Artificial intelligence in education: Next-gen teacher perspectives

Sămărescu, Nicoleta; Bumbac, Robert; Zamfiroiu, Alin; Iorgulescu, Maria-Cristina Article Artificial intelligence in education: Next-gen teacher perspectives Amfiteatru Economic Provided in Cooperation with: The Bucharest University of Economic Studies Suggested Citation: Sămărescu, Nicoleta; Bumbac, Robert; Zamfiroiu, Alin; Iorgulescu, Maria-Cristina (2024) : Artificial intelligence in education: Next-gen teacher perspectives, Amfiteatru Economic, ISSN 2247-9104, The Bucharest University of Economic Studies, Bucharest, Vol. 26, Iss. 65, pp. 145-161, https://doi.org/10.24818/EA/2024/65/145 This Version is available at: https://hdl.handle.net/10419/281814 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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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 145 ARTIFICIAL INTELLIGENCE IN EDUCATION: NEXT-GEN TEACHER PERSPECTIVES Nicoleta Sămărescu1, Robert Bumbac2, Alin Zamfiroiu3 and Maria-Cristina Iorgulescu4 1) National University of Science and Technology Politehnica Bucharest, University Centre Pitești, Pitești, Romania. 2)3)4) Bucharest University of Economic Studies, Bucharest, Romania. 3) National Institute for Research & Development in Informatics – ICI Bucharest, Bucharest, Romania. Please cite this article as: Sămărescu, N., Bumbac, R., Zamfiroiu, A. and Iorgulescu, C.M., 2024. Artificial Intelligence in Education: Next-Gen Teacher Perspectives. Amfiteatru Economic, 26(65), pp. 145-161. DOI: https://doi.org/10.24818/EA/2024/65/145 Article History Received: 30 September 2023 Revised: 23 November 2023 Accepted: 25 December 2023 Abstract The progress made by artificial intelligence (AI) worldwide has led, through the Beijing Consensus, UNESCO to recommend to governments, in 2019, to include AI in educational policies and processes. While numerous studies inventory students' and teachers’ opinions on the use of AI in education (AIEd), this article differs by analysing the factors that influence the behavioural intention to use AI from the training stage of future primary and secondary teachers in Romania. Through exploratory quantitative research, carried out on a sample of 270 students from the Faculty of Education, Social Sciences and Psychology, the interaction of subjects with AI and the intention of integrating AIEd are followed using binary logistic regression. The analysis conducted shows that among the six variables of the model, “the confidence in personal ability to use AI” and “the perception of a greater number of advantages” have a positive and significant impact on the willingness to use AI in the educational process, more than “being previously used”, “the knowledge level” or “student requirements”. These findings are of particular importance for the revision of teacher training programmes and the development of educational policies that increase the confidence of future teachers in the ability to use AI, eliminating fears or misconceptions about AI. Keywords: artificial intelligence; education; technology; future teachers; teacher training; behaviour intention; Technology Acceptance Model (TAM). JEL Classification: O33, I20  Corresponding author, Bumbac Robert – 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. © 2023 The Author(s). AE Artificial Intelligence in Education: Next-Gen Teacher Perspectives 146 Amfiteatru Economic Introduction Artificial intelligence has seen considerable development since 1943, when the first mathematical model that computerised the biological neurone was developed (McCulloch and Pitts, 1943), and 1956, when the Dartmouth conference, considered the birthplace of AI, took place (McCarthy, 1998). Recent developments in AI caused UNESCO (2019b), through the Beijing consensus, an event attended by 500 representatives of more than 100 member states, to provide guidance and recommendations on developing policies and practices in education that harness AI technologies. Based on the identified literature, it is considered that the perception of teachers' behavioural intention to use AI (identified in the Technology Acceptance Model - TAM) has been under researched since the training stage (Davis, 1989). According to Choi, Jang and Kim (2023), “teachers hardly integrate AI educational tools and little is known about their perceptions of AI tools”. There is also limited knowledge about how students and teachers use AI systems and how AI can be integrated into schools, colleges, and other educational institutions (Williamson and Eynon, 2020). Given the role of future teachers in implementing AI tools in their teaching and how they will inspire and influence generations of students, it is essential to understand their perceptions of AI and the factors that influence their behavioural intention to use the technology. Current research contributes to the development of the existing literature by identifying and testing a first set of factors that influence the use of AI in education. Based on these findings, as AI evolves and is better integrated into education, additional factors can be identified and a more detailed perspective can be generated on the best way to develop teacher training programmes so that teachers become interested and prepared to use AI in their work. For a proper integration of AI elements in education, the aim is to get a clear picture of what stakeholders involved in the educational process want, by conducting as many studies as possible. AI has made considerable progress in fields such as healthcare and business, while in education AI is still in an incipient stage (Singh and Miah, 2019). Unlike most of the papers and books published so far, which focus on students’ attitudes and perceptions of using AI in the medical field in the UK (Sit et al., 2020; Chan and Hu, 2023; Kairu, 2023), the present study investigates the perception of current students - prospective primary or pre-school teachers regarding the integration of AI in the educational process. Thus, the objective of the article is to explore the behavioural intention of future teachers to use AI and to identify factors that influence this intention. To this end, a model based on binary logistic regression was developed to determine the main factors that lead prospective teachers to use AI. The specific objectives of our research are: (1) to understand the degree of familiarity of future teachers with AI; (2) to study the perceptions of future teachers of the change brought about by AI in the educator’s role; (3) to create a model that predicts the behavioural intention of future teachers to use AI and to identify the main factors that influence them. For this purpose, exploratory research was carried out with 270 respondents, current students of the Faculty of Education Sciences of the National University of Science and Technology Politehnica Bucharest - Pitești University Centre and the Râmnicu Vâlcea Territorial Centre. Most of the respondents attend these teacher training courses, being current or prospective teachers in primary or pre-school education in Romania. One of the main desiderata identified worldwide is to increase the degree of “positive use” of AI elements in education (UNESCO, 2019b). Although there are policies and programmes that seek to integrate elements of AI into the education system, the success of this approach Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 147 will largely depend on the knowledge and perception of teachers at the bottom of the education system. Therefore, studying the intention to use AI is important, as it will be passed on, directly or indirectly, to the generations of children that future teachers will train. The article is divided into three parts: (1) review of the existing scientific literature on the use of AI in education; (2) presentation of the research methodology, including a description of the method, sample, and its characteristics; (3) presentation of the results and discussions of the intention to use AI in future teaching careers and the factors that influence it, followed by the conclusions of the article. 1. Literature review Today, the elements of AI are a reality that future teachers must understand and appropriately integrate into their teaching practices to ensure the efficiency and effectiveness of the educational process. Research by Kim, Soyata and Behnagh (2018) shows how AI can provide teachers with real-time feedback during presentations, analysing audio and visual elements of the presenter to determine the quality of the presentation and improve the audience experience. A similar approach is found in Woolf et al. (2013) who appreciate that AI can help improve education in the long term by personalising learning, improving student experiences by evaluating datasets of teaching behaviour, motivation, and social interaction. However, there are also negative aspects of AI, identified by Păvăloaia and Necula (2023), such as the high costs of integrating technology, the fact that it can lead to job losses and higher electricity consumption. It should also be considered that prolonged virtual time can cause dependency, lack of empathy, and communication deficiencies; moreover, there is concern about the security of the data used by AI, in case the data have vulnerabilities, can be compromised or stolen (Pisica et al., 2023). Although most stakeholders understand the benefits and progress brought about by AI, its implementation is not as clear and there are many challenges and pressures on educational institutions to set guidelines and standards in the use of AI (Bonsu and Baffour-Koduah, 2023). Moorhouse and Kohnke (2023) showed that AI tools will generate substantial changes in curriculum and that teacher educators need support to develop the necessary skills to use AI appropriately. Integrating AIEd is a complex process, which requires from the outset to establish the correct working coordinates, so that teachers and students alike perceive the benefits of AI, the safe and constructive ways of using technology in their work. Designing the right way to integrate AI into educational institutions requires first a clear understanding of the knowledge, skills, and perceptions of teachers and students alike. Numerous scientific papers explore the knowledge and preconceptions of teachers related to technologies, robots, or elements that generate artificial intelligence (GAI). KaplanRakowski et al. (2023) show that the more teachers have a better outlook on GAI, the more often they use it. Based on this finding, the present research will analyse the extent to which future teachers in Romania perceive more advantages or disadvantages in using AI. The benefits of AI for teachers do not seem to be confined to generating content, Chounta et al. (2022) highlight that, despite their limited knowledge of this type of technology, Estonian teachers use AI as a tool to facilitate the access and use of multilingual content. Unfortunately, there is still no consensus on methods and techniques to use AI in teaching and research. Fahrman et al. (2020) explain this situation by the constantly changing skills of teachers and the fact that “researching, exploring and understanding this knowledge and skills AE