=Paper=
{{Paper
|id=Vol-3918/paper000
|storemode=property
|title=AREdu 2024: Where augmented reality meets augmented intelligence
|pdfUrl=https://ceur-ws.org/Vol-3918/paper000.pdf
|volume=Vol-3918
|authors=Serhiy O. Semerikov,Andrii M. Striuk,Maiia V. Marienko,Olha P. Pinchuk,Yuliia V. Yechkalo,Viktoriia V. Tkachuk,Iryna S. Mintii,Anna V. Iatsyshyn,Olena V. Gorda,Olga B. Kanevska,Ivan I. Donchev
|dblpUrl=https://dblp.org/rec/conf/aredu/X24
}}
==AREdu 2024: Where augmented reality meets augmented intelligence==
https://ceur-ws.org/Vol-3918/paper000.pdf
Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–28
AREdu 2024: Where augmented reality meets augmented
intelligence
Serhiy O. Semerikov1,2,3,4,5 , Andrii M. Striuk4,1,5 , Maiia V. Marienko2 , Olha P. Pinchuk2 ,
Yuliia V. Yechkalo4 , Viktoriia V. Tkachuk4 , Iryna S. Mintii6,2,1,3,7,8,5 , Anna V. Iatsyshyn2 ,
Olena V. Gorda9 , Olga B. Kanevska1 and Ivan I. Donchev10
1
Kryvyi Rih State Pedagogical University, 54 Universytetskyi Ave., Kryvyi Rih, 50086, Ukraine
2
Institute for Digitalisation of Education of the NAES of Ukraine, 9 M. Berlynskoho Str., Kyiv, 04060, Ukraine
3
Zhytomyr Polytechnic State University, 103 Chudnivsyka Str., Zhytomyr, 10005, Ukraine
4
Kryvyi Rih National University, 11 Vitalii Matusevych Str., Kryvyi Rih, 50027, Ukraine
5
Academy of Cognitive and Natural Sciences, 54 Universytetskyi Ave., Kryvyi Rih, 50086, Ukraine
6
University of Łódź, 68 Gabriela Narutowicza Str., 90-136 Łódź, Poland
7
Lviv Polytechnic National University, 12 Stepana Bandery Str., Lviv, 79000, Ukraine
8
Kremenchuk Mykhailo Ostrohradskyi National University, 20 University Str., Kremenchuk, 39600, Ukraine
9
Kyiv National University of Construction and Architecture, 31 Povitroflotskyi Ave., Kyiv, 03680, Ukraine
10
South Ukrainian National Pedagogical University named after K. D. Ushynsky, 26 Staroportofrankivska Str., Odesa, 65020,
Ukraine
Abstract
The 7th International Workshop on Augmented Reality in Education (AREdu 2024) brought together researchers
and practitioners to explore the convergence of augmented reality and artificial intelligence in educational contexts.
This paper presents an overview of the workshop’s proceedings, comprising 22 peer-reviewed papers spanning
diverse areas. Key themes include immersive learning environments, augmented intelligence in education, learning
analytics, innovative educational technologies, and AR/VR applications. The papers collectively demonstrate how
the integration of AR and AI technologies can enhance learning experiences, improve educational outcomes, and
support the development of critical skills. Despite ongoing challenges in Ukraine, AREdu 2024’s format enabled
global participation and knowledge sharing. The proceedings provide valuable insights to guide future research
and implementation efforts in AR-enhanced education.
Keywords
augmented reality, artificial intelligence, educational technology, immersive learning, learning analytics, STEM
education, teacher training, educational data mining
1. Introduction
Augmented Reality in Education (AREdu) is a peer-reviewed in-
ternational Computer Science workshop focusing on research ad-
vances and applications of virtual, augmented and mixed reality in Figure 1: AREdu 2024 logo.
education.
The 2024 edition of the workshop marks a significant evolution in the field, particularly highlighting
the convergence of augmented reality with artificial intelligence technologies. This intersection presents
AREdu 2024: 7th International Workshop on Augmented Reality in Education, May 14, 2024, Kryvyi Rih, Ukraine
" semerikov@gmail.com (S. O. Semerikov); andrey.n.stryuk@gmail.com (A. M. Striuk); popelmaya@gmail.com
(M. V. Marienko); opinchuk100@gmail.com (O. P. Pinchuk); uliaechk@gmail.com (Y. V. Yechkalo);
viktoriya.tkachuk@gmail.com (V. V. Tkachuk); mintii@iitlt.gov.ua (I. S. Mintii); anna13.00.10@gmail.com (A. V. Iatsyshyn);
anaelg@ukr.net (O. V. Gorda); o.b.kanevska@gmail.com (O. B. Kanevska); donchev@pdpu.edu.ua (I. I. Donchev)
~ https://acnsci.org/semerikov (S. O. Semerikov); http://mpz.knu.edu.ua/andrij-stryuk/ (A. M. Striuk);
https://acnsci.org/mintii/ (I. S. Mintii); https://kdpu.edu.ua/personal/obkanevska.html (O. B. Kanevska)
0000-0003-0789-0272 (S. O. Semerikov); 0000-0001-9240-1976 (A. M. Striuk); 0000-0002-8087-962X (M. V. Marienko);
0000-0002-2770-0838 (O. P. Pinchuk); 0000-0002-0164-8365 (Y. V. Yechkalo); 0000-0002-5879-5147 (V. V. Tkachuk);
0000-0003-0789-0272 (I. S. Mintii); 0000-0001-8011-5956 (A. V. Iatsyshyn); 0000-0001-7380-0533 (O. V. Gorda);
0000-0003-1703-7929 (O. B. Kanevska); 0000-0002-3373-6562 (I. I. Donchev)
© 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
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Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–28
new opportunities for creating more adaptive, intelligent, and personalized learning experiences.
The workshop’s focus on both AR and AI reflects the growing recognition that these technologies,
when combined, can provide powerful tools for addressing contemporary educational challenges and
supporting innovative pedagogical approaches.
The 7th International Workshop on Augmented Reality in Education (AREdu 2024), held on May 14,
2024, in Kryvyi Rih, Ukraine, provided a dynamic platform for researchers, educators, and technology
developers to share their latest findings and experiences in the rapidly evolving field of AR and AI
in education. Building on the success of previous editions [1, 2, 3, 4, 5, 6, 7], AREdu 2024 attracted a
diverse array of contributions exploring the design, implementation, and evaluation of AR/AI-based
learning environments across various educational levels and subject areas.
This year’s workshop covers a wide range of topics related to the application of augmented reality
and artificial intelligence in various educational contexts:
• Immersive learning environments and tools
• Augmented intelligence in education
• Learning analytics and educational data mining
• Innovative educational technologies and approaches
• AR/VR applications and case studies
• Best practices and lessons learned
This volume represents the proceedings of the AREdu 2024. It comprises 22 contributed papers that
were carefully peer-reviewed and selected from 27 submissions. At least three program committee
members reviewed each submission.
