Chapter 5. Integration of artificial intelligence technologies into the digital transformation of professional higher education in technical fields

110 CHAPTER 5 CHAPTER 5 INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL HIGHER EDUCATION IN TECHNICAL FIELDS ABSTRACT This chapter explores current trends in the integration of artificial intelligence technologies into the professional training of students in technical higher education institutions. The theoretical section highlights models of digital transformation, the structure of digital competences, the role of AI in adapting educational programs, as well as strategic initiatives implemented at Lviv Polytechnic National University under the leadership of rector N. Shakhovska. Special attention is given to an empirical study based on a survey of students and instructors in technical fields. The findings identify the most anticipated benefits and barriers to AI adoption in the educational process and reveal correlations between selected advantages and the respondents’ level of digital readiness. A series of visualizations is presented, including a digital competence pyramid, network graphs of stakeholder interaction, and a map of multiple associations between AI-driven educational outcomes. The results underscore the need for a systemic approach to fostering AI literacy in technical universities, the importance of digital pedagogical support for instructors, and the development of an ethical culture in the use of intelligent tools in professional education. KEYWORDS Artificial intelligence in education, technical higher education, digital transformation, AI literacy, digital competences, educational technologies, professional training, ethical use of AI, instructors’ digital readiness, learning innovation. In the 21st century, professional higher education faces the imperative of transformation under the influence of digital technologies, particularly artificial intelligence (AI). Technical universities, such as Lviv Polytechnic National University, are expected not only to adapt to new conditions but also to become flagships of innovative change. Today, the mission of technical education extends beyond the transfer of specialized knowledge to include the development of digital competences aligned with the demands of a dynamic labor market and contemporary digital reality. DOI: 10.15587/978-617-8360-16-0.CH5 Anatolii Dmytruk, Vitaliia Hrytsiv, Maiya Babkina, Iryna Skril, Iryna Vyslobodska, Maryna Smilevskal © The Author(s) of chapter, 2025. This is an Open Access chapter distributed under the terms of the CC BY license 111 chapter 5. INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL HIGHER EDUCATION IN TECHNICAL FIELDS CHAPTER 5 AI functions not only as a subject of study but also as a powerful tool for transforming the educational process. Intelligent systems are already being applied to analyze educational data, personalize learning, create simulations and digital twins, automate knowledge assessment, and support students through virtual tutors and chatbots. At the same time, the implementation of such technologies requires updated teaching methodologies, ethical responsibility, and equitable access to digital solutions for all participants in the educational ecosystem. The theoretical underpinnings of AI in mathematics and education are well articulated in the frameworks reflecting current advances [1–3], emphasizing the importance of descriptive models and system categorizations. This topic is particularly relevant in the context of the strategic development of Lviv Polytechnic National University, led by Rector Prof. Nataliia Shakhovska, Doctor of Technical Sciences and a leading expert in intelligent information technologies. The university consistently advances a policy of educational digital transformation and the expansion of digital competences among both instructors and students. The purpose of this study is to provide a theoretical framework and empirical exploration of the integration of artificial intelligence technologies into the educational process of a technical higher education institution. Special emphasis is placed on the analysis of digital and AI-related competences, the expected benefits and challenges of implementation, and the interconnections between key components of digital transformation – as illustrated by the case of Lviv Polytechnic National University. The object of the study is the process of developing digital and AI competences in the professional higher education system of technical profile. The subject of the study includes the methods, models, and tools for implementing AI technologies in the educational process of a technical university, as well as the attitudes of key stakeholders toward their use. Research objectives: – to analyze the current state of AI implementation in professional education within technical universities; – to identify the levels of digital and ai-related competences developed among students in technical fields; – to investigate the expected benefits and challenges associated with integrating ai into the educational process; – to develop a visual model illustrating the interconnections between key components of digital transformation; – to propose a structural model for integrating AI into the system of professional higher education in technical institutions. The methodological framework of the study includes an analysis of scientific literature and strategic documents, a comparative review of educational practices, as well