Artificial Intelligence in Education: Next-Gen Teacher Perspectives 148 Amfiteatru Economic is also complex”. Teacher technical skills, knowledge, and perceptions greatly influence student knowledge; therefore, this research will assess the extent to which prospective teachers used AI and will consider that they have sufficient knowledge of it. The differences in perceiving AI can also be explained by age; Chan and Lee (2023) show that Gen Z (born 1990-2000) students are optimistic about the potential of AI technologies, while Gen X (born 1960-1980) and Gen Y (born 1980-1990) teachers acknowledge its benefits, but also express concerns about overreliance on, ethical and pedagogical implications of this technology. From this point of view, this research will examine teachers’ perceptions of students’ requirements to use AI and, respectively, the appreciation of students for teachers who use this technology. Therefore, AIEd is at the same time a challenge in respecting the authenticity of computer-generated content, which can lead to the “sacrifice of uniqueness and creativity” of the human mind, so it is imperative to adopt standards for responsible and ethical behaviour in the use of this technology (Dalalah and Dalalah, 2023). Holmes et al. (2022) point out that the low interest in the ethics of using AI in education may lead to an underdevelopment of this field, limiting the impact of AI and the usefulness of this technology. A second broad category of scientific papers investigates the perception and interaction of students with AI. Chao et al. (2021) observe that attitudes toward AI among students tend to be predominantly positive. However, as Ravi Kumar and Raman (2022) note, students consider that AI can be used in teaching and learning activities or other administration processes, but do not trust its use in the assessment processes such as admissions or examinations. Doumat et al. (2022) complement this finding by pointing out that, although more than 57% of students believe that “assessment by AI is more objective”, only 26% of them “want to be assessed by AI”. As with teachers, students understand the potential of technology, but see its implementation and use in their future careers as a distant and long process. Sit et al. (2020) confirm this with the example of medical students who feel unprepared but understand the growing importance of AI in their future work and therefore would like to learn more about it. Advances in artificial intelligence (AI) have also revealed problems with the accuracy of computergenerated data. Research by Dalalah and Dalalah (2023) shows that the use of AI in various professions, such as medicine, can be harmful by providing erroneous positive or negative test results, leading to delays in caring for health problems or the suggestion of unnecessary procedures or therapies. Such errors can also occur in the use of AI in student assessment. This may explain the opinion of students that “AI cannot replace the teacher”, but at the same time they want more AI resources to help them achieve their educational goals (Zou et al., 2020). The difference between different types of AI resources stems from the perceived usefulness and simplicity of communication, as Kim et al. (2020) show, and the use of AI can work especially when face-to-face interaction is not possible. Unlike previous works, the present research investigates the perception of AI among future teachers during their training stage and focusses on identifying the main factors and how they influence the behavioural intention to use AI. Future teachers will play a crucial role in integrating and using AI in the development and education of children and students from an early age, helping them to develop cognitive, social, and communication skills. This study takes place in a context where modern early childhood and school education is considered a key stage for the cultivation and AI literacy among children, as demonstrated by various Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 149 initiatives undertaken by organisations such as Stanford - AI4ALL or the International Society for Technology in Education (ISTE). Studies such as those by Al Darayseh (2023), Limna, Kraiwanit and Jangjarat, (2023) or Leong and Chaichi, (2021) prove the importance of the scientifically established Technology Acceptance Model (TAM) used to explain the acceptance and intention to use new technologies (Davis, 1989). Building on this model, the present research will examine the intention of the behavioural to use AI (identified in the TAM) AI among prospective teachers. Just as Hasib et al. (2022) use logistic regression to predict student performance based on historical data, the present research aims to use binary logistic regression as a method to predict teachers’ intention to use AI based on attitude and perception of its usefulness. 