A systematic analysis of new approaches to digital economic education based on the use of AI technologies

Mihai, Laurențiu; Mănescu, Leonardo-Geo; Vasilescu, Laura; Băndoi, Anca; Sitnikov, Catalina Soriana Article A systematic analysis of new approaches to digital economic education based on the use of AI technologies Amfiteatru Economic Provided in Cooperation with: The Bucharest University of Economic Studies Suggested Citation: Mihai, Laurențiu; Mănescu, Leonardo-Geo; Vasilescu, Laura; Băndoi, Anca; Sitnikov, Catalina Soriana (2024) : A systematic analysis of new approaches to digital economic education based on the use of AI technologies, Amfiteatru Economic, ISSN 2247-9104, The Bucharest University of Economic Studies, Bucharest, Vol. 26, Iss. 65, pp. 201-219, https://doi.org/10.24818/EA/2024/65/201 This Version is available at: https://hdl.handle.net/10419/281817 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 201 A SYSTEMATIC ANALYSIS OF NEW APPROACHES TO DIGITAL ECONOMIC EDUCATION BASED ON THE USE OF AI TECHNOLOGIES Laurențiu Mihai1, Leonardo-Geo Mănescu2, Laura Vasilescu3, Anca Băndoi4 and Cătălina Sitnikov5 1)2)3)4)5) University of Craiova, Romania Please cite this article as: Mihai, L., Mănescu, L.G., Vasilescu, L., Băndoi, A. and Sitnikov, C., 2024. A Systematic Analysis of New Approaches to Digital Economic Education Based on the Use of AI Technologies. Amfiteatru Economic, 26(65), pp. 201-219. DOI: https://doi.org/10.24818/EA/2024/65/201 Article History Received: 29 September 2023 Revised: 19 November 2023 Accepted: 20 December 2023 Abstract Since the start of the global COVID-19 pandemic, most higher education institutions have been forced to exchange the traditional teaching environment for online education, and many chose to continue to use digital education platforms after its end, especially through the use of artificial intelligence (AI) applications and technologies. Our research represents a systematic literature review of a number of 60 scientific papers, aiming to study how the concept of digital economic education based on artificial intelligence is approached in the scientific literature, how artificial intelligence applications are used in digital economic education, and which are the critical success factors and the challenges that this domain is facing. Our findings have shown that most researchers define digital education as the use of technology to support educational activities, while highlighting artificial intelligence and its different applications as an essential element of current digital education, which has the potential to fundamentally transform the economic processes. The large-scale adoption of e-learning systems based on artificial intelligence is influenced by technology, by their superior capabilities in terms of coherently correlating the learning and studying processes, by the teachers’ trust in the results generated by these technologies and by cultural factors, while facing several challenges related to the users’ resistance to change, digital competences, the systems’ accessibility, as well as financial issues. Furthermore, based on this research endeavour, a model system of correlations and elements has been developed, specifically for the digital economic education based on artificial intelligence. This model includes both the success factors and the unique challenges inherent in the particular application areas. Keywords: Artificial Intelligence (AI), digital economic education, systemic model regarding digital economic education based on AI JEL Classification: D83, I21, I23, O33  Corresponding author, Laurențiu Mihai – e-mail: [email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2024 The Author(s). AE A Systematic Analysis of New Approaches to Digital Economic Education Based on the Use of AI Technologies 202 Amfiteatru Economic Introduction based on the current state of knowledge Artificial intelligence represents a critical element of the contemporary surge of emerging technologies, which, lately, have significantly transformed the landscape of economics education. Artificial intelligence (AI) refers to the ability of a software application to execute tasks commonly attributed to intelligent beings (Salas Pilco and Yang, 2022), comprising several branches, including digital communication, machine learning, big data analysis and processing, and natural language processing (Chen et al., 2020). The rapid expansion of this technology is progressively reshaping the way individuals interact, communicate, reside, acquire knowledge, and engage in professional activities. Artificial intelligence has various applications within the realm of digital economic education. For example, AI has been included in several educational technologies, including chatbots (Kasneci et al., 2023), intelligent tutoring systems, and automated assessment software (Hwang and Tu, 2021). According to Chen et