Strategy for the Use of Artificial Intelligence
at the National Aerospace University
“Kharkiv Aviation Institute”
Approved by:
the Academic Council of
the National Aerospace University
“Kharkiv Aviation Institute”
Minutes No. 11 of May 21, 2025
Enacted by Order No. 233 of May 21, 2025
SUYA KHAI-YV-S/001:2025
Date of Enactment: May 22, 2025
Edition No. 1
1. General Provisions
1.1. The Strategy for the Use of Artificial Intelligence (hereinafter referred to as the Strategy) at the National Aerospace University “Kharkiv Aviation Institute” (hereinafter referred to as the University) is aimed at endorsing the policy on the use of artificial intelligence (hereinafter referred to as AI) and defining the principles and mechanisms for the responsible and ethical use of AI technologies in all workflows of the University.
1.2. The Strategy has been developed in accordance with:
- Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act);
- provisions of the Constitution of Ukraine;
- Laws of Ukraine “On Higher Education”, “On Education”, “On Scientific and Technical Information”, “On Scientific and Scientific-Technical Activity”, “On Copyright and Related Rights”;
- the Concept of Artificial Intelligence Development in Ukraine, approved by the Resolution of the Cabinet of Ministers of Ukraine dated 02.12.2020 No. 1556-r;
- Recommendations for the Responsible Implementation and Use of Artificial Intelligence Technologies in Higher Education Institutions, prepared by the Ministry of Education and Science of Ukraine and the Ministry of Digital Transformation of Ukraine, and published on 29.04.2025 (see link) (hereinafter referred to as the MES Recommendations);
- the Charter of the University;
- the Code of Academic Integrity of the University and other local regulatory acts of the University that regulate the provision and improvement of educational, research, organizational, and administrative processes at the University.
1.3. The University recognizes the importance of AI in modern educational and scientific processes and strives to ensure its safe and effective use at all levels of study.
1.4. The Strategy applies to all activities of the University, including educational, scientific, innovative, and administrative activities, and to all participants in the educational and scientific processes: teachers, students, researchers, educational support staff, as well as administrative (management) personnel and other persons who have access to the University's resources for using AI technologies in academic, administrative, and other activities of the University.
1.5. The structure and content of the Strategy are designed to ensure transparency, ethics, and efficiency of its application. The Strategy and other documents related to it are systematically improved and updated.
1.6. The Strategy defines the algorithms for implementing AI systems that will ensure the improvement of education quality, stimulate innovation in research, efficiency in management, and contribute to the sustainable development of the University.
1.7. The Strategy guarantees respect for human rights, intellectual property rights, protection of personal data, and compliance with the principles of academic integrity at the University.
1.8. Terms in the field of artificial intelligence use:
Automated / automatic decision-making - a process of making a decision using information technologies with a low level of human involvement / without human involvement.
AI Security - a property of an AI system consisting in its ability to counter threats of model behavior disruption or unauthorized acquisition of data or parameters during operation. The security of an AI system is one of the aspects of information systems security.
Big Data - a general term for large, complex sets of digital data whose storage, analysis, management, and processing require equally complex technological tools and significant computing power.
Large Language Model (LLM) - a class of language models that use deep learning algorithms and are trained on large datasets that may contain not only text but also other modalities (images, audio, etc.).
Hallucination - a phenomenon in which the output of a generative AI system contains inaccurate or false information deceptively presented as factual.
Generative artificial intelligence - a type of AI used to create new content, including audio, code, images, text, video, etc.
Request (prompt) - input text, instruction, or task for an AI system to which the system must respond by generating content.
Machine learning - a type of AI that involves training algorithms to learn from input data and improve their performance over time. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Computing resource (in the context of cloud computing) - a cloud or local service, such as a virtual machine, used to perform computational tasks.
Artificial intelligence - an organized set of information technologies with the application of which it is possible to perform complex integrated tasks through the use of a system of scientific research methods and information processing algorithms obtained or independently created during operation, as well as to create and use one's own knowledge bases, decision-making models, information processing algorithms, and to determine ways to achieve the set goals. The term AI is used synonymously with the term “AI system”.