as a quantitative empirical study based on surveys conducted among students and instructors in technical fields. The obtained results are presented through graphical visualizations – including pyramidal models 112 PROFESSIONAL EDUCATION AND PERSONNEL TRAINING CHAPTER 5 of digital competence, network diagrams, and histograms – which help interpret the structure of perceived benefits and barriers to AI integration in education. Structurally, the chapter comprises six thematic blocks, which the chapter combines analytical depth with practical orientation, illustrating the opportunities and prospects for expanding digital competences in modern professional higher education. Most existing research focuses either on general overviews of IT/AI in higher education or on applications within specific disciplines (e.g., language learning or medical training). In contrast, this study presents a systematic analysis of AI implementation specifically within a technical university, taking into account internal educational policies and practices at Lviv Polytechnic National University. The proposed original three-level model of digital and AI competences (literacy – professional use – research level) offers a framework for structuring the preparation of technical students for real-world participation in the digital economy. An associative visualization method is applied to reveal the connections between selected perceived benefits of AI, a technique rarely used in pedagogical studies. This approach allows not only for tracking frequency of responses but also for exploring the cognitive context, identifying which advantages are interconnected in respondents’ perceptions. The study reflects real initiatives implemented at Lviv Polytechnic National University: the digital transformation policy led by Rector Nataliia Shakhovska, cooperation with the IT industry, participation in Jean Monnet and BUP projects, and the development of digital infrastructure. This gives the research a practical dimension and offers a reference model for other technical universities in Ukraine. The chapter also includes the original survey instrument, which may be reused by other institutions to assess their readiness for AI integration. Additionally, the proposed infographics and network-based models can serve as tools for educational management and strategic planning. 5.1 Theoretical foundations for the implementation of artificial intelligence in vocational education 5.1.1 Evolution of AIED paradigms (artificial intelligence in education) The idea of applying artificial intelligence in education has a long history, dating back to the 1970s and 1980s when the first concepts of Intelligent Tutoring Systems (ITS) were developed. These systems were based on the assumption that a computer could model individual student needs and adapt educational materials accordingly. The main theoretical approaches in this paradigm include: – the intelligent tutoring paradigm, where AI acts as a mentor: it monitors progress, detects knowledge gaps, and adjusts the learning trajectory; 113 chapter 5. INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL HIGHER EDUCATION IN TECHNICAL FIELDS CHAPTER 5 – the collaborative learning coordination paradigm, which emerged later and focuses on supporting student interactions, task distribution, and enhanced group learning through AI mechanisms [4]. Contemporary AI tools go beyond these early concepts. Generative models such as GPT, Claude, and Copilot not only adapt learning content but actively create new educational material. This requires a fundamentally new understanding of their pedagogical role. As a result, AIED is transforming from a reproductive environment into one of shared cognitive partnership between humans and digital agents. 5.1.2 Hybrid intelligence as a conceptual framework for ai integration in education The traditional view of AI as an autonomous system is gradually being replaced by the concept of hybrid intelligence, in which AI does not replace the human but enhances cognitive capabilities. This approach is based on the idea of synergy: human intuition, creativity, and ethical judgment are combined with the computational power, analytical speed, and adaptability of AI. M. Cukurova notes that human – AI hybrid interaction is one of the key trends in educational technology. They emphasize that effective learning systems should function as extended learning environments in which AI acts not just as a knowledge mediator but as a partner in problem-solving, reflection, and self-directed learning [5]. In the context of vocational and technical education, hybrid intelligence is realized through: – automated assessment with expert correction; – interactive learning systems that model behavior and provide feedback; – joint project development between students and digital agents, e.g., during code creation, diagram design, or data modeling [6]. 5.1.3 Ethical and inclusive dimensions of AIED As the influence of AI in education expands, the issue of ethical responsibility becomes increasingly important. The integration of AI changes both pedagogical approaches and the relationships among teachers, students, and digital agents. Therefore, there is a growing need to establish an ethical framework for AIED use. According to W. Holmes et al., the main risks associated with AIED include [4]: – algorithmic opacity and the inability to explain system-generated recommendations; – hidden bias due to skewed training data; – privacy violations and irresponsible collection of personal educational data; – reduced student autonomy, with a risk of turning education into an overly controlled process. 