Abstract
As interest in artificial intelligence (AI) within the education sector continues to grow, many experts believe that the roles of teachers, schools, and educational leaders are likely to evolve. This study aims to explore potential scenarios arising from the integration of AI into education and to examine its possible implications for the future of schools. Utilizing a phenomenological approach—a qualitative research method—the study gathered insights from participants across various professional backgrounds. The findings suggest that the introduction of AI in education will bring both advantages and challenges for schools and educators. While many participants expressed generally positive attitudes toward AI, some concerns were raised, particularly by teachers and academics, about its impact on the future of teaching. Legal professionals focused primarily on the legal frameworks and potential issues AI may pose in educational settings, whereas engineers viewed AI as a valuable tool capable of enhancing quality and delivering benefits across the sector.
Keywords: Artificial intelligence; education; school management
Introduction
Artificial Intelligence (AI), often described by the general public as machines or computers having the ability to think and behave like humans, reflects the ambition to create digital systems that can replicate human thought and action (Wartman & Combs, 2018). In this context, AI can be defined as the proficient simulation of human reasoning and behavior through tools or software programs (Mohammed & Watson, 2019). As Timms (2016) explains, it may be unrealistic to think of AI only in the form of home computers. Instead, AI is expected to integrate into our daily lives in diverse shapes and functionalities. Ng (2017) refers to AI as the “new electricity” of our time. With its strong potential to contribute to economic progress, AI is predicted to become a foundational element of the Fifth Industrial Revolution (see, Golic, 2019). This could help explain why China made a record-breaking $40 billion investment in AI in 2017 (Mou, 2019). Estimates show that AI may raise China’s GDP by 26%, or around $7 trillion, by the year 2030. In contrast, North America is projected to experience a 14.5% increase in GDP, roughly $3.7 trillion during the same timeframe (PwC, 2017). These projections help to better grasp the added value and global influence of AI on the future economy—and more specifically, how it may shape the education sector, which influences workforce development and serves as a catalyst for economic growth and technological progress. The widespread advancement of AI will affect many aspects of life, including reconfiguring social structures and transforming administrative and instructional processes within schools. Educational institutions, which are being pushed to adapt to the digital world and embed 21st-century competencies, are among the entities most likely to experience significant change due to AI’s growth. Karsenti (2019) notes that technological tools are increasingly filling daily life and becoming central to young people’s experiences, meaning schools may have little choice but to integrate them. In this light, this study aims to analyze how professionals from different fields—such as law, business, education, and engineering—perceive the development of AI and foresee its role in education. The core purpose is to assess what the use of AI in educational settings implies and what possible outcomes it might present for the future of schooling, as viewed through the perspectives of individuals across various industries.
Roll and Wylie (2016) reference Henry Ford’s famous quote: “If I had asked people what they wanted, they would have said faster horses.” This analogy suggests that current education systems have become more efficient—perhaps even ‘faster classes’ delivering quicker results. But the question remains: will these fast-paced classrooms remain effective, or will we need to rethink education for the challenges of the 21st century? As the 22nd century approaches, is providing skills, critical thinking, and metacognition enough? Or must we design educational models never seen before? What distinct opportunities could AI offer that will help preserve and enhance human emotional and social characteristics, setting us apart from intelligent machines or robotic systems? These discussions are already surfacing, especially around whether AI might one day replace teachers (see, Felix, 2020). Manyika et al. (2017) argue that effective teachers will still play a crucial role, particularly in cultivating students’ creativity, emotional intelligence, and interpersonal skills. They believe AI and automation may even serve to amplify human traits. When summarizing findings in AI-related educational research, Haseski (2019) mentions that AI has the potential to personalize learning, offer richer educational experiences, unlock student potential, stimulate creativity, and ease the workload of educators. Nonetheless, alternative perspectives exist. Some experts argue that transferring key teaching responsibilities to machines could pose serious risks (Humble & Mozelius, 2019). To navigate this future, national education systems need to prepare a new generation of teachers who can collaborate with AI-based systems (Wogu, Misra, Olu-Owolabi, Assibong & Udoh, 2018).
