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Вестник Томского государственного университета. 2023. № 494. С. 180-186 Vestnik Tomskogo gosudarstvennogo universiteta - Tomsk State University Journal. 2023. 494. рр. 180-186
Original article
UDC 372.881.111.1
doi: 10.17223/15617793/494/19
Learning and teaching in the contexts of artificial intelligence: Transforming content for the humanities
Darya V. Aleynikova1,2, Lyudmila V. Yarotskaya1,3
1 Moscow State Linguistic University, Moscow, Russian Federation 2festabene@mail. ru 3 lvyar@yandex.ru
Abstract. The incredible spread of artificial intelligence and its integration in business processes around the world pose a challenging task for foreign language teachers. Current research focuses on improving teaching strategies within the scope of artificial intelligence on an interdisciplinary basis, where foreign language could be seen as an integrating ground for unifying professional and digital knowledge. The authors suppose such an approach aims at educating competitive specialists.
Keywords: annotation, artificial intelligence, foreign language teaching, labeling, social sciences and humanities
For citation: Aleynikova, D.V. & Yarotskaya, L.V. (2023) Learning and teaching in the contexts of artificial intelligence: Transforming content for the humanities. Vestnik Tomskogo gosudarstvennogo universiteta - Tomsk State University Journal. 494. pp. 180-186. doi: 10.17223/15617793/494/19
Introduction
In recent decades, we have witnessed an incredible rise of modern technologies. Various innovative instruments appear at a new cross-border level. Subsequently, they act as a basis for changing social and economic relations within the scope of the digital society. The crucial influence is attributed to the new wave of artificial intelligence development, the "summer of AI" [1]. Artificial intelligence is regarded as a foundation for a wide range of companies and an instrument for the complete digital transformation. It helps both efficiently and quickly carry out a growing array of tasks. The fact that it increases overall productivity by several times makes most companies consider it as a professional tool.
Current trends, previously related to the industry of IT, today change the corporate world, creating a digitally unified labour market. Such processes highlight the necessity for organizations to verify their strategy goals. The COVID-19 pandemic has significantly accelerated the pace of digital tool application in the social sciences and humanities. To some extent, it led to the scaling of a number of services provided and, at certain stages, the replacement of a "human" with automated algorithms. Innovations influence the ways of performing professional activities and contribute to the emergence of new professions. Obviously, artificial intelligence is not likely to substitute for humans in most professional areas. However, it is to completely alter careers themselves, including duties performed. For instance, are the areas of responsibility in law [2] or records management [3] to be transformed? If so, how will it change teaching approaches? At the same time, one of the biggest matters of concern is the preparation of
future specialists to deal with constantly transforming digital contexts.
Historically, university graduates who want to be competitive must possess a high level of subject knowledge, language proficiency in one or more foreign languages, and well-developed soft skills. The critical point is that knowledge in demand today is also undergoing transformation, adding a digital component to the primary specialists' needs in any professional field. That sheds the light on the idea of revisiting higher education considering widespread artificial intelligence application. The point to highlight is to revise the social sciences and the humanities. We suppose that the discipline Foreign Languages has a great potential for unifying subject and digital knowledge while sharpening foreign language fluency on an interdisciplinary basis.
The scope of our teaching deals with teaching psychologists, lawyers, specialists in records management and intercultural professional communication. It should be noted that professional communication goes beyond borders with the help of innovations and artificial intelligence. New demands call for adequate measures, posing a challenging task for foreign language teachers in higher educational institutions. We aim at educating competitive specialists in the area of social sciences and the humanities with comprehensive skills and knowledge. These above-mentioned perceptions created the intrinsic motivation that guided this research.
Literature review
Artificial intelligence finds its way into various areas, revealing technical aspects of its application as well as a human factor. In terms of professional activities, we will
© Aleynikova, D.V., Yarotskaya, L.V., 2023
examine how corporate areas react to the ongoing transformation.
Artificial intelligence is widely applied in psychology [4, 5], jurisprudence [2, 6-8], records and information management [3, 9, 10], human resource management [11, 12], etc.
