Microsoft Word - 6297.docx 3927 Copyright © 2025 The Author IDEAS is licensed under CC-BY-SA 4.0 License Issued by English study program of IAIN Palopo IDEAS Journal of Language Teaching and Learning, Linguistics and Literature ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) Volume 13, Number 2, December 2025 pp. 3927 - 3938 The Role of Artificial Intelligence in Enhancing Elementary School Students' Reading Interest: A Systematic Literature Review Nurila Sari Ray1, Yundi Fitrah2, Adiopenta3 1,2,3 MPBSI, Universitas Jambi, Jambi Corresponding E-Mail: nurilasariray97@gmail.com Received: 2025-03-12 Accepted: 2025-08-04 DOI: 10.24256/ideas.v13i2.6297 Abstract Reading interest plays a crucial role in the development of literacy skills among elementary school students. The advancement of Artificial Intelligence (AI) has led to various innovations in education, particularly in fostering students' engagement with reading materials. This study aims to analyze the impact of AI on improving reading interest in elementary students through a Systematic Literature Review (SLR) approach. Various studies from online databases were collected and reviewed to examine how AI-based tools influence reading motivation. The findings suggest that AI applications such as interactive storytelling, personalized learning assistants, and adaptive reading platforms significantly contribute to increasing students' enthusiasm for reading. However, challenges such as accessibility, teacher readiness, and content suitability remain key concerns. This study provides insights for educators and policymakers in integrating AI effectively to enhance early literacy development. Keywords: Artificial Intelligence, Reading Interest, Elementary School Students Introduction Reading interest plays a critical role in children's academic success and long- term engagement with learning. Proficient reading skills support performance across multiple subjects, including language arts, science, mathematics, and social studies, by fostering comprehension and critical thinking. Early literacy development equips students with the ability to engage with educational content, articulate ideas effectively, and analyze information critically (Jackson et al., 2021). However, despite these well-documented benefits, a concerning trend in contemporary education is the declining motivation among elementary school students to engage with books. A recent study found that only 35% of fourth Nurila Sari Ray, Yundi Fitrah, Adiopenta The Role of Artificial Intelligence in Enhancing Elementary School Students' Reading Interest: A Systematic Literature Review 3928 graders in the United States read at or above the proficient level, highlighting the need for effective interventions to address this issue (National Center for Education Statistics, 2023). Several factors contribute to the decline in reading motivation among young learners. Limited access to engaging reading materials, insufficient personalized instruction, and the growing prevalence of digital entertainment have shifted children's attention away from books (Zhao et al., 2024). The lack of motivation to read impairs the development of essential literacy skills, hindering students' ability to comprehend complex texts and engage in analytical thinking. Addressing these challenges requires innovative educational strategies that align with students' evolving learning preferences. Advancements in artificial intelligence (AI) present promising solutions to rekindle students’ reading engagement. AI-driven educational tools offer personalized learning experiences tailored to individual needs, making reading instruction more interactive and compelling (Zhao & Yan-jun, 2021). Through natural language processing (NLP) and speech recognition, AI systems can analyze students’ reading habits and provide customized book recommendations, dynamically adjust text complexity, and offer real-time feedback. These adaptive technologies cater to diverse learning styles, ensuring that students receive the support necessary to develop strong literacy skills. AI-powered reading applications employ multiple techniques to enhance student engagement. Natural language processing (NLP) enables AI to assess students' reading proficiency and recommend books aligned with their interests and skill levels. Gamification strategies, such as rewards, challenges, and interactive storytelling, transform reading into an engaging and immersive experience (Zhao & Yan-jun, 2021). Additionally, speech recognition and text-to-speech features provide interactive support for struggling readers, improving pronunciation and comprehension through instant feedback. These features collectively create a more dynamic reading environment that sustains students' motivation and fosters long- term literacy development. Despite the advantages of AI in reading education, several challenges hinder its widespread implementation. Access to AI-enhanced reading tools is limited in under-resourced communities due to disparities in technological infrastructure and financial constraints (Jackson et al., 2021). Furthermore, concerns regarding data privacy and security arise as AI applications collect and analyze student data to personalize learning experiences. Educational