al. (2020), AI-based systems offer increased opportunity for all individuals involved in the process of economics learning and training. Previous studies have demonstrated the benefits of incorporating AI into economics education, such as enhanced collaboration among students, customised learning experiences, efficient scheduling of learning activities (Chatterjee and Bhattarcharjee, 2020), adaptive feedback on learning processes (Salas Pilco and Yang, 2022), reduced administrative burden for teachers (Xu and Ouyang, 2022), and automation of exam evaluation and other educational tasks specific to economics education (Maslova et al., 2020). This paper focuses on AI in Education (AIED), which encompasses the use of artificial intelligence technologies, including intelligent tutoring systems, chatbots, digital assistants, and other digital automation components that contribute to the advancement and enhancement of digital economic education (Micu et al., 2021; Bearman et al., 2022). AIED possesses significant potential for enhancing learning, teaching, evaluation, and administration in economics higher learning institutions, offers the opportunity to customise and adapt the economics educational content to the specific requirements of students, while supporting academics in the educational process, making it a significant emerging technology in the field of digital economic education (Felix, 2021; Ooi et al., 2023). The current research addresses AIED as a central element in optimising teaching-learning processes, yielding significant benefits in personalising the educational experience (Felix, 2021). AIED applications demonstrate the capacity to analyse substantial volumes of data generated during the learning process, discerning patterns and adapting content based on individual learners' needs. This adaptability and customisation of instruction represent a notable advantage of employing AIED, contributing to the optimisation of levels of knowledge assimilation (Chen et al., 2022). Thus, the use of AI in digital education opens substantial perspectives for enhancing the efficiency and relevance of the educational process in the current context characterised by accelerated technological advancements (Celik et al., 2022). Thus, by conducting a systematic review of the relevant literature published in the last four years (2020-2023), our paper aims to contribute to the ongoing research on AIED. Our research endeavour focused on answering four research questions: (1) How is the concept of digital economic education based on AI presented in the recent literature; (2) How is AI used in digital economic education? (3) What are the critical success factors influencing the use of artificial intelligence applications? (4) What are the challenges that the use of AI applications in digital economic education is facing? Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 203 In order to answer the research questions, this study will discuss on the general features of AIED, the AI’s impact on the economics learning process, the factors which affect the adoption of these technologies, the most used AIED applications as well as the challenges that these technologies have to overcome in order to be adopted on a larger scale. Consequently, the paper is structured into two main sections: Research Methodology which refers to search query used in the Web of Science database, the inclusion and exclusion criteria, the qualitative assessment and the coding process, and Results and Discussion which includes: the structure on categories of the papers included in the analysis, the concept of economics digital education based on AI, the use of AI in economics education, critical success factors affecting the use of AI applications, and the challenges which economics digital education based on AI is facing and finally, the conclusions and recommendations formulated considering the research results are presented. 1. Research Methodology 1.1. Search query used in the Web of Science database In order to fulfil our research objective and to answer the four research questions, we have conducted a comprehensive search of papers that were related to AIED, using the Web of Science Database. The search query used the following string ((TS=“e-learning” OR TS=“digital economics education” OR TS=“online economics learning”) AND (TS=“digital tools” OR TS=“AI” OR TS = Artificial Intelligence”)), which yielded 12,962 results. In order to conduct a first stage analysis, we have used VOSViewer software, which has generated several bibliometric maps, based on which we concluded that the journals which have published the most papers on the subject of AIED are Education and Information Technologies (416 papers), followed by Education Sciences (370 papers) and International Journal of Emerging Technologies in Learning (175 papers). At the same time, the most cited journal (as of July 2023) is Education