Educational Collaboration : Teachers and Artificial Intelligence

: This study aims to analyze the collaboration between teachers and Artificial Intelligence that can meet the needs and goals of better education. This study uses a qualitative descriptive method with a literature review research design by collecting information from reputable international journal articles indexed in Scopus over the last five years (2018-2023) using the Publish or Perish search tool. The process of meta-analysis in this research begins with formulating the research question, andthen collecting relevant literature data that aligns with the research question. Next, the data is prepared by evaluating the literature's relevance to the research topic. Afterwards, the data from the selected literature is analyzed and synthesized. The results show that collaboration between teachers and Artificial Intelligence can be a good solution to improve learning effectiveness and help students learn in a personalized way. Although Artificial Intelligence technology can assist teachers in the learning process, teachers still play an important role in guiding and inspiring students. Therefore, collaboration between teachers and Artificial Intelligence technology should also be done carefully and directed towards improving a better and more meaningful learning experience for students


Introduction
The development of science and technology, which initially focused on the development of machines and mechanical technology such as steam engines and cars, has increasingly expanded to various fields such as computers and telecommunications in the 20th century (S. P. Huang, 2018). In particular, the development of computer technology in Academics who hold negative views are concerned that the use of AI technology may bring new problems due to the lack of human interaction in the learning process (Wogu et al., 2019). In some cases, AI technology can replace the role of the teacher or reduce the interaction between students and teachers, which can affect the quality of learning. The use of AI technology in education also raises privacy issues, especially in terms of student data collection (Holmes et al., 2022). Some academics are concerned that excessive and detailed data collection can jeopardize student privacy. Issues of bias and discrimination can also arise in the use of AI technology in education. AI systems can be biased if the data used to train the model is uneven or does not reflect the diversity of the student population. As pointed out by Cope et al. (2020), we need to be aware of the intrinsic limitations of AI, which are the limits of transposing human meaning into numbers. Schiff (2020) also hopes to promote critical reflection on the development of AI in education. The conflicting views among groups of academics regarding the use of AI in education can hinder progress in the field of education. Therefore, a middle ground is needed to find solutions and agreements that can meet the needs and goals of better education. This study aims to analyze the collaboration between teachers and Artificial Intelligence that can meet the needs and goals of better education.

Research Method
This research uses a qualitative descriptive method with a type of systematic review of the literature (Pati & Lorusso, 2018), which is collecting information or scientific writings related to a literature review. The literature review involves analysis and synthesis of various sources of information or literature relevant to the research topic being studied (Ramdhani et al., 2014). This method aims to collect, review, and analyze literature related to the topic of AI usage in education, so as to help build a framework or basic concept in the research. In the literature review method, the researcher conducts a search and selection of literature from reputable international journal articles indexed in Scopus within the last five years (2018)(2019)(2020)(2021)(2022)(2023) with the help of the Publish or Perish search tool. The process of meta-analysis in this research, as suggested by (Paul & Barari, 2022), begins with formulating the research question, andthen collecting relevant literature data that aligns with the research question. Next, the data is prepared by evaluating the literature's relevance to the research topic. Afterwards, the data from the selected literature is analyzed and synthesized. Finally, the findings are interpreted, and conclusions are drawn to answer the research question.
The initial stage of this research is to determine the problem formulation, which includes: 1) What is the concept of Artificial Intelligence; 2) How is Artificial Intelligence used in Education; and 3) How is the collaboration between teachers and Artificial Intelligence. Next is to gather, review, and analyze literature related to the research topic, with steps including: 1) identifying the research topic to be studied (Wang, 2017); 2) searching for relevant literature on the topic (Haddaway et al., 2015); 3) selecting the most relevant and high-quality literature to be used in the research (Dowd et al., 2018); 4) analyzing the selected literature (Cruz-Benito et al., 2018); 5) interpreting and synthesizing the results of the literature analysis (Pati & Lorusso, 2018). These results can be used to build a framework or basic concept for the research (Kamble et al., 2018). From the search process using the Publish or Perish search tool, 200 papers were obtained, consisting of 119 Articles, 1 Book, 7 Book Chapters, 32 Conference Papers, 8 Editorials, 1 Erratum, 1 Letter, 2 Notes, 1 Retraction, and 28 Reviews. Among the 10 types of data, primary data in the form of articles were selected, totalling 119 articles. Next, from the 119 articles, the most relevant titles related to the topic of AI in education were selected for analysis. The interpretation and synthesis of the results of the literature analysis are presented in the following sub-topics: 1) Artificial Intelligence, 2) Artificial Intelligence in Education, and 3) Teacher and Artificial Intelligence.