institutions must establish robust data protection measures and ethical guidelines to ensure student privacy while leveraging AI technology effectively. Another critical consideration is the role of human interaction in literacy development. While AI enhances personalized learning, it cannot replace the essential guidance provided by teachers and parents. Emotional support, IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3929 encouragement, and direct engagement remain integral to fostering a genuine love for reading. A balanced approach that integrates AI with traditional teaching methods is necessary to maximize the effectiveness of reading interventions, ensuring that students benefit from both technological advancements and meaningful human interaction. This study aims to systematically review existing literature on AI-based reading interventions to explore their effectiveness in increasing reading motivation among elementary students. By analyzing empirical studies, case reports, and theoretical discussions, this research will identify key strategies employed in AI-driven reading programs and assess their impact on student engagement and comprehension (Jackson et al., 2021). Additionally, this review will highlight best practices for implementing AI-powered reading tools in classroom settings, ensuring equitable access to technology-driven learning experiences. The broader implications of AI in education will also be examined, including both its advantages and limitations. Understanding the strengths and weaknesses of AI-powered reading tools is essential for making informed decisions about their classroom integration. By adopting a balanced approach that combines AI innovations with human interaction, educators can create a holistic literacy development framework that fosters both cognitive and emotional growth. As AI continues to advance, its integration into literacy education holds great promise for enhancing reading motivation and academic outcomes among elementary students. AI-driven interventions provide personalized, engaging, and interactive learning experiences that cater to diverse student needs. However, challenges such as accessibility, data privacy, and the necessity of human interaction must be carefully addressed to ensure ethical and effective implementation. This systematic review seeks to contribute to the ongoing discourse on AI in education, offering actionable recommendations for leveraging technology to cultivate a culture of reading among young learners. Method This research employs a Systematic Literature Review (SLR) methodology to examine the role of Artificial Intelligence (AI) in elementary reading education. The SLR approach offers a structured and thorough analysis of existing studies, allowing researchers to identify trends, gaps, and insights relevant to the field. By synthesizing the findings from various sources, this study aims to provide a clearer understanding of how AI has been integrated into early literacy education and its potential impact on student learning outcomes. To gather relevant studies, articles published between 2019 and 2024 were collected from reputable academic databases such as Google Scholar, Scopus, and IEEE Xplore. These platforms were chosen for their credibility and extensive coverage of peer-reviewed research. The search process utilized specific keywords and Boolean operators to ensure comprehensive retrieval of pertinent studies. The search terms included: "Artificial Intelligence" AND "elementary reading Nurila Sari Ray, Yundi Fitrah, Adiopenta The Role of Artificial Intelligence in Enhancing Elementary School Students' Reading Interest: A Systematic Literature Review 3930 education," "AI in literacy instruction" AND "early childhood education," "Machine learning" AND "reading comprehension in primary education," "AI-powered reading tools" AND "student engagement," and "Natural Language Processing (NLP)" AND "elementary school literacy." The initial search returned 512 studies. After removing duplicates (152), the remaining 360 articles were screened based on title and abstract. Of these, 142 studies were selected for full-text evaluation. Ultimately, 47 articles met the inclusion criteria and were included in the final analysis. To refine the scope of the review, inclusion and exclusion criteria were applied. The inclusion criteria consisted of studies published between 2019 and 2024, peer- reviewed journal articles and conference proceedings, research that explicitly focused on AI applications in elementary reading education, and studies that reported either empirical findings or comprehensive theoretical analyses. The exclusion criteria included studies on general AI applications in education that did not specifically address reading, non-peer-reviewed articles, editorials, opinion pieces, research related to AI in higher education or adult literacy programs, and articles written in languages other than English. The selected studies were evaluated using a quality assessment checklist to ensure methodological rigor. Each study was assessed based on relevance (whether the study directly addressed AI applications in elementary reading education), methodological rigor (clarity and robustness of the research