and Information Technologies (2926 citations), followed by Computer and Education (2245 citations), Education Sciences (1980 citations), International Journal of Emerging Technologies in Learning (1209 citations), and Interactive Learning Environments (1128 citations). Lastly, the most cited papers that address the topic of digital economic education based on artificial intelligence until July 2023 (Figure no.1) are Radianti et al. (2020) (419 citations), Köenig et al. (2020) (244 citations), Watermeyer et al. (2021) (310 citations), and Almaiah et al. (2020) (308 citations). AE A Systematic Analysis of New Approaches to Digital Economic Education Based on the Use of AI Technologies 204 Amfiteatru Economic Figure no. 1. Bibliometric analysis regarding the most cited papers on the topic of AIED 1.2. Inclusion and exclusion criteria and quality assessment In order to refine our search, only research articles, conference proceedings, and review paper (12,843 papers), published after 2020 (6,212 papers) were included, from three Web of Science categories: Education Educational Research, Education Scientific Disciplines and Social Issues or Education Special (2,379 papers), that have been written in English (2,604 papers). Moreover, we have excluded all the papers that have been published in medical journals, resulting in 1,887 papers. Finally, we limited our search only to open access papers, which yielded a final number of 915 articles. The decision to limit the search to open access articles was made based on considerations of accessibility, open access articles being available to all individuals interested in a specific thematic area, enabling them to read and analyse the content without restrictions. Moving forward, 183 papers were allocated to each of the five authors. After reading the titles and the abstracts, those articles which were considered irrelevant to our research were excluded. In order to be considered relevant, the papers should have focused on artificial intelligence used as a digital education tool. There were excluded papers which were purely technical, focused on programming, or focused on technical, humanities, or healthcare. During this first quality assessment stage, 143 articles were identified that met the selection criteria. Among them, each of the five authors thoroughly read 30-31 papers, conducting a critical analysis of each article, using their experience as reviewers for various scientific journals. During this second stage of quality assessment, we eliminated the papers which were found inconclusive, poorly written, employed questionable methods or in which the findings were not thoroughly presented and discussed, reaching a final number of 59 relevant articles. Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 205 Finally, we have included one more paper (Zawacki-Richter et al., 2019) in our analysis, although it was published before 2020, because it was being cited in many of the articles included in the final portfolio by July 2023 (409 citations). Therefore, 60 papers were used in this current research (Figure no. 2). Figure no. 2. PRISMA sample design diagram for systematic evaluation Source: adapted after Bond et al., 2020, p.9 1.3. The coding process This stage aimed to code the 60 articles into four categories: digital education based on AI, the use of AI in digital education, and critical success factors the challenges this area is facing. The authors independently coded the selected articles, initially coding the first 10 papers based on the four categories. After establishing and testing the coding methodology, relevant information was extracted for further analysis. Other variables included the journal, the publication year, the paper type, and the digital tool type (Table no.1). All authors participated in the final coding process, coding 12-13 papers, and reached a consensus regarding the ambiguous findings. 2. Results and discussion 2.1. The structure of the papers included in the study Our systematic review encompasses a total of 60 research papers, which have been classified into four general categories (Table no. 1). Thus, our sample consist of 22 papers categorised as “The concept digital economics education based on AI”, 24 papers included in the category “The use of artificial intelligence in digital economic education”, 5 papers categorised as “Critical success factors affecting the use of Artificial Intelligence applications in digital economic education” and 7 papers included in the category “Challenges facing the use of artificial intelligence applications in economic education”. Moreover, one paper was found to be relevant to both the “The concept of e-learning” and “Critical influence factors” categories, while another paper was included in both the “Critical influence factors” and “Challenges” categories. AE A Systematic Analysis of New Approaches to Digital Economic Education Based on the Use of AI Technologies 206 Amfiteatru Economic Table no.1. Paper classification Variable Percent Frequency Variable Percent Frequency Publication year AI applications used in the paper 2019 1.67% 1 AI 31.66% 19 2020 41.67% 25 MOOCs, LMSs 46.67% 28 2021 25% 15 Gamification 1.67% 2 2022 23.33% 14 Blockchain 1.67% 1 2023 8.33% 5 VR 1.67% 1 Journal Others 16.66% 10 Int. J. Emerg. Technol. Learn. 