1) Artificial Intelligence
Artificial Intelligence, commonly abbreviated as AI, is a technology that imitates human abilities to think, understand, learn, and perform actions that sometimes require human intelligence, such as speaking, language processing, recognizing images or sounds, and performing complex tasks (L. Chen et al., 2020). AI systems are designed using techniques such as machine learning, neural networks, natural language processing, computer vision, and decision trees. a) Machine learning is a technique that enables AI systems to learn from data without being explicitly programmed (Cope et al., 2020). In machine learning, the AI system will analyze data and identify patterns or trends that are hidden within it. The AI system can use this information to make predictions or decisions. b) Neural network is a mathematical model that imitates the way the human brain works (Cope et al., 2020). The input data will be processed by a large number of neurons or processing units. Each neuron will receive input from other neurons and produce output that is sent to other neurons. In this process, the AI system can recognize complex patterns and make decisions based on available information. c) Natural language processing (NLP) is a technique that enables AI systems to understand human language. The AI system will analyze human text or speech and try to understand the meaning behind the words. With the help of NLP, AI systems can perform tasks such as translation, sentiment analysis, and speech recognition (Guan et al., 2020). d) Computer vision is a technique that allows AI systems to process images and videos, and recognize objects, faces, or actions within them (Ward et al., 2020). The AI system will analyze the visual features of the image or video, and compare them with existing databases. With the help of computer vision, the AI system can perform tasks such as facial recognition, object detection, and medical image analysis. e) Decision tree is a technique that allows AI systems to make decisions based on a series of rules or conditions. The AI system will build a decision tree based on the available information. Each branch of the tree represents a condition or rule, and each leaf of the tree represents a decision or action. With the help of decision tree, AI systems can perform tasks such as data classification or decision-making (Mahbooba et al., 2021). AI works by using algorithms that are built into computer systems (Ouyang & Jiao, 2021). These algorithms can be programmed to make decisions, analyze data, and solve problems quickly and efficiently. AI requires input data to learn and understand the patterns and concepts necessary for the given tasks. The collected data is usually processed by the machine and processed by algorithms used to develop AI models (Wogu et al., 2019). The more data that is input into the system, the better the performance of AI in processing data and making more accurate decisions. Additionally, AI can also be integrated with other systems, such as the Internet of Things (IoT) and big data, to obtain more and more comprehensive data (Li et al., 2022).
AI has a significant impact on various aspects of life, including industry, healthcare, transportation, and education. Some impacts of AI include: increasing efficiency and productivity in various sectors; providing solutions to complex and complicated problems; providing ease in interacting and communicating with technology; improving the quality of life by assisting in healthcare and environmental fields; and causing changes in the job market and the need for job qualifications (Acemoglu & Restrepo, 2019).
The main goal of developing AI is to create technology that is smarter and more efficient in solving complex tasks, thereby helping humans to increase efficiency and productivity in various fields (Yang et al., 2021). In addition, another goal is to create technology that can learn and develop on its own, so that it can provide more accurate and effective solutions in various situations and problems faced. AI can also help address complex problems and support better innovation development in the future (Verganti et al., 2020).

2) Artificial Intelligence in Education
In this digital era, AI can be used to enhance students' learning experience, assist teachers in assessment, and provide recommendations to personalize learning (Chen, 2022). a) Adaptive Learning Adaptive learning is a learning technique that can be set up to adjust to students' level of understanding. In this case, AI can help create a more adaptive learning environment by using data analysis from students' responses (Guan et al., 2020). AI can read data from tests or assignments given and analyze students' answers automatically. Then, AI can provide recommendations or improvements for students who are struggling. Additionally, AI can personalize learning for each student by providing material that suits their level of understanding. AI can utilize data from students' learning experiences and analyze their abilities to recommend suitable material based on their needs and level of understanding (Zawacki-Richter et al., 2019). AI can also provide customized learning materials based on students' interests and preferences, thus increasing their motivation and interest in learning. In this case, AI can assist teachers in personalizing students' learning experiences and maximizing their learning potential. Therefore, learning can be more effective and efficient because students can learn in a more appropriate way for their needs (Lin et al., 2018).

b) Personalized Learning
Personalized learning is one of the positive impacts of AI in education. With AI technology, teachers can obtain data from students' learning activities and identify individual strengths and weaknesses (Ouyang & Jiao, 2021). Thus, teachers can provide more personalized and tailored learning to meet the needs of each student, which can enhance learning effectiveness. Moreover, AI can help offer relevant learning resources and materials that match students' needs, such as videos or articles related to specific topics (Chatterjee & needs and help educational institutions prepare students for a better future. AI can assist educational institutions in monitoring and evaluating the effectiveness of the current curriculum. By examining learning data, AI can help identify areas where students are struggling and need improvement. This can help educational institutions continually improve the curriculum and ensure that students receive the best education (Chiu & Chai, 2020). In an ever-changing and evolving educational environment, AI's ability to adapt and adjust to these changes can assist educational institutions in staying relevant and providing the best education for students (Huang & Rust, 2018).