It is necessary to note that artificial intelligence significantly changes traditional professional activities. Artificial intelligence is part of legal practices all over the world [13-15]. Lawyers face the need not only to keep track of legal innovations but also to correlate algorithms in accordance with the changes made. Thus, scientists from the University of Pennsylvania and Sheffield created a form of artificial intelligence that can predict the decisions of the European Court of Human Rights with an accuracy of 79% [16].
As things stand, most legal services will soon be carried out online, and basic Web services will replace lawyers. It is essential that lawyers develop new strategies to build a future in which the legal profession is digitalized. Such an approach logically leads us to the conclusion that legal education should employ new techniques that are technologically appropriate. The current changes in the legal services market mean that restrictions will inevitably disappear, and new lawyers and other legal professionals will have to adapt to a new reality [17].
The influence of artificial intelligence in psychology is also seen as inevitable though assessments of its role in the field differ. According to the survey conducted by C.B. Frey, the integration of AI technologies in the area of psychology is not significant; however, it has increased since 2013 [18]. Researchers see great prospects for the use of artificial intelligence in the area of psychological help [19, 20]. There is now a plethora of smartphone applications (Cogniant, Woebot, BioBase, Youper, Replika, Talkspace, Tess, Moodmission) that can help monitor current psychological state and provide advice. For instance, app users report their bad mood or anxiety symptoms to MoodMission, and AI algorithms recommend five strategies to help them cope with anxiety, improve their mood, and reduce general anxiety. These strategies include: relaxation strategies, cognitive reframing exercises, physical activity, etc. The participants of the experiment reported their mood before and after applying the strategies [21]. Electronic therapy is becoming increasingly popular. It can serve as an additional diagnostic tool for psychologists as well as an independent technology. It should be noted that the work of modern psychologists is evolving in the digital context, which should have an impact on the training of future specialists [22].
The field of electronic documents and records management also faces transformations due to AI impact [23, 24].
Obviously, the transformation of professional activities requires shaping learning and teaching content. Researchers in different professional fields highlight that it is of utmost importance to revise teaching social sciences and humanities students considering the apparent artificial intelligence growth [22], which results in accentuating the potential of the
discipline - foreign language teaching as an interdisciplinary basis for such digital integration.
The idea proposed called into a question: How should the educational content be revised? Artificial intelligence is regarded as inefficient in solving problems that are presented as abstract, that do not have a clear answer, imply persuasion or arbitrary talk, and are associated with the awareness of humanistic concepts of the real world [25]. At the same time, issues of a non-standardized nature that contain real-life problems are not unlikely to be solved. Moreover, artificial intelligence algorithms work with datasets and there is a clear correlation -the more tasks to be solved, the more data to be provided. Artificial intelligence algorithms need quality data to be successfully trained [26]. Furthermore, more complex tasks require high-quality data.
Artificial intelligence systems are trained rather than programmed. The creation of datasets is regarded as challenging [27]. In some areas, datasets are not available, but even when they are available, labeling efforts can require huge human resources. When starting to work with data, the subject of the data is analyzed according to their belonging to one of the categories (structured or unstructured). Missing data could refer to publicly available information that the system will have easy access to, or it could refer to closed-access databases.
In law and medicine, databases are frequently inaccessible. In such cases, synthetic data is used, which is created artificially from scratch or based on real data. It is useful when there is insufficient data to train the algorithm. After analyzing the available data, a conclusion can be drawn about what data is missing and how to expand the data sets [28].
A very interesting point in this regard is that in most cases, in specialized areas, those who work with data (label data) must be professional lawyers, psychologists, records managers, etc., who are highly trained in data labeling [29]. At the same time, we observe an exponential growth in the AI labeling market from USD 1.5 billion in 2019 to USD 3.5 billion in 2024 [30]. The analysis of these factors brought us to the following idea: future professionals' teaching should include basic labeling techniques. We suppose that such an approach could be implemented within the framework of foreign language teaching on an interdisciplinary basis.