114 PROFESSIONAL EDUCATION AND PERSONNEL TRAINING CHAPTER 5 In addition, issues of digital equity are critically important. Studies conducted within our project confirm that not all learners have equal access to modern digital tools and high-speed internet, particularly under war conditions or in socioeconomically disadvantaged regions. Leading organizations such as UNESCO, IEEE, and the European Commission – recommend adhering to the following principles in AI integration: – transparency (AI systems should be interpretable to users); – fairness (avoiding discrimination or exclusion); – accountability (clear assignment of responsibility for AI actions); – security (protection of educational data); – human-centeredness (AI should serve as a support tool, not a control mechanism) [7, 8]. 5.1.4 Theory of socially generative systems A novel direction in AI and education research is the concept of socially generative systems, which views AI not as a static tool but as a co-participant in the social learning process. M. Sharples proposes interpreting generative models (such as ChatGPT, Claude AI, and Copilot) as communication participants capable of supporting, transforming, or even simulating pedagogical interactions. Learning, in this context, becomes a triadic process: teacher – student – Ai [9]. Social generativity is reflected in: – AI participation in dialogues, where it not only answers but also asks clarifying questions or provides counterarguments; – co-construction of knowledge, where students “discuss” ideas with AI, refine arguments, and train logical thinking; – shaping learning behavior through AI-generated recommendations that influence time planning or learning strategies. This theory helps explain why perceived benefits of AI among teachers and students are interrelated. In our research, a network structure of perceived benefits was identified, where effects such as personalization, motivation, and innovation are interconnected. This is a manifestation of social generativity. Thus, treating AI as a social agent allows us to expand traditional educational models and align them with 21st-century learning concepts – co-creation, partnership, and multidirectional interaction [10, 11]. 5.1.5 Digitalization as a driver of professional competence development Digital transformation affects not only educational tools but also the structure of professional competencies formed in students of technical disciplines. The focus is shifting from traditional 115 chapter 5. INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL HIGHER EDUCATION IN TECHNICAL FIELDS CHAPTER 5 knowledge and skills to integrated digital abilities, the capacity to adapt to emerging technologies, and the ability to collaborate effectively within digital environments. A recent framework for vocational and technical education argues that developing digital competencies effectively requires a whole-institution approach – engaging institutional leaders, teachers, and learners together in co-creating the digital learning environment. Systematic reviews in higher education point out that digital transformation demands not only technical fluency but also pedagogical skillsets: critical media literacy, ethical awareness, and methodological innovation are highlighted as essential capabilities for both students and educators. A study on graduates’ employability reveals significant skills gaps: employers increasingly require data literacy, online research competence, digital communication, and basic cybersecurity. According to research conducted within our project (Section 5.3), both students and instructors acknowledge that the use of AI services contributes significantly to the development of key professional competencies (Fig.5.1), including: – analytical thinking, developed through working with large datasets, querying AI, and interpreting results; – digital literacy, enhanced through hands-on interaction with modern tools such as GitHub Copilot, Notion AI, and similar platforms [6, 11]; – adaptability and flexibility, fostered by navigating the unpredictability of generative AI responses; – project-oriented thinking, supported by new formats such as learning case studies, hackathons, and collaborative work environments [12, 13].  Fig. 5.1 Structure of the relationship between digital skills and components of professional competence Professional competency Digital literacy Adaptability to technological change Technical knowledge Critical thinking CreativityTeamwork 116 PROFESSIONAL EDUCATION AND PERSONNEL TRAINING CHAPTER 5 In the context of technical and vocational education, these competencies are especially important. Future professionals are expected not only to operate AI tools, but also to understand their architecture, ethical limitations, and practical relevance to their field of expertise [14, 15]. 5.1.6 The lifelong learning paradigm in the digital society In the 21st century, the concept of lifelong learning has evolved from an abstract ideal into a practical necessity. The rapid advancement of digital technologies, particularly artificial intelligence, is reshaping the labor market, altering the qualifications expected from professionals, and shortening the life cycle of knowledge. In this context, higher education institutions are no longer limited to delivering foundational knowledge but are increasingly responsible for developing skills in self-directed learning, re-skilling, and critical adaptation (Fig.5.2).  