The meanings of other terms in the field of AI are provided in the MES Recommendations and the “Glossary of Terms in the Field of Artificial Intelligence”, prepared and published by the Ministry of Digital Transformation of Ukraine (compilers: Chumachenko D., Mishkin D., Andriienko O., Krakovetskyi O., Turuta O., Dubno O., Khrushchova D., Kobrin A., Avdieieva T., Kravets I., Gerasymiak V., Shabanov O., Bystrytska A. Kyiv: Ministry of Digital Transformation of Ukraine, 2024. 37 p.); see the link.
2. Purpose and Tasks of Using Artificial Intelligence
2.1. The purpose of developing the Strategy is to establish clear rules and recommendations for the use of AI at the University to ensure academic integrity, improve the quality of education and promote innovation in learning, scientific research, the efficiency of administrative procedures, ensure ethical standards, and standardize the application of AI in academic and other activities of the University.
2.2. The main goals of the Strategy are:
- improving the quality of higher education through the implementation of the best practices of AI integration into educational and scientific activities within the educational process;
- ensuring compliance with ethical principles, confidentiality, and data security;
- supporting innovation by creating conditions for research and experimentation with AI, which contribute to the development of new teaching methodologies, educational process management, and the use of AI in scientific research;
- compliance with academic integrity by staff and higher education students, understanding the capabilities of generative AI tools and being aware of its risks, guaranteeing transparency regarding the use of AI;
- efficiency and optimization of administrative and educational processes through the automation of routine tasks, analysis of large volumes of data, and adaptation of educational programs to the individual needs of higher education students;
- automation of the preparation of various types of documents, including planning and reporting documentation, organizational and administrative documents, etc.; data analysis for making management decisions;
- supporting the professional development of research and pedagogical staff and employees through training and professional development in the field of AI, which contributes to the effective use of new technologies;
- social responsibility of the University towards higher education students, research and pedagogical staff, and employees by ensuring the ethical and transparent use of AI;
- implementation of international standards and regulations in the field of AI, which ensures high quality of educational services and prepares higher education students for the demands of the global labor market;
- supporting the strategic development of the University through the integration of modern technologies that help to remain competitive and meet the challenges of the times.
2.3. The Strategy is aimed at defining the fundamental principles for the use of AI by all participants in the educational process, scientific, innovative activities, and University management processes, and outlines the directions for raising awareness and building competence in the use of AI.
2.4. Tasks of the Strategy:
2.4.1. Development of AI infrastructure:
- creation and use of intelligent platforms to support and develop AI technologies on the basis of existing educational, scientific, and other departments of the University or new laboratories and centers;
- providing access to computing power and resources necessary for training and research in the field of AI;
2.4.2. Integration of AI into the educational process:
- development and implementation of courses and programs that include the study of AI and its applications in various fields of knowledge;
- ensuring innovative approaches to teaching and learning with the help of AI;
- using AI to adapt educational programs to individual needs of higher education students, ensuring personalized learning;
- using AI in the process of accreditation of educational programs;
- using AI in managing the educational process at the University;
- supporting inclusivity for higher education students with special educational needs;
- implementation of online platforms that use AI algorithms to increase learning efficiency.
2.4.3. Professional development of teachers:
- conducting trainings, seminars, and courses for teachers on the use of AI in teaching and scientific work;
- creating a cooperation network between universities and enterprises for the mutual exchange of knowledge in the field of AI.
2.4.4. Ethical and legal aspects of AI use:
- development of ethical standards and principles for the use of AI in education, including data privacy protection, non-discrimination, and fairness;
- ensuring the compliance of AI technologies with the current legislation of Ukraine and international standards in the field of education and research.
2.4.5. Improving management efficiency:
- implementation of AI for the automation of administrative processes of University management;
- using AI in the preparation of draft documents based on standardized forms;
- creating incubators for AI-assisted projects aimed at sustainable development, particularly in the transport and aerospace industries;
- development of AI systems to assess the compliance of the University's activities with the UN Sustainable Development Goals;
- using big data and AI analytical tools to analyze data regarding higher education students, the learning process, and other aspects of the University's activities to make more informed decisions, particularly in public procurement, determining pricing policy, etc.