3) Teachers and Artificial Intelligence
Although AI can provide many benefits in education, there are some tasks that cannot be fully replaced by AI, thus requiring collaboration between teachers and AI. a) Building Interpersonal Relationships Although AI can provide responses and feedback on student performance, the interpersonal relationships and emotional connections between teachers and students remain important in helping students feel connected to their learning environment. Interpersonal relationships and emotional connections between teachers and students are essential aspects of education (Lameras & Arnab, 2022). Good teachers can be a source of inspiration, motivation, and support for students. In addition, good interpersonal relationships can help build students' trust in their teachers and make them feel more comfortable in the learning environment. Although AI can provide responses and feedback, AI cannot provide the same interpersonal experience as teachers (Seo et al., 2021). AI does not have the ability to form the emotional relationships and interpersonal connections required in education. Therefore, the role of teachers remains important in building interpersonal relationships with students and providing the emotional support needed by students (Tyson & Sauers, 2021)

. b) Understand Students' Needs
Although AI can assist in monitoring student progress and suggesting learning strategies, understanding the unique needs and learning styles of each student requires direct observation and interaction by the teacher. AI can provide learning recommendations for each student based on the collected data (Bagunaid et al., 2022). To better understand individual student needs, teachers need to interact directly and observe each student, as well as build a personal relationship with them. In this way, teachers can understand the learning style, special needs, and preferences of each student in learning. This can help teachers prepare appropriate curricula and provide the necessary support for each student. In addition, personal relationships can also help improve student motivation and interest in learning, as well as help them feel accepted and valued as individuals in the learning environment (Hwang & Tu, 2021). c) Teaching Social and Emotional Skills Teaching social and emotional skills requires interaction between teachers and students who are trained to help students learn how to interact with others, manage their emotions, and build confidence. AI may be able to provide some resources to help teachers teach social and emotional skills, but it cannot fully replace the role of teachers in helping students understand and practice these skills (Joshi et al., 2021). Teaching social and emotional skills requires direct interaction between teachers and students, as well as requiring observation skills and a personal approach. For example, teaching students about empathy and building positive relationships with others requires communication skills and interpersonal interaction, which are difficult to simulate or learn through AI platforms. Therefore, the role of teachers is still very important in helping students learn social and emotional skills well (Malik et al., 2019).

d) Creativity
Teachers often have to use their creativity in teaching to make students interested and engaged in learning. This involves the ability to think outside the box, create engaging learning activities, and motivate students to learn. Although AI can provide some ideas or resources, it does not have the ability to generate creative ideas that are suitable for the situation and needs of the students. The creativity of teachers in teaching cannot be fully replaced by AI (Tao et al., 2019). Although AI can provide assistance in designing and compiling interesting teaching materials, it cannot mimic the uniqueness and intelligence possessed by each teacher. The ability of teachers to think creatively and generate new ideas is an important factor in creating an inspiring and effective learning environment. In addition, teachers can also pay attention to the individual needs of students in teaching creative skills, such as finding ways to solve problems and encouraging students to think critically (Chiu & Chai, 2020). Therefore, although AI can assist in some aspects of learning, teachers still have an important role in teaching creative skills and motivating students to learn better. e) Assessing More Complex Skills Some tasks that require more complex skill assessment, such as assessing writing or verbal skills, may not be accurately done by AI. Decision-making involving values and subjective assessment also requires the presence of a teacher (Chaturvedi et al., 2023). Although AI can assist in providing initial assessment or measurable evaluation in some specific aspects, such as analyzing grammar, spelling, or syntax, the ability to produce accurate and meaningful assessments in the broader context of learning is still a teacher's task. Teachers can have a deeper understanding of students' abilities, capture individual difficulties or needs, and provide specific and measurable input and feedback to help students improve their skills (Markauskaite et al., 2022). f) Subjective Assessment Although AI can provide objective assessment for some types of tasks, such as multiple-choice exams, AI cannot provide subjective or contextual assessment like in writing or presentation tasks (Baryannis et al., 2019). Subjective assessment requires deeper assessment and can be influenced by context, style, and individual preferences. Subjective assessment often involves considerations beyond simply right or wrong answers. Teachers can provide more holistic assessments, including quality and creativity of answers, and provide appropriate feedback to help students improve their skills. AI may be able to help in preparing or developing assessment criteria, but ultimately, subjective assessment still requires the involvement and wisdom of teachers (Ver Milyea et al., 2021). g) Creating a Safe and Inclusive Learning Environment Although AI can assist in facilitating online learning, teachers still have an important role in creating a safe and inclusive learning environment for all students (Ilić et al., 2021). Teachers help build a positive learning culture and stimulate student growth, as well as manage relationships between students. AI cannot fully replace the role of teachers in creating a safe and inclusive learning environment (De Bruyn et al., 2020). Teachers have an important role in building and maintaining positive relationships between students and helping students understand and appreciate diversity. In addition, teachers play a crucial role in reinforcing students' ethics and morals, as well as helping students understand their responsibilities to themselves and society. All of these require direct interaction and interpersonal relationships that cannot be fully replaced by technology (Chatterjee & Bhattacharjee, 2020).