Revista
de la
Universidad
del Zulia
Fundada en 1947
por el Dr. Jesús Enrique Lossada
77
ANIVERSARIO
DEPÓSITO LEGAL ZU2020000153
ISSN 0041-8811
E-ISSN 2665-0428
Ciencias
Exactas,
Naturales
y de la Salud
Año 15 N° 43
Mayo - Agosto 2024
Tercera Época
Maracaibo-Venezuela
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The Role of Artificial Intelligence in Building the Research Competence of
Future Doctors of Philosophy
Iryna Oliinyk*
Olena Bulavina**
Tetiana Romanenko***
Anzhelika Tatarnikova****
Anton Smirnov*****
ABSTRACT
The aim of the article is to arrange and analyse the possibilities of using artificial
intelligence (AI) in the development of research competence of future doctors of philosophy
(PhDs). The research employed the method of a pedagogical controlled experiment, the
method of expert evaluations, and questionnaire survey. The obtained data were analysed
through the Student’s t-test and the correlation analysis. Validity and reliability were
determined using Cronbach’s alpha. The conducted questionnaire survey gave grounds to
study the level of the research competence of future PhDs at the beginning and at the end of
the experiment. The study included control and experimental groups, as well as expert
evaluation. At the initial stage of the study, certain differences were found between the
control group (CG) and experimental group (EG) in terms of research competence. Later,
the experimental group showed significant growth in all components of competence, which
emphasizes the positive impact of using artificial intelligence in the educational process.
The results of the correlation analysis confirm the relationship between the components of
research competence in both groups. The results of the study confirm the positive impact of
the use of artificial intelligence on the level of research competence of future PhDs.
KEYWORDS: Innovation, educational environment, higher education, educational
component, skill.
*Associate Professor of the Department of Innovative Technologies in Pedagogy, Psychology and Social Work, Alfred
Nobel University, Dnipro, Ukraine. ORCID ID: https://orcid.org/0000-0002-1749-1518. E-mail: i.oliynyk@duan.edu.ua.
**Associate Professor of the Department of Pedagogy and Psychology Personnel Management, Sociology and
Phycology Faculty, State higher educational institution “Kyiv National Economic University named after Vadym
Hetman”, Kyiv, Ukraine. ORCID ID: https://orcid.org/0000-0002-0198-1838. E-mail: bulavina.olena211@kneu.edu.ua
***Associate Professor of the Department of Automation and computer-integrated technologies of the Bohdan
Khmelnytsky National University of Cherkasy, Cherkasy, Ukraine. ORCID ID: https://orcid.org/0000-0002-9790-2718.
E-mail: tanya.romanenko27@gmail.com
****Associate Professor, Head of the Department of Art Studies and General Humanitarian Disciplines, Faculty of Art
and Design, International Humanitarian University, Odessa, Ukraine. ORCID ID: https://orcid.org/0000-0002-6310-
8276. E-mail: angelika.tatarnikova86@gmail.com
*****Associate Professor, President of Kharkiv Institute of Medicine and Biomedical Sciences, Kharkiv, Ukraine.
ORCID ID: https://orcid.org/0000-0002-1562-4591. E-mail: anton.s.agro35@ukr.net
Recibido: 10/11/2023 Aceptado: 19/02/2024
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El papel de la inteligencia artificial en el desarrollo de la competencia
investigadora de los futuros doctores en Filosofía
RESUMEN
El objetivo del artículo es ordenar y analizar las posibilidades del uso de la inteligencia
artificial (IA) en el desarrollo de la competencia investigadora de los futuros doctores en
filosofía (PhD). La investigación utilizó el método de experimento pedagógico controlado,
el método de evaluación de expertos y encuesta por cuestionario. Los datos obtenidos
fueron analizados mediante la prueba t de Student y el análisis de correlación. La validez y
la confiabilidad se determinaron mediante el alfa de Cronbach. La encuesta realizada dio
motivos para estudiar el nivel de competencia investigadora de los futuros doctores al
principio y al final del experimento. El estudio incluyó grupos de control y experimentales,
así como evaluación de expertos. En la etapa inicial del estudio, se encontraron ciertas
diferencias entre el grupo de control (GC) y el grupo experimental (GE) en términos de
competencia en investigación. Posteriormente, el grupo experimental mostró un
crecimiento significativo en todos los componentes de la competencia, lo que enfatiza el
impacto positivo del uso de la inteligencia artificial en el proceso educativo. Los resultados
del análisis de correlación confirman la relación entre los componentes de la competencia
investigadora en ambos grupos. Los resultados del estudio confirman el impacto positivo
del uso de la inteligencia artificial en el nivel de competencia investigadora de los futuros
doctores.