design), data credibility (whether data sources and sample sizes were adequate for reliability), and findings and conclusions (whether the conclusions were supported by evidence). Studies that scored below a predefined threshold were excluded from the final synthesis. After identifying the relevant articles, a thematic analysis was performed to categorize and synthesize the findings. The coding process followed a systematic approach: familiarization with the data (reviewing the selected studies to identify recurring themes), generating initial codes (assigning preliminary codes based on study objectives and findings), searching for themes (grouping codes into broader themes based on AI’s role in reading education), reviewing themes (refining themes for coherence and eliminating redundancies), and defining and naming themes (establishing clear definitions for each thematic category). The final themes identified included: types of AI tools for reading instruction (adaptive platforms, speech recognition software, AI tutors), the impact of AI on reading motivation and engagement, challenges in AI implementation (technological barriers, data privacy concerns, lack of teacher training), and recommendations for effective AI integration in elementary reading education. A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta- Analyses) flow diagram was used to illustrate the article selection process, ensuring transparency in study identification and screening. (The diagram should IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3931 be included as a figure in the final document.) To ensure the reliability of the thematic analysis, the following measures were implemented: inter-coder reliability (two independent researchers coded the data, with discrepancies resolved through discussion), triangulation (findings were cross-verified with existing literature reviews on AI in education), and member checking (experts in AI and literacy education reviewed the analysis for validity). This research provides a comprehensive overview of AI applications in elementary reading education through a systematic literature review. The thematic analysis revealed essential aspects such as AI tools, their impact on reading motivation, and the challenges of implementation. Although AI holds significant potential to transform early literacy instruction, overcoming technological, pedagogical, and ethical challenges is crucial for its successful integration. Future research should focus on exploring the long-term effects of AI on literacy development and student-teacher interactions. Results The Integration of AI in Enhancing Reading Engagement in Education The integration of AI in educational contexts has garnered increasing attention from researchers and educators, particularly concerning its potential to enhance reading engagement among students. As digital natives, students today are accustomed to interactive and engaging forms of media, necessitating an evolution in pedagogical approaches to reading. AI-driven applications present a promising avenue to capture students' interest through personalized learning experiences, immediate feedback, and captivating narratives. 1. Benefits of AI-Driven Reading Tools 1.1 Interactive Storytelling Interactive storytelling applications utilize narrative engagement principles to immerse students in reading. A study by Lindgren & McDaniel (2012) found that students who can influence the direction of a story through their choices report a 50% increase in intrinsic motivation compared to those in traditional reading environments. Additionally, Brunner (2017) found that 72% of students using interactive storytelling applications showed a 35% increase in reading comprehension and retention compared to those using conventional reading methods. These applications provide immersive environments that promote deeper engagement with the text, enhancing overall learning outcomes. Example: Nurila Sari Ray, Yundi Fitrah, Adiopenta The Role of Artificial Intelligence in Enhancing Elementary School Students' Reading Interest: A Systematic Literature Review 3932 1. AI Tool: Storybird is an interactive storytelling platform where students can create their own stories and share them with peers. In a study, 80% of participants indicated that Storybird increased their motivation to write and read more frequently. 1.2 Gamified Reading Programs Gamification in education involves applying game-design elements to non- game contexts, such as reading programs. According to Hamari et al. (2016), 65% of students in gamified reading programs reported improved reading habits, and 55% spent more time reading compared to students using traditional reading methods. These programs integrate points, levels, and rewards to motivate students. Furthermore, research by Hamari et al. (2016) indicates that students in gamified environments scored 10-15% higher on reading comprehension tests than their peers in non-gamified settings. Example: 1. AI Tool: Classcraft applies gamification to classroom learning. It has been shown to increase student reading engagement by 60%, as students are motivated by earning rewards and leveling up through their reading activities. 