The workshop’s proceedings showcase the breadth and depth of current research on educational
AR. From theoretical frameworks to empirical studies and practical applications, the papers collec-
tively demonstrate AR’s immense potential to enhance learning experiences, foster engagement and
motivation, and develop critical 21st-century skills.
2. AREdu 2024 committees
Organizing committee
• Andrii Striuk, Kryvyi Rih National University, Ukraine [8]
• Serhiy Semerikov, Kryvyi Rih State Pedagogical University, Ukraine [9]
• Maiia V. Marienko, Institute for Digitalisation of Education of the NAES of Ukraine, Ukraine [10]
• Olha Pinchuk, Institute for Digitalisation of Education of the NAES of Ukraine, Ukraine [11]
Program committee
• Irina Georgescu, Bucharest University of Economic Studies, Romania [12]
• Filip Górski, Poznan University of Technology, Poland [13]
• Dragoş Daniel Iordache, National Institute for Research and Development in Informatics - ICI
Bucuresti, Romania [14]
• M.-Carmen Juan, Universitat Politècnica de València, Spain [15]
• Michael Kerres, University Duisburg-Essen, Germany [16]
• Gabor Kiss, Selye János University, Slovakia & Óbuda University, Hungary [17]
• Ranesh Kumar Naha, Queensland University of Technology, Australia [18]
• Nina Rizun, Gdańsk University of Technology, Poland [19]
• Tetiana Vakaliuk, Zhytomyr Polytechnic State University, Ukraine [20]
• Nataliia Veretennikova, Lviv Polytechnic National University, Ukraine [21]
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Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–28
Additional reviewers
• Olha Bondarenko, Kryvyi Rih State Pedagogical University, Ukraine [22]
• Roman Danel, VŠTE České Budějovice, Czechia [23]
• Vita Hamaniuk, Kryvyi Rih State Pedagogical University, Ukraine [24]
• Hamraz Javaheri, German Research Center for Artificial Intelligence (DFKI), Germany [25]
• Christos Kaltsidis, Democritus University of Thrace, Greece [26]
• Oleksandr Kolgatin, Simon Kuznets Kharkiv National University of Economics, Ukraine [27]
• Yaroslav Krainyk, Petro Mohyla Black Sea National University, Ukraine [28]
• Hennadiy Kravtsov, Kherson State University, Ukraine [29]
• Volodymyr Kukharenko, Kharkiv National Automobile and Highway University, Ukraine [30]
• Svitlana Lytvynova, Institute for Digitalisation of Education of the NAES of Ukraine, Ukraine [31]
• Maiia V. Marienko, Institute for Digitalisation of Education of the NAES of Ukraine, Ukraine [32]
• Iryna Mintii, Institute for Digitalisation of Education of the NAES of Ukraine [33]
• Andrii Morozov, Zhytomyr Polytechnic State University, Ukraine [34]
• Pavlo Nechypurenko, Kryvyi Rih State Pedagogical University, Ukraine [35]
• Yulia Nosenko, Institute for Digitalisation of Education of the NAES of Ukraine, Ukraine [36]
• Vasyl Oleksiuk, Ternopil Volodymyr Hnatiuk National Pedagogical University, Ukraine [37]
• Kateryna Osadcha, Norwegian University of Science and Technology, Norway [38]
• Viacheslav Osadchyi, Borys Grinchenko Kyiv Metropolitan University, Ukraine [39]
• Liubov Panchenko, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic
Institute”, Ukraine [40]
• Olha Pinchuk, Institute for Digitalisation of Education of the NAES of Ukraine, Ukraine [41]
• Serhiy Semerikov, Kryvyi Rih State Pedagogical University, Ukraine [42]
• Yevhenii Shapovalov, Junior Academy of Sciences of Ukraine, Ukraine [43]
• Andrii Striuk, Kryvyi Rih National University, Ukraine [44]
• Nataliia Valko, Kherson State University, Ukraine [45]
• Kateryna Vlasenko, National University of “Kyiv-Mohyla Academy”, Ukraine [46]
• Yuliia Yechkalo, Kryvyi Rih National University, Ukraine [47]
3. Proceedings overview
3.1. Immersive learning environments and tools
The paper “Immersive learning tools for teaching mathematics to high school students in general
secondary education institutions” by Lytvynova and Rashevska [48] explores the potential of immersive
technologies in developing mathematical and information-communication competencies among high
school students in specialized classes of general secondary education institutions in Ukraine. The
authors provide a literature review, highlighting the growing importance of immersive technologies in
education, particularly in the context of the COVID-19 pandemic and the need for innovative teaching
approaches. The study focuses on four main immersive learning platforms: AR Book, mozaBook,
GeoGebra 3D, and Desmos. For each platform, the authors offer a detailed description of its features,
benefits for teachers and students, and specific applications in teaching mathematics to high school
students. The paper provides insights into how these platforms can be used to create interactive and
engaging learning experiences, personalize learning trajectories, and foster the development of critical
thinking and problem-solving skills. The authors conclude by emphasizing the need for further research
on the systematic integration of immersive technologies into the Ukrainian education system, the
development of adaptive curricula, and the training of teachers to effectively implement immersive
learning in their classrooms.
In the paper “Bridging minds and machines: AI’s role in enhancing mental health and productivity
amidst Ukraine’s challenges”, Bondar, Bilozir, Shestopalova, and Hamaniuk [49] explore the convergence
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Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–28
of human intelligence and artificial intelligence, focusing on its potential to enhance education in the
domain of mental health, particularly within Ukrainian educational institutions following the pandemic
and amid wartime conditions. The authors delve into the concepts of “digital mental health”, “e-mental
health”, “mental health technology”, and “digital mental health”, highlighting their significance in the
current context. The paper examines the standards for university courses in mental health technologies
and introduces a variety of mental health apps, including wearables, platforms, data analytics resources,
and other tools. The authors emphasize the importance of integrating artificial intelligence into both
the education and economic sectors, providing a detailed account of an experiment integrated into a
standard university curriculum involving master’s psychology students at a pedagogical university.
The results and conclusions of this experiment are thoroughly presented, offering valuable insights
into the practical application of AI in mental health education. Furthermore, the paper investigates
the impact of transactional distance on the learning experience of students pursuing mental health
technology courses online at Kryvyi Rih State Pedagogical University during the 2023-2024 academic
year. The study’s findings affirm the critical role of synergizing human and artificial intelligence in
addressing pressing challenges, enhancing mental health education, honing data analysis skills, and
shaping a brighter future for well-being.