2. Research methodology The purpose of the present research is to explore the intention of prospective teachers to use AI and to identify its determining factors. For this purpose, a quantitative exploratory research was carried out, using a questionnaire-based survey, which tests the perceptions of current students enrolled in teacher training programmes at the Pitești University Centre, the Faculty of Education, Social Sciences and Psychology, and its regional branch in Râmnicu Vâlcea. The questionnaire included 10 questions using the five-step Likert scale, from “to a very small extent” to “to a very large extent”, 4 multiple choice questions, 1 open question, and 4 demographic questions. Given the difficulty of conducting research on a representative random sample, the sample included respondents based on the criterion of relevance, i.e., people studying education sciences and teacher education who are aware of the evolution of AI technology and use (Jurconi et al., 2022). The choice of quantitative research is justified by the objective of creating a model that predicts the use of AI by future teachers and identifies factors that influence this behaviour. This required recording as many responses as possible, in an organised way, allowing evaluation and validation of the data using statistical methods of analysis (Bell and Waters, 2018). In addition, quantitative research has the advantage of being more objective, by generating quantifiable results that allow for a possible generalisation of findings for the whole population. The questionnaire was sent by e-mail to all undergraduate students enrolled in the Pedagogy of Primary and Pre-school Education section and to those enrolled in the Early Childhood Education Master’s programme at the Faculty of Education, Social Sciences and Psychology, National University of Science and Technology Politehnica Bucharest - Pitești University Centre and Râmnicu Vâlcea Territorial Centre, most of them working or intending to work in primary or pre-school education in Romania. The responses were collected between May and August 2023 using the Google Forms platform, and data analysis and interpretation were performed using SPSS statistical software. Therefore, of a total of 370 students, 270 valid responses were recorded, resulting in a response rate of 73%. Given that the integration of AI in education is still in its infancy stage, especially in primary and secondary education in Romania, the present research aimed to open the door for prospective teachers to use AI, and the respondents were not required to have practical experience teaching with the help of AI. Participation in the research was optional, voluntary, and completely anonymous in order to protect the identity of the respondents. When the questionnaire was distributed, the information message included the information that the completion of the questionnaire implied consent to the aggregate dissemination of the AE Artificial Intelligence in Education: Next-Gen Teacher Perspectives 150 Amfiteatru Economic research results. As illustrated in Table 1, 97.4% of the sample is female dominated, which is a characteristic of pre-school and primary education not only in Romania but worldwide. When it comes to technology, the age variable is seen as important in openness to new technologies. Thus, a balance between Generation Z respondents (41.48%) and Generation Y respondents (45.18%), and only a small share of Generation X participants (13.33%) is observed. Therefore, it can be said that the study is largely conducted in the age segment familiar with new technologies. In terms of education level, all research participants are pursuing Bachelor’s or Master’s degrees and are in the process of training for their future careers. Participants in the study included 21.1% of current students and teachers in preschool and primary education, 39.62% of current students who want to become teachers in pre-school or primary education, and 39.25% of respondents who do not yet have a job and do not know what career path they will follow. Table no. 1. Distribution of the sample by gender, age, level of education and current occupation or career plan Gender Age (years) Level of education Current occupation or career plan Female 263 18-23 112 undergrad uate student 238 Currently a preschool teacher 38 24-39 122 Currently a primary school teacher 19 Male 7 40-60 36 MA student 32 Future pre-school teacher 66 > 60 0 Future primary school teacher 41 Not yet employed and no career plan 106 270 270 270 270 Source: Authors’ own calculations The study compares the perspective of two distinct groups: Group 1 – those students who have previously used AI and Group 2 – those who did not use AI. Several factors influencing the relationship between teacher and AI were examined, such as the level of knowledge of AI, the degree of interaction and previous exposure to AI elements, the readiness to use AI in teaching activity, the desire of future teachers for further training in AI, changes brought about by AI in student-teacher interaction, in improving the learning experience, the simplification of the administrative activities, the expectations of pre-school children / schoolchildren for the use of new technologies and their vision of integrating AI in the teaching and learning process. Following this analysis, a binary logistic regression was performed to predict the intention of future teachers to use AI and to understand its influencing factors. The following variables were established for this purpose: ● dependent variable: I17 – Behavioural Intention to Use AI in future teaching career (identified in the TAM); ● independent variables: I1 – Use of AI up to now; I3 – I have sufficient knowledge and can explain what AI means; I4 – I know how to exploit the benefits of AI in school/kindergarten; I6 – Pupils appreciate more the teachers who use AI; I8 – AI changes the role of the educator; I10 – AI will help improve student-teacher interaction; I14 – I consider myself able to teach with AI (identified in TAM as ease of use); I15 – A future Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence Systems AE Vol. 26 • No. 65 • February 2024 151 teacher needs to master and use AI; I16 – I consider there are more advantages to AI than disadvantages (identified in TAM as perceived usefulness). The study of behavioural intention to use AI