Research
There has been a great increase of artificial intelligence application. The incorporation of artificial intelligence into various companies' day-to-day operations has resulted in significant changes to professional activities. Taking into account the fact that professional activity is reflected in higher education, consequently, the educational system must respond to these new challenges by providing innovative educational content. This study intends to examine how artificial intelligence is changing the content of teaching social
sciences and humanities. The following research questions guided the study:
- How is AI changing careers and how should the educational content be revised?
- What innovative techniques could be introduced in the studying process?
- What is the outcome of the new instruments' integration?
Research Methodology
In this study we employed a qualitative research design combining observation with an open questionnaire survey.
Participants
The participants were second-year (8 students), fourth-year (11 students), and fifth-year (8 students) learners of English majoring in social sciences and humanities (records management and law) at Moscow State Linguistic University.
Materials
At the pre-teaching stage, we applied YouTube videos and research articles presenting current trends in artificial intelligence. For stages 2 and 3, authentic texts from the areas of law and records management were used. At the post-teaching stage, an open questionnaire was shared via Google Forms.
Procedure
The whole survey procedure lasted for two months and was divided into four successive stages:
Stage 1. AI discourse exploration (At this pre-teaching stage, learners discovered AI trends in their professional areas. Students were asked to reveal the areas of concern considering the spread of AI technologies).
Stage 2. Techniques for labeling (At this point, we introduced labeling techniques in response to demand).
Stage 3. Labeling: From theory to practice (This stage involved a professional task simulating a real-life situation).
Stage 4. Feedback: Open questionnaire (At this stage we asked the students to analyze the importance of the AI training component. The open questionnaire survey was implemented to accumulate the students' feedback).
We applied the qualitative approach to analyze the responses provided. We distinguished two categories: a) benefits of AI module integration; b) students' impressions.
Results and discussion
The process of marking up a text document or various elements of its content is known as data labeling. Human language has its own specific features that quite frequently pose problems for machines. Labeling (annotation) envisages classifying images, text structures, and other objects according to distinguished criteria in order to prepare datasets for training a model. Any successful professional activity in the area of machine
learning starts with highly qualitative datasets. Inaccuracy or lack of professional expertise will bring inefficiency and cost lots of money. Subsequently, teaching social sciences and humanities students to label should be seen as one of the priority tasks. Moreover, considering cross-disciplinary approach, this aspect could be taught during foreign language classes.
Stage 1. AI Discourse Exploration
Labeling is a vast domain directly related to artificial intelligence. It includes all sorts of labeling, considering the tasks proposed.
Therefore, we decided to specify which types of labeling may necessitate the involvement of experts from various professional communities. To determine relevant types of labeling, we felt it was critical to develop criteria that they must meet. Among such criteria, we distinguished the following:
- text material for labeling;
- critical thinking inclusion;
- linguistic and discourse analysis application.
The choice of the above-mentioned criteria is directly related to the specific features incorporated in the activities of social science and humanities specialists. Firstly, at large, they deal with text and not images / video / audio files. Secondly, their professional activity is inextricably linked with analysis. For instance, records management specialists gather, input, monitor, and classify given information. By the same token, lawyers think analytically, precisely, and rigorously to describe legal procedures and find reasonable solutions.
Stage 2. Labeling techniques
With regard to what has been said, we believe that these types of labeling meet the criteria (Figure 1):
1) Named entity recognition (NER). It enables us to extract valuable data information from utterances. It aims at finding words and classifying them as entities. Extracted entities could be used to work with databases, make decisions, and generate phrase maps. NER is best suited for marking up key information in text that could include people, geographic locations, and any frequently occurring objects [31].
2) Key word labeling includes identifying key words and phrases. Quite often AI applies wrongfully the factor of "frequency" to mark the most important information. At the same time, for the human it is obvious that even rarely appearing words and phrases may be the ones in need.
3) Named entity linking (NEL). NER is linked to finding entities from the text, while NEL is the process of connecting those named entities to an existing base. Entity annotations correlate with discourse annotation and, therefore, discourse analysis.