A key strength of the paper “Harnessing immersive technologies for enhancing Japanese language
acquisition: Methodological insights for prospective language educator” by Gayevska [50] is its thor-
ough classification and analysis of immersive technologies, clearly delineating the differences between
various VR and AR approaches. The authors’ systematic breakdown of VR into five categories and AR
into three types provides a clear framework for understanding these technologies’ educational applica-
tions. The visual representations through figures effectively communicate these complex technological
relationships.
The empirical research component is notably robust, employing both quantitative and qualitative
methods to assess student attitudes and learning outcomes. The survey results from 31 students provide
valuable insights into learners’ perceptions of immersive technologies, while the comparative analysis
of exam results (95 points vs 85 points average) offers concrete evidence of the technology’s educational
impact.
The paper makes a unique contribution by focusing specifically on the challenges of teaching
character-based writing systems. The discussion of using AR for learning Kanji characters is particularly
insightful, offering practical solutions for one of the most challenging aspects of Japanese language
acquisition. The detailed analysis of student preferences for different learning approaches, including the
high rating (5.0) for creating AR-based educational materials, provides valuable guidance for curriculum
development.
In the paper “Harnessing immersive technologies for enhancing mathematical logics education in
secondary schools”, Velychko, Fedorenko, Kaidan, and Kaidan [51] examine the current state and practi-
cal applications of immersive technologies in enhancing mathematical logics education in secondary
schools. The authors emphasize the importance of visual presentation and quality of new knowledge in
the learning process, particularly in light of the rapid development of information and communication
technologies. The study explores the possibilities and specifics of employing virtual worlds in the
educational process and provides practical results of approbating virtual tools in the classroom.
The paper begins with a literature review, highlighting the growing significance of immersive
technologies in education and the potential of edutainment, or learning through play, in engaging
students and improving learning outcomes. The authors discuss various types of immersive technologies,
including Real Reality (RR), Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), and
360-degree photos and videos, and their applications in educational settings.
A key contribution of this study is the analysis of existing virtual learning environments, such as
Lifeliqe’s Digital Science Curriculum, Minecraft: Education Edition, and Tinkercad, and their potential
for teaching mathematical logics. The authors provide practical examples of how these platforms can
be used to create interactive and engaging learning experiences, fostering a deeper understanding of
abstract concepts and logical reasoning.
The research methodology includes a survey conducted among students at the Donbas State Ped-
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Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–28
agogical University, studying secondary education in mathematics, physics, and informatics. The
survey results reveal that most students are familiar with the concept of gamification, are ready to learn
through play, and support the integration of computer games in the teaching process. However, the
study also highlights the limited exposure of students to learning through computer games and the
need for incorporating gamification elements in teacher training programs.
The paper presents a case study of introducing gamification elements in teaching the topic “Logical
Operators” using Minecraft EDU and Tinkercad environments. The results demonstrate a significant
increase in student interest and improved learning outcomes, indicating the effectiveness of immersive
technologies in teaching mathematical logics.
Figure 2: An example of creating a scheme for the logical operator “AND” in Tinkercad [51].
In conclusion, the authors emphasize the potential of gamification in enhancing the quality of
education for learners of all ages, deepening the level of acquired knowledge, and enabling more
effective use of skills and abilities. However, they also acknowledge the need for establishing clear
procedures and adherence to key stages in creating game mechanisms for successful implementation in
the education system.
3.2. Augmented intelligence in education
In the paper “A novel pedagogical approach to equipping prospective IT professionals with skills in 3D
modelling and reconstruction of architectural heritage”, Hevko, Potapchuk, Lutsyk, Yavorska, Hiltay,
and Stoliar [52] present a novel pedagogical methodology for teaching prospective IT professionals
cutting-edge 3D technologies for the graphical reconstruction of architectural heritage. The effectiveness
of the proposed approach is demonstrated through a case study involving the reconstruction of the
Parochial Cathedral of St Mary of Perpetual Assistance from the 1950s. The methodology encompasses
a comprehensive set of stages: analysis, modeling, design, and 3D printing, underpinned by a synthesis
of archival data analysis, parallax estimation from stereo image pairs, and contemporary 3D modeling
techniques. The authors detail the selection of 3DS Max as the optimal software for creating the detailed
3D model and Cura for preparing the model for 3D printing. The experimental evaluation confirms
the efficacy of the proposed teaching methodology in equipping students with a robust theoretical and
practical foundation for deploying modern digital technologies in the reconstruction and preservation
of architectural heritage. The proposed approach has the potential to foster the development of essential
skills among prospective IT professionals while contributing to the preservation and dissemination of
cultural heritage.
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Figure 3: The printed miniature of the Parochial Cathedral of St Mary of Perpetual Assistance [52].
In the paper “A novel neuro-fuzzy approach for evaluating educational programme quality and
institutional performance in higher education”, Ryabko, Vakaliuk, Zaika, Kukharchuk, Kukharchuk,
and Novitska [53] present a novel methodology for evaluating the quality of educational programmes
and institutional performance in higher education institutions using advanced artificial intelligence
techniques, specifically the Adaptive Neuro-Fuzzy Inference System (ANFIS) and multi-layer neural
networks. The study addresses the challenges of subjectivity in self-assessment processes and aims
to proactively identify potential issues and deficiencies in educational activities prior to accreditation
reviews.
The paper begins with a theoretical background, discussing the complexity of evaluating educational
quality and the need for quantitative methods to assess non-numerical characteristics. The authors
highlight the importance of student-centredness and academic freedom in the accreditation process
and emphasize the potential of involving students in the evaluation of educational programmes and
institutional activities.
The proposed approach utilizes student ratings on a four-level assessment scale as input data for the
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Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–28
multi-layer neural network, while the criteria for assessing educational programme quality serve as
input variables for the ANFIS model. The underlying hypothesis is that students with higher academic
performance would provide more objective assessments of the quality criteria.
The authors provide a detailed description of the ANFIS network architecture and the process of
training and testing the neural networks using the MATLAB environment. The results demonstrate that
the multi-layer neural network exhibits superior predictive accuracy compared to the ANFIS model,
with an average absolute error of 0.0321 and a relative error of 7.08% when compared to expert estimates.
The authors also highlight the potential of using student and graduate assessments to prepare training
datasets for configuring and training artificial neural networks capable of performing comprehensive
evaluations of educational programmes and institutional activities.
Figure 4: The results of network testing on known values of expert estimates [53].
The discussion section critically evaluates the study’s findings, acknowledging the debatable nature
of using students as experts in the evaluation process and emphasizing the need for further research
with larger datasets to refine the neural network architecture and improve its predictive capabilities.
The authors also suggest involving teachers from other educational institutions and increasing the
volume of the input vector to include estimates from teachers, stakeholders, and experts.