Fig. 5.2 Cross-structure of formal, non-formal, and informal learning in the digital society Formal learning Non-formal learning Informal learning • Lectures • Practicals • Laboratories • Webinars • Open online courses Student as an architect of their own educational trajectory AI Personalization New expectations for graduates of technical universities include: – the ability to quickly update professional knowledge; – readiness to master new digital tools independently, without external assistance; – self-assessment skills for tracking one’s educational progress; – intrinsic motivation for continuous learning, especially in online environments [16, 17]. Artificial intelligence plays a key role in supporting lifelong learning through: – adaptive learning systems, which adjust content and pace based on learner performance; – personalized learning pathways, aligned with learner goals and current competencies; 117 chapter 5. INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL HIGHER EDUCATION IN TECHNICAL FIELDS CHAPTER 5 – AI-based assistants that provide suggestions, generate explanations, or administer diagnostic tests (e.g., Copilot, recommender systems used by platforms like Coursera); – knowledge verification tools based on intelligent testing algorithms [18]. This shift requires a rethinking of both educational content and methodologies. Educators are now expected to cultivate learning-to-learn strategies, enabling students to function effectively in dynamic, digital knowledge environments. 5.1.7 Transformation of the educational environment in the context of digital transition As digital technologies continue to expand, the educational environment of technical universities is transforming into a multi-dimensional ecosystem that combines physical, virtual, blended, and simulated learning spaces. Within this evolving context, artificial intelligence functions as a modulator of educational flows, enabling the customization of learning processes to meet the individual needs of each participant. Key characteristics of the modern educational environment include: – hybrid learning formats, combining offline instruction, online learning, asynchronous modules, and simulation-based experiences; – digital mobility, where students access content via mobile apps, cloud platforms, and virtual laboratories; – integration of intelligent systems, such as AI-powered scheduling tools, progress tracking dashboards, and personalized recommendation engines; – continuous feedback loops, supported by learning management systems (LMS), chatbots, and educational analytics platforms [4, 5]. Examples of integrated solutions: – Moodle with AI modules – for participation analytics and automatic generation of personalized assignments; – MS Teams with Copilot – assisting instructors in creating quizzes, answering student questions, and managing course materials; – Open edX with adaptive pathways – delivering differentiated instruction based on learner performance and preferences. This transformation redefines the educational space from a static location into a dy-namic learning ecosystem, responsive to changes in learner behavior and technological advan-cements. The Fig. 5.3 illustrates how core elements of the digital environment – administrative platforms, learning platforms, cloud services, simulators, and AI modules – interact through a central educational analytics hub. This hub collects data on user activity, performance, and learning dynamics to generate individualized educational scenarios. 118 PROFESSIONAL EDUCATION AND PERSONNEL TRAINING CHAPTER 5  Fig. 5.3 Digital learning environment of a technical university: components, interactions, and functions ACTIVITY AI moduls Administrativate platform Educational and learning platforms Cloud services LEARNING ANALYTICS RESULTS RECOMMENDATIONS Digital Learning Environment in HEI: Components, Interaction, Functions INDIVIDUALIZED LEARNING SCENARIOS PROGRESS DYNAMICS 5.1.8 Adapting the regulatory framework for AI integration in education The growing integration of artificial intelligence into educational processes requires not only technical modernization but also the updating of regulatory frameworks governing the operation of technical higher education institutions. At both national and institutional levels, the lack of clearly defined policies regarding the use of generative AI, student data analysis, and automated assessment systems creates legal uncertainty and raises concerns over academic integrity. The model (Fig. 5.4) outlines regulatory alignment at three interconnected levels: national policy (macro), institutional governance (meso), and classroom practices (micro). It reflects how top-down and bottom-up regulatory dynamics shape ethical, transparent, and effective use of AI in education. Key areas for regulatory adaptation include: – institutional AI policies: formalizing guidelines for the permitted use of AI tools in student projects, theses, laboratory reports, and other academic work; – revised assessment procedures: incorporating open formats, elements of oral verification, and hybrid assessment models to ensure authenticity of learning outcomes; – ethical code of AI usage: requiring proper attribution for AI-assisted content (similar to academic citations), and prohibiting the use of AI for cheating, manipulation, or data fabrication. Student data protection: aligning institutional practices with GDPR principles, even for internal data platforms used for educational analytics [19, 20]. 