While interest in AI’s role in education is recent, the foundational concepts of general AI can be traced back to the 14th century, with significant theoretical advancements made by Alan Turing in 1937 (Humble & Mozelius, 2019). AI has now become a central subject within academic research and scientific debate. Discussions about its influence have even extended into organizational management through emerging ideas like “artificial intelligence leadership” (see, Canbek, 2020). As AI becomes more integrated into educational practice, it is likely to bring major changes to educational systems and their operations. Sekeroglu, Dimililer, and Tuncal (2019) suggest that AI technologies can support teachers in delivering more customized learning paths. In addition, AI can improve access to education for marginalized groups—such as those with disabilities, refugees, out-of-school youth, and individuals living in isolated or underserved areas (Pedro, Subosa, Rivas, & Valverde, 2019). Research shows that intelligent learning environments and AI-driven tools can offer personalized and adaptive instruction (Mohammed & Watson, 2019). Although high-quality education still requires the involvement of human teachers, AI promises to elevate learning outcomes—especially by supporting tailored instruction (Grosz & Stone, 2018). Pedro et al. (2019) discuss a dual-teacher model, where AI functions as an assistant. While teachers spend much time on repetitive or administrative duties—such as answering routine questions—AI systems can handle these tasks, freeing up teachers to focus more on guiding students individually and strengthening classroom engagement.
Research Method
This research was conducted as a phenomenological study, a widely used approach in qualitative research. Qualitative methods are typically chosen when a detailed exploration of a particular issue or topic is needed (Creswell, 2013). Studies that aim to reveal individual interpretations and understandings are classified as phenomenological research (Yildirim & Simsek, 2008). In this context, we sought to understand the views of participants regarding the application of AI in educational settings. To capture perceptions about artificial intelligence in education from professionals in four distinct disciplines, a triangulated data collection strategy was utilized.
Participants
Participants in the study were selected using purposeful sampling, a method that focuses on choosing individuals who are likely to provide rich and relevant information aligned with the research objectives (Buyukozturk, Cakmak, Akgun, Karadeniz & Demirel, 2018). The researchers identified a total of 19 individuals, categorized into four distinct professional groups, to capture diverse perspectives on the role of artificial intelligence in education. These groups included academicians working in the field of educational sciences (5 individuals), legal professionals such as practicing lawyers and judges (5 individuals), technical experts specializing in artificial intelligence and employed in public or private institutions (4 individuals), and teachers currently working in public schools (5 individuals). In order to maintain participant anonymity, pseudonyms were assigned for use in the reporting process. The names used for this purpose were: Ahmet, Ayşe, Fatma, Hatice, Emine, Ali, Huseyin, Ismail, Suleyman, Tahsin, Kemal, Elif, Kubra, Mahmut, Burak, Mehmet, Ziya, Recep, and Esref.
Collection of Data and Analysis
The research used semi-structured interviews for data collection. Participants were initially sent an online form with questions asking their opinions on AI, its future in education, its integration, as well as the potential advantages and challenges it may present. These responses were used to analyze how they perceive the role of artificial intelligence in the educational landscape. Interview questions were created with input from three field experts in educational sciences. Initially, participants completed an online form independently. To gather more nuanced data, follow-up face-to-face interviews were conducted, allowing participants to expand upon their initial written responses. This two-stage process was based on the idea that participants might express themselves more freely in writing first, while the second stage aimed to clarify and deepen understanding of key points based on the researchers’ observations. All data were evaluated through content analysis, progressing from coding to thematic grouping. The objective was to analyze each participant’s responses line by line. From initial codes, themes were developed and later cross-referenced with existing literature. Every line of data was included in the analysis process during the coding and theme creation stages. In the results section, more detailed explanations were provided for themes highlighted through coding, such as the theme of “individualized learning.” The participants’ thoughts were interpreted through the lens of their current experiences with AI and how they envisioned future scenarios. Only one of the questions (the final one) was analyzed using a predefined numerical coding system, which is presented in Table 1.