The paper concludes by underlining the practical implications of the proposed methodology, which can
enable higher education institutions to detect shortcomings and potential problems before accreditation
examinations. The authors also identify prospects for further research, such as applying neural network-
based software products to automate various aspects of the educational process and introducing neural
network software for direct student training in specific disciplines.
In the paper “Using intelligent agent-managers to build personal learning environments in the e-
learning system”, Burov, Pasko, Viunenko, Agadzhanova, and Ahadzhanov-Honsales [54] present a
novel approach to developing the structure of a multi-agent environment for e-learning systems and
propose a computer technology to ensure student activities in e-learning modular systems. The study
addresses the low level of adaptation of modern e-learning systems to individual student characteristics
and the lack of ability to predict learning outcomes.
The introduction highlights the importance of delivering dynamic learning materials and managing
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Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–28
training course systems promptly in modern e-learning systems. The authors emphasize the role of
intelligent agent-managers in referring students to relevant communities, examining materials accessed
by other community members, and connecting students and experts. The main disadvantage of current
learning management systems is identified as the failure to provide students with assistance in the
distance learning process, necessitating the integration of metacognitive agents for each student.
The paper proposes a three-level multi-agent management architecture for distance learning in
e-learning systems, which includes Tutor Agents, Lesson Planning Agents, Learner Agents, and Per-
sonalization Agents. The authors focus on learning agents, also known as autonomous intelligent
agents, and present the flow of work of an agent-manager as part of the Learning Management System
Figure 5: Flow of work of an agent-manager as part of LMS [54].
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Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–28
(LMS). The formalized description of modular e-learning systems to ensure the ergonomic quality of
human-machine interaction is addressed through a complex of component and morphological models.
The developed models define the concept of forming the knowledge and data bases of the learning
management system in the “agent – manager for e-learning” software package. The basic functional
blocks and principle of operation of the agent-manager for e-learning are illustrated, highlighting
the interaction between the student model, knowledge model, teaching model, and student-teacher
interaction model.
Experiments conducted at Sumy National Agrarian University demonstrate the effectiveness of the
developed models and computer technology. The quality expertise and evaluation of the parameters
of electronic training modules “Informatics” for first-year students of the “Agronomy” specialty were
carried out.
The proposed technology enables the consideration of factors affecting students’ learning outcomes
from a holistic perspective and the formation of an individual trajectory for each learning session. The
authors conclude by emphasizing the potential of their approach to provide users with the opportunity
to collect, analyze, distribute, and use knowledge in the e-learning system from various independent
sources.
In the paper “AI tools for sustainable primary teacher education: literary-artistic content generation”,
Nezhyva, Palamar, Semenii, and Semerikov [55] explore the possibilities of using AI tools for generating
literary-artistic content in preparing primary school teachers for professional activities. The study aims
to determine the familiarity and readiness of future primary school teachers to use AI in the literary
field, reveal the possibilities of applying AI to prepare teachers for organizing the study of literary
works, describe AI tools that can motivate young students to read, and highlight the advantages and
disadvantages of using AI programs to generate literary-musical and video content based on literary
material.
The introduction provides an overview of the growing interest in AI applications in education and
the potential of AI tools in transforming teaching and learning methods. The authors emphasize the
importance of preparing future primary school teachers to effectively integrate AI technologies into
their professional activities, particularly in the context of literary education.
The study’s methodology employs a mixed-methods approach, combining surveys, hands-on experi-
ences with AI tools, and the analysis of student-generated artifacts. The participants, 138 bachelor’s
students specializing in “Primary Education” at Borys Grinchenko Kyiv Metropolitan University, were
introduced to various AI tools for generating literary-musical and video content, such as Suno, Udio,
Boomy, Pictory, Lumen5, and InVideo AI. Data were collected through surveys, pedagogical cases,
multimedia didactic tools, and observations.
Figure 6: An example of the generation of the tale “Kotygoroshko” in the Pictory program [55].
The results section provides a detailed analysis of the AI tools tested by the students, highlighting
their features, advantages, and disadvantages for creating literary-musical and video content. The
authors present practical examples of how these tools can be used to enhance the study of literary
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Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–28
works in primary education, such as creating songs based on poems, generating dialogues between
characters, and visualizing writers’ biographies.
The discussion section underscores the potential of AI tools in creating engaging and interactive
literary-artistic content, facilitating personalized learning, and supporting the development of students’
creativity and critical thinking skills. The authors also address the challenges and limitations identified
during the study, such as the need for content validation, technical support, and addressing ethical
concerns.
The study proposes measures to ensure the responsible and beneficial implementation of AI in primary
education, including rigorous content validation processes, teacher training, and the development of
clear policies for transparent and ethical AI usage. The authors emphasize the importance of integrating
AI technologies into teacher education programs to foster digital literacy skills, promote innovative
teaching methods, and cultivate a mindset of continuous learning and adaptation.
The conclusion highlights the significance of the study’s findings in contributing to sustainable
education practices by preparing future primary school teachers to effectively harness the potential of
AI technologies while navigating the challenges and opportunities presented by the digital age. The
authors call for further research to explore the long-term impact of AI integration on student learning
outcomes, develop comprehensive AI competency frameworks, and refine ethical guidelines for AI use
in educational settings.
In the paper “A model for improving the accuracy of educational content created by generative AI”,
Talaver and Vakaliuk [56] present a novel approach for text processing and factual claims verification
to address the critical challenge of ensuring the reliability of AI-generated educational content. The
study focuses on extracting factual claims, retrieving evidence from authoritative sources, verifying
content, and rewriting it to ensure accuracy while maintaining pedagogical effectiveness.
The introduction provides an overview of the transformative impact of AI in education, highlighting
the potential of large language models (LLMs) in enhancing the accessibility, quality, and relevance of
learning materials. The authors emphasize the need for a system that complements manual peer review
processes by providing detailed annotations and evidence-based notes related to facts retrieved from
different sources.
The theoretical background section offers a thorough review of the growing influence of AI in
education, the SME-driven approach to content creation, and the challenges and limitations of generative
AI for learning. The authors discuss the risks of bias, inaccuracies, and ethical concerns, underscoring
the importance of robust, multi-layered validation frameworks. They also highlight the utility of the
Swiss Cheese Model and the need for adaptable guardrail solutions to mitigate these challenges.
The methods section outlines the development of the proposed system, which employs a multi-
layered approach to content verification. The workflow incorporates multi-stage processing, structured
claim extraction, evidence classification, and revision strategies, with a focus on prompt engineering
techniques and adherence to factual accuracy. The testing methodology, which includes articles with
altered factual information, demonstrates the system’s ability to detect inaccuracies.
The results section presents a simple web UI that supports the visualization of the verification process.
The processed text contains highlighted parts representing analyzed claims, with color-coding indicating
the certainty of the decision and the presence of revisions. The popup functionality provides detailed
information about each claim, including the revised paragraph, an explanation of the changes, certainty
scores, and aggregated evidence counts.