4) Text classification involves labeling texts, namely, attaching documents to certain categories. For instance, document classification, contract classification, and text sentiment labeling. It is necessary to tag a single label that will sort large amounts of textual information for document classification [31].
Fig. 1. Data labeling types relevant to the social sciences and humanities specialists
Stage 3. Labeling: From theory to practice The next challenge we faced after specifying the types was how to teach and what exactly to teach. So, we worked out a set of rules that may be helpful for those who start on a journey of labeling:
Settings Documents Metrics Downloads
1) Read the given text and analyze it thoroughly.
2) Summarize the text in your own words using bullets.
3) Specify the categories / phrases / key concepts that are relevant for labeling (Figure 2).
Guidelines
Document Labels
Entities
Dictionaries
Entity Labels
Relations
Requirements
Annotatables
Annotations
Entity Types DOCS
Here you define the categories to highlight in text. An entity can be any concept, for example: protein name, risk, organization, paragraph highlight, customer feedback, etc.
To automatically annotate and disambiguate entities, use Dictionaries or/and Machine Learning.
Save
CivLaw Optional description Delete
CrimLaw Optional description Delete
FamLaw Optional description Delete
ImmLaw Optional description Delete
Fig. 2. Entity types (created with tagtog.com)
4) Do not forget about margins. They may be helpful for writing questions and notes.
5) While labeling text use abbreviations or symbols.
6) If you deal with text classification, it may be useful to work out features that are applicable to the specified text type.
7) While labeling, avoid including unnecessary data in your labels and only include what you want extracted.
8) Do not forget that the same entity should be labeled the same way across all documents. Be consistent!
9) After labeling, check the text to verify if you managed to label all the important pieces.
Having discussed labeling and its possible strategies, we asked the students to label the texts. Our law students
were supposed to deal with text classification and categorize legal cases in different branches based on the content of the cases (Figure 3).
Text classification may mistakenly be seen as an easy task for artificial intelligence algorithms. However, when we look at the example, we notice some indicators that are only visible to humans: "civil litigation" may mislead to the idea of civil law, when in fact it belongs to the area of family law. "An offer of employment" may be incorrectly attributed to the sphere of labor law, though in this context it is about civil law.
Records management includes labeling documents. Subsequently, records management students dealt with entity labeling - tagging entities such as names, department, location, and key phrases (Figure 4).
|_(The civil legislation
shall be applied to the property and to the personal non-property
relations between family members, Indicated In Article 2 of the present Cods, which are not regulated by the family legislation (AitictoS of the present Code), insofar as this does not contradict the essence of family relations.
1) The civil legislation shall be applied to the property and to the personal non-property relations between family members, indicated in Article 2 of the present Code, which are not regulated by the family legislation {Article 3 of the present Code), insofar as this does not contradict the essence of family relations.
2) Compulsory medical treatment, as envisaged in this Code, may be Imposed by a court of law on a person who has committed a socially dangerous deed in a state of Insanity.
3)The individual at no point in the contractor's selection process prior to receiving an offer of e
it from the contractor
s himself or herseff from further consideration or otherwise indicates that he or she is no longer interested in the
position.
4) The objectives of this Act with respect to immigration are
(a) to permit Canada to pursue the maximum social, cultural and economic benefits of immigration;
> [jClvLaw
It 4 o(o.oo%)
LlThe individual at no point in the contractor's selection process prior to receiving an offer of employment from the contractor, removes himself or herself from further consideration or otherwise indicates that he or she is no longer interested in the position.
I mm Law 1
Ii f 0(0.00%)
Jfhe objectives of this 1 Act with respect to Immigration are (a) to permit Canada to pursue the maximum social, cultural Bnd economic benefits of immigration;
CrimLaw 1
11 * 0(0.00%)
Compulsory medical 1
treatment, as envisaged in this Code, may be imposed by a court of law on a person who has committed a socially dangerous deed in a state of insanity.
Fig. 3. Text classification (created with tagtog.com; compiled from: the Civil Code of Russian Federation; the Family Code of the Russian Federation, the Criminal Code of the Russian Federation; the Immigration and Refugee Protection Act, Canada, 2001)
Contract of Purchase and Sale I, Paris London
(Buyer, herein called the Purchaser) Of 31, Rue Jeanvier, Paris, France.