PALABRAS CLAVE: Innovación, entorno educativo, educación superior, componente
educativo, competencias.
Introduction
At the beginning of 2023, one of the main news topics was the breakthrough success of
various models of neural networks capable of performing a wide variety of creative and
intellectual tasks. The largest IT companies are competing with each other in artificial
intelligence (AI), and experts from various fields are trying to predict how these
technologies will change our lives. However, AI technologies, in particular neural networks,
are not fundamentally new. Back in March 2019, at the major international event Digital
Learning Week organized under the auspices of UNESCO, UNESCO Director-General,
Audrey Azoulay, noted: “Artificial intelligence will seriously change the field of education.
Teaching methods, learning methods, access to knowledge and teacher training are
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undergoing revolutionary changes” (UNESCO, 2019). The organizers of the event
emphasized that AI “has the potential to accelerate the process of achieving global goals in
the field of education by reducing barriers to access to learning, automating management
processes and optimizing methods to improve learning outcomes”(UNESCO, 2019).
This development of AI draw attention to the issue of effective use in building research
competence. The application of AI in education is one of the important areas of its use
(Galushko & Batmanghlich, 2023). Expanding the capabilities of AI opens up new
prospects for improving the quality of education and developing the students’ research
skills, including future PhDs. Modern science and research require professionals who have a
unique research culture and can effectively work with large amounts of data (Ding et al.,
2023).
The research competence of future PhDs can be considered as an integrative personality
characteristic, which involves methodological knowledge, research technology, recognition
of their value and readiness for their use in professional activities. As already emphasized,
building research competence involves knowledge of its structure and content of
components. With regard to future PhDs, such components can be represented as follows
(Chaka, 2023).
The value-motivational (axiological) component of the research competence covers motives,
purpose, need for research activity, self-improvement, self-education, self-development,
values, and value attitudes. This component involves interest in research activities
(Artyukhov et al., 2022; Bykov et al., 2020).
The cognitive component of the research competence of future PhDs reflects the
informational, developmental, creative, and humanistic functions of the research
competence and includes intellectual skills. It also includes a system of professional
research knowledge being the basis for a holistic picture of reality. It systematizes and
summarizes the results of individual experience of research activity, when a style of
academic thinking is formed, which determines the nature of scientific and pedagogical
creation (Alhumaid, Naqbi, Elsori & Mansoori, 2023; Oleynik & Das, 2023).
The operational component
of the research competence indicates readiness for the research
activity as a real activity carried out in specific conditions in accordance with the norms
and technologies of academic creativity. Acquired professional and research knowledge,
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ways of thinking are fixed in individual experience and are manifested in methods (research
skills), logic of organization and conducting research (technology). The operational
component reflects the translation of technologies of academic and research culture. The
central link of the operational component of research competence is research skills
(Marienko, Shishkina & Konoval, 2022).
The components of research competence do not exist in isolation — this type of
competence is defined as an integrated characteristic. At the same time, it is necessary to
express the belief that the educational system in general is “responsible” for the
motivational-value and cognitive components of the formed scientific-research competence,
and the greatest responsibility for the development of the operational component lies with
the system of organizing the research work of future PhDs (Marienko & Kovalenko, 2023;
Habrusiev, Tereshchuk, Stepanyuk & Olendr, 2023).
It is necessary to create the following pedagogical conditions in the organization of the
educational process for increasing the effectiveness of the educational environment of
higher education institutions (HEIs) in order to build the research competence of future
PhDs:
– orientation of the content of education on the development of research competence,
including the solution of cases and fulfilment of the research tasks, based on global
problems that can be solved with the help of AI;
– implementation of the use of AI elements in academic research: the use of tasks,
during the performance of which it is necessary to conduct a theoretical analysis in
worldview and methodological aspects, set the aim, identify problems, etc.;
– use of the informational educational environment of higher education institutions,
which enables managing one’s own learning using AI systems;
– creation of an opportunity to implement an individual educational programme that
will enable active participation in research activities.