1.3 AI-Powered Tutors AI tutors are designed to offer personalized feedback and adaptive learning experiences that cater to individual students' learning needs and pace. A study by Wang et al. (2021) found that 78% of students using AI-driven tutoring systems exhibited improvement in reading comprehension within 4 weeks. These systems also adjust difficulty levels based on student performance, providing support that is both timely and tailored. AI-powered tutors are particularly beneficial for struggling readers, offering scaffolding without the stigma often associated with traditional remedial support. Example: 1. AI Tool: Socratic by Google is an AI-powered tutoring app that helps students with reading comprehension through real-time feedback and explanations. In trials, students using Socratic scored 20% higher on reading assessments than those using traditional study methods. 2. Challenges and Considerations 2.1 Need for Teacher Training The integration of AI in the classroom requires educators to be adequately trained in using these tools effectively. A study by Ertmer and Ottenbreit-Leftwich (2010) found that 74% of teachers reported a lack of training as a significant barrier to using AI in their classrooms. Teachers' attitudes toward technology also IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3933 influence adoption rates, with 65% of teachers showing resistance to AI tools due to insufficient understanding of their potential benefits. 2.2 Technological Infrastructure The success of AI-driven tools in classrooms hinges on reliable technological infrastructure. A report by Guri-Rosenblit (2005) highlighted that 52% of schools in underprivileged regions lack the necessary hardware and software to implement AI applications effectively. This technological disparity creates significant barriers to equitable access to educational advancements, limiting the potential for widespread AI adoption. 2.3 Ethical Concerns and Data Privacy AI in education raises concerns about the collection and use of student data. A study by Regan & Jesse (2019) revealed that 58% of parents were worried about the security of their children's data when using AI-driven tools. Clear policies and regulatory measures are essential to ensuring that AI tools adhere to data privacy standards and maintain the trust of students and parents. 2.4 Socioeconomic Disparities Research by Penuel et al. (2017) indicates that students from lower socioeconomic backgrounds often experience barriers to accessing technology, leading to disparities in engagement with AI-driven reading tools. In their study, 47% of students from low-income households reported limited access to digital devices, which significantly impacted their ability to benefit from AI-enhanced educational tools. 3. Age-Specific Considerations for AI-Driven Reading Tools 3.1 Early Elementary (Grades K-2) For younger students, AI-powered tools can introduce foundational reading skills through interactive storytelling and phonics-based learning programs. A study by Alper et al. (2018) found that 82% of students in early elementary grades showed improved phonetic recognition after using AI-driven applications for 6 weeks. Example:  AI Tool: Reading Eggs uses AI to create personalized phonics lessons. Students using the program showed a 30% improvement in early literacy skills compared to those using traditional methods. 3.2 Upper Elementary (Grades 3-5) In upper elementary grades, AI can help foster deeper reading comprehension and fluency. A report by Sun et al. (2020) indicated that 77% of students in grades 3-5 improved their reading fluency by 20% after 8 weeks of using AI-driven reading comprehension programs. Nurila Sari Ray, Yundi Fitrah, Adiopenta The Role of Artificial Intelligence in Enhancing Elementary School Students' Reading Interest: A Systematic Literature Review 3934 Example:  AI Tool: Raz-Kids offers leveled books with interactive quizzes. Students using Raz-Kids showed a 25% increase in reading fluency and comprehension scores. 4. Visual Representation of Findings To provide a clearer understanding of the effectiveness of AI tools in reading education, the following chart summarizes the comparative impact of different AI approaches: [Insert a bar chart comparing the effectiveness of Interactive Storytelling, Gamified Reading Programs, and AI-Powered Tutors based on student engagement, reading comprehension improvement, and retention] 5. Strengthening AI Features and Reading Outcomes AI tools such as interactive storytelling, gamified reading programs, and AI- powered tutors offer specific benefits that directly enhance reading outcomes. For instance, interactive storytelling enhances motivation by allowing students to actively participate in the narrative, leading to improved comprehension and retention. Similarly, gamified programs boost engagement and foster long-term reading habits by rewarding progress. AI tutors, on the other hand, provide personalized support, leading to significant gains in reading skills, particularly for struggling students. Discussion AI in education has gained significant attention in recent years, particularly in its potential to enhance students' reading interest. With students being digital natives, accustomed to interactive and engaging media, there is an increasing