Having inspected the real property (Provence design apartment), hereby offer to
purchase
from
Maryland York
(Full names of Seller, herein called the Vendor)
Entities
total 0 not normalized Q Group/filter entities v
r> 1
ll f 0 (0.00%)
Paris London
1
V 1
1.1 f 0(0.00%)
Maryland York
1
PROP 1
ll + 0(0.00%)
Fig. 4. Named entity recognition (created with tagtog.com)
It is necessary to highlight that all the above mentioned labeling types come in useful. Moreover, some labels may mislead the AI algorithms. For instance, Paris is a city and a name. Maryland is a state in the USA, but, in the contract provided, it is the vendor's name (Figure 4).
Stage 4. Feedback: Open questionnaire The post-teaching stage provided feedback. At this point, we asked the students to assess the significance of the AI training component. The open questionnaire survey was used to collect student feedback.
To analyze the responses provided, we used a qualitative approach. We divided our findings into two
categories: a) the benefits of AI module integration, and b) students' impressions. The categories evaluated by the students are presented below (Figure 5).
Evaluating the benefits of the AI module integration, the students found it necessary, beneficial, and up-to-date. Most students commented positively on the idea of investigating new profession-related instruments. A practice-oriented approach was emphasized. It was pointed out that this is of utmost importance for their future careers. The students specified that before the AI module they lacked knowledge in the sphere of AI application, especially in their professional areas.
30 25 20 15 10 5 0
Results
I .1 I I
AI (general knowledge)
Positive attitude towards AI
I Before (number of people)
Labeling Labeling (practical techniques aspect awareness) (theoretical aspect awareness)
■ After (number of people)
Fig. 5. Results (Open questionnaire analysis)
Conclusions
The widespread inclusion of AI algorithms in professional areas is not to be discussed. Most companies all over the world keep looking for the fastest, highest-quality decisions to solve the ever-increasing number of everyday problems. Considering this idea, it is obvious that AI changes professional activities and future careers, including the social sciences and humanities. Many careers are undergoing transformation. Such reshaping processes are to be reflected in teaching practices.
Our research focused on improving current teaching strategies within the scope of artificial intelligence on an interdisciplinary basis, where foreign language course is seen as an integrating ground for unifying professional and digital knowledge.
One of the professional areas that is in high demand is labeling. Labeling is the process of categorizing
images, text structures, and other objects using specific criteria in order to prepare datasets for training a model. Any successful professional activity in machine teaching and learning begins with high-quality datasets. We suppose that teaching labeling to students in the social sciences and the humanities should be regarded as one of the priority tasks.
It is also important to highlight that our research revealed that not all texts can be easily labeled automatically. This sort of activity is usually reserved for humans. Further investigation undertaken by the authors of the article after completing the pre-teaching stage of the research described above revealed that social sciences and humanities students should study current trends and learn to differentiate between them. Therefore, foreign languages professors should be provided with special manuals, recommendations, and other teaching and learning aids to ensure the expected educational results.
References
1. Francesconi, E. (2022) The winter, the summer and the summer dream of artificial intelligence in law. Artificial Intelligence Law. 30. pp. 147-161.
doi: 10.1007/s10506-022-09309-8
2. Marques Martins, J. (2020) A system of communication rules for justifying and explaining beliefs about facts in civil trials. Artificial Intelligence
Law. 28. pp. 135-150. doi: 10.1007/s10506-019-09247-y
3. Jaillant, L., Aske, K., Goudarouli, E. & Kitcher, N. (2022) Introduction: challenges and prospects of born-digital and digitized archives in the
digital humanities. Archival Science. 22. pp. 285-291. doi: 10.1007/s10502-022-09396-1
4. Malgaroli, M., Hull, T.D., Wiltsey Stirman, S. & Resick, P. (2020) Message delivery for the treatment of posttraumatic stress disorder: longitudinal
observational study of symptom trajectories. Journal ofMedical Internet Research. 22 (4). Art. e15587. doi: 10.2196/15587
5. Rickard, N., Arjmand, H.-A., Bakker, D. & Seabrook, E. (2016) Development of a Mobile Phone App to Support Self-Monitoring of Emotional
Well-Being: A Mental Health Digital Innovation. JMIR Mental Health. 3. Art. e49. doi: 10.2196/mental.6202
6. Billi, M., Calegari, R., Contissa, G., Lagioia, F., Pisano, G., Sartor, G. & Sartor, G. (2021) Argumentation and Defeasible Reasoning in the Law.