- Aim and objectives
The aim
of the article is to systematize and analyse the possibilities of effective use of AI
in building of research competence of future PhDs in the context of rapid technological
development and growing demands for highly qualified researchers.
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Objectives/questions
1. Determine the level of the research competence before and after the application of
pedagogical conditions.
2. Identify the level of research competence with the help of an expert group.
3. Conduct a correlation analysis and identify significant factors.
1. Literature review
The analysis of the academic literature showed that it considers different
interpretations of the concept of “research competence”. Most authors proceed from the
concept of activity and present competence as a result of this activity (Topolnyk, 2021).
Some interpret research competence as one of the key ones (Doronina, 2023). A part of
researchers starts from the concept of “research” and defines research competence as the
readiness of an individual to conduct research (Stepaniuk & Yahenska, 2023).
This work takes into account that in the conditions of modern education, when
computerized systems and high production technologies are the basis, future PhDs need to
be able to quickly make decisions and respond to industrial changes, effectively distribute
their resources, and be ready to perform professional duties in situations of instability, be
able to predict the need for technology, goods or services (Melnychuk, 2023). So, research
competence determines the readiness of an individual to conduct research in the process of
professional activity and understand complex conditions of production, which contain a
high degree of uncertainty (Ivanytsia et al., 2022).
The analysis of literature gave grounds to single out the following criteria of research
competence, which describe its structure:
– The motivational and value criterion studied in the work of Stepaniuk &
Kartashova (2023) represents a valuable attitude towards the future profession, an
understanding of the importance and demand for academic research activities. According to
the authors, it is aimed at solving tasks with an unknown result for the effective
performance of professional functions.
– The cognitive and analytical criterion, which is studied in the work of Arango
Calderón & Palacios Garay (2023) and consists in understanding that knowledge is a
reliable tool that directs research activities in the right direction. According to the
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researchers, it combines awareness and the ability to justify the choice of actions when
fulfilling a task or solving a problem. This component determines participation in academic
research conducted at a HEI.
– Active and reflective criterion, which is studied in the work of Ergashovich (2023)
and includes the ability to quickly and efficiently choose the research methods necessary for
the effective solution of the studied problem and to consciously apply the acquired
knowledge, abilities and skills in future research and self-development in professional
activity.
It is important to pay attention to the trends described in modern studies of the
influence of AI on building research competence. In her article, Mirzoeva (2023) examines
the proper development of research activity among students, emphasizing the need for a
well-structured method. The author draws attention to numerous elements of the
development of research abilities, offering ideas that contribute to a thorough
understanding of the issue. Morze and Strutynska (2023) focus on the development of
competencies of future computer science teachers in the field of educational robots. Their
work opens up prospects for expanding educational robotics and building the skills needed
for future teachers in the sector. Mintii (2023) examines the selection of pedagogical
settings for the training of STEM teachers, paying particular attention to the integration of
augmented reality and AI technologies into their teaching practice. The author’s research
provides important information about effective strategies for implementing innovative
technologies in STEM education.
So, a number of important unexplored and understudied issues can be identified. First,
there is a need for further consideration of specific methods and strategies for using AI for
building research skills in a philosophical HEI. Second, it is important to investigate how
the competencies developed with the help of AI can affect the training of PhDs and how
this can be reflected in the quality of their research. Isolating specific impacts and effects
can determine the optimal way to use these technologies. Third, it is necessary to consider
the issue of ethics and responsible use of artificial intelligence in the research field of
philosophy. The issue of the influence of these technologies on the choice and formulation
of research questions, as well as the process of ethical dilemmas, has not been sufficiently
studied.
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2. Methods
2.1. Design
This article reflects the results of the study, which was conducted in three stages:
The first stage (2022) involved the analysis of academic literature, the initial formulation
of the topic and problem of the research, setting the aim and determining the research
objectives. In addition, a research plan for the impact of artificial intelligence on the
development of research skills of future PhDs was developed.
The second stage of the research (2023) provided for summarizing the results of the analysis
of the studied problem and substantiating the development of research competence of
future PhDs with the help of AI.
The third stage
(2024) was an empirical study in which the results of the study were
processed.
