=Paper=
{{Paper
|id=Vol-3918/paper020
|storemode=property
|title=Bridging minds and machines: AI’s role in enhancing mental health and productivity amidst Ukraine’s challenges
|pdfUrl=https://ceur-ws.org/Vol-3918/paper020.pdf
|volume=Vol-3918
|authors=Kateryna M. Bondar,Olha S. Bilozir,Olena P. Shestopalova,Vita A. Hamaniuk
|dblpUrl=https://dblp.org/rec/conf/aredu/BondarBSH24
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==Bridging minds and machines: AI’s role in enhancing mental health and productivity amidst Ukraine’s challenges==
https://ceur-ws.org/Vol-3918/paper020.pdf
Kateryna M. Bondar et al. CEUR Workshop Proceedings 43–59
Bridging minds and machines: AI’s role in enhancing
mental health and productivity amidst Ukraine’s
challenges
Kateryna M. Bondar1,2 , Olha S. Bilozir2 , Olena P. Shestopalova2 and Vita A. Hamaniuk2,3
1
International Psychoanalytic University, 1 Stromstraße, Berlin, 10555, Germany
2
Kryvyi Rih State Pedagogical University, 54 Universytetskyi Ave., Kryvyi Rih, 50086, Ukraine
3
Academy of Cognitive and Natural Sciences, 54 Universytetskyi Ave., Kryvyi Rih, 50086, Ukraine
Abstract
The article explores the convergence of human intelligence with artificial intelligence, emphasizing its potential
to enhance education in the realm of mental health. This synergy is especially crucial in Ukraine, particularly
within its educational institutions, following the pandemic and amid wartime conditions. The article delves into
the concepts of “digital mental health” and “e-mental health,” shedding light on the significance of “mental health
technology” and “digital mental health.” It also examines the standards for university courses in mental health
technologies and introduces a variety of mental health apps, encompassing apps, wearables, platforms, data
analytics resources, and other tools. The article underscores the importance of integrating artificial intelligence
into both the education and economic sectors. It provides a comprehensive 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 detailed. Moreover, the article investigates the impact
of transactional distance on the learning experience of students pursuing mental health technology courses in an
online format at Kryvyi Rih State Pedagogical University during the 2023-2024 academic year. Indicators of the
transaction distance of the sample are researched and presented in detail. The influence of evaluation, satisfaction
and their interaction on the level of transactional distance is analyzed too. Applied logical and statistical tests
were used, in particular, using the Pearson test for correlation analysis. 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.
Keywords
human-AI synergy, digital mental health, e-mental health, mental health technology, artificial intelligence in
education, transactional distance, online learning, higher education, mental health apps, wearables, data analytics,
psychological education, university curriculum, pedagogical innovations, Pearson correlation analysis, wartime
education, Ukraine, sustainable well-being, mental health pedagogy, education technology, digital transformation
1. Background context
The fusion of human intellect and artificial intelligence algorithms has ushered in a realm of unsurpassed
opportunities for advancing mental health technologies and treatments in an age that is defined by
rapid technological progress and data-driven decision-making processes in psychotherapy. This article
delves into the vast potential of collaborative synergy between humans and AI that focuses on two key
realms: enhancing crisis online counselling education of psychologists in war conditions in Ukraine
and data-driven decision making within HEI.
Amidst the backdrop of the Ukrainian conflict from 2022 to 2024, numerous enterprises and institutions
operating within Ukraine have been confronted with significant challenges. They not only grapple with
adapting to volatile work conditions and employee needs but also contend with the enduring effects
AREdu 2024: 7th International Workshop on Augmented Reality in Education, May 14, 2024, Kryvyi Rih, Ukraine
" katerynabondarr@gmail.com (K. M. Bondar); olechkabiloz@gmail.com (O. S. Bilozir); e.shestopalova@kdpu.edu.ua
(O. P. Shestopalova); vitana65@gmail.com (V. A. Hamaniuk)
~ https://kdpu.edu.ua/personal/kmbondar.html (K. M. Bondar); https://kdpu.edu.ua/personal/bilozir.html (O. S. Bilozir);
https://kdpu.edu.ua/personal/opshestopalova.html (O. P. Shestopalova); https://kdpu.edu.ua/personal/vagamanuk.html
(V. A. Hamaniuk)
0000-0002-2441-4203 (K. M. Bondar); 0000-0002-0655-865X (O. S. Bilozir); 0000-0002-3401-1790 (O. P. Shestopalova);
0000-0002-3522-7673 (V. A. Hamaniuk)
© 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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Kateryna M. Bondar et al. CEUR Workshop Proceedings 43–59
of the COVID-19 pandemic [1, 2]. The once-prosperous Ukrainian economy of 2021, a driving force
behind the nation’s growth, has been particularly susceptible to disruptions caused by rolling blackouts,
shelling incidents, and labor displacement.
Recognizing the substantial impact of professionals’ mental well-being on overall business perfor-
mance, there arises a critical need for future organizational psychologists to possess skills in monitoring
mental health, resilience, and relevant organizational metrics. Addressing these challenges requires an
urgent optimization of psychology curricula to align with the demands imposed by the Ukrainian war
context.
To tackle these pressing issues, artificial intelligence capabilities come into play. By leveraging
AI-based tools, we can overcome hurdles related to tracking and interpreting vital mental health
indicators. Moreover, equipping HR and psychologists with practical skills in utilizing psychometric
data is essential. This innovative approach not only enhances our understanding of mental health
outcomes but also empowers enterprises and institutions to make well-informed decisions crucial for
supporting employee well-being and overall efficiency amidst wartime conditions in Ukraine.
2. Literature review
2.1. Mental health technologies for online crisis counselling
Mental health technology is a multi-faceted field that represents the convergence of technology and
mental well-being [3]. This broad vision includes various digital innovations carefully designed to
support and improve mental health care and overall psychological well-being. At its core, mental health
technology is a catchall term that covers a wide range of digital tools, apps and devices, where each of
them is strategically designed to serve different aspects of mental health care [3]. These innovations
span the entire mental health spectrum [4], from the critical areas of prevention and early intervention
to treatment and ongoing support (figure 1) [5].
Figure 1: Mental health spectrum of technologies.
Firstly, prevention is at the fore of mental health technology, offering proactive tools and plat-
forms designed to prevent the occurrence of mental health problems [6]. For example, these may
be stress-reduction apps, mood-tracking software, and digitally attentive programs that help people
build resilience and maintain mental balance. All this tools should supported by statistical analisis and
recomendations from psychologist.
Secondly, recognizing the importance of early detection and interference, mental health technologies
offer screening and assessment tools that can identify potential mental health problems in their initial
stages [7]. These tools enable intervention and access to appropriate resources of support promptly,
mitigating the severity of conditions and improving prognosis.
In addition, some researches state that AI can be used as an alternative data source in scientific
research, in particular to collect synthesized prior knowledge on the topic under study. Through a
research process to study the impact of the global health crisis caused by COVID-19 on education, based
on the joint analysis of human intelligence and artificial intelligence [8], it was demonstrated that the
use of such technologies can take the process of scientific research a step forward and accelerate the
scale and speed of knowledge production for the benefit of humanity [9].
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Kateryna M. Bondar et al. CEUR Workshop Proceedings 43–59
Thirdly, digital mental health solutions offer a variety of options in the treatment space [10]. There
are evidence-based digital therapies such as cognitive behavioral therapy (CBT) and dialectical behavior
therapy (DBT) delivered through mobile apps and online platforms [11] as well as individual and group
therapy zoom-meetings, chat-bots. These solutions allow people to participate actively in treatment
and recovery.
Further, after the initial stages of treatment, mental health technology continues to play a decisive
role in supporting well-being [12]. Supportive communities and peer networks promote a sense of
belonging and reduce feelings of isolation. Wearable devices and monitoring tools can track biometric
data, helping individuals and their healthcare teams path progress and make data-driven adjustments
to treatment plans [13].
Moreover, the use of big data and advanced analytics is a growing aspect of mental health technology
[14]. It becomes possible to identify trends, predict mental health crises and adapt measures on a larger
scale with the help of combining and analyzing large data sets. This data-driven approach has the
potential to revolutionize mental health care.
Mental health technology also supports research by providing a platform for studying mental health
patterns and treatment outcomes [15]. It is a valuable educational resource for both mental health
professionals and the general public, offering ideas, guidance, and training materials.
Although AI offers benefits in academic environments and mental health research, it has evoked a
mixture of awe and apprehension among educators and researchers, prompting efforts to understand and
potentially mitigate its impact. There are some studies that seek to illuminate the current perceptions
of AI in academic literature, exploring its implementations and the perceived risks it may pose to the
educational landscape. Disputes about the ethics of using AI have been going on for several years now,
and even legal aspects are being discussed. However, its influence continues to grow, with researchers
studying both the positive and negative aspects of its use for educational and research purposes [16].
Last but not least, the human brain can acquire knowledge, generate new ideas and make decisions
based on internal data and machines, which is known as machine learning [17, 18]. At the same time,
neural networks are a tool for their implementation and imitate human skills [19, 20]. Despite the
ethical and social impact of rapidly developing AI, it paves the way for more research in a multilingual
society [21].
2.2. Standards for university courses of crisis online counselling with
implementation mental health technologies
Artificial intelligence is rapidly transforming many fields, including psychology. AI has the potential to
improve psychological research, practice, and education. For example, AI can be used to develop new
diagnostic tools, create personalized treatment plans, and improve the delivery of mental health services.
Notwithstanding, for psychologists to fully embrace AI, they need to be trained in the technology. A
recent study found that psychology students are interested in AI, but they need more training in order
to use it effectively. The study also found that students are concerned about the ethical implications of
AI [22].
In addition to this, in study by Gado et al. [22] developed and tested a new model to explain what
factors are relevant to predict psychology students’ attitude towards AI and their intention to use it. The
study found that perceived usefulness, perceived social norm, and attitude towards AI were significant
predictors of intention to use AI. Perceived knowledge of AI was also a significant predictor of intention
to use AI, especially for female participants. The study suggests that psychology training programs
should focus on fostering a positive attitude towards AI among students by emphasizing its usefulness
and ease of use in psychologists’ work contexts. Additionally, programs should help students to develop
the knowledge and skills they need to use AI effectively.
Another example is the article “Training the next generation of counselling psychologists in the
practice of telepsychology” discusses the need for training programs to prepare counseling psychologists
for the future of service delivery in psychology, which increasingly includes the use of telepsychology.
The authors note that there are few options available for trainees seeking to acquire experience in
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Kateryna M. Bondar et al. CEUR Workshop Proceedings 43–59
telepsychology and that guidelines for training programs in this area are virtually non-existent [23].
However, in a study organized by Perle et al. [24], researchers surveyed 782 psychological and medical
professionals about their interest in videoconferencing telehealth training and mental health telehealth
referral. Results showed that both groups were interested in telehealth training, with psychological
professionals more likely to be interested. The most desired training topics were efficacy data, ethical
issues, and legal concerns.
Developing comprehensive university standards for online crisis counselling is vital to preparing
students for the dynamic demands of this field. Key components and considerations include curriculum
design:
1) core courses: cover fundamental topics such as technology integration, ethics, and cutting-edge
innovations (figure 2);
2) elective courses: allow specialization in areas like teletherapy, digital interventions, data analytics,
or app development.
Figure 2: Fundamental topics of mental health technologies for online crisis counselling university courses.
That is why, the online crisis counselling course should adopt an interdisciplinary approach, encour-
aging cooperation between psychology, computer science, data science, and public health departments
to promote a comprehensive view. An integral component of the curriculum focused on ethical founda-
tions, highlighting ethical principles such as safeguarding data privacy, ensuring informed voluntary
consent, and the responsible usage of AI in diagnosis and treatment [13].
Additionally, the main attention in course development revolves around development proficiency
in the evaluation and application of mental health technologies [25]. This includes the acquisition of
required technological skills for evaluation and effective use of an array of mental health apps, including
apps, wearables, telehealth platforms, and data analytics resources (figure 2).
First of all, the course should include the specifics of working with mental health apps [15] and digital
therapeutics (DTx) [26], virtual reality (VR) Therapy [11] as tools for supporting clients (figure 3). A lot
of mobile applications have been created to assist individuals in overseeing their mental well-being.
These applications include functionalities like monitoring emotional states, meditation and mindfulness
practices, use of cognitive-behavioural therapy (CBT) methods and fostering peer connections for
support. It is quite important for psychology students to understand how to use these tools to support
community mental health.
VR technology is increasingly used in exposure therapy for PTSD and phobias. It allows individuals
to confront and manage their fears in a controlled and immersive environment. As described in the
research by Usmani et al. [11], the future of mental health within the metaverse lies in the potential use
of immersive digital realms, referred to as the metaverse, for solving mental health issues.
The course should also cover how to use telehealth and teletherapy to support clients specifically.
Telehealth and teletherapy platforms have revolutionized the provision of therapeutic and consulting
services, enabling individuals to access these vital services remotely through video calls, phone calls,
or text messaging. For instance, Miu et al. [27] investigates the impact of the COVID-19 pandemic
on psychotherapy, with a specific focus on individuals who have serious mental illness (SMI). These
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Kateryna M. Bondar et al. CEUR Workshop Proceedings 43–59
Figure 3: Mental health apps.
findings shed light on the viability and effectiveness of telehealth for individuals with serious mental
illness amidst the challenges posed by COVID-19.
It is equally important that the course included the specifics of working with online screening and
assessment tools [7, 11], wearable devices [28] as tools for client self-diagnosis. Certain wearable fitness
trackers and smartwatches are already equipped with functions to monitor stress levels, sleep patterns,
and physical activity [28].
Last but not least, data analytics and AI topics should be included in university courses. Advanced an-
alytics and machine learning can help healthcare providers and researchers identify patterns and trends
in mental health data. This can lead to more personalized treatment plans and a better understanding
of mental health disorders.
While mental health tech is a valuable resource, it should complement, but not replace, professional
mental health care. It can provide additional tools for managing mental well-being, but seeking guidance
and treatment from trained professionals remains essential for severe or persistent issues. Traditionally,
the online crisis counselling course has been perceived as challenging by students due to its reliance
on statistical analysis tools and a complex process involving manual decoding of raw survey data,
organizing the data, and defining variables using SPSS statistical packages or R coding. These tasks
require additional software knowledge and programming skills, which psychology students often find
challenging extracurricular tasks.
2.3. Student engagement and satisfaction of online learning
Student engagement and satisfaction are essential to successful online learning. The Zhang Scale of
Transactional Distance (RSTD) is a valuable tool for educators to measure and address transactional
distance, a key factor influencing student engagement and satisfaction in online online crisis counselling
course [29]. Transactional distance refers to the psychological-pedagogical space that separates students
from their peers, instructors, course content, and learning interface. This can be caused by various
factors, such as:
1. Online students may feel isolated from their peers and professors, leading to decreased engagement
and satisfaction [30].
2. Poorly designed or uninteresting online courses can lead to longer distances between transactions.
Significant development and widespread adoption of artificial intelligence and no-code software
in early 2023 are driving demands for the adoption of AI technologies in course design. Therefore,
offering a module that integrates artificial intelligence into online crisis counselling course
provides an exceptional opportunity to explore the sought-after convergence of technology and
mental well-being. This interdisciplinary approach not only reflects the evolving landscape of
mental health support but also gives students additional time to make informed decisions about
the data [29, 30].
3. Students may experience technical difficulties accessing course content or using statistical pack-
ages purchased by the university, which may also increase the distance between transactions.
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However, the developed public platforms are widely available and do not require a presence at
the university [31].
RSTD quantifies transaction distance along four key dimensions [32]:
1. transactional distance between students (TDSS) (measures the perceived psychological and
educational gap between students in an online learning environment);
2. transactional distance between student and instructor (TDST) (measures the perceived separation
and interaction dynamics between students and their instructors in an online course);
3. transactional distance between learners and content (TDSC) (measures the perceived distance or
cognitive space between learners and course content or materials);
4. transactional distance between learner and interface (TDSI) (measures the perceived distance
between learners and the technology interface or platform used for learning).
In our case, we use RSTD to identify areas for improvement in an online course to evaluate the
effectiveness of instructional interventions aimed at reducing transaction distance and increasing
student engagement and satisfaction. Given the aforementioned prerequisites, the research inquiry
will encompass the following: evaluate the transactional distance encountered by students and their
satisfaction levels while utilizing an AI in online crisis counselling course and Data Analytics?
3. Methodology
This study aimed to assess the academic performance and satisfaction of psychology students in their
study of the module “Mental Health Technology and AI” in the pilot course “Crisis Online Counselling”,
explicitly emphasizing the integration of artificial intelligence and machine learning. The research took
place in an online format during the 2023-2024 academic year against the backdrop of the ongoing
conflict in Ukraine. The research employed a mixed-methods approach to investigate the transactional
distance experienced by students enrolled in the course.
3.1. Sampling and procedures
In total, 60 students (table 1) participated in the course. The participants included 80.5% females and
19.5% males. There was no significant difference in the distribution of participants into groups based on
gender (𝜒2 (2) = .444, 𝑝 = .79). The average age of participants was 𝑀 = 20.3 years (𝑆𝐷 = 2.4). Most
participants were preparing for careers as practical psychologists in educational institutions (53.1%),
while the rest aimed to become private (46.9%) psychologists. These two conditions did not significantly
differ regarding participants’ educational paths (𝜒2 (1) = 1.793, 𝑝 = .18).
Table 1
Demographics of the present sample (𝑁 = 60, mean age = 20.5; 𝑆𝐷 = 1.07).
Variable Type Frequency
Gender Male 12
Female 48
Age categories 18–24 years old 58
Age categories 25–34 years old 2
Academic status Diploma BA 60
Department Psychology 44
Moodle served as the asynchronous platform, while Zoom facilitated synchronous learning and lab
work presentations. This approach allowed for a comprehensive exploration of the research problem
by combining both quantitative and qualitative data collection and analysis, shedding light on the
dynamics of teaching and learning in the specific context of wartime.
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Figure 4: The research model of the study.
The procedure for our controlled experiment was conducted as part of the pilot module (1 ECTS) of
an “online crisis counselling” course (3 ECTS). The study involved students studying for a BA degree in
psychology at a pedagogical university.
Research on the use of student course evaluations has demonstrated a wide range of applications as
quality indicators of the system, for enhancing the expansion of students’ rights and opportunities, and
as tools for measuring educational quality. Accordingly, this study hypothesized that course evaluation
is associated with quality of content.
Based on previous research and the development of the model of evaluation, the following hypotheses
were formulated, and the proposed research model is illustrated in figure 4.
Here’s a breakdown of the procedure:
1. The entire module lasted a total of 4 weeks and consisted of four course topics. Each course topic,
“Diagnostics of mental health in technology”, “Exploratory Data Analysis (EDA)”, “Setting up a
chatbot model” and “Evaluation”, lasted one week.
• The topic “Diagnostics of mental health in technology” was aimed at studying the analysis of
data on mental health for IT companies; we have selected a list of diagnostic tools to analyze
the mental health, burnout and resilience of university IT stakeholders (“Mental Health
Rating Scale”, “Resilience Rating Scale”; individual and organizational stressors, internal and
external, as well as 5 open-ended questions about energy demands at work and resilience
practices, managerial encouragement, and types of company assistance during war). The
SurveyMonkey platform was used to collect mental health data.
• The topic “Exploratory Data Analysis (EDA)” was the data analysis task used several tools:
ChatGPT or Bard to code Python commands for data preparation (including raw data clean-
ing, data quality assurance, anonymization and protection of confidential information for
ethical reasons). Also, used Dataiku EDA tools for visual data exploration and descriptive
statistics of a sample. The primary goal was to identify patterns, correlations, and potential
anomalies in data on mental health, resilience, and stress levels among wartime IT profes-
sionals. Next, the goal was to find relevant characteristics or variables that could be useful
for analyzing mental health, such as average stress levels, identifying periods of high stress,
or classifying employees based on their mental health status.
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Kateryna M. Bondar et al. CEUR Workshop Proceedings 43–59
• The topic “Setting up a chatbot model” included content and cluster analysis of open questions
(setting up a chatbot model, specifying the context, querying a model with relevant questions,
templates based on text data, on which GPT is trained) and skills in using Dataiku for machine
learning without coding.
• The topic “Evaluation” explained the components of the report and the formulation of
conclusions, problems associated with human verification and validation of models.
2. Presentation of course content: students attended lectures and two practical sessions conducted
by the same teacher for all students.
3.2. Research design and setting
Research into the utilization of student course evaluations has unveiled a diverse array of applications,
serving as barometers of system quality, tools for enhancing student empowerment, and metrics for
assessing educational excellence. Consequently, this study posited an association between course
evaluations and teaching quality.
Drawing upon prior research and the model, the ensuing hypotheses were formulated. The research
model proposed in figure 4 encapsulates these research questions:
• Research question 1. How does the utilization of AI tools (AT) influence the factors contributing
to student satisfaction in online learning (SfS)?
• Research question 2. What is the relationship between student factors of satisfaction in online
learning (SfS) and their achievement of learning goals (SS)?
A web-based email surveys were designed for asynchronous data collection to gather feedback
through the student evaluation of teaching (SET) [33] that includes Zhang’s transactional distance scale
and online survey proposed by the National Agency for Higher Education (Methodology of independent,
external, on-site evaluation of the quality of legal education in Ukraine) (A and B).
1. The instruments evaluated both student satisfaction with the course and the perceived transac-
tional distance between students and their instructor, as well as between students and course
materials. To minimize potential biases, both Zhang’s scale and the student satisfaction question-
naire utilized a Likert scale with five response options, ranging from “completely disagree” to
“completely agree.”
2. Respondents’ perceptions regarding the quality of teaching are examined based on four indicators:
teaching style, student-centered learning, learning resources and support, certification, and
program design. The survey on teaching quality in universities under war conditions, conducted
through computer-based Google Forms, comprised 20 questions and 4 statements regarding
perceived course benefits, associated factors, and participant behavioral characteristics during
the study process. Responses were scored on a 5-point Likert scale from “never true” to “almost
always true,” and each item was analyzed individually to provide specific insights into its content.
3. Following the completion of the course module, students underwent an online knowledge test. To
pass the required knowledge test (Student’s graduates) on the Moodle platform, students initially
needed a minimum of 50% correct answers on the multiple-choice questions.
Data analysis will be analyzed using Jamovi. Descriptive statistics, including correlation analysis using
the Pearson criterion, were used to quantitatively assess the relationship between academic achievement
and the effectiveness of Online crisis counseling training, determining the strength and direction of
this association. Quantitative data from the Zhang scale will undergo descriptive statistical analysis to
uncover patterns and trends in students’ perceptions of transactional distance [34]. Inferential statistical
tests, correlation analysis, specifically employing the Pearson criterion, was used to determine the
relationship between transactional distance scales, and the effectiveness of training quantitatively. This
analysis aimed to establish the strength and direction of the association between these variables. [35].
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4. Data analysis
The overall satisfaction with the course among participants was notably high, with an average rating
of 4.37 and a standard deviation of 0.991, on a scale ranging from 1 to 5 (SS = Student satisfaction
“Overall, I am satisfied with this course”). Particularly, students expressed a strong appreciation for the
interaction facilitated by the AI tools utilized in the classes (SI = Transaction distance between students
and interface, averaging 3.97 with a standard deviation of 0.68) (table 2).
Table 2
Indicators of the transaction distance of the sample (𝑁 = 60).
Scales Mean SE SD
S-C 3.65 0.0884 0.685
S-I 3.97 0.0750 0.581
S-T 4.00 0.1258 0.974
S-S 4.32 0.1176 0.911
LG 3.93 0.1383 1.071
SS 4.37 0.1279 0.991
However, there was a perceived decrease in student ratings concerning the transactional distance
between students and course content, with a mean rating of 3.65 and a standard deviation of 0.68. On
the other hand, students rated their transactional distance with teachers and peers relatively high, with
average values of 4.00 (𝑆𝐷 = 0.974) and 4.32 (𝑆𝐷 = 0.911), respectively (table 2).
Transactional distance is associated with a grade on most dimensions. Post hoc analysis revealed the
most significant differences (table 3).
Based on our empirical analysis, we applied logical and statistical tests, particularly using the Pearson
criterion for correlation analysis. We tested the quantitative relationships between:
1. Assessment of the quality of the knowledge obtained in the online test and the effectiveness of
participation in the course.
2. The total number of timely submitted reports and their correlation with the quality assessment.
We used Pearson’s correlation to illustrate these relationships, recognizing that the correlation
coefficient may not reach a perfect. We also applied non-parametric significance tests to establish
statistical significance due to the limited distribution information of in the data.
Table 3
Indicators of students’ work systematicity on the basis of assignments (𝑁 = 60).
Grade
Assignment title Number𝑎 Correlation𝑏
satisfaction𝑐
The topic “Diagnostics of mental health in technology” 41 – 0.68
The topic “Exploratory Data Analysis (EDA)” 36 0.67 0.56
The topic “Setting up a chatbot model” 49 0,45 0,62
The topic “Evaluation” 56 0,51 0,63
Note: 𝑎 – number of reports that were uploaded in time; 𝑏 – correlation with the grade for quality (online
knowledge test); 𝑐 – correlation with the grade for quality the total number of reports that were uploaded in time.
Based on the provided correlations (table 4), the factors can be ranked from powerful to less powerful
associations and organized into groups :
Group 1: Moderately strong
1. Transactional distance between students and content (S-C) correlates positively with:
• Students’ social presence (SP) (𝑟 = 0.61)
• Learning goals (LG) (𝑟 = 0.43)
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Table 4
Course evaluation correlations (𝑁 = 60).
Scales S-C S-I DP LG SG SP
Transactional distance between student and content (S-C) – 0.31 0.27 0.43 0.39 0.61
Transaction distance between students and interface (S-I) – 0.33 0.38 0.56 0.36
Design of programs (DP) – 0.52 0.44 -0.26
Learning goals (LG) – 0.44 0.45
Student grades (SG) – 0.56
Students’ social presence (SP) –
• Student grades (SG) (𝑟 = 0.39)
• Transactional distance between students and interface (S-I) (𝑟 = 0.31)
• Design of programs (DP) (𝑟 = 0.27)
2. Transactional distance between students and interface (S-I) correlates positively with:
• Student grades (SG) (𝑟 = 0.56)
• Learning goals (LG) (𝑟 = 0.38)
• Design of programs (DP) (𝑟 = 0.33)
Group 2: Moderately strong relationships
4. Learning goals (LG) show positive correlations with:
• Student grades (SG) (𝑟 = 0.45)
• Students’ social presence (SP) (𝑟 = 0.45)
Group 3: Moderately strong with weaker negative relationship
3. Design of programs (DP) correlates positively with:
• Learning goals (LG) (𝑟 = 0.52)
• Student grades (SG) (𝑟 = 0.44)
• but negatively with:
• Students’ social presence (SP) (𝑟 = −0.26)
5. Student grades (SG) display a positive correlation with:
• Students’ social presence (SP) (𝑟 = 0.56)
This organization highlights the strengths of associations between different factors, categorizing
them into groups based on their correlation values. Here are the definitions for each group based on
the revised organization of factors:
Group 1: Strong correlations. This group highlights significant correlations between the transactional
distance between students and content (S-C) and both students’ social presence (SP) and
learning goals (LG). These relationships of factors indicate robust connections, suggesting
that when students feel engaged with course content, they are more likely to be socially
present and focused on achieving learning objectives.
Group 2: Moderate correlations. Comprising moderate correlations, this group underscores the rela-
tionships between Student-Content and various other factors, including student grades (SG),
transactional distance between students and interface (S-I), and design of programs (DP).
While these relationships are not as strong as those in Group 1, they still suggest moderately
strong links between different aspects of the course evaluation.
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Group 3: Moderate correlations with a weaker negative relationship. Characterized by moderate
associations with a weaker negative relationship, this group reveals the complex interplay
between design of programs (DP), learning goals (LG), student grades (SG), and students’
social presence (SP). Despite the presence of negative correlations, the overall associations
within this group are moderate, indicating nuanced relationships among these factors.
This refined organization provides a comprehensive understanding of the varying strengths of
associations among different factors in course evaluations, offering valuable insights for future research
and course improvement initiatives.
5. Discussion
Study results highlight the effectiveness of training and the achievement of favorable educational
outcomes in the context of integrating artificial intelligence and no-code machine learning for mental
health data analysis. Not only does the popularity of discussing these tools ensure that students are
involved in studying the subject, but also in conditions of forced online learning against the backdrop
of war, it creates a reduction in the distance regarding the use of interfaces and software that does not
require the use of programs purchased by the university and stay on campus.
• Research question 1 aimed to investigate the influence of AI tools (AT) on factors
contributing to student satisfaction in online learning (SfS).
The study found that overall satisfaction with the course was high, with an average rating of 4.37
and a standard deviation of 0.991. Notably, students appreciated the interaction facilitated by AI tools,
as indicated by a mean transaction distance between students and interface (SI) of 3.97 with a standard
deviation of 0.68 (table 1). However, there was a perceived decrease in ratings for transactional distance
between students and course content (S-C), with a mean rating of 3.65 and a standard deviation of 0.68.
Conversely, transactional distance with teachers (S-T) and peers (S-S) was rated relatively high, with
average values of 4.00 (𝑆𝐷 = 0.974) and 4.32 (𝑆𝐷 = 0.911), respectively.
In addition, the study highlights the specific satisfaction of students using learning analytics tools
integrated with artificial intelligence. The main objective of the module was to provide psychology
students with fundamental competencies to analyze mental health data and solve problems relevant
to the ongoing conflict in Ukraine. However, it is critical to understand both the general and specific
trends identified in the data set.
Transactional satisfaction distance has also demonstrated a correlation with measures of successful
task completion within a given time frame. According to our previous research [33], students who
had difficulty meeting assignment deadlines tended to perceive greater transactional distance when
interacting with both the course interface and course content, particularly the topic of data ethics.
However, their interaction with peers during group assignments remained minimal.
The student research team’s primary goal was to identify patterns, correlations, and potential
anomalies in a data set related to mental health, resilience, and stress among wartime IT professionals.
Deploying trained models into Dataiku enabled real-time predictive analysis. Notably, among students
classified as absent, a key factor influencing assignment quality was the presence or absence of strong
educational goals. These results are close to the conclusions of research on student motivation [36].
Emphasis during the practicum was on the ethical handling of sensitive mental health data and
adherence to confidentiality protocols. However, open-ended responses indicated that students had
difficulty completing assignments within the time limits and were only able to engage superficially
with ethical considerations. It’s close to the results of research [37], where Moodle was found to be
ineffective and 92.4% of students considered it a time-wasting tool. Notably, in our research as well as
research by Best there were no strong contrary opinions; most respondents were neutral [37].
• Research question 2 investigated the relationship between student factors of satisfaction
in online learning (SfS) and their achievement of learning goals (SS).
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Kateryna M. Bondar et al. CEUR Workshop Proceedings 43–59
The study found that students with higher grades, such as those in the “B” category, primarily focused
on reducing transactional distance related to content, interface, and peer interactions. Conversely, stu-
dents with lower grades, specifically those in the “C” category, reported dissatisfaction and concentrated
efforts on reducing transactional distance associated with content, interface, and teacher parameters.
Based on the provided correlations (table 4), the factors were organized into groups based on their
correlation values. In Group 1, characterized by moderately strong associations, transactional distance
between students and content (S-C) showed positive correlations with students’ social presence (SP),
learning goals (LG), student grades (SG), transactional distance between students and interface (S-I),
and design of programs (DP). Group 2 highlighted moderately strong relationships, with learning goals
(LG) positively correlating with both student grades (SG) and students’ social presence (SP). Group 3,
displaying moderately strong associations with a weaker negative relationship, revealed that the design
of programs (DP) correlated positively with learning goals (LG) and student grades (SG) but negatively
with students’ social presence (SP). Additionally, student grades (SG) in Group 3 displayed a positive
correlation with students’ social presence (SP).
This organization provides valuable insights into the strengths of associations between different fac-
tors, allowing for a clearer understanding of their relationship with student satisfaction and achievement
of learning goals in online learning environments.
6. Conclusion
This research explores the potential of AI to improve mental health education and data analysis learning,
demonstrating the significant benefits it can bring to individuals and organizations. As technology
advances, the synergy between human intelligence and artificial intelligence will play a key role in
shaping the future of work and well-being. The results of this study provide information about the
influence of relevance of course content on student perception.
The hypothesis, that the declining transactional distance between student-content and interface
correlates with their grade (academic performance) while utilizing an AI in Mental Health Tech and
Data Analytics, was supported. This finding suggests that active participation in the course, coupled
with understanding how artificial intelligence tools can be applied in a psychological context, can
positively influence students’ career intentions. This emphasizes the role of practical experience and
practical application in shaping students’ professional trajectories.
Conducting this study in an online format during the conflict in Ukraine adds a unique dimension to
the research. This reflects the adaptability and resilience of students and teachers in the face of difficult
circumstances. The findings emphasize that even under these conditions, effective teaching strategies
can make a significant difference in students’ learning experiences and outcomes.
7. Significance and consequences
The research is particularly noteworthy for several reasons. For educators, this highlights the value of
incorporating real-world relevance into curriculum development and providing students with opportu-
nities to work with emerging technologies. Conducted during the 2022-2023 academic year amidst the
ongoing conflict in Ukraine, this study navigates the unique challenges presented by the online format.
This context adds relevance and urgency to understanding student engagement and achievement in
such conditions. The study addresses the complexity associated with the subject matter, which students
often consider challenging. This complexity stems from the reliance on statistical analysis tools and the
need for skills in decoding raw data, data organization, and using statistical packages. Investigating how
students cope with these demands is of substantial significance. By examining how students perceive
the relevance of course content and their intentions to apply artificial intelligence tools in their future
careers, this research sheds light on the effectiveness of educational approaches in preparing students
for the evolving demands of their field.
54
Kateryna M. Bondar et al. CEUR Workshop Proceedings 43–59
8. Ethical considerations
Students were assured anonymity and that survey responses wouldn’t affect their course assessment.
Before the survey, participants consented to data use, including knowledge test scores. Two months
post-data collection, participants received a thorough debrief with initial findings. This process ensured
ethical and systematic experiment execution in the university course context.
Funding: Project ERASMUS-EDU-2023-CBHE101129379 “Boosting University Psychological Resilience and Wellbeing in
(Post-) War Ukrainian Nation”.
Declaration on Generative AI: During the preparation of this work, the author) used GPT-4o in order to: Improve writing
style, Content enhancement. After using this tool, the author(s) reviewed and edited the content as needed and takes full
responsibility for the publication’s content.
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A. Student course evaluation “Survey on the quality of teaching
disciplines”
Instruction: “Dear Student! The University Administration invites you to take part in a survey on the level
and quality of teaching disciplines. Your answers will help to improve the educational process and improve
the quality of education at the university. The survey is conducted anonymously. Thank you in advance for
participating in the survey!”
Zhang’s scale of transactional distance
ST = Transactional distance between students and teacher
1. The instructor generally answers the student’s questions
2. The instructor pays no attention to me
3. I receive prompt feedback from the instructor on my academic performance
4. The instructor was helpful to me
5. The instructors are available to answer my questions
6. The instructor can be turned to when I need help in the course
SC = Transactional distance between student and content
7. The content of this course is of great interest to me
8. I don’t know why I have to learn this
9. The examinations in this course have challenged me to do my best work
10. This course emphasized SYNTHESIZING and organizing ideas, information, or experiences into
new, more complex interpretations and relationships
11. This course emphasized MAKING JUDGEMENTS about the value of information, arguments,
or methods such as examining how others gathered and incorporated data and assessing the
soundness of their conclusions
12. This course emphasized APPLYING theories and concepts to practical problems or in new situa-
tions
SS = Transactional distance between students and students
13. I learned a lot from observing the interactions among the students
14. The students in this online class challenged me to do my best work
15. I get along well with my classmates
16. I feel valued by the class members in this online class
17. My classmates in this online class value my ideas and opinions very highly
18. My classmates respect me in this online class
19. I am good at working with the other students in this online class
20. I feel a sense of kindred spirit with my fellow classmates
21. The class members can be turned to when I need help in the course
22. There are students I can turn to in this online class
23. The class members are supportive of my ability to make my own decisions
SI = Transactional distance between students and interface
24. It is difficult to pay attention to the instructor in the web environment
25. I have adequate access to the web resources I need
26. The fact that I am online does not inhibit my class participation
27. An efficient system is provided for students and instructors to exchange materials
28. I am comfortable using the computer
29. I hate using the web
30. It was easy for me to use the technology involved with this online class
31. The technology used in this course is difficult to learn and use
SL = Student learning
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Kateryna M. Bondar et al. CEUR Workshop Proceedings 43–59
I have learned a great deal in this online class
LG = Learning goals
I have made tremendous progress towards my goal in the subject area of this course
SS = Student satisfaction
Overall, I am satisfied with this course
Please give 3 recommendations to improve course design
B. Student course evaluation “Survey on the quality of teaching
disciplines”
Instruction: “Dear Student! The University Administration invites you to take part in a survey on the level
and quality of teaching disciplines. Your answers will help to improve the educational process and improve
the quality of education at the university. The survey is conducted anonymously. Thank you in advance for
participating in the survey!”
20 questions on a 5-point scale, 3 questions with answer options, and 1 open-ended question. For all
psychological variables, respondents gave answers on a 5-point Likert scale from “(1) never true” to “(5)
almost always true” in Google Forms.
Design of programs:
1.1. I need discipline for my future professional activity.
1.2. The discipline contains useful material.
1.3. The discipline is logically connected with other disciplines.
1.4. The discipline contributes to the formation of skills and abilities.
1.20. In general, it was interesting for me to master this discipline.
Style of teaching (teaching and assessment):
1.6. The professor is fluent in educational material and modern scientific information;
1.7. The professor motivates students to independently search for information in depth;
1.8. The professor clearly formulates the goals and the training plan;
1.9. The educational material is presented in an accessible and interesting way;
1.10. The professor uses the latest interactive teaching methods.
Student-centered learning:
1.12. I have the desire to continue studying with this teacher (other disciplines, coursework, qualification
work);
1.13. The teacher is open and friendly with students;
1.14. I always had the opportunity to turn to the teacher for clarification or advice;
1.16. The teacher is tactful and knows how to establish contact with students.
Learning resources and student support, certification:
1.5. The discipline is provided with the necessary textbooks and teaching materials.
1.11. The teacher clearly defines the criteria for assessing students’ knowledge.
1.17. The professor always conducts classes on time and according to the schedule.
1.18. Distance learning was well organized by the professor.
1.19. But, in my opinion, it would be more correct to teach the discipline personally.
59