2.4.6. Support for innovations and startups:
- creating conditions for higher education students and young scientists who use AI to develop innovative solutions, supporting startups and projects in this field;
- organizing hackathons, competitions, and incubation programs to stimulate creativity and the development of AI projects;
2.4.7. International cooperation:
- expanding cooperation with international scientific institutions, universities, and enterprises to exchange experience and implement advanced practices in the field of AI;
- participating in international programs and projects that promote the development of AI in education and science.
3. Values and Principles of Using Artificial Intelligence
3.1. The University supports the rational use of AI tools, provided that the requirements for information security, data confidentiality, compliance with copyright norms, and academic integrity are taken into account.
3.2. The use of AI must be aimed at supporting and developing human potential, rather than replacing it. AI technologies are oriented towards ensuring the well-being of higher education students, teachers, and all participants in the educational process.
3.3. AI should help ensure equal opportunities for all higher education students, teachers, scientists, and other specialists, taking into account diverse needs, training levels, and other factors. AI technologies must be unbiased and prevent discrimination based on gender, race, age, disability, or other grounds.
3.4. The Strategy does not consist in declaring general restrictions on the use of generative AI, but in emphasizing the specifics of its conscious and responsible use.
3.5. The use of AI must be transparent for all participants in the educational process, scientific research, as well as for persons involved in making administrative and management decisions. AI algorithms and their decisions must be understandable and explainable to ensure trust in the results obtained using the technologies.
3.6. The Strategy establishes that all participants in the educational and scientific process must adhere to the core principles of AI use that promote its fair, ethical, safe, and productive utilization, including:
- academic integrity - the use of AI must comply with the principles of open science, integrity, and objectivity of scientific research;
- safety - cybersecurity measures to prevent unauthorized access to data, protection of personal data of educational process participants, as well as ensuring the reliability and safety of systems using AI;
- responsibility - each participant in the educational process is responsible for the final result of AI use at the University in accordance with the requirements of current legislation. In any case, the responsibility for the content of academic and administrative decisions and documents that establish / confirm rights, achievements, and other circumstances regarding the educational, scientific, or administrative activity of a person, made with the help of AI, is borne by the human who made such a decision or signed such a document. The use of AI serves as a supporting tool that expands human capabilities, enabling them to make informed decisions. Mechanisms must be created at the University to ensure accountability for the use of AI and a clear distribution of duties and responsibilities for its application among developers, teachers, administrators, and higher education students. An audibility option plays a key role in this, allowing for the evaluation of algorithms, data, and design processes, especially in critical areas of application;
- ethics - ensuring the use of artificial intelligence in compliance with ethical norms and values, which includes the responsible use of AI technologies to achieve positive results without negative social or ethical consequences;
- protection against harm - AI technologies must be used in a way that minimizes the possibility of causing harm, whether through unauthorized use of personal data or making incorrect decisions;
- confidentiality - protection of personal and confidential information of educational process participants, including rules and policies regarding data collection, storage, and processing, as well as ensuring compliance with legislation on personal data protection;
- reliability of results - decisions made with the help of AI must be accurate, verified, and validated on real data to guarantee reliability and consistency with expected outcomes;
- support for continuous learning - creating programs for professional development and training of all educational process participants who use or interact with AI must be an important component of the policy;
- support for persons with special educational needs - AI should facilitate the integration of higher education students with diverse needs, particularly through adaptive technologies that improve access to learning for persons with disabilities;
- principle of fairness - the use of AI in the educational process must be oriented towards ensuring fair and honest conditions for all its participants. Algorithms must operate without bias and ensure equal opportunities for higher education students, taking into account their individual characteristics;
- transparency - ensuring clarity and transparency in the use of AI, which guarantees the comprehensibility of algorithms used for decision-making, the possibility of verifying results, and access to information about what data is collected and used.
4. Directions of Artificial Intelligence Application at the University
4.1. Directions of AI use in the educational process:
1) Personalized learning and adaptive systems:
- individual learning trajectories (adaptive learning platforms that take into account data of higher education students; intelligent tutors that can provide additional explanations, assistance with problem solving and tests, and individually customize materials and tasks for each higher education student);
- recommendation systems (AI can offer higher education students recommendations regarding learning materials, courses, or additional resources, books, articles, online courses, or other educational materials corresponding to the needs of each higher education student);
- using AI to compare educational programs at the University with the National Qualifications Framework, the requirements of the National Agency for Higher Education Quality Assurance (NAQA), and international standards;
- integration of AI modules into internal education quality systems for generating reports in formats adopted by NAQA.
2) Automation of assessment and testing:
- automatic generation of tests;
- automatic grading of assignments (automatic grading of multiple-choice or short-answer choices; use of tools for assessing written works, such as grammar and style checking, as well as plagiarism detection);
- real-time progress assessment (real-time analysis of the academic performance of higher education students, providing teachers and the University administration with information on problematic aspects of learning, which allows for quick adjustments to the teaching approach).
3) Intelligent learning assistants:
- chatbots and virtual assistants (answering questions from higher education students regarding schedules, courses, deadlines, and the organization of learning in a 24/7 mode).
4) Automation and visualization of the educational process (learning materials):
- translation and localization of materials (automatic translation of learning materials into different languages, making them accessible to students from different countries);
- using AI to monitor and visualize educational outcomes, creating interactive dashboards to track the dynamics of competence acquisition by higher education students, in particular, when analyzing feedback and processing data from questionnaires (of higher education students, employers, and research-pedagogical / pedagogical staff) regarding the quality of the educational process using Natural Language Processing (NLP);
5) Data analysis and forecasting:
- forecasting the academic performance of higher education students (machine learning-based systems can predict which students might face difficulties in learning and provide timely recommendations to support them);
- application of educational process analytics based on big data (Big Data) to substantiate strategic management decisions (enrollment, workload, funding, etc.);
- using AI to analyze risks of understaffed programs, personnel losses, resources overexpenditure, etc.;
- analysis of audio and video recordings of classes, higher education students' grades, and feedback using AI.
6) Behavioral analysis of higher education students:
- data analysis from learning management systems (using information from learning management systems to identify potential problem areas in courses or in the ways materials are presented).
7) Inclusivity and accessibility of learning:
- adaptive interfaces and resources for persons with special educational needs (automatic subtitles, voice assistants, and translation, interactive systems for people with disabilities);
- flexibility in learning (creating flexible learning models that allow students to choose the most comfortable learning formats for them, taking into account individual needs and learning styles).
4.2. Directions of AI use in research and development work:
1) Big Data analysis:
- data processing and classification (using machine learning algorithms to classify, detect patterns and trends in large datasets);
- analysis of textual data (processing textual data, such as scientific articles and reports, which allows for the automatic extraction of important information and the analysis of trends in scientific publications).
2) Forecasting and modeling:
- forecasting scientific trends (analyzing existing research and trends to predict future directions in science);
- modeling complex systems (modeling physical, chemical, and biological systems and processes).
3) Automation of scientific processes:
- automatic execution of experiments (automating the conduct of experiments, collection, and primary processing of results, which increases the speed and accuracy of research);
- automation of literature review systems (automatically finding and analyzing the latest scientific articles on specific topics, allowing researchers to quickly familiarize themselves with relevant studies).
4) Generation of new hypotheses and innovations:
- algorithms for discovering new connections between data (automatically detecting correlations or subtle patterns in data that can become the basis for new hypotheses);
- intelligent systems for the development of new materials (predicting the properties of new materials or molecules, which is of great importance for scientific achievements in physics, chemistry, and biotechnology).
5) Intellectual analysis of scientific publications:
- plagiarism checking;
- citation and impact analysis (using citation metrics to analyze the impact of articles, studies, or authors, helping scientists navigate relevant sources).
6) Pattern recognition and video analytics:
- image analysis (detecting patterns and features in arrays of images).
7) Search for new technologies and innovations:
- analysis of scientific innovations (processing large volumes of patents and scientific articles, helping to identify innovative ideas that can become the basis for new technologies);
- research optimization (optimizing experiment parameters by identifying the most effective conditions to achieve the desired result).
8) Collaboration and network research:
- analysis of collaboration between scientists (analyzing scientific networks to identify the most effective collaborations and suggest potential research partners);
- development of scientific communications (automatic translation of scientific publications, facilitating communication between scientists from different language groups).
9) Ethical requirements for the use of AI in scientific research:
- when using AI in scientific papers, the author is obliged to clearly indicate which part of the results, texts, hypotheses, or illustrations was obtained using AI and which was created directly by the researcher;
- AI tools (name, version, date of use, developer) must be specified in the footnotes or in the list of references;
- it is prohibited to submit scientific papers that are completely or predominantly generated using AI without significant participation of the author;
- all results arising from the application of generative AI are subject to internal verification for compliance with academic integrity and must be edited in adherence to scientific standards.
- the use of AI must not lead to scientific falsification, data fabrication, or copyright infringement.
10) Control over the reliability of results obtained with the involvement of AI:
- scientific research results partially or fully obtained using AI must be confirmed by alternative methods: experiments, manual calculations, or comparisons with literature sources;
- a procedure for internal auditing of AI tools used in research is being implemented at the University, involving specialists in digital security, ethics, and artificial intelligence;
- reviewers of internal and competitive scientific papers must evaluate the validity of AI use, the presence of errors or hallucinations, especially when using large language models or generative systems;
- for complex technical models (for example, material forecasting, system properties forecasting, etc.), the following must be documented:
- the type and source of training data,
- model operation logic;
- repeatability of results in a test environment.
11) Internal certification of AI tools used in scientific research:
- before starting a research project that involves the engagement of new AI tools, the researcher must submit a brief application to the authorized person containing a description of:
- AI functionality;
- purpose of application;
- potential risks to data, results, or the University's reputation;
- the University maintains an internal register of AI tools approved for use, which includes verified and tested systems;
- the decision to include a new AI tool in the register is made by the AI implementation working group in coordination with the University's Scientific and Technical Council;
- the register is reviewed and updated once a year, taking into account current risks, new technologies, safety standards, and recommendations of the Ministry of Education and Science.
4.3. Directions of AI use in administrative management:
1) Automation of administrative processes:
- automation of document workflow at the University, in particular, drafting documents based on approved standard forms;
- schedule management;
- interaction with higher education students.
2) Performance evaluation and monitoring:
- academic performance evaluation of higher education students;
- analysis of the effectiveness of teachers and scientists;
- education quality monitoring.
3) Recruiting and personnel management:
- automation of candidate selection;
- employee performance analysis.
4) Finance and resource management:
- budget forecasting;
- cost optimization;
- application of AI for energy resource management of the University and its separate structural units, modeling CO2 emissions, and detecting energy losses.
5) Development of AI systems to assess the compliance of the University's activities with the UN Sustainable Development Goals.
5. Use of Artificial Intelligence by Teachers
5.1. Use of AI by teachers to create educational content.
1) Generation of learning materials
Teachers can use AI to automatically generate test questions, exercises, and even lecture presentations based on already existing learning material. This enables teachers to save time on routine work, focusing more attention on unique tasks and the development of critical thinking in higher education students. In particular, AI can help in creating individual or group assignments that correspond to the level of students' knowledge, which increases learning efficiency.
2) Adaptive learning
AI systems can analyze the performance of each student and offer them the most appropriate tasks or materials to overcome gaps in knowledge. Teachers can use such tools for a personalized approach to teaching, which allows for covering more higher education students and ensuring more effective assimilation of the material. Teachers must manage the use of AI, interpret its results, and integrate them into broader learning strategies.