Trustworthiness
Trustworthiness holds significant importance in qualitative research. To ensure a reliable and valid process, an academic expert in the field reviewed the entire research process, including data collection, coding, tabulation, and reporting. Moreover, the researchers disclosed their initial assumptions about the topic. Participants were also asked to share their preconceptions. Independent assessments were carried out by three academic experts, ensuring analytical triangulation. Data triangulation (Streubert & Carpenter, 2011) was also employed to enhance credibility. The study additionally reviewed research involving artificial intelligence in a wide range of sectors—from healthcare to industry—to allow for perspective triangulation. In qualitative research, triangulation refers to utilizing multiple data sources or methodologies to achieve a deeper understanding of the issue at hand (Patton, 1999). The inclusion of experts from different fields in this study on AI in education helped create a diverse and rich data pool. The data were compared with findings from similar studies in existing literature. This content-based comparison drew from multiple sources and findings. The researchers made efforts to minimize their own biases. Both raw and processed data were retained for any potential future verification. The aims, procedures, and methodology of the study were explained transparently to the participants. Detailed descriptions of the process and participant selection were provided to support the study’s transferability. Ethical standards were observed, and participants were informed of their right to withdraw from the study at any point.
Findings
The core themes presented in this section are derived from the most frequently mentioned codes identified in participant responses. These codes, developed under each thematic heading, are not arranged by frequency but reflect key areas emphasized by participants. Additionally, the codes and related insights presented here were validated by participants across multiple professions, following their approval. Analysis of the collected data revealed that participants initially focused on the tangible and functional elements—such as tools, applications, and educational outcomes—that artificial intelligence might introduce to the education sector. Each of the remaining themes explored in this section builds upon that initial focus. Given the broad range of ideas participants offered regarding products, potential disadvantages, benefits, and recommendations, a few representative quotes were included under each thematic heading to reflect the general perspective conveyed by the respondents.
Products (Outcomes)
This theme focuses on the anticipated tools, systems, and educational outputs that participants believe artificial intelligence could introduce into academic environments. The scope of “products” as defined by the participants is not limited to physical technologies but includes a wide variety of software systems, instructional models, and educational frameworks. Notable expectations include advanced software platforms, robotic teaching assistants, intelligent classroom environments, and systems designed to personalize instruction according to student needs. Other product-related elements discussed by participants involved interactive simulations for lessons, programs capable of generating real-life scenarios and case studies, systems that analyze students’ interests and abilities, and tools supporting career guidance. Several interviewees mentioned AI-driven attendance tracking, fully automated classroom management, systems designed to detect individual learning outcomes, and tools capable of customizing instruction for each student. Additional tools expected by participants included attention monitoring software, performance analysis systems to enhance academic achievement, cloud-based learning platforms, systems for adjusting and editing curricula, and platforms for identifying and reporting students’ learning behaviors and styles. Supporting this theme, Tahsin, an academic, noted that artificial intelligence could facilitate various educational processes, including student evaluation, facial recognition, and attendance management, and that it might promote more personalized learning experiences. Ziya, a legal professional, spoke of the potential for delivering individualized instruction via virtual AI-powered platforms. Mahmut, a technology expert, emphasized that curriculum development and planning could be managed through AI mechanisms using machine learning models tailored to educational settings.
Drawbacks
This section outlines the concerns and risks identified by participants regarding the integration of artificial intelligence into education. Several key risks were mentioned, including the promotion of mechanical thinking over intuitive reasoning, the erosion of humanistic values in favor of utilitarian principles, and the potentially problematic classification of individuals based solely on cognitive measures such as IQ. Additional drawbacks included a possible increase in data-centric and impersonal approaches to education, the reduced need for human involvement in teaching roles, the risk of uncontrolled and possibly invasive AI technologies, and the potential for social disconnection among students. Teacher participants in particular expressed strong concerns about these risks. Ayşe, a public school teacher, remarked that artificial intelligence could eventually dominate education to the point that human intervention may no longer be needed. Ali, an academic, echoed similar concerns by suggesting that AI could foster a society of machine-like, emotionally detached individuals. Emine, another teacher, voiced fears that educators themselves may become obsolete over time. Similarly, Burak, a legal professional, speculated that AI systems might eventually take over all aspects of the teaching process, potentially eliminating the need for human teachers. Some participants attributed these concerns to the dystopian portrayals of AI in films and media, which they feared could become reality.