The discussion section highlights the strengths of the proposed method, including its clear and
practical framework, the use of prompt-chaining techniques, and its cost efficiency. The authors also
acknowledge the challenges faced by the system, such as occasionally missing specific claims or failing
to provide adequate context. They suggest fine-tuning the model with more varied and representative
training examples to enhance its contextual understanding and precision.
The conclusion emphasizes the potential of the proposed approach in addressing text verification
and content refinement challenges. The authors encourage researchers and developers to explore and
refine the system in real-world settings to ensure its full potential is realized across domains.
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Serhiy O. Semerikov et al. CEUR Workshop Proceedings 1–28
Figure 7: An example of the verification tool UI includes a field with inserted text and processed output that
provides suggestions for different parts and a popup with details that are opened upon hovering highlighted
parts of the text [56].
3.3. Learning analytics and educational data mining
In the paper “The role of educational and scientific studies taxonomies in a centralised informational
web-oriented educational environment”, Shapovalov, Shapovalov, Tarasenko, Usenko, Paschke, and
Shapovalova [57] explore the use of educational and scientific studies taxonomies in a centralized
informational web-oriented educational environment. The authors propose structuring scientific and
educational studies using the formalization of the IMRAD approach to provide data interoperability.
The study focuses on using study results as part of a centralized informational web-oriented educational
environment and applies this structurization to two specific studies related to the utilization of methane
tank waste and effluent. The authors describe the use of specific tools from CIT Polyhedron to process
Figure 8: Using of same terms to provide interoperability between educational programmes ontology and
scientific studies ontologies [57].
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study data, including an audit tool that compares newly inputted data to existing taxonomies and
highlights cases of full correspondence between elements of different works. The paper also presents
the approach of integrating studies with educational ontologies within the centralized informational
web-oriented educational environment, along with a mathematical formalization of this process.
In the paper “Digital modeling of the ecophilic tendencies of university students’ consciousness”,
Klochko, Fedorets, Sharyhin, and Kaplinskyi [58] present a digital modeling approach to study the
ecophilic tendencies of university students’ consciousness, based on the integrative application of
digital, mathematical, anthropological, and psychological methods. The study aims to contribute to
the understanding of the psychological, value, cognitive, and behavioral factors in the development
of environmental consciousness and human behavior, which are crucial for achieving the Sustainable
Development Goals.
The introduction provides a comprehensive overview of the importance of developing ecologically
oriented human qualities as a prerequisite for ecologically oriented behavior. The authors emphasize
the role of ecophilic tendencies of consciousness as a psychological prerequisite for the formation of
these qualities, justifying the need for their digital and mathematical modeling.
The methodology section describes the application of various methods, including system analysis,
cluster analysis, mathematical statistics, digital modeling, and digital data visualization. The authors
employ the Hubert index, hierarchical clustering with Ward’s minimum variance method, and distance
matrix to determine the optimal number of clusters. They also use the K-Means and Canopy clustering
methods to structure the objects into clusters.
The results section presents the findings of the study, revealing that the optimal number of clusters
is 3, but the authors decide to divide the set of objects into 4 clusters to capture the unique structure
of the cluster for diagnosing the ecophilic intentions and values of university students. The cluster
models obtained using the K-Means and Canopy methods are similar, confirming the effectiveness of
the algorithms and the adequacy of the constructed cluster structures.
The discussion section compares the study’s findings with the work of other researchers, highlighting
the conceptual proximity and the potential of the existential and harmonizing aspect of greening as a
strategy for shaping consumer environmental behavior and fostering creativity, innovation, altruism,
and compassion.
The conclusion emphasizes the importance of the study’s results in developing strategies for the
ecologization of higher education students. The authors conceptualize two directions of ecologization:
existentially harmonizing and aesthetically harmonizing. These directions are used to develop ecophilic
tendencies of consciousness in future mathematics teachers, students of information technology, and to
improve the health-saving competence of physical education teachers.
3.4. Innovative educational technologies and approaches
In the paper “Enhancing personal financial management skills through a machine learning-powered
business simulator”, Antoniuk, Vakaliuk, Didkivskyi, Vizghalov, Oliinyk, and Yanchuk [59] introduce
a novel web-based business simulator equipped with machine learning capabilities to facilitate the
development of personal financial management skills. The paper begins by highlighting the importance
of effective personal financial management as a critical life skill and the lack of sufficient financial
literacy among university students in Ukraine. The authors present a comprehensive methodology
for utilizing the simulator, including its content, objectives, formats, methods, and tools. The key
features and sections of the simulator are described in detail, along with the specific personal finance
management skills it aims to cultivate. To enhance the simulator’s effectiveness, elements of machine
learning, particularly reinforcement learning, have been incorporated. The authors emphasize the
simulator’s versatility, as it is designed to cater to a wide audience, from school-aged children to adults,
and can be integrated into economics courses at both secondary and tertiary education levels in Ukraine.
The paper concludes with a discussion on the future prospects of using such simulators to develop
managerial and financial competencies among students from diverse specialties.
The paper “Collaborative learning in the system of training future information technologies specialists
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Figure 9: Business simulator [59].
as an educational strategy for the fundamentalization of the sustainable development of education”
delves into the role of collaborative learning in the training of future information technology (IT)
specialists. Authored by Tverdokhlib, Klochko, Sharyhin, and Fedorets [60], the study explores how
collaborative learning can be integrated into IT education to enhance teamwork skills, problem-solving
abilities, and contribute to the sustainable development of education. The research is grounded in both
empirical and theoretical frameworks, with a particular focus on the distinction between cooperative
and collaborative learning, and proposes a methodology for using collaborative learning in the context
of team sports programming.
The paper provides a clear distinction between cooperative and collaborative learning. Cooperative
learning involves the division of tasks among group members, whereas collaborative learning emphasizes
a more integrated, psychologized, and intellectualized approach, where group members work together to
solve problems and achieve common goals. This distinction is crucial for understanding the synergistic
effects of group work in educational settings.
The authors propose a structured methodology for implementing collaborative learning in IT edu-
cation, particularly in the context of team sports programming. The methodology includes iterative
processes such as team formation, role definition, training, and evaluation. Specific collaborative tech-
niques like pair programming, joint code development, code review, retrospectives, and code sessions
are recommended to enhance teamwork and problem-solving skills.
(a) (b)
Figure 10: Word cloud of keywords characterizing the concept of cooperative learning (a) and collaborative
learning (b) [60].
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The study includes a survey of university lecturers involved in IT education. The survey results,
analyzed using system analysis, Natural Language Processing (NLP), and statistical methods, reveal that
while cooperative learning is more commonly used, collaborative learning has a significant positive
impact on students’ teamwork skills, problem-solving abilities, and emotional and cognitive interactions
within groups.