5.2. Use of AI by teachers for assessing student works.
1) Automated grading
AI systems can automatically check the correctness of multiple-choice tests or evaluate mathematical solutions. This significantly reduces the time spent on grading and ensures the accuracy of results. For more complex assignments, such as essays or scientific papers, AI can be used for the preliminary assessment of text structure, grammar, style, and plagiarism detection. However, the final grading of such works must remain with the teacher, as they can provide a more well-founded assessment, taking into account the context and depth of argumentation.
2) Plagiarism detection
AI can automatically check the works of higher education students for plagiarism using specialized software. These systems compare the submitted work with large databases of scientific articles, dissertations, Internet resources, and other sources. The use of such tools enables teachers to quickly detect cases of unauthorized borrowing of materials, which promotes compliance with academic integrity in the educational process.
5.3. Use of AI in administrative educational processes.
1) Planning and organization of the educational process
AI can significantly improve the organization of the educational process through the automation of course and class scheduling, as well as the optimization of resource utilization. For example, AI can help teachers create the most efficient schedules for lectures and practical classes, taking into account various factors: availability of classrooms, students' schedules, and maximum time utilization. AI can assist in planning courses and curricula by analyzing data on student performance and suggesting changes to the program or the structuring of the course to improve learning outcomes.
2) Evaluation of the effectiveness of educational programs
AI can monitor the results of the educational process, evaluating the effectiveness of educational programs based on data regarding the academic performance of higher education students, their grades, and their level of engagement in the course. This allows teachers and staff of educational departments to promptly adjust courses, increasing their efficiency and compliance with modern requirements.
5.4. Ethical aspects and risks of AI use by teachers.
1) Transparency in the use of AI
Teachers are obliged to inform higher education students about the use of AI in the educational process. For example, if an assignment or grading is partially processed using AI, the teacher must inform the students about this and explain exactly how the AI affects the assessment or performance of the work. This helps avoid misunderstandings and creates an atmosphere of trust between the teacher and higher education students.
2) Avoiding bias in algorithms
Teachers must be aware that some AI systems may have built-in biases, especially if they are based on big data containing social, cultural, or other types of bias. Teachers should thoroughly evaluate such systems to avoid possible negative consequences for higher education students, particularly taking into account the social and cultural aspects of learning.
3) Student data protection
The use of AI for processing student data requires special attention to the issues of security and protection of personal information. Teachers must comply with legislative requirements regarding data confidentiality and ensure its proper storage, processing, and use. It is important that all AI tools used in the learning process meet safety and data protection standards.
5.5. Training of teachers for the use of AI.
For the effective use of AI, teachers need to undergo special training within professional development programs, which includes both the technical aspects of using software and ethical issues. This will help them understand the capabilities and limitations of AI, properly integrate these technologies into the educational process, and prevent violations of academic integrity.
6. Use of Artificial Intelligence by Higher Education Students
6.1. Higher education students have the right to use AI as a powerful tool to achieve academic goals; however, this must be done in accordance with the principles of academic integrity and ethics. The use of AI must be aligned with the University's existing requirements and its policies on educational activities.
6.2. AI cannot replace the individual efforts of higher education students, but only assists in information processing, data analysis, text creation, or solving mathematical problems. The University encourages higher education students to use AI tools to improve their skills and achieve results, provided that ethical norms and integrity standards are observed.
6.3. Permissible use of AI by higher education students.
1) Assistance in learning and research.
AI can be a useful tool to facilitate the learning process and conduct research. Students can use various AI-based programs to search for literature, generate ideas for essays, presentations, or scientific articles, test knowledge and prepare for exams, check text grammar and style, and receive feedback on completed assignments.
2) Text generation.
AI systems for text generation can assist higher education students when writing essays, term papers, laboratory reports, practical works, or other textual assignments. They allow users to use prompts to obtain ideas for starting work, structure text, or find appropriate arguments and examples to develop a topic. However, it should be noted that such an approach must not lead to plagiarism or the automatic use of generated texts without one's own modifications or additions. Higher education students must carefully edit and adapt generated texts to match their own ideas and research, as well as properly cite the sources used.
