reporting processing based on stack trace analysis
No full textThe rapid growth of large-scale software systems has led to the adoption of automatic error reporting platforms collecting millions of crash reports from real users. Central to these reports is the stack trace — a record of function calls leading to failure — which serves as a crucial diagnostic resource. However, the sheer volume, diversity, and redundancy of reports create bottlenecks: developers are overwhelmed by duplicates and highly variable submissions from the same defect, impeding efficient issue resolution.
Existing deduplication and triage solutions in industry and academia mainly rely on string-matching, information retrieval, or graph-based heuristics. While efficient, string and IR methods often miss semantic and contextual nuances; graph-based models lose detail about individual reports, reducing accuracy. These limitations cause missed linkages between related errors and fragmentation of bug databases. The lack of scalable algorithms, real-world benchmarks, and advanced learning methods further restricts current tools.
This dissertation advances automation of error report processing via stack trace analysis. It introduces (1) hybrid similarity metrics extending traditional techniques, (2) deep learning models for robust similarity estimation, (3) aggregation strategies leveraging group-level information, (4) scalable solutions for industrial use, (5) the first models for automated developer assignment in stack trace–centered triage, and (6) methods for interpreting and highlighting the most informative stack frames. The research is validated on multiple proprietary and open datasets, including new benchmarks released as part of this work. Together, these contributions provide a unified, reproducible foundation for scalable, accurate, and actionable error report deduplication, grouping, assignment, and tooling in real-world software engineering
Characterization of geological settings related to intrusive magmatism on the Moon and Mars
No full textIn this research, I explored the geology of igneous intrusive domes in the Moon and Mars. These structures have not been widely investigated outside Earth, mainly due to the difficulty in locating them. I decided to do a detailed analysis of two systems: the Valentine Domes on the Moon, and the Utopia Planitia Dome field on Mars, focusing on their properties at the surface. I followed a cartographic approach in this research, using geostratigraphic units to characterize the locations and define their geological evolution. While analyzing the Valentine Domes, I noticed the lack of an open-source tool to work with the spectral data of the Moon, this led to the creation of the MoonIndex library, a tool to process spectral cubes and generate spectral indexes for the Moon. With the aid of MoonIndex, I performed the geological analysis of the Valentine Domes. The first result was the discovery of a new dome, which was detected by using the aspect parameter. I also found that several smaller structures such as rilles, dykes, and secondary domes are associated with the main domes. The dome field in Utopia Planitia is different from the lunar location, hundreds of domes were emplaced in a large area. The study of the domes showed they originated from an intrusive-to-extrusive system, since their shapes range from cryptodomes to volcanic domes. The lunar and Martian domes show some similarities, they are basaltic, have a small incidence in the surface morphology, and their parental magmas took advantage of structural features to reach the surface. However, the genesis of the system is different. The Valentine Domes formed under a polygenetic style, while the dome field in Utopia Planitia originated in a monogenetic system. This research will open the door to discovering new intrusive systems and to better understand the ones already known