The paper aligns collaborative learning with the broader goals of sustainable development in educa-
tion, particularly SDG 4, which aims to ensure inclusive and equitable quality education. By fostering
teamwork, critical thinking, and problem-solving skills, collaborative learning prepares students to
address global challenges and contribute to a sustainable future.
The findings suggest that collaborative learning can be particularly effective in preparing students
for team-based activities such as sports programming. The iterative process of team formation and
role definition, combined with collaborative techniques, can lead to better performance and a deeper
understanding of complex tasks.
The paper “Harnessing online services for creating augmented reality enhanced comics in primary
education” by Bodnenko, Lokaziuk, Poryadchenko, and Proshkin [61] explores the use of cloud services
to create augmented reality (AR)-enhanced comics in primary education. It highlights the didactic
potential of comics as an educational tool and provides a comparative analysis of modern cloud services,
programs, and applications for comic creation. The study also examines the functionality of AR programs
like Vuforia, EasyAR, and ARCore. Through a survey of teachers, the most popular cloud services
(Pixton, Marvel HD, Comica) are identified, and algorithms for developing AR-enhanced comics are
presented. The paper concludes with the development of educational and methodological support for
teachers and students, aiming to improve their readiness to use these technologies in primary education.
The paper “Methodological foundations of teaching the basics of artificial intelligence to lyceum
students” by Tarasova and Doroshko [62] makes a significant contribution to the field of AI education by
developing and validating a comprehensive methodological framework for teaching artificial intelligence
concepts to lyceum (upper secondary) students in Ukraine. The research addresses a critical gap in
current educational practices as countries worldwide grapple with how to effectively integrate AI
education into secondary school curricula.
The authors present a well-structured multi-phase research approach that combines theoretical
analysis with practical implementation. Their review of existing textbooks and educational materials
reveals important gaps in current AI coverage, finding that only 3.5% of content in Ukrainian informatics
textbooks addresses AI-related topics. This quantitative analysis is particularly valuable as it provides
concrete evidence of the need for more comprehensive AI education resources.
A key strength of the paper is its development and evaluation of an innovative three-part web quest
complex. This educational tool demonstrates how theoretical concepts can be effectively translated into
engaging, interactive learning experiences. The pilot study with 20 lyceum students yielded promising
results, with 85% reporting increased engagement and 90% showing improved understanding of AI
concepts. These findings provide important validation of the proposed teaching methodology.
The methodological framework presented is notably comprehensive, addressing four crucial com-
ponents: core AI concepts and skills, teaching approaches, curriculum design guidelines, and teacher
professional development recommendations. This holistic approach recognizes that successful AI
education requires not just well-designed content, but also appropriate pedagogical strategies and
teacher support systems.
The paper’s emphasis on incorporating real-world applications and ethical considerations into AI
education is particularly timely and relevant. This approach helps prepare students not just technically,
but also to think critically about the societal implications of AI technologies.
The paper “The use of ICT by teachers for the development of students’ critical thinking in the context
of sustainable development in Ukraine” by Ovcharuk, Marienko, Hrytsenchyk, Kravchyna, and Malytska
[63] provides crucial insights into how Ukrainian educators are adapting to wartime conditions through
the use of ICT to develop students’ critical thinking skills. The research is particularly valuable as it
documents the challenges and innovations in education during an unprecedented period of disruption,
with implications for understanding educational resilience in crisis situations.
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Figure 11: Example of a comic created using the Pixton online service [61].
The authors present compelling data on the current state of Ukrainian education, noting that only
about one-third of schools operate fully in-person, while the remainder must rely on remote or blended
learning approaches. Their nationwide survey of teachers’ ICT usage reveals important patterns in
tool adoption, with Viber (78.4%), Zoom (65.4%), and Google Apps for Education (53.1%) emerging as
the most widely used platforms. The significant increase in Google Apps adoption from 20.2% in the
previous year suggests rapid digital transformation in response to crisis conditions.
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Figure 12: Relationships and interaction of teachers and students in the process of developing critical thinking
with the use of digital tools [63].
The research makes a valuable contribution by linking ICT usage to both critical thinking development
and sustainable development goals. The authors effectively demonstrate how digital tools can support
the twin objectives of maintaining educational continuity during crisis conditions while fostering
essential 21st-century skills. Their framework connecting teacher actions, ICT methods, and student
outcomes provides a practical model for educators working in challenging circumstances.
A particular strength of the paper is its attention to teacher professional development needs. The
finding that 79.2% of teachers rely on administrative emails for information about ICT training, while
only 54% receive such information from professional development institutions, highlights important
gaps in teacher support systems that need to be addressed.
The authors’ recommendations for improving ICT integration and critical thinking instruction are
well-grounded in their empirical findings. Their call for more systematic teacher training, parent
engagement, and psychological support reflects a holistic understanding of the challenges facing
education systems in crisis situations.
While the paper effectively documents current practices and challenges, it could benefit from more
detailed discussion of successful pedagogical strategies for developing critical thinking through ICT.
Additionally, greater attention to the specific needs of displaced students and teachers would strengthen
its practical applications.
The paper “Blended learning: definition, concept and relevance to education for sustainability”
by Mintii [64] makes a significant contribution to the field of educational technology by providing
a comprehensive analysis of blended learning terminology and conceptualization, with particular
attention to its implementation in the Ukrainian educational context. The paper’s thorough examination
of terminology across multiple European languages and educational systems offers valuable insights
for standardizing educational terminology in the context of European integration.
The author’s methodology is particularly noteworthy, employing a systematic analysis of both
academic literature and practical implementations across different European countries. The detailed
comparison of terminology usage, visualized through geographical mapping, provides a clear and
convincing argument for the adoption of “kombinovane navchannia” (combined learning) as the most
appropriate Ukrainian term for blended learning.
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Figure 13: Definition of “blended learning” on the European map: countries that use the word “combining” are
marked with a green marker, “mixing” with a red marker, “mixing” and “combining” with a blue marker [64].
A key strength of the paper is its thorough examination of the conceptual foundations of blended
learning. The author moves beyond simple definitions to explore the complex interplay between face-
to-face and online learning modalities, formal and informal educational approaches, and the role of
intelligent technologies in creating adaptive learning environments. The resulting definition of blended
learning as a “planned, pedagogically balanced, adaptive combination” represents a sophisticated
understanding of the concept that goes beyond mere technological integration.
The paper makes a particularly valuable contribution by linking blended learning to sustainable
development goals and European digital education initiatives. This connection demonstrates how
blended learning can serve as a strategic tool for achieving broader educational and social objectives,
particularly in the context of Ukraine’s European integration processes.