3) Exam preparation.
AI can be helpful for exam preparation. Higher education students can use intelligent systems to test their knowledge, create questions for self-assessment, or get feedback based on completed tasks. The systems can offer adaptive learning, where the difficulty of tasks changes depending on the level of knowledge, making it possible to prepare more effectively for exams or tests.
4) Improving communication.
AI can be used to improve communication between higher education students and teachers. Higher education students can use chatbots to get answers to frequently asked questions, as well as to schedule meetings or request updates about the schedule, which saves time on routine administrative issues and allows them to focus on more important aspects of learning.
6.4. Obligation to declare the use of AI.
1) Academic integrity.
The use of AI must be honest and transparent. Higher education students are obliged to declare when they used AI tools to complete assignments, especially if AI plays a significant role in the process of text creation or problem-solving. This must include specifying the tools and programs used, as well as the resources provided by AI, if relevant to the work. For example, if a higher education student uses AI to generate part of the text or to verify the correctness of their calculations, this information must be indicated in the materials accompanying the work.
2) Plagiarism and authorship.
AI must not be used to create texts that do not comply with the standards of academic integrity. Higher education students are strictly prohibited from submitting works completely or partially created with the use of AI without indicating this fact. Such actions will be considered a violation of academic integrity rules and may lead to appropriate sanctions, including the cancellation of exam results or expulsion from the University. The use of AI must not replace the original work of the student but should only serve as an auxiliary tool.
6.5. Prohibited use of AI by higher education students.
1) Automatic execution of assignments.
AI cannot be used for the automatic execution of assignments or writing works without the participation of the higher education student. This includes using AI to generate texts, essays, term papers, practical, laboratory, or graduation works without significant student involvement in the process. Although AI can help in shaping ideas, creating drafts, or analyzing information, the final product must be the result of the intellectual efforts of the higher education student themselves.
2) Violation of copyright rules.
Higher education students cannot use AI to create content that violates copyrights or other intellectual rights. They must ensure that all sources used to create their works are legal and that appropriate references to the sources are cited correctly.
6.6. Sanctions for violations.
The use of AI by higher education students without compliance with academic integrity requirements can lead to negative consequences. In case of violation of the Policy on the Use of AI, the higher education student may be subject to disciplinary sanctions, including:
- a written warning;
- retaking exams or courses;
- expulsion from the University.
All violations must be documented and reviewed by the relevant Academic Integrity Committee of the University, which makes a decision in accordance with internal regulations.
7. Ethics and Integrity in the Use of Artificial Intelligence
7.1. Higher education students, scientific, research-pedagogical, and pedagogical staff of the University must be informed about the proper use of AI in learning and research.
7.2. Higher education students, scientific, research-pedagogical, and pedagogical staff of the University are obliged to strictly adhere to academic integrity when using AI during their activities.
7.3. The procedure for detecting and establishing facts of academic integrity violations, the procedure for reviewing facts of academic integrity violations, and the procedure for filing and reviewing appeals regarding cases of dishonest use of AI are conducted in accordance with the Code of Academic Integrity (СУЯ ХАІ-НМВ-К/001:2023) and the Regulations on the Academic Integrity Committee of the University (СУЯ ХАІ-НМВ-П/008:2019, edition 2).
8. Developing Competence in the Use of Artificial Intelligence and Measures for Implementing Artificial Intelligence Systems
8.1. Activities aimed at developing competence in the use of AI can be carried out in the following areas:
- including topics on the use of AI into educational components;
- professional development of research-pedagogical, pedagogical, and scientific staff;
- educational events (providing recommendations, consultations, conducting seminars, round tables, trainings, etc.);
- public discussions;
- special actions and awareness campaigns.