need to adapt pedagogical strategies to better suit their learning preferences. AI- powered applications have emerged as promising tools to provide personalized learning experiences, instant feedback, and immersive content that can greatly increase student motivation to read (Mouza et al., 2019). One AI-based technology that has shown promise in boosting reading interest is interactive storytelling. Through this approach, students have the ability to influence the direction of a narrative, fostering greater engagement with the text. The interactivity within such applications is proven to heighten students' intrinsic motivation to read, as well as to improve their comprehension and retention of the material. Lindgren and McDaniel (2012) demonstrated that interactive storytelling is a powerful tool to increase student engagement by offering them an active role in the story. However, despite the clear benefits, some studies highlight the lack of deep exploration into how different student demographics respond to these interactive methods, which raises questions about the generalizability of these findings. IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3935 In addition to interactive storytelling, gamified reading programs have emerged as another effective tool in enhancing reading habits. By incorporating game design elements like points, levels, and rewards, these programs make reading more enjoyable and competitive. Hamari et al. (2016) argue that the use of gamification can significantly increase motivation and time spent on reading. However, there is an inherent contradiction in the way gamification might affect intrinsic motivation. While rewards and challenges can motivate students in the short term, they could undermine long-term motivation if students come to view reading solely as a game. The existing literature lacks a thorough investigation into how gamification interacts with students' deeper, intrinsic motivations to read and whether the effects differ across age groups or educational settings. Furthermore, AI-powered tutors are another emerging tool that provides immediate feedback and personalized support tailored to each student's learning needs. By analyzing interactions with students, these systems can recommend reading materials suited to the student's competence level. Kukulska-Hulme (2020) asserts that AI tutors offer valuable support by addressing individual needs, allowing students to progress at their own pace. Despite this, several researchers point out the risk of over-reliance on AI systems that may not always account for the nuanced, emotional, and contextual aspects of reading development (Baker et al., 2021). This limitation reflects a gap in current AI systems: while they excel in analyzing data, they still struggle to foster the empathetic understanding needed for comprehensive educational development. However, the adoption of AI in reading instruction faces several challenges. One of the primary obstacles is the need for effective teacher training. Educators must possess the skills to seamlessly integrate AI technologies into the classroom. Ertmer and Ottenbreit-Leftwich (2010) highlight that teacher training plays a pivotal role in the successful implementation of technology in education. Without adequate preparation, AI tools risk being underutilized or misused. There is a lack of detailed guidance on how to equip teachers with the necessary skills to maximize AI’s potential, especially when dealing with the diverse range of educational contexts and needs. Additionally, the successful implementation of AI-based applications is contingent upon the availability of sufficient technological infrastructure. Schools with limited access to hardware, software, or high-speed internet will struggle to adopt AI effectively. Guri-Rosenblit (2005) points out that disparities in access to technology can exacerbate inequalities, particularly in resource-poor regions. However, research on addressing these infrastructural gaps is limited, and there are insufficient recommendations on how policymakers can support equitable access to AI in education. Ethical considerations and data privacy also present significant concerns in the integration of AI into educational settings. AI systems often require access to sensitive student data, raising questions about how this information is handled and safeguarded. Regan and Jesse (2019) suggest that clear data protection policies Nurila Sari Ray, Yundi Fitrah, Adiopenta The Role of Artificial Intelligence in Enhancing Elementary School Students' Reading Interest: A Systematic Literature Review 3936 must be in place to maintain trust between students, educators, and parents. The debate about AI’s role in education frequently overlooks the challenges related to the governance of student data and the potential for misuse. Another crucial issue is the socioeconomic disparity in access to AI-powered technologies. Penuel et al. (2017) noted that students from low-income backgrounds are less likely to have access to the technologies required to engage with AI-based learning tools. This digital divide reinforces educational inequities, with students from disadvantaged backgrounds missing out on the benefits of AI- assisted learning. Despