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 afirm 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.
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 efects
of the COVID-19 pandemic [
Recognizing the substantial impact of professionals’ mental well-being on overall business performance, 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 eficiency amidst wartime conditions in Ukraine.
Mental health technology is a multi-faceted field that represents the convergence of technology and
mental well-being [
Firstly, prevention is at the fore of mental health technology, ofering proactive tools and
platforms designed to prevent the occurrence of mental health problems [
Secondly, recognizing the importance of early detection and interference, mental health technologies
ofer screening and assessment tools that can identify potential mental health problems in their initial
stages [
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 [
Thirdly, digital mental health solutions ofer a variety of options in the treatment space [
Further, after the initial stages of treatment, mental health technology continues to play a decisive
role in supporting well-being [
Moreover, the use of big data and advanced analytics is a growing aspect of mental health technology
[
Mental health technology also supports research by providing a platform for studying mental health
patterns and treatment outcomes [
Although AI ofers benefits in academic environments and mental health research, it has evoked a
mixture of awe and apprehension among educators and researchers, prompting eforts 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 [
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 [
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 efectively. The study also found that students are concerned about the ethical implications of
AI [
In addition to this, in study by Gado et al. [
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
telepsychology and that guidelines for training programs in this area are virtually non-existent [
However, in a study organized by Perle et al. [
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.
That is why, the online crisis counselling course should adopt an interdisciplinary approach,
encouraging 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
foundations, highlighting ethical principles such as safeguarding data privacy, ensuring informed voluntary
consent, and the responsible usage of AI in diagnosis and treatment [
Additionally, the main attention in course development revolves around development proficiency
in the evaluation and application of mental health technologies [
First of all, the course should include the specifics of working with mental health apps [
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. [
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. [
It is equally important that the course included the specifics of working with online screening and
assessment tools [
Last but not least, data analytics and AI topics should be included in university courses. Advanced analytics 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.
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 [
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,
ofering 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 [
RSTD quantifies transaction distance along four key dimensions [
In our case, we use RSTD to identify areas for improvement in an online course to evaluate the efectiveness 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?
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.
In total, 60 students (table 1) participated in the course. The participants included 80.5% females and 19.5% males. There was no significant diference in the distribution of participants into groups based on gender ( 2(2) = .444, = .79). The average age of participants was = 20.3 years ( = 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 difer regarding participants’ educational paths ( 2(1) = 1.793, = .18).
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.
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 cleaning, 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 professionals. 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. • 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.
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) [
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 efectiveness 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 [
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).
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 ( = 0.974) and 4.32 ( = 0.911), respectively (table 2).
Transactional distance is associated with a grade on most dimensions. Post hoc analysis revealed the most significant diferences (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 efectiveness 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 coeficient 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.
Note: – number of reports that were uploaded in time; – correlation with the grade for quality (online knowledge test); – 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) ( = 0.61) • Learning goals (LG) ( = 0.43) • Student grades (SG) ( = 0.39) • Transactional distance between students and interface (S-I) ( = 0.31) • Design of programs (DP) ( = 0.27) 2. Transactional distance between students and interface (S-I) correlates positively with: • Student grades (SG) ( = 0.56) • Learning goals (LG) ( = 0.38) • Design of programs (DP) ( = 0.33) Group 2: Moderately strong relationships
• Student grades (SG) ( = 0.45) • Students’ social presence (SP) ( = 0.45) Group 3: Moderately strong with weaker negative relationship 3. Design of programs (DP) correlates positively with: • Learning goals (LG) ( = 0.52) • Student grades (SG) ( = 0.44) • but negatively with: • Students’ social presence (SP) ( = − 0.26) 5. Student grades (SG) display a positive correlation with:
• Students’ social presence (SP) ( = 0.56)
This organization highlights the strengths of associations between diferent 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 relationships 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 diferent aspects of the course evaluation. 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 diferent factors in course evaluations, ofering valuable insights for future research and course improvement initiatives.
Study results highlight the efectiveness 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 ( = 0.974) and 4.32 ( = 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 [
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 [
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
dificulty completing assignments within the time limits and were only able to engage superficially
with ethical considerations. It’s close to the results of research [
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, students with lower grades, specifically those in the “C” category, reported dissatisfaction and concentrated eforts 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 diferent factors, allowing for a clearer understanding of their relationship with student satisfaction and achievement of learning goals in online learning environments.
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 dificult circumstances. The findings emphasize that even under these conditions, efective teaching strategies can make a significant diference in students’ learning experiences and outcomes.
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 opportunities 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 efectiveness of educational approaches in preparing students for the evolving demands of their field.
Students were assured anonymity and that survey responses wouldn’t afect 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.
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!”
Overall, I am satisfied with this course
Please give 3 recommendations to improve course design
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.