J. 4 (4). pp. 897-914. doi: 10.3390/j4040061
7. Huttner, L. & Merigoux, D. (2022) CATALA: Moving towards the future of legal expert systems. Artificial Intelligence Law. doi: 10.1007/s10506-
022-09328-5 [Online] Available from: https://hal.inria.fr/hal-02936606v3/document (Accessed: 01.02.2023).
8. Nowotko, P.M. (2021) AI in judicial application of law and the right to a court. Procedia Computer Science. 192. pp. 2220-2228. doi:
10.1016/j.procs.2021.08.235
9. Chabin, M.-A. (2020) "he potential for collaboration between AI and archival science in processing data from the French great national debate.
Records Management Journal. 30 (2). pp. 241-252. doi: 10.1108/RMJ-08-2019-0042
10. Xie, S.L., Siyi, L. & Han, R. (2022) Competing with artificial intelligence - can the records and information management profession withstand the challenge? Records Management Journal. 32 (2). pp. 151-169. doi: 10.1108/RMJ-08-2021-0033
11. Nawaz, N. (2020) Exploring artificial intelligence applications in human resource management. Journal of management Information and Decision Sciences. 23 (5). pp. 552-563.
12. Tambe, P., Cappelli, P. & Yakubovich, V. (2019) Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. California Management Review. 61 (4). pp. 15-42. doi: 10.1177/0008125619867910
13. Reiling, A.D. (2020) Courts and Artificial Intelligence. International Journal for Court Administration. 11 (2). Art. 8. doi: 10.36745/ijca.343
14. Shi, C., Sourdin, T. & Li, B. (2021) The Smart Court - A New Pathway to Justice in China? International Journal for Court Administration. 12 (1). Art. 4. doi: 10.36745/ijca.367
15. Zou, M. (2020) 'Smart Courts' in China and the Future of Personal Injury Litigation. Journal of Personal Injury Law. [Online] Available from: https://ssm.com/abstract=3552895 (Accessed: 12.12.2022).
16. Aletras, N., Tsarapatsanis, D., Preojiuc-Pietro, D. & Lampos, V. (2016) Predicting judicial decisions of the European Court of Human Rights: A natural language processing perspective. PeerJ Computer Science. 2. doi: 10.7717/peerj-cs.93. [Online] Available from: https://peerj.com/articles/cs-93/ (Accessed: 10.11.2022).
17. Susskind, R. (2017) Tomorrow's Lawyers: An Introduction to Your Future. 2nd Ed. Oxford University Press.
18. Frey, C.B. (2019) The Technology Trap: Capital, Labor, and Power in the Age of Automation. Princeton Press.
19. Fitzpatrick, K.K., Darcy, A. & Vierhile, M. (2017) Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health. 4 (2). doi: 10.2196/mental .7785
20. Stepanov, S.Yu., Orzhekovsky, P.A., Ushakov, D.V. (2020) Problems of digitalization and development stratergies of lifelong learning. Lifelong education: theXXIcentury. 2 (30). doi: 10.15393/j5.art.2020.5684. (In Russian).