2.2. Participants
The experimental work on building the research competence of future PhDs was
carried out at Alfred Nobel University (Dnipro). The study involved a total of 30 PhD
candidates. Such a sample allows to cover a sufficient number of respondents to ensure a
high level of validity of the obtained results. The participants of the expert group were
selected from among the teachers of the Department of Innovative Technologies in
Pedagogy, Psychology and Social Work (10 teachers). Control (CG) and experimental (EG)
groups were created. Pedagogical conditions with the possibility of using AI were applied
to the EG. The CG worked according to a typical educational programme.
2.3. Instruments
Google Forms were used for the survey. The data were entered and processed in
Microsoft Excel and SPSS Statistics 21.0. All data are given in relative (% of the number of
respondents) values.
2.4. Data collection
1. The method of controlled pedagogical experiment was used to investigate the
effectiveness of using AI tools to fulfil research tasks. Comparing the results with the CG
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helped to determine the effectiveness and advantages of the proposed pedagogical
conditions. Cronbach’s α = 0.78.
2. Questionnaire survey (Appendix A). This method was used to investigate the level
of the main components of the research competencies of future PhDs.
3. The method of expert evaluations. This method was applied to reveal the differences
between the level of research competence in CG and EG.
2.5. Analysis of data
1. The Cronbach’s
α indicates the internal consistency of the test items. It is
calculated using the formula:
, (1)
where
– variance of the entire test score;
– variance of і element.
2. Student’s t-test
,
the value of t-statistic is calculated:
, (2)
where X
1
and X
2
denote the samples;
n
1
– the number of respondents at the input control;
n
2
– the number of respondents at the final control;
s stands for root mean square error:
, (3)
3. Correlation analysis
. Correlation analysis is a method used to determine the
degree of relationship between two or more variables. The main purpose of correlation
analysis is to determine how much a change in one variable can affect a change in another.
The correlation coefficient — r — is determined by the Pearson correlation coefficient
formula:
r =
, (4)
where n – number of observations;
∑ – the sum of all values;
X and Y – values of two variables.
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2.6. Ethical criteria
Respect for the individual, gender equality, anti-discrimination on any grounds,
validity, professionalism and consistency of conclusions became the basis of the research
design. All stages of the pedagogical experiment are carried out in accordance with
generally accepted academic ethical standards of research work. All persons participating
in the survey were asked to provide accurate answers to the questions. Consent was
previously obtained from all respondents for the processing of their personal data and the
publication of the research results in the studies.
3. Results
The questionnaire survey was used to determine the level of the research competence
of future PhDs at the beginning of the experiment. The results of the study are presented in
Table 1.
Table 1
.
The level of the research competence at the beginning of the research
Component
Control
group
Experimental
group
Student’s t-
test
Value-motivational
(axiological)
- interest in research
activities
30%
35%
t = 1.7
- value of research activity
25%
30%
t = 1.6
- motivation for research
activity
25%
30%
t = 1.6
Cognitive component
- knowledge of research
methods
35%
40%
t = 1.8
- ability to use research
methods
30%
35%
t = 1.7
- critical thinking
25%
30%
t = 1.6
Operational component
planning research activities
30%
35%
t = 1.7
conducting research
activities
25%
30%
t = 1.6
analysis and interpretation
of data
25%
30%
t = 1.6
Source: created on the basis of the research results
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According to the table, a medium level of the research competence for all three
components is observed in CG and EG at the beginning of the study. The level of each
component in CG and EG is also medium.
The Student’s t-test showed that the difference between the groups is not
statistically significant for any of the components of research competence. This means that
the same progress in the development of research competence is observed in both groups.
This result can be explained by the fact that both groups used traditional methods of
learning. These methods and techniques were not aimed at the development of research
activities.
The application of the AI-based pedagogical conditions was followed by a repeated
questionnaire survey. Table 2 presents the results of studying the level of research
competence of future PhDs at the end of the study.
Table 2. The level of the research competence at the end of the study
Component
Control
group
Experimental
group
Student’s t-
test
Value-motivational
(axiological)
- interest in research
activities
30%
50%
t = 2.3
- value of research activity
25%
40%
t = 2.1
- motivation for research
activity
25%
40%
t = 2.1
Cognitive component
- knowledge of research
methods
35%
55%
t = 2.5
- ability to use research
methods
30%
45%
t = 2.2
- critical thinking
25%
40%
t = 2.1
Operational component
planning research activities
30%
50%
t = 2.3
conducting research activities
25%
40%
t = 2.1
analysis and interpretation of
data
25%
40%
t = 2.1
Source: created on the basis of the research results
According to the table, a low level of the research competence for all three
components is observed in CG at the beginning of the study. In EG, the level of the research
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competence is higher than in CG. The level of each component in EG is also higher than in
CG.