is of particular importance in the TAM model because it is the precursor to actual use (Davis, 1989). As can be seen in Table 2, for both constructs (attitude toward AI use and perception of AI usefulness), Cronbach Alpha coefficient (α) > 0.700, Average Variance Extracted (AVE) > 0.500, and Composite Reliability (CR) > 0.700, demonstrating an appropriate level of internal consistency between the variables used in the study (Henseler and Sarstedt, 2013; Nemțanu et al., 2021). Table no. 2. Validity of the data used in the questionnaire Construct Variables Loading α / AVE / CR Attitude towards the use of AI I3 – I have sufficient knowledge and can explain what AI means I4 – I know how to exploit the benefits of AI in school/ kindergarten I6 – Pupils appreciate more the teachers who use AI I8 – AI changes the role of the educator I10 – AI will help to improve pupil-teacher interaction 0.764 0.860 0.764 0.332 0.712 α: 0.737 AVE: 0.504 CR: 0.826 Perception of AI usefulness I1 – Use of AI up to now I14 – I consider myself able to teach using AI I15 – A future teacher must master and use AI I16 – I think there are more advantages to AI than disadvantages I17 – Willingness to use AI in future teaching career 0.179 0.751 0.874 0.850 0.764 α: 0.744 AVE: 0.533 CR: 0.833 Source: The authors’ own calculations Several hypotheses have been established on the basis of the papers published to date. The research conducted by Labrague et al. (2023) shows that previous exposure to AI technologies, knowledge about AI and competence in using AI lead to a positive perception and implicitly to a greater willingness to incorporate AI into subsequent practices. On the basis of this finding, the following two hypotheses are formulated: H1: The previous use of AI significantly and positively influences the intention of behavioural use of AI in the future teaching career. H2: Knowledge of AI significantly and positively influences the behavioural intention to use AI in future teaching careers. Furthermore, the study by Ali (2017) supports the idea that teachers’ willingness to use AI is directly influenced by what their students want and need. Therefore, teachers exposed to the enthusiasm of students and their positive experiences of using AI led to increased chances of incorporating AI into teaching methods, resulting in the formulation of the following hypothesis: H3: Students’ expectations and requirement to use new AI technologies significantly and positively influence the behavioural intention to use AI in future teaching career. It is interesting to note how some teachers understand their role in helping students use technology, as well as how they view the use of AI in their own work (Ali, 2017). Specifically, teachers will play a key role in ensuring that AI is used correctly and ethically AE Artificial Intelligence in Education: Next-Gen Teacher Perspectives 152 Amfiteatru Economic to measure and assess student performance, that the data obtained is accurate, reliable, and true (Owan et al., 2023). All these observations led us to formulate the fourth hypothesis: H4: The perception of the change brought about by AI in the educator’s role significantly and positively influences the behavioural intention to use AI in the future teaching career. The use of AI is slowed by external barriers (lack of easy access to hardware, software, tools and training) and internal barriers (lack of trust, negative beliefs and attitudes about using AI in school), thus, beliefs, confidence and attitude significantly influence the teachers’ decision to use AI in their work (Rowston, Bower and Woodcock, 2022). The following hypothesis thus emerges: H5: The perception of own ability /confidence to use AI in teaching significantly and positively influences the behavioural intention to use AI in the future teaching career. A survey of 399 Hong Kong university students found that perceptions of the usefulness and willingness to use GAI technology increased as they identified its benefits in learning, writing and studying activities (Chan and Hu, 2023). This finding prompts the formulation of the following hypothesis: H6: The perception of more advantages than disadvantages of AI in education (perceived usefulness) significantly and positively influences the behavioural intention to use AI in a future teaching career. 3. Results and discussion 3.1. Degree of familiarity of future teachers with AI Upon analysis of the data, it clearly appears that up to the time of the research, 77.41% of the students enrolled in teacher education programmes have heard about AI, 27.04% have further researched AI, and only 20% used AI. To understand to what extent the use of AI contributes to changing perceptions, the results of the two groups were analysed: Group 1 – those who have actually used AI; and Group 2 – those who did not use AI, although they have heard or read about this technology. Thus, 59.26% of the respondents included in Group 1 used AI for educational purposes, 48.15% out of curiosity, or 37.04% for various work or even entertainment tasks. The low rate of use of AI is explained by the early stage of development of the technology. The survey respondents rate their knowledge and understanding of AI as average, with higher values for Group 1 – those who have previously used this technology, as shown in Table 3. It is important to note that, regardless of the group, there is a high interest in improving the use of AI in the teaching process, with more than 76% of the respondents being interested in attending training courses on this topic. Table no. 3. 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