18.33% 11 Subject Category Educ. Inf. Technol. 18.33% 11 Digital education based on AI 38.33% 23 Educ. Sci. 8.33% 5 AI in digital educaiton 40% 24 Comput. Educ. 6.67% 4 Critical success factors 10% 6 Comput. Educ.: Artif. Intell. 5% 3 Challenges 15% 9 Others 43.34% 2 2.2. The concept digital economic education based on AI Digital education, also referred to as online education, e-learning, distance education, or distance learning, encompasses teaching and learning processes conducted within an online setting (El-Sabagh, 2021). O'Neill (2023) defines e-learning as the use of technology to aid instructional activities, with the aim of achieving specified educational goals without the need for both the learner and the instructor to be physically present in the same area, and includes various elements, including technologies, educational systems, surroundings, and the primary users, namely students and teachers. In the last decade, the educational environment has undergone a significant shift from the traditional structure to a more engaging and stimulating learning environment (Butler-Henderson and Crawford, 2020). In this context, the global adoption of digital technologies and tools is having a profound impact on the methods and practises employed in education. Among the many digital tools that can be used in online education, artificial intelligence represents an innovative and revolutionary approach that facilitates the customisation of both educators' and students' experiences. Consequently, the use of artificial intelligence in education has the potential to enhance and adapt pedagogical practises by different forms of personalised education, intelligent content delivery, automation of educational tasks, tutoring, and ensuring inclusive educational opportunities for students with special requirements. In order to properly implement an AIED-based platform, several elements need to be considered such as the students’ involvement (Bond et al., 2020; Theresiawati et al., 2020; El-Sabagh, 2021; Chiu et al., 2021; Ng et al., 2023), digital literacy (Falloon, 2020; Köenig et al., 2020; Muammar et al., 2023), satisfaction (Giray, 2021; Shams et al., 2021; Zhao et al., 2021), overall students s’ performance (Butler-Henderson and Crawford, 2020; Rakic et al., 2020; Rajabalee and Santally, Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 207 2021), and local factors such as technological resources and internet connectivity (Anthony et al., 2020; Khalil Awan et al., 2021; Lloret-Irles et al., 2022). Several authors argue that the concept of e-learning is closely associated with student engagement (Bond et al., 2020; Theresiawati et al., 2020; Chiu et al., 2021; El-Sabagh, 2021; Ng et al., 2023), which is driven by motivation, which is, in turn, influenced by the students’ autonomy, competency, and relatedness. Thus, there is a need to enhance student involvement in terms of behaviour, affect, and cognition, which should be a primary focus for educators (Ng et al., 2023). Bond et al. (2020) revealed that the most often identified variables are behavioural, affective, and cognitive engagement. El Sabagh (2021) highlighted that students’ engagement is closely related to the personalisation of the learning process according to their individual learning styles, which also positively influences their academic performance. AI applications have an essential role in this adaptive approach to e-learning, which aims to promote direct learning, facilitate knowledge development, and enhance the overall learning experience (Theresiawati et al., 2020; El Sabagh, 2021). Another element under discussion is the level of student satisfaction which is influenced by the platforms’ quality (Dangaiso et al., 2020; Shams et al., 2022), design (Giray, 2021) and accessibility (Shams et al., 2020), as well as the way they are implemented (Almusharraf and Kharo, 2020). Theresiawati et al. (2020) and Giray (2021) suggest three fundamental elements which influence the quality of an AI based e-learning system: quality of teaching staff, the learning management system’s quality, and content quality. Students’ engagement and satisfaction have been shown to be closely related, as highlighted by Rajabalee et al. (2020), while Rakic et al. (2020) indicate a noteworthy correlation between students' academic performance and the utilisation of AI as a digital education tool. Several papers examined the educators’ experience with AI-based e-learning platforms, as well as their perception regarding