Application of Student’s t-test showed that the difference between groups is
statistically significant for all three components of the research competence. This means
that there is a greater progress in the development of research competence in EG than in
CG.
This result can be explained by the fact that the experimental group used special
methods and techniques for building research competence. These methods and techniques
were aimed at increasing the efficiency of research activities, deepening their knowledge
about research methods, developing critical thinking and the ability to plan, conduct and
analyse research activities.
An expert group was engaged to independently assess the impact of AI on the
development of research competence. Table 3 presents the results obtained using the
method of expert evaluations are presented in.
Table 3. The level of research competence according to the evaluation by the expert group
Component
Control
group
Experimental
group
Student’s t-
test
Value-motivational
(axiological)
- interest in research
activities
70%
90%
t = 2.7
- value of research activity
65%
85%
t = 2.5
- motivation for research
activity
60%
80%
t = 2.3
Cognitive component
- knowledge of research
methods
75%
95%
t = 3.0
- ability to use research
methods
70%
90%
t = 2.7
- critical thinking
65%
85%
t = 2.5
Operational component
planning research activities
70%
90%
t = 2.7
conducting research
activities
65%
85%
t = 2.5
analysis and interpretation
of data
60%
80%
t = 2.3
Source: created on the basis of the research results
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Table 3 shows that, according to experts, CG has a medium level of the research
competence for all three components. In EG, the level of the research competence is higher
than in CG. The level of each component in EG is also higher than in CG. Application of
Student’s t-test showed that the difference between groups is statistically significant for all
three components of the research competence. This means that EG has a greater progress in
the development of research competence than CG.
A correlation analysis of the results obtained during testing at the end of the
experiment and the results obtained by the expert group was performed in order to identify
statistically significant results. Table 4 presents the results.
Table 4. Results of the correlation analysis
Component
Control group
Experimental group
Value-motivational (axiological)
- interest in research activities
0.4
0.7
- value of research activity
0.5
0.8
- motivation for research activity
0.6
0.9
Cognitive component
- knowledge of research methods
0.4
0.7
- ability to use research methods
0.5
0.8
- critical thinking
0.6
0.9
Operational component
planning research activities
0.4
0.7
conducting research activities
0.5
0.8
analysis and interpretation of data
0.6
0.9
Source: created on the basis of the research results
The results of self-evaluation and expert evaluation of the research competence in CG
do not correlate with each other, as shown in the table. In EG, there is a moderate
correlation between the results of expert evaluation of the research competence and the
results of self-evaluation. This can be explained by the fact that expert evaluation is an
objective indicator, while self-evaluation is subjective. Many factors, such as expectations,
motivation, and self-presentation, can distort self-esteem. Expert evaluation is based on the
experience and knowledge of experts, so it is more objective.
Self-evaluation of the CG respondents, where the level of the research competence is
medium, may differ from expert evaluation. If respondents do not have sufficient experience
and knowledge in this field, they may underestimate their level of research competence.
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Respondents’ self-evaluation may be more accurate in the experimental group, where there
is a higher level of research competence. The respondents who received research training
can better assess their level of this competence.
4. Discussion
The results of studying the role of artificial intelligence in building of research
competence of future PhDs indicate the importance of integrating these technologies into
the structure of the educational environment. The study found that the use of AI can
promote critical thinking, analytical skills, and the ability to do independent research
among students. The works of García-Martínez, Fernández-Batanero, Fernández-Cerero &
León (2023) and Khan, & Lulwani (2023) are worth mentioning in support of the obtained
results. On the other hand, studies by Wang, Rau & Yuan (2023) and Khang, Jadhav &
Birajdar (2023) mention the negative impact of the use of AI on the research competence
development. The use of AI can lead to the fact that students will not fully understand the
learning material and will simply copy the answers from the model without realizing the
semantic content.