Benefits
The perceived advantages of incorporating artificial intelligence into educational settings were explored through this theme. Participants mentioned several benefits, including improved systems for evaluating student performance, enhanced capacity for learners to progress at their own pace, and more accurate assessments of individual learning needs. Other benefits involved the potential for resolving long-standing issues in schools, such as excessive paperwork and time inefficiencies, as well as the general improvement in educational quality. Respondents also discussed how AI might simplify administrative work, facilitate data-informed decision-making, and allow educators to tailor instruction based on student learning capacity and speed. The use of AI in analyzing learning patterns was seen as a way to support the selection of effective teaching methods and foster smaller, more efficient class settings. In addition, participants believed AI could improve individualized instruction and help education policymakers, for example by simulating future population growth to guide investment decisions in education infrastructure. İsmail, an academic, explained that artificial intelligence could be used to monitor students’ progress, offer evaluations, and even suggest career paths based on academic performance. Elif, an engineer, emphasized the value of regular data reporting about student status, suggesting that AI systems could generate insights and recommendations to share with stakeholders. Esref, representing the legal field, said that AI could be helpful in evaluating exam results, tracking student behavior, and improving communication. Ahmet, a teacher, added that AI systems might analyze student speech to measure comprehension and provide targeted feedback. These responses align with the concept of “learning analytics” often discussed in educational research literature.
Suggestions
This theme encompasses participants’ recommendations regarding the use of artificial intelligence in education. A range of suggestions was put forward that reflect both technical and human-centered considerations. First, participants emphasized the importance of having a structured and measurable system in place when implementing artificial intelligence in educational settings. They advocated that applications or systems developed for AI in education should first undergo pilot testing, and only after evaluating the outcomes of such trials should they be integrated into wider systems. Another critical suggestion involved the need for academic research focused on these AI-based systems, with detailed analyses to ensure their efficacy and alignment with educational goals.
Infrastructure development was also seen as a foundational requirement, along with the establishment of a strong audit mechanism to maintain quality and accountability. Several participants cautioned against ignoring the psychological impact of AI on individuals, recommending that AI use should be paired with supportive and preventive software tools. It was suggested that systems should measure how AI applications influence people’s decision-making abilities, especially in personal and social contexts. There was a clear consensus that the integration process must be designed carefully to avoid adverse effects on social relationships. Participants reiterated that AI should not be viewed as a catch-all solution but rather applied selectively in areas where it adds value. Furthermore, they emphasized the need for interdisciplinary collaboration among all stakeholders—educators, engineers, psychologists, legal experts, and others—rather than limiting the process to a narrow group.
Illustrating these points, Hatice, a public school teacher, expressed that artificial intelligence should be used mindfully and only in areas where its application is necessary. Ali, an academician, noted the importance of caution, highlighting the need for academic inquiry and pilot testing before broad adoption. Kubra, an expert engineer, stressed that AI should not replace the human aspect of teaching but serve as a supportive element under human control to mitigate potential risks. As technology most directly impacts humanity, several participants underlined the importance of basing AI integration on a strong legal foundation to prevent harm. Recep, a legal professional, proposed that legal frameworks could help minimize risks associated with AI in education. Mehmet, another jurist, focused particularly on the issue of personal data privacy. He emphasized that the storage, protection, and confidentiality of sensitive data in upcoming AI systems must be guaranteed, especially given rising concerns over mental profiling. He proposed a two-step strategy: first, states must build the necessary infrastructure and allow access through strict legal protocols; second, any breach of privacy should be addressed swiftly and effectively, using deterrent penalties to discourage future violations.
To conclude this section, participants were asked a final descriptive question: “How do you define AI tools in education when artificial intelligence-supported educational environments are considered?… Please give us a clear percent if AI is beneficial or problematic?” The aim of this question was to extract a numerical evaluation of participants’ overall perception of AI’s integration into education and society. The respondents, representing diverse fields, provided percentage-based insights reflecting their expectations about the balance of benefits and risks. Their responses considered both the advantages and challenges of AI usage in education. A breakdown of these answers by professional group is provided in Table 1.
| Groups | Benefit Average | Drawback Average |
| Academicians | % 56.00 | % 44.00 |
| Law Personnel | % 72.20 | % 27.80 |
| Expert Engineers | % 95.00 | % 5.00 |
| Teachers | % 62.00 | % 38.00 |
| General | % 68.67 | % 31.33 |
Table 1. Distribution of benefit – drawback percentages by groups
In this respect, it can be said that the participants generally viewed artificial intelligence developments in education positively. Academicians seemed to assess the potential benefits and drawbacks primarily in relation to the teaching profession, expressing concern over possible future challenges for teachers, while still acknowledging the advantages AI can offer in instructional processes. In contrast, expert engineers were more optimistic, focusing on the systemic improvements AI could bring and suggesting that it would enhance the overall quality and efficiency within the education sector.