125 chapter 5. INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL HIGHER EDUCATION IN TECHNICAL FIELDS CHAPTER 5 significant offices in Lviv – have been central to this effort. These alliances have shaped curriculum and lab offerings: SoftServe and peers actively contributed to developing new AI, data science, cybersecurity, and IoT programs through the Lviv IT Cluster, with support for educational tracks and access to industry-grade tools. Events like the Lviv IT Cluster’s “IT Future Conf” with SoftServe as gold partner regularly assemble leading firms (including EPAM, N-iX, Avenga, Intellias) for lectures, workshops, and student recruitment. Beyond education, companies like SoftServe have launched real-world AI pilot programs such as integrating generative AI into development workflows, boosting productivity up to 45% creating internship and research opportunities for students at Lviv Polytechnic . Additionally, joint spaces like the SmartIndustry conference and the IoT lab, supported by both the IT Cluster and companies like SoftServe and GlobalLogic, foster continuous collaboration among academia, business, and students. This engagement enables the university to co-create applied AI solutions, while students benefit from hands-on projects, industry mentoring, and direct paths to employment. The key stakeholder groups – students, instructors, administration, IT departments, and external partners (IT companies, EdTech developers) – and the directions of their interaction during digital transformation are presented in Fig 5.9.  Fig. 5.9 Network structure of stakeholder interaction in the process of AI integration into technical higher education Government Learners Quality assurance agencies Employers NGOs Employer organizations Higher educational institutions Instructors serve as intermediaries between the administration and students, while also collaborating with IT companies in the development of educational content. Students interact not only with instructors, but also indirectly – through LMS interfaces – with technical services. This network structure highlights that successful AI implementation in education is not merely a technological shift, but an organizational one, where coordination among all educational environment participants plays a crucial role. 126 PROFESSIONAL EDUCATION AND PERSONNEL TRAINING CHAPTER 5 5.3.1 Integration of AI into academic courses The development of digital and AI-related competences is supported through a set of dedicated courses introduced into the academic curriculum, such as: 1. Artificial Intelligence, Machine Learning, and Intelligent Data Analysis – offered to students in programs like Computer Science (122), Software Engineering (121), Applied Mathematics (113), and Information Systems (126). 2. Foundations of AI and Digital Transformation – available as an elective for students from non-technical fields. 3. Decision Support Systems, Python Programming, and Neural Networks and Computational Intelligence – included in master’s programs. Some courses are co-designed in collaboration with leading IT companies such as SoftServe and EPAM [34]. This partnership facilitates the inclusion of industry-oriented content and enables students to work with real-life case studies. 5.3.2 Digital learning environments enhanced by AI tools Lviv Polytechnic National University actively employs blended learning platforms, particularly Moodle, which is integrated with analytics modules and predictive algorithms [35]. In pilot settings, the university has introduced: – automated code assessment systems; – pattern recognition in student responses; – generation of personalized assignments using generative AI. Tools such as ChatGPT, GitHub Copilot, and Notion AI are increasingly used by students to assist in study preparation, solution modeling, writing reflections, and preparing presentations. Personalized learning systems using neural networks have demonstrated success in adapting individual study plans for technical students [36]. Furthermore, the use of sentiment analysis and neural-network quality-management tools in education and healthcare has been validated in similar educational settings [37–39]. According to the survey results [Fig. 5.10], the most widely recognized benefit associated with the use of AI in professional education is personalized learning (84%). This indicates a strong demand for individual learning paths supported by intelligent systems. Other significant factors include improved quality of education (75%) and the development of digital skills (66%). Less than half of the respondents identified assessment optimization (39%) as a key benefit, which may reflect a lack of awareness about the technical capabilities of AI in knowledge evaluation. In response, instructors have developed new assessment formats – including analytical reports, mini-projects, and case studies – aimed at fostering critical thinking and promoting deeper learning. 127 chapter 5. INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL HIGHER EDUCATION IN TECHNICAL FIELDS CHAPTER 5  Fig. 5.10 Survey results on the perceived benefits of using artificial intelligence in professional education 100 % 90 % 80 % 70 % 60 % 50 % 40 % 40 % 30 % 20 % 10 % 0 % Personalized learning Increased learning Development cources Access to resources Adaptative learning Automatic grading Other 84 % 75 % 66 % 53 % 47 % 39 % 9 % The Fig. 5.11 illustrates how many AI-related benefits each respondent selected. Most respondents identified five to seven key advantages, indicating a broad perception of AI’s value within the student community. This distribution reflects a high level of awareness among participants regarding the diverse potential of AI in education, particularly in areas such as adaptive learning, AI ethics, and big data analytics.  