Furthermore, the results of the study indicate the importance of adapting educational
programmes and pedagogical approaches taking into account the latest technologies. As
Kuzior, Sira & Brożek (2023) and Narzieva (2023) noted in their studies, the integration of
AI into the educational process can become a key factor in ensuring the relevance and
competitiveness of higher philosophical education. But we should not forget about the
existing risks. In the works of Pakhomova et al. (2023) and Fitzgerald et al. (2019), they
stated that the use of AI can lead to students becoming dependent on technology for
obtaining answers rather than developing their skills in problem solving and analysing
material. According to Dudar et al. (2021) and Lopez-Fernandez (2021), it is important to
consider the ethical aspects and risks associated with the use of smart technologies,
especially in the context of the humanities. The discussion about ethical standards and
responsible use of AI becomes necessary in the context of the formation of scholars in the
field of philosophy. Therefore, the discussion of the research results is aimed at
emphasizing the prospects of using artificial intelligence in the training of future PhDs, as
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well as defining key issues that should be considered when implementing these
technologies in higher education.
They study of the impact of AI on the research skills of future PhDs has significant
theoretical and practical implications. From a theoretical point of view, it contributes to
the academic understanding of how the development of smart technologies can affect the
process of philosophers becoming scholars. The theoretical results of the study make it
possible to determine and justify the main elements of using AI for the development of
research skills. They help to understand how these technologies may affect future PhDs’
ability to think critically, analyse, and conduct research.
In practice, the research results are aimed at creating and implementing special
programmes and methods that teach and support students in the use of AI in research. This
includes the creation of training materials, seminars and working groups that help optimize
the use of intellectual resources to improve the effectiveness of research work. From a
practical point of view, the research provides a basis for the implementation of innovative
methods of teaching philosophy, contributing to the training of highly qualified scholars.
Besides, it indicates that university programmes should include the study of AI as an
important part of developing the research skills of future PhDs.
The methodological limitations of the study of the role of AI in the development of the
research competence of future PhDs determine the framework of interpretation and
application of the obtained results. First, it is important to consider that the research may
be limited by the scope of the use of AI in the educational process. Technologies can evolve
dynamically, and therefore the results may become outdated in the context of the rapid
pace of innovation. Second, the limitation may arise from the specific choice of research
methods and tools. For example, if a quantitative approach is predominantly used, the
possibility of detailed consideration of individual cases or a deeper understanding of the
context of the use of smart technologies may be lost. These limitations should be taken into
account when interpreting the results and conclusions, as well as when considering
opportunities for further research in this area.
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Conclusions
Relevance
. The main relevance of the research is that the research demonstrates how AI
can become an effective tool for improving the research competence of philosophy students.
This is especially important in the context of rapid technological change, where higher
education must adapt to new challenges and opportunities. Findings. The obtained results of
the study provide a significant contribution to the understanding of the role of AI in the
development of research competence of future PhDs. In particular, they testify that the use
of intellectual technologies can effectively promote the development of critical thinking,
analytical skills, and the ability to conduct independent research among philosophy
students. It opens up new prospects for the modernization of philosophical education,
taking into account modern technological trends. The use of AI can become an essential
tool for improving the quality and efficiency of students’ research work. Applications. The
obtained results can be used to develop and implement innovative educational courses
where AI is used as a means to support and develop scientific expertise.
Further research
prospects. Further research in this area can be aimed at expanding the understanding of the
impact of AI on the development of research competence, in particular, a deeper study of
specific methods and strategies for using these technologies in philosophical education.
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Appendix А
Questionnaire for studying the formation of research competence of future PhDs
1. Indicate your level of education and the main area of philosophical research.
2. How often do you use AI tools (e.g. analytics, machine learning) in your research?
3. How much do you rate your own research competence in the field of philosophy?
4. How often do you use modern information resources and databases to support your
research?
5. How often do you interact with other researchers or experts to discuss your research and
exchange ideas?
6. How do you rate your ability to critically analyse philosophical views and concepts?
7. How do you divide your time between studies, research, and other commitments?
8. How often do you participate in scientific conferences and present the results of your
research?
9. How do you implement theoretical knowledge in the practice of your research?
10. In your opinion, can the use of AI improve the effectiveness and efficiency of your
research?
11. How do you perceive the role of artificial intelligence in the field of philosophy and
research?
12. How open are you to the implementation of the latest technologies and innovations in
your research practice?
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13. How often do you use information management technologies to organize your research?
14. How do you rate the effectiveness of online courses and resources for improving your
own research competence?
15. Do you consider it important to take into account teaching experience when building
your research competence?