Conclusions
The interviews conducted with participants produced four main themes and one descriptive theme concerning artificial intelligence in education. The first theme focused on products, which referred to tangible and intangible AI outputs and applications expected to emerge in the near future. These included simulation programs, evaluation and testing systems, virtual reality classrooms, assistant robots, and personalized learning platforms. One of the most frequently mentioned applications was AI’s role in advancing personalized learning. AI is expected to significantly support this process by tailoring education to meet individual learners’ needs, thereby enabling a more effective and targeted learning experience (Chang & Lu, 2019). Goksel and Bozkurt (2019), in their analysis of AI in education research, identified adaptive learning, personalization, and learning styles as central concepts, suggesting that the traditional one-size-fits-all approach may soon be replaced by AI-powered individualized learning. These innovations indicate that education systems may better meet students’ learning requirements through AI support, significantly aiding both learners and teachers. Abdelsalam (2014), for instance, proposed an intelligent tutoring system (ITS) grounded in a mastery learning strategy, further exemplifying how AI can enrich personalized education.
The second theme addressed drawbacks and risks. Participants expressed concerns that widespread use of AI in education might lead to overly mechanical forms of thinking, the dominance of pragmatic approaches over humanistic values, reduced emphasis on emotional and aesthetic aspects of learning, decreased demand for teachers, ethical concerns, security vulnerabilities, and deteriorating social relationships. These concerns reflect a broader anxiety over the potential consequences of excessive reliance on technology. Some participants noted that the current overuse of mobile phones already contributes to social, emotional, and behavioral problems, as previously observed by Choliz (2010), and they worry that similar or more severe issues could arise with the unconscious integration of AI into everyday life. Many participants predicted a reduction in human presence within educational environments, with increased use of robotic assistants. These views align with Picciano (2019), who warned that AI-induced job displacement will likely impact white-collar professions such as teaching, law, and medicine. However, Picciano also emphasized that it is not AI itself but those who harness it effectively who will replace others in the workforce. Roll and Wylie (2016) supported this view by asserting that teachers must adapt by taking on roles as mentors, teaching lifelong skills and fostering human interaction beyond traditional classroom duties.
Although current AI developments have not yet reached the levels depicted in television and film, participants expressed a growing awareness of the rapid pace of AI progress. Cultural references, particularly Isaac Asimov’s I, Robot and its adapted film, influenced their perspectives. Some participants even cited Asimov’s famous “Three Laws of Robotics” and the subsequent addition of a “Zeroth Law,” underscoring the importance of moral and ethical safeguards in AI systems (Asimov, 2004). These laws emphasize preventing harm to humans, obeying human commands, and preserving robot existence only when it does not conflict with the previous laws. The theme of proportionality also emerged as a key safeguard against the potential risks of AI, with participants calling on professionals—particularly academics and engineers—to adopt a conscious and responsible approach in developing and implementing AI technologies.
The third theme dealt with the benefits of artificial intelligence, particularly its functional contributions. Unlike the first theme, which focused on tangible products, this theme explored how AI could improve learning processes and decision-making. Participants believed AI-enabled systems would align educational content with individual learners’ pace, better assess their needs, minimize wasted resources, facilitate faster data analysis, and support more accurate decision-making. One participant noted that AI could even guide government investment in education by predicting population movements. Subrahmanyam and Swathi (2018) argued that predictive AI tools could track students’ learning habits and propose optimized study schedules. Similarly, Roll and Wylie (2016) emphasized the need for more personalized support for students and teachers, stating that AI could better address learners’ individual needs and facilitate self-paced education. According to Subrahmanyam and Swathi (2018), AI systems can also help students achieve mastery by repeating lessons as needed and creating dynamic, individualized learning plans.