8.2. The following AI implementation measures are conducted or may be conducted at the University:
1) Within 1 month from the date of approval of the Strategy:
- designate an authorized person for AI use at the University and a working group composed of representatives from educational, scientific, and administrative areas to coordinate AI implementation measures at the University, as well as to obtain additional information and consultations (hereinafter referred to as the working group);
- the working group and the University administration, jointly with teachers and higher education students, shall conduct surveys or focus groups to determine where and how AI can be useful in learning, research, and administrative processes;
- identify the existing qualifications among teachers, scientists, and IT personnel, as well as assess the available technical resources (servers, software, databases) that can be involved in AI implementation projects.
- the authorized person for AI use at the University and the working group shall, on the basis of this paragraph, the survey results, and the qualifications assessment, prepare a draft order on conducting AI implementation measures at the University, defining their specific content, execution deadlines, and responsible persons.
2) Within 3 months from the date of approval of the Strategy:
- upon the proposal of the vice-rectors according to their areas of work, AI implementation groups shall be established in educational, scientific, and administrative structural units to develop and implement action plans for the use of AI in the respective field of the University's activity;
- the Scientific and Methodological Committees of the University (NMC-1, NMC-2, NMC-3), taking into account this Strategy and the Recommendations of the Ministry of Education and Science, must develop basic recommendations for educational programs (specialties) regarding the use of AI in educational activities;
- the Scientific and Technical Council of the University, taking into account this Strategy and the Recommendations of the Ministry of Education and Science, must summarize recommendations regarding the use of AI in scientific activities;
- each department must develop an Instruction for implementing the policy on the use of generative AI in the educational process at the department, defining the stages of implementing the policy regarding the use of generative AI in learning, teaching, and assessment, taking into account the specifics of the educational programs and courses provided by the department and Appendix No. 6 of the Recommendations of the Ministry of Education and Science;
- each educational and scientific unit of the University must include the issue of AI use into current plans and report on it in accordance with the established procedure;
- within the framework of professional development for research-pedagogical and pedagogical staff, a course on the use of AI must be developed and implemented;
- a course (or individual topics) on the fundamentals of AI must be included in the educational programs for higher education students of all specialties;
- a criterion for the use of AI is introduced into the performance evaluation of teachers, scientists, heads of departments, deans of faculties, heads of other educational, scientific, and administrative units, and vice-rectors;
- the authorized person for AI use at the University, together with the working group and taking into account the recommendations of the Ministry of Education and Science, shall compile suitability assessments of specific AI systems for further use at the University, considering the risks of violating human rights, intellectual property rights, personal data protection, etc.
3) Within 6 months from the date of approval of the Strategy:
- AI systems must be integrated with existing educational systems (learning platforms, distance learning platforms, etc.);
- measures for plagiarism checking and checking for the unauthorized use of AI in the works of higher education students and in scientific papers must be strengthened;
- results before and after AI implementation must be comparable (for example, the quality of learning, student satisfaction levels, speed and accuracy of assessment).
4) Within 9 months from the date of approval of the Strategy:
- expanding the use of AI - after successful testing, scale up the use of AI technologies to other courses and administrative processes.
5) Permanently:
- continuous monitoring and improvement.
9. Final Provisions
9.1. The Strategy comes into force from the moment of its approval by the Academic Council of the University and its implementation by the order of the Rector.
9.2. The Strategy, along with other regulatory acts of the University regarding the regulation of relations associated with the use of AI, methodological and educational materials, practical case studies for teachers, scientists, and higher education students, roadmaps, and examples of permissible / prohibited uses of AI, must be brought to the attention of all employees and higher education students by publishing them on the official website of the University and must be taken into account in their work.
9.3. The authorized person for AI use at the University and the working group, guided by this Strategy and approved action plans in the respective fields of activity, participate in the development of draft regulatory acts for consideration by the Academic Council of the University. These acts aim to regulate the application of AI, define the rights and duties of participants in educational, scientific, and administrative processes, establish criteria for the unauthorized use of AI in the works of higher education students, scientists, and teachers, define procedures for detecting academic integrity violations, and outline the procedure for declaring the use of AI.
9.4. Amendments and/or additions to the Strategy are made in the manner established for its adoption.
9.5. Relations not regulated by this Strategy are governed by the legislation of Ukraine.