21. Bakker, D. & Rickard, N. (2019) Engagement with a cognitive behavioral therapy mobile phone app predicts changes in mental health and wellbeing: MoodMission. Australian Psychology. 54. pp. 245-260. doi: 10.1111/ap.12383
22. Gratzer, D. & Goldbloom, D. (2020) Therapy and E-therapy - Preparing Future Psychiatrists in the Era of Apps and Chatbots. Acad Psychiatry. 44. pp. 231-234. doi: 10.1007/s40596-019-01170-3
23. Katuu, Sh. (2022) Managing records in enterprise-wide systems and some artificial intelligence considerations. International Conference on Artificial Intelligence and the development of Records and Archives Management. doi: 10.13140/RG.2.2.23271.52644 [Online] Available from: https://www.researchgate.net/publication/361119203_Managing_records_in_enterprise-wide_systems_and_some_artificial_intelligence_considerations (Accessed: 10.02.2023).
24. Yarotskaya, L.V. (2017) Electronic documents and records management in Russian andforeign companies. Moscow: Triumph. (In Russian).
25. Remus, D. & Levy, F.S. (2016). Can Robots Be Lawyers? Computers, Lawyers, and the Practice of Law. SSRN Electronic Journal. doi:
10.2139/ssrn.2701092. [Online] Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2701092# (Accessed: 10.02.2022).
26. Pugliese, R., Regondi, S. & Marini, R. (2021) Machine learning-based approach: global trends, research directions, and regulatory standpoints. Data Science and Management. 4. pp. 19-29. doi: 10.1016/j.dsm.2021.12.002
27. Whang, S.E. & Lee, J.-G. (2020) Data collection and quality challenges for deep learning. Proceedings of the VLDB Endowment. 13 (12). pp. 3429-3432. doi: 10.14778/3415478.3415562
28. Polachowska, K. (2019) AI in education: can AI improve the way we teach and learn? In: Neoteric. Software House That Helps You Innovate. [Online] Available from: https://neoteric.eu/blog/ ai-in-education-can-ai-improve-the-way-we-teach-and-learn/ (Accessed: 09.08.2022).
29. Baidakova, D. (2021) Data Labelers in Highly Specialized Fields: How and Where Do We Get Them? [Online] Available from: https://towardsdatascience.com/data-labelers-in-highly-specialized-fields-how-and-where-do-we-get-them-26306a4952b9 (Accessed: 05.12.2022).
30. Cognilytica.com. (2020) Data engineering, preparation and labeling for AI, 2020. [Online] Available from: https://www.cognilytica.com/document/data-preparation-labeling-for-ai-2020/ (Accessed: 13.08.2022).
31. Ovchinnikov, I. (2021) Named entity recognition with Dasha AI Platform in practice. [Online] Available from: https://dasha.ai/en-us/blog/named-entity-recognition (Accessed: 01.12.2022).
32. Kamruzzaman, S.M. (2010) Text classification using artificial intelligence. arXiv preprint arXiv:1009.4964. [Online] Available from: https://ui.adsabs.harvard.edu/abs/2010arXiv1009.4964K/abstract (Accessed: 03.02.2023).
Information about the authors:
D.V. Aleynikova, Cand. Sci. (Pedagogics), associate professor, Moscow State Linguistic University (Moscow, Russian Federation). E-mail: festabene@mail.ru ORCID: 0000-0001-5397-7999
L.V. Yarotskaya, Dr. Sci. (Pedagogics), head of the Department of Psychology and Pedagogical Anthropology, Moscow State Linguistic University (Moscow, Russian Federation). E-mail: lvyar@yandex.ru ORCID: 0000-0001-6539-3085
The authors declare no conflicts of interests.
Информация об авторах:
Алейникова Д.В. - канд. пед. наук, доцент кафедры лингвистики и профессиональной коммуникации в области права Института международного права и правосудия Московского государственного лингвистического университета (Москва, Россия). E-mail: festabene@mail.ru
Яроцкая Л.В. - д-р пед. наук, зав. кафедрой психологии и педагогической антропологии Института гуманитарных и прикладных наук Московского государственного лингвистического университета (Москва, Россия). E-mail: lvyar@yandex.ru
Авторы заявляют об отсутствии конфликта интересов.
Статья поступила в редакцию 23.02.2023; одобрена после рецензирования 26.09.2023; принята к публикации 29.09.2023.
The article was submitted 23.02.2023; approved after reviewing 26.09.2023; accepted for publication 29.09.2023.