The fourth theme of this study encompassed participants’ suggestions for implementing artificial intelligence (AI) in education through precautionary and structured approaches. Participants emphasized the necessity of adopting supervised systems and deliberate, pre-analysed steps to ensure responsible AI integration. They advocated for various levels of oversight—both technical and judicial—highlighting the importance of limiting AI’s intrusion into personal domains. Participants agreed that AI should not be treated as a universal solution but rather as a facilitative tool, intended to enhance and streamline educational processes where appropriate. Without proper regulation, AI systems that imitate human intelligence may give rise to complex legal issues, such as questions around accountability, authorship and intellectual property, data privacy, and security. These concerns suggest that legal frameworks must evolve alongside technological advances, with specific legislation tailored to address the roles of AI entities like robotic teaching assistants or intelligent software within educational settings.
AI in education represents a field rich in promise and innovation. However, its integration must be guided by careful consideration of contextual, ethical, pedagogical, psychological, and sociological implications. While AI can offer transformative potential, participants consistently warned against over-reliance, reminding us that technology, if unregulated or misapplied, may harm rather than help. Given the centrality of human welfare in technological progress, all AI-related developments should proceed on a robust legal foundation to prevent unintended consequences. The fifth and final theme highlighted the general outlook on AI, which was predominantly positive. Most participants, especially expert engineers and professionals directly involved in AI systems, viewed the technology as beneficial, particularly in terms of improving system performance and alleviating human workload. Teachers also expressed a degree of optimism, recognizing the potential of AI to support educational processes. However, academicians were somewhat more reserved, focusing instead on the broader implications and possible negative impacts, especially regarding the future of teaching professions. The participants’ perspectives appeared to be influenced by a combination of professional expertise, personal experience with current technologies, and representations of AI in media and popular culture, including dystopian and utopian narratives.
Overall, this study included the views of a diverse group of stakeholders—teachers, academicians, engineers, and legal experts—offering a comprehensive and multidimensional perspective on AI in education. The data revealed four core themes: (a) Products, referring to anticipated tools and applications of AI; (b) Drawbacks, addressing potential risks and harms; (c) Benefits, focusing on the functional advantages of AI systems; and (d) Suggestions, outlining guidelines for responsible and effective AI integration. While AI presents significant opportunities for advancing education—particularly through personalization, efficiency, and data-driven insights—it also introduces new challenges. Schools must adopt proactive strategies to prepare for these changes, ensuring that technological innovation is matched with thoughtful policy and human-centered design. Policymakers and education leaders are urged to consult both empirical research and practitioner feedback to maximize the benefits of AI while minimizing its potential drawbacks, preparing education systems for the demands of the future.
Acknowledgement
This study was supported within the project named Artificial Intelligence Education for Children (2019-1-TR01-KA201- 07704) by the Erasmus+ Programme of the European Union. European Commission and Turkish National Agency cannot be held responsible for any use which may be made of the information contained therein.
References
Abdelsalam, U. M. (2014, March). A proposal model of developing intelligent tutoring systems based on mastery learning. Paper presented the Third International Conference on E-Learning in Education (pp. 106–118). Retrieved from http://paper.researchbib.com/view/paper/14102
Asimov, I. (2004). I, Robot. New York: Bantam Books.
Buyukozturk, S., Cakmak, E. K., Akgun, O. E., Karadeniz, S., & Demirel, F. (2018). Bilimsel araştırma yöntemleri
[Scientific research methods]. Ankara: Pegem A Yayıncılık.
Canbek, M. (2020). Artificial Intelligence Leadership: Imitating Mintzberg’s Managerial Roles. In Business Management and Communication Perspectives in Industry 4.0 (pp. 173–187). IGI Global.
Chang, J., & Lu, X. (2019, August). The study on students’ participation in personalized learning under the background of artificial intelligence. In 10th International Conference on Information Technology in Medicine and Education (ITME) (pp. 555-558). IEEE.
Choliz, M. (2010). Mobile phone addiction: a point of issue. Addiction, 105(2), pp. 373–374.
Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches, SAGE publications Felix, C.V. (2020). The Role of the Teacher and AI in Education. Sengupta, E., Blessinger, P. and Makhanya, M.S. (Ed.)
International Perspectives on the Role of Technology in Humanizing Higher Education (Innovations in Higher Education Teaching and Learning, Vol. 33), Emerald Publishing Limited, pp. 33–48. https://doi.org/10.1108/S2055- 364120200000033003
Goksel, N., & Bozkurt, A. (2019). Artificial intelligence in education: current insights and future perspectives. In S. Sisman-Ugur & G. Kurubacak (Eds.), Handbook of Research on Learning in the Age of Transhumanism (pp. 224– 236). Hershey, PA: IGI Global.