Fig. 5.11 Distribution of the number of AI benefits selected by respondents 40 35 30 25 20 15 10 10 5 05 4 3 Number of benefits selected Percentage of respondents 2 1 128 PROFESSIONAL EDUCATION AND PERSONNEL TRAINING CHAPTER 5 5.3.3 Student initiatives and research projects Lviv Polytechnic National University actively supports youth-led research initiatives related to artificial intelligence. University-organized AI hackathons, startup competitions, and cross-faculty innovation hubs engage students in developing projects in areas such as: – smart city technologies; – energy efficiency; – digital assistants; – educational platforms with adaptive learning features. Some student theses and master’s projects already implement ML algorithms using tools such as TensorFlow, scikit-learn, and OpenCV, demonstrating the gradual integration of AI into students’ professional skill sets during their studies [40]. At the same time, pedagogical research at Lviv Polytechnic National University highlights the role of educational coaching and interdisciplinary learning in enhancing student motivation and cognitive engagement. Studies show that activating students’ learning potential through coaching methods and integrating foreign language instruction within professional education contributes to higher autonomy and readiness for digital learning environments. 5.3.4 International projects and collaborations Lviv Polytechnic National University actively participates in a range of international educational initiatives that support the integration of artificial intelligence and digital transformation into higher education. Among them are the Erasmus+ Jean Monnet projects focused on the digitalization of governance and education in Ukraine, as well as the Baltic University Programme, which promotes sustainable development modeling using analytical and AI-based tools. Additionally, the university is involved in specialized Erasmus+ Key Action 2 (KA2) consortia, such as: – “Effectiveness of Medicine E-learning Distance Courses”, an international collaborative project co-led by Prof. N. Shakhovska in partnership with the University Lumière Lyon 2 (France), the Polytechnic University of Valencia (Spain), Linnaeus University (Sweden), and others. This project focuses on the application of AI-based adaptive learning systems in medical education and digital pedagogy; – “iCare4Next” emphasizes inclusive digital learning, accessibility, and digital support mechanisms for students with disabilities and veterans. AI is used in this context to develop intelligent tutoring and support systems that adapt to users’ cognitive and emotional states; – “SmallAIM (AI in Medicine)”, coordinated under the Eurizone initiative, explores the application of explainable AI models in medical diagnostics and e-learning systems, integrating ethical considerations and transparency. These international collaborations not only raise awareness about the potential of artificial intelligence among students and faculty but also enable the transfer of innovative instructional 129 chapter 5. INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL HIGHER EDUCATION IN TECHNICAL FIELDS CHAPTER 5 approaches into the Ukrainian educational context. Through such projects, Lviv Polytechnic National University contributes to the formation of a shared European educational space based on digital inclusion, sustainability, and data-driven pedagogy. 5.3.5 Industry-supported educational programs: the case of Lviv IT cluster In response to the growing demand for industry-relevant competencies, Lviv Polytechnic National University has partnered with the Lviv IT Cluster to modernize its bachelor’s degree programs. This collaboration resulted in the creation and implementation of cutting-edge curricula across multiple disciplines, reflecting the latest trends in artificial intelligence, digital systems, and data analytics. The updated programs include: – Robotics (G6 Information-Measuring Technologies) – targeting applications in medicine, defense, and space; – Internet of Things (F3 Computer Sciences, Systems Engineering) – training specialists to design smart, internet-connected systems; – Cybersecurity (F5 Cybersecurity and Information Protection) – preparing experts to protect digital infrastructure; – Artificial Intelligence (F3 Computer Sciences, AI Systems) – focusing on developing AI-based technologies and applications; – DevOps & Data Engineering (F6 Information Systems and Technologies) – teaching students how to manage complex digital ecosystems; – Business Analysis & Data Science (F4 System Analysis) – equipping future professionals with analytical and decision-making skills; – IT Sales Management (F4 System Analysis, IT Product Management) – training students in product management and market strategies; – UI/UX Design (G20 Publishing and Polygraphy) – merging technology with aesthetics to create user-centered interfaces. These programs are developed with the active participation of IT professionals and regularly updated to reflect the needs of the digital labor market. Thanks to this initiative, students gain access not only to up-to-date theoretical knowledge but also