their efficiency as a learning instrument, highlighting several drawbacks, such as limited time available for teachers to enhance their digital literacy (Köenig et al., 2020; Khalil Awan et al., 2021), inadequate training (Zhao et al., 2021; Ng et al., 2023), and improper management of technical issues (Dhillon and Murray, 2021; Cranfield et al., 2021). The accelerated adoption of e-learning systems based on artificial intelligence has led to the recognition of the importance of educators’ digital competence (Falloon, 2020; Köenig et al., 2021; Zhao et al., 2021). In their works, Ng et al. (2023) and Muammar et al. (2023) have discussed the DigCompEdu framework, a guiding principle for educators in effectively integrating resources and designing AI-based learning programmes. The model includes several elements such as interaction, digital resources (including AI applications), teaching and learning, assessment, empowering students, and enabling learners’ digital proficiency (Ng et al., 2023), while aiming to evaluate the academic community’s ICT proficiency and their current digital and AI related competences (Muammar et al., 2023). 2.3. The use of AI in digital economics education Our research has shown that several types of digital tools can be used in order to properly implement an e-learning system, among which AI is highlighted as having the potential to transform the educational process. Hillmayr et al. (2020) identified five categories of AI-based digital education tools: exercise and practice programmes, tutoring systems, AE A Systematic Analysis of New Approaches to Digital Economic Education Based on the Use of AI Technologies 208 Amfiteatru Economic hypermedia systems, intelligent tutoring systems and simulations, the last two being the most effective. Bonfield et al. (2020), discusses other four types of AI applications: smart campuses, digital assistants, massive open online courses (MOOCs), and Learning Management Systems (LMSs). Lizcano et al. (2020) highlighted the existence of a number of other blockchain-based applications, which enable the decentralised validation of students' acquisition of economics competencies, ensuring that their training aligns with the prevailing job landscape and the market demands. Radianti et al. (2020) focuses on virtual reality economics educational applications, which are shown to be an excellent educational instrument for higher education but are still in an experimental stage. Abdulaziz Alsuhbhi et al. (2020) dealt with the topic of introduction of game elements in economics learning systems, which includes the use of levels, experience points, badges, dashboards, progress bars, content unlocking, and leader boards. Futhermore, Mhlanga and Moloi (2020) aimed to study the use of AI technologies in the economics educational sector and their findings suggest that the COVID-19 pandemic determined the development of virtual learning platforms, the use of educational applications and websites, the establishment of STEM digital schools, but also a widespread transition to distance learning. Bearman et al. (2022) defined artificial intelligence used as a digital education tool (AIED) as a digital technology capable of revolutionising conventional education, offering more dynamic and enhanced educational approaches using highly personalised, scalable, and costeffective alternative solutions. Xu and Ouyang (2022) defined AIED as an emerging interdisciplinary domain that uses AI applications to transform educational design and enhance student learning. According to Lameras and Arnab (2022), AIED covers the development, implementation, and assessment of tools, pedagogical models, instructional frameworks, ethical considerations, and teaching staff competencies. Chen et al. (2020) states that the development and execution of AIED technologies involves the collaboration of system designers, data scientists, product designers, statisticians, linguists, cognitive scientists, psychologists, education experts, and numerous other professionals. Felix (2021) highlights cost and time saving as two main significant advantages of AIED, which is particularly relevant in the current economic climate characterised by limited financial resources and the need for effective time management. Using AI to assume some aspects of the educational process has the potential to minimise salary costs, while allowing faculty members to focus on other areas, such as teacher-student interpersonal relationship or academic research which contributes to their professional reputation. Kasneci et al. (2023) as well as Chen et al. (2020) discussed several AIED systems, such as natural language processors, collaborative robots, large language models, and chatbots, designed to either assist teachers in their tasks or operate autonomously, carrying out activities similar to those of an educator. 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