Grosz, B. J., & Stone, P. (2018). A century-long commitment to assessing artificial intelligence and its impact on society. Communications of the ACM, 61(12), pp. 68–73.
Golic, Z. (2019). Finance and artificial intelligence: The fifth industrial revolution and its impact on the financial sector. Zbornik radova Ekonomskog fakulteta u Istočnom Sarajevu, (19), pp. 67–81.
Haseski. H.I. (2019). What do Turkish pre-service teachers think about artificial intelligence? International Journal of Computer Science Education in Schools, 3(2), Doi: 10.21585/ijcses.v3i2.55
Humble, N., & Mozelius, P. (2019, October). Artificial Intelligence in Education-a Promise, a Threat or a Hype?. In European Conference on the Impact of Artificial Intelligence and Robotics 2019 (ECIAIR 2019), Oxford, UK (pp. 149–156). Academic Conferences and Publishing International Limited.
Karsenti, T. (2019). Artificial intelligence in education: the urgent need to prepare teachers for tomorrow’s
schools. Formation et profession, 27(1), pp. 112–116. Doi:10.18162/fp.2019.a166.
Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.
Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., & Dewhurst, M. (2017). A future that works: Automation, employment, and productivity. Chicago: McKinsey Global Institute.
Mohammed P.S., & Watson E. N. (2019). Towards inclusive education in the age of artificial intelligence: perspectives, challenges, and opportunities. In: Knox J., Wang Y., Gallagher M. (eds) Artificial Intelligence and Inclusive Education. Perspectives on Rethinking and Reforming Education. Singapore: Springer. https://doi.org/10.1007/978- 981-13-8161-4_2
Mou, X. (2019). Artificial intelligence: investment trends and selected industry uses. EMCompass; No. 71. Washington, D.C.: World Bank Group.
Ng, A. (2017, January 25). Artificial intelligence is the new electricity. Speech presented at Stanford MSx Future Forum in California, Stanford. https://www.youtube.com/watch?v=21EiKfQYZXc.
Patton, M. Q. (1999). Enhancing the quality and credibility of qualitative analysis. Health services research, 34(5/2), pp. 1189–1208.
Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Paris: UNESCO.
Picciano, A. (2019). Artificial intelligence and the academy’s loss of purpose. Online Learning, 23(3), Doi:10.24059/olj.v23i3.2023
PwC. (2017). Sizing the prize What’s the real value of AI for your business and how can you capitalise? Retrieved from https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf.
Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), pp. 582–599.
Sekeroglu, B., Dimililer, K., & Tuncal, K. (2019). Artificial intelligence in education: application in student performance evaluation. Dilemas Contemporáneos: Educación, Política y Valores, 7(1), pp. 1–21.
Streubert, H. J., & Carpenter, D. R. (2011). Qualitative research in nursing. (5th ed.). Philadelphia: Lippincott Williams and Wilkins.
Subrahmanyam, V. V., & Swathi, K. (2018). Artificial intelligence and its implications in education. In Int. Conf. Improv. Access to Distance High. Educ. Focus Underserved Communities Uncovered Reg. Kakatiya University (pp. 1–11).
Timms, M. J. (2016). Letting artificial intelligence in education out of the box: educational cobots and smart classrooms. International Journal of Artificial Intelligence in Education, 26(2), pp. 701–712, Doi: 10.1007/s40593- 016-0095-y
Wartman, S. A., & Combs, C. D. (2018). Medical education must move from the information age to the age of artificial intelligence. Academic Medicine, 93(8), pp. 1107–1109.
Wogu, I. A. P., Misra, S., Olu-Owolabi, E. F., Assibong, P. A.. & Udoh, O. D. (2018). Artificial intelligence, artificial teachers and the fate of learners in the 21st century education sector: Implications for theory and practice. International Journal of Pure and Applied Mathematics, 119(16), pp. 2245–2259.
Yildirim, A., & Simsek, H. (2008). Sosyal bilimlerde nitel araştırma yontemleri [Qualitative research methods in the social sciences]. Ankara: Seckin Publication.