The analysis of intelligent technologies’ role in blended learning environments is forward-thinking,
highlighting how machine learning, learning analytics, and AI-driven systems can enhance personaliza-
tion and effectiveness. This aspect of the research is especially relevant given the rapid advancement of
AI technologies in education.
While the paper’s comprehensive literature review and linguistic analysis are impressive, future
research could benefit from more empirical evidence about the effectiveness of different blended learning
approaches in the Ukrainian context. The author acknowledges this in the proposed future research
dimensions, which appropriately encompass philosophical, psychophysiological, sociological, and
technological aspects.
The paper “Social media as a tool for career guidance in higher education” by Tkachuk, Yechkalo,
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Bielikova, Kolomoiets, Zinchenko, and Semerikov [65] makes a contribution to understanding how
social media can be effectively leveraged for career guidance in higher education. The research is
particularly valuable for its systematic methodology and evidence-based approach to integrating digital
platforms into career counseling practices.
A key strength of the paper is its thorough theoretical framework that builds upon Social Career
Cognitive Theory, demonstrating how digital interactions influence career choices and professional
identity formation. The authors effectively illustrate this through well-designed figures that map out
implementation strategies and theoretical relationships.
The methodology section is notably robust, presenting a clear three-tiered system of engagement,
monitoring, and evaluation. The comparative analysis of different social media platforms (LinkedIn,
Instagram, Facebook, and Twitter (X)) provides actionative insights for practitioners, with LinkedIn
showing superior career outcomes (68%) despite Instagram’s higher student adoption rate (89%).
The experimental study is particularly compelling, using a controlled design to test specific ped-
agogical conditions. The results show meaningful improvements across all measured criteria in the
experimental group, with increases ranging from 9% to 18.2%. This empirical validation strengthens
the paper’s theoretical propositions and provides concrete evidence for the effectiveness of social
media-based career guidance when properly implemented.
The authors’ visualization of performance trends throughout the academic year effectively demon-
strates the progressive impact of their methodology, with student engagement increasing from 65% to
88% and career progress improving from 58% to 82%. These metrics provide valuable benchmarks for
other institutions implementing similar programs.
3.5. AR/VR applications and case studies
The paper “Leveraging augmented reality in a mobile application for effective advertising of educational
services: an efficiency analysis” by Marchuk, Levkivskyi, Graf, Marchuk, and Panarina [66] makes
a contribution to understanding the practical application of augmented reality technology in higher
education marketing and recruitment. The research provides both technical implementation details and
empirical evidence of AR’s effectiveness in engaging prospective students.
A key strength of the paper is its comprehensive documentation of the technical development process
using Unity and Vuforia. The authors clearly outline the creation of 3D models, system architecture,
and interface design, providing a practical blueprint for other institutions interested in implementing
similar solutions. The inclusion of detailed diagrams and screenshots enhances the reproducibility of
their approach.
The empirical analysis is particularly noteworthy, examining data from 1,086 applicants across
multiple dimensions. The findings reveal significant engagement with the AR application, with 58% of
applicants utilizing the technology. The detailed breakdown by specialty provides valuable insights
into the varying effectiveness of AR across different programs, with Software Engineering showing
particularly strong results (60% of applications).
The statistical analysis is thorough and well-presented, using appropriate descriptive statistics
and exploratory analysis techniques. The visualization of results through multiple figures effectively
communicates key findings about enrollment patterns, advertising exposure, and competitive scores.
The analysis of gender differences in application patterns adds an important dimension to understanding
the technology’s impact across different demographic groups.
The authors effectively highlight both successes and limitations of their implementation. For in-
stance, they note that while Computer Science had more enrollments than AR views, this was due to
geographical limitations in the application’s availability rather than a failure of the technology itself.
This kind of critical analysis strengthens the paper’s credibility.
One minor limitation is that while the paper effectively demonstrates correlation between AR usage
and application rates, it could have provided more discussion of causation versus correlation in the
relationship between AR exposure and enrollment decisions. Additionally, a more detailed examina-
tion of the cost-benefit aspects of implementing such technology would be valuable for institutions
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Platform selection and setup
Create dedicated career guid- Establish posting schedule: 3x
Develop content calendar
ance accounts on LinkedIn, weekly on Instagram/LinkedIn,
aligned with academic timeline
Instagram, and YouTube 2x monthly on YouTube
Content development
Career path
showcases Day-in-the-life Infographics on Live Q&A sessions Student success
through industry videos of various required qualifi- with career stories and
professional professions cations and skills counselors testimonials
interviews
Engagement strategy
Host weekly live Create topic- Peer mentor- Conduct
Share industry
sessions addressing specific hashtags ing groups polls/surveys
news and trends
career queries for easy navigation through LinkedIn to gather stu-
relevant to student
dent interests
Monitoring and analytics
Monitor post per- Collect feedback
Track engagement Analyze most engag-
formance and stu- through comments
metrics across platforms ing content types
dent interaction and direct messages
Personalization and support
Provide one-on-one guidance Create targeted content Connect students
through direct messages based on student interests with industry mentors
Evaluation
Monthly assess- Quarterly review Student feed- Track career Measure student
ment of engage- of content back surveys placement rates satisfaction levels
ment metrics effectiveness
Figure 14: Implementation steps of methodology of using social media [65].
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Figure 15: The result of pressing the “I” button in the mobile application [66].
considering similar initiatives.
The paper “Experience in developing and implementing virtual tours using 360° video technology
in the educational environment” by Pushkar, Bobarchuk, Denysenko, and Halchenko [67] makes a
contribution to understanding both the technical and pedagogical aspects of implementing 360° video
technology in higher education. The research is particularly valuable for its practical demonstration of
how immersive technologies can enhance educational experiences while remaining more accessible
than full VR/AR solutions.
A key strength of the paper is its comprehensive analysis of 360° video technology in comparison to
other immersive technologies. The authors effectively demonstrate through clear comparisons how 360°
video offers a balanced approach between immersion and accessibility, making it particularly suitable
for educational applications. Their detailed comparison table of AR, VR, and 360° video technologies
provides valuable insights for institutions considering immersive technology adoption.
The paper’s methodology section is exemplary, providing a detailed, step-by-step framework for
implementing 360° video projects in educational settings. The five-phase implementation process
outlined in figure 16 offers a practical blueprint that other institutions can follow. The authors’ attention
to technical details, from equipment selection to post-processing requirements, makes this framework
particularly valuable for practical implementation.
The discussion of pedagogical applications is thorough and well-reasoned, particularly in the context
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Preparatory phase
Basic training Theoretical 360° introduction Practical 360° introduction
Task setting
Goal definition Work planning Equipment & software selection
Project implementation: content creation
Reference Study Concept definition Shooting preparation Material recording
Project implementation: processing and assembly
Video processing Audio processing Video-audio sync Metadata addition
Publication and distribution
Publication file
preparation Platform selection Publication Distribution
Figure 16: Stages of implementation of an educational project to create a 360° video [67].
of publishing and printing education. The authors identify multiple specific applications, from produc-
tion process demonstration to risk-free training scenarios, demonstrating a deep understanding of both
the technology’s capabilities and educational needs.
The case study of creating a university virtual tour provides concrete evidence of the methodology’s
effectiveness. The detailed documentation of the technical process, including frame sketching, video
stitching, and post-processing, offers valuable practical guidance for similar projects. The inclusion of
visual examples strengthens the paper’s instructional value.
The paper “Theoretical and practical aspects of using artificial intelligence technologies in the field
of sound design” by Bobarchuk, Halchenko, Hnidenko, and Zavadetskyi [68] makes a contribution
to understanding both the theoretical foundations and practical applications of AI in sound design.
The research successfully bridges the gap between technical capabilities and creative implementation,
providing insights relevant to both researchers and practitioners.
A key strength of the paper is its comprehensive analysis of the evolution of sound design, from
traditional methods to modern AI-driven approaches. The authors effectively demonstrate how AI
technologies are transforming the field while acknowledging both their potential and limitations. The
clear delineation between classical sound design principles and emerging AI capabilities provides
valuable context for understanding this transformation.
The practical implementation section is particularly noteworthy, presenting a concrete case study of
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Dark ambient;
Low frequency noise;
Wind noise;
Melancholic classical
instruments.
Humming of wires Cracking of branches
Figure 17: Determining sounds for the scene [68].
creating sound design for visual novels using various AI tools. The step-by-step documentation of using
tools like Suno AI, AudioGen, and Synplant2 provides valuable insights into the practical challenges
and opportunities of AI-assisted sound design. The authors’ candid discussion of both successes and
limitations in their implementation adds credibility to their findings.
The paper makes a unique contribution by integrating multiple AI tools in a complementary work-
flow, demonstrating how different technologies can be combined to overcome individual limitations.
Their systematic approach to breaking down scenes into component sounds and matching them with
appropriate AI tools provides a useful framework for similar projects.
3.6. Best practices and lessons learned
The paper “Implementing MLOps practices for effective machine learning model deployment: A meta
synthesis” by Hanchuk and Semerikov [69] makes a significant contribution to the field of machine
learning operations by systematically analyzing and synthesizing findings from multiple systematic
reviews on MLOps practices. The paper’s methodological approach, following Chrastina’s framework,
provides a rigorous foundation for integrating insights across the literature.
The paper’s key strength lies in its systematic organization and thorough examination of MLOps
across multiple dimensions – from theoretical foundations to practical implementation challenges. The
authors effectively synthesize findings about MLOps workflows, tools, frameworks, and deployment
methods, providing a holistic view of the field that will be valuable to both researchers and practitioners.
Particularly noteworthy is the paper’s treatment of MLOps maturity models and assessment frame-
works. The authors identify and analyze several approaches to measuring MLOps maturity, from
Amershi’s adaptation of the Capability Maturity Model to Lwakatare’s five-stage development frame-
work. This analysis provides organizations with concrete benchmarks for assessing and improving
their MLOps practices.
The clear delineation of roles and responsibilities in ML model operationalization is another valuable
contribution. The paper effectively maps out the interactions between data scientists, engineers, domain
experts, and management, highlighting the cross-functional nature of successful MLOps implementation.
The authors’ analysis of challenges and open issues is particularly insightful, identifying both technical
hurdles (like managing model lifecycles and ensuring scalability) and organizational challenges (such
as skill gaps and communication issues). The discussion of future trends and opportunities provides
valuable direction for both research and industry development.
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4. Conclusion
The proceedings of the 7th International Workshop on Augmented Reality in Education (AREdu 2024)
showcase the rapid evolution and maturation of AR and AI technologies in educational contexts. The
22 peer-reviewed papers demonstrate significant advances in combining immersive technologies with
artificial intelligence to create more effective, engaging, and personalized learning experiences. From
theoretical frameworks and methodological innovations to practical applications and empirical studies,
the contributions reflect the diversity and dynamism of this emerging field.
The papers highlight several key trends and developments:
• The growing sophistication of immersive learning environments that leverage both AR and AI
capabilities
• The emergence of augmented intelligence as a framework for enhancing educational processes
• The increasing importance of learning analytics and educational data mining in understanding
and optimizing learning experiences
• The development of innovative approaches to integrate these technologies into various educational
contexts
• The valuable insights gained from real-world applications and case studies
These proceedings also underscore the resilience and innovation of the educational technology
community, particularly in Ukraine, where researchers and practitioners continue to advance the field
despite ongoing challenges. The Academy of Cognitive and Natural Sciences (https://acnsci.org/),
Kryvyi Rih State Pedagogical University, and Kryvyi Rih National University’s successful hosting
of AREdu 2024 demonstrates the community’s commitment to maintaining international academic
collaboration and advancing educational technology research.
Looking ahead, the field faces both opportunities and challenges. While the integration of AR and AI
technologies shows great promise for transforming education, questions remain about implementation,
scalability, and equity of access. Future research will need to address these challenges while continuing to
explore the potential of these technologies to enhance teaching and learning across different educational
contexts and disciplines.
The workshop’s success in fostering dialogue and collaboration among researchers, practitioners, and
technology developers bodes well for the future of AR and AI in education. We look forward to seeing
how the ideas and insights shared at AREdu 2024 will influence the next generation of educational
technologies and pedagogical approaches.
We had excellent presentations and fruitful discussions that broadened our professional horizons,
and we trust that all participants derive immense satisfaction from this workshop. We look forward to
the day when we will be able to meet again in person under more tranquil and peaceful circumstances.
Acknowledgments: We extend our sincere gratitude to the authors who submitted their papers and the delegates for their
active participation and unwavering interest in our workshops, which have provided a platform for the exchange of ideas and
innovation. Our heartfelt appreciation goes to the program committee members for their continuous guidance and to the peer
reviewers, whose diligent efforts have substantially enhanced the quality of the papers by providing constructive criticisms,
improvements, and corrections. We acknowledge and thank the authors for their significant contributions to the workshop’s
success.
Furthermore, we express our most profound appreciation to the CEUR-WS.org team (https://ceur-ws.org/), the only sponsor
of the AREdu workshop series since 2018.
Declaration on Generative AI: During the preparation of this work, the authors used Claude 3 Opus in order to: Drafting
content, Abstract drafting, Peer review simulation. After using this service, the authors reviewed and edited the content as
needed and takes full responsibility for the publication’s content.
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aspects of using artificial intelligence technologies in the field of sound design, CEUR Workshop
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model deployment: A meta synthesis, CEUR Workshop Proceedings (2025) 329–337.
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