to real-world practices and internships with partner companies. Conclusion to Section 5.3 The implementation of artificial intelligence at Lviv Polytechnic National University exemplifies a strategic and comprehensive approach to educational innovation. Through the integration of 130 PROFESSIONAL EDUCATION AND PERSONNEL TRAINING CHAPTER 5 AI-related courses, the use of intelligent digital learning environments, and active engagement in international projects, the university has established itself as a leader in fostering AI competencies among both students and faculty. Importantly, these initiatives go beyond technology adoption – they reshape the pedagogical culture, stimulate interdisciplinary collaboration, and align educational outcomes with the demands of the digital economy. The ongoing institutional commitment to AI-driven transformation reflects not only current global trends but also a proactive vision for the future of technical education. 5.4 Challenges and ethical aspects of using AI in the educational process Despite the numerous benefits that artificial intelligence technologies bring to professional higher education, their implementation is accompanied by a range of challenges: technical, pedagogical, and ethical. In technical universities, where AI is used not only as a learning tool but also as a component of professional practice, the issue of responsible AI use becomes particularly important. 5.4.1 Academic integrity in the age of generative AI One of the most debated challenges is the use of generative AI models (such as ChatGPT, Claude, and GitHub Copilot) by students to produce texts, answers, code, or reports. In the absence of clearly defined policies on AI usage in higher education, several risks arise: – academic plagiarism; – loss of independent critical thinking skills; – automation of tasks without real understanding of the content. In response to these risks, instructors at Lviv Polytechnic are developing new assessment formats: analytical tasks with personalized elements, open-ended discussions, and mini-projects that require students to justify their thought processes. There is also ongoing debate around acceptable AI usage, aiming to distinguish between responsible assistance and inappropriate substitution of human work. 5.4.2 Survey results on barriers to AI adoption To identify barriers to the effective integration of AI into professional education, a survey was conducted among students and faculty of technical disciplines. The results are presented in Fig. 5.12. The most significant barrier, according to respondents, is the lack of appropriate user skills (65%), highlighting the urgent need for systematic training of both students and instructors. A considerable percentage also emphasized ethical risks (53%) and technical infrastructure limitations (49%). 131 chapter 5. INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL HIGHER EDUCATION IN TECHNICAL FIELDS CHAPTER 5 Other barriers, such as lack of funding (45%) and legal constraints (18%), point to the importance of external support and regulatory frameworks for the integration of digital innovations in education.  Fig. 5.12 Survey results on barriers to the use of artificial intelligence in the educational process Lack of relevant skills Technical limitations 70 60 50 40 30 20 10 10 0 65 % 53 % 49 % 45 % 18 % Insufficient funding Legislative barriers Ethical issues Percentage of respondents 5.4.3 Perception of problem complexity: How many barriers do respondents identify? The Fig. 5.13 illustrates how many barriers each respondent marked as significant. This allows us to assess whether the problem of AI integration in education is perceived narrowly or broadly by stakeholders.  Fig. 5.13 Distribution of the number of AI-related barriers identified by respondents Number of respondents, % Number of obstacles selected 5 4 3 2 1 60 50 40 30 20 10 0 0 132 PROFESSIONAL EDUCATION AND PERSONNEL TRAINING CHAPTER 5 The data suggest a near-normal distribution: most respondents selected three key barriers, indicating a balanced and comprehensive perception of the issue. A small portion identified only one or as many as five barriers, reflecting varying levels of awareness or personal experience with AI integration in education. This distribution underscores the need for a differentiated approach to addressing these challenges – from basic training to institution-wide support policies. 5.4.4 Digital access inequality Not all learners have equal access to modern digital tools or stable internet connections – particularly under wartime conditions or in blended/remote learning settings. This raises concerns that the integration of AI technologies may deepen educational inequality. In this context, it is crucial for universities to provide: – open-access resources and local servers with AI capabilities (within internal infrastructure); – baseline digital literacy training for all students regardless of major; – onboarding sessions on using AI tools (e.g., Notion AI, Copilot, OpenCV, RapidMiner) at the beginning of the academic year. 5.4.5 Instructor training and support The successful integration of AI requires not only technical upgrades but also a shift in the role of instructors. Not all educators possess sufficient experience with digital tools, which can lead to: – anxiety about new technologies; – challenges in managing the learning process; – resistance to change due to lack of support or increased workload. Lviv Polytechnic gradually implements professional development programs in EdTech, digital pedagogy, and AI tools. These include training workshops, summer schools, and involvement of faculty in cross-departmental digitalization projects. 5.4.6 Ethical use of AI in education There is growing global attention to AI ethics. Key principles that educational institutions should adhere to include: – algorithmic transparency (understanding how AI systems make decisions); – non-discrimination (eliminating bias in data or models); – data protection (especially when analyzing student performance or handling personal data); – respect for human autonomy (AI as an assistive tool, not a replacement for human input). 133 chapter 5. INTEGRATION OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES INTO THE DIGITAL TRANSFORMATION OF PROFESSIONAL HIGHER EDUCATION IN TECHNICAL FIELDS CHAPTER 5 Universities adopting AI should develop their own ethical guidelines for digital tools, clearly defining boundaries, accountability, confidentiality, and openness. Conclusion to Section 5.4 The integration of artificial intelligence into the educational process of technical universities brings both transformative opportunities and critical challenges. While AI can significantly enhance learning personalization, content generation, and data-driven decision-making, its implementation must be approached with caution and responsibility. The findings indicate that insufficient user skills, ethical concerns, and infrastructure limitations remain key barriers to effective AI adoption. Moreover, unequal digital access, lack of instructor preparedness, and the potential erosion of academic integrity due to misuse of generative AI tools highlight the need for institutional strategies that combine technical, pedagogical, and ethical safeguards. Universities must therefore not only invest in digital infrastructure and professional development but also establish transparent and inclusive policies to guide the responsible use of AI. By addressing these challenges through a holistic and equity-focused approach, higher education institutions can ensure that the integration of AI strengthens rather than undermines the quality and integrity of academic processes. 5.5 A model for implementing AI in professional education at a technical university Successful digital transformation of the educational process at technical higher education institutions requires not a fragmented adoption of individual digital tools, but a systematic model for integrating artificial intelligence (AI) into all stages of professional training. Such a model should be based on an interdisciplinary approach, practical orientation, adherence to ethical principles, and the development of both student and faculty digital competencies. 5.5.1 Implementation levels The model can be represented as a three-level structure: a) Level 1 – AI literacy: development of basic knowledge about the principles of AI, machine learning, algorithms, and their societal impact. This level should be accessible to all students, regardless of their field of study. Implementation methods: integrated lectures, online courses, and seminars; b) Level 2 – professional application of AI: using AI as a tool within the framework of a specific discipline: for example, forecasting in economics, digital twins in mechanical engineering, data analysis in the energy sector, code generation and verification in IT. 134 PROFESSIONAL EDUCATION AND PERSONNEL TRAINING CHAPTER 5 Implementation: through specialized courses, laboratory work, and practical training; c) Level 3 – research and innovation Level: engaging students in interdisciplinary research projects, hackathons, and thesis projects using AI technologies. Active collaboration with IT companies, partici pation in international educational initiatives, and submitting startup ideas to innovation competitions. 5.5.2 Key components of the model Institutional Policy: – defining a clear strategy for digital transformation; – developing an ethical code for the use of AI in education; – supporting educational initiatives at the rectorate level. Educational Programs and Standards: – updating academic programs to include AI-oriented components; – designing interdisciplinary courses; – implementing micro-qualifications and certification modules (e.g., AI for Engineers, AI for Teachers). Faculty Development: – offering professional development courses in AI/EdTech; – facilitating experience exchange among departments and faculties; – providing mentorship for junior faculty in working with digital tools. Infrastructure: – access to open resources (Google Colab, Hugging Face, Kaggle); – availability of AI laboratories and GPU-supported servers; – equipping classrooms for hybrid and simulation-based learning. Integration with the Labor Market: – collaboration with IT companies in program development; – student internships in AI-focused teams; – organization of workshops, guest lectures, and certifications involving industry professionals. 5.5.3 Visualization of the benefits of AI integration The multiple interconnections between the benefits of AI use in education are shown in Fig. 5.14. The nodes with the highest number of associative connections are personalized learning, access to resources, and preparation for the digital labor market. This indicates that these components form the core perception of AI effectiveness in professional education. The Other node is linked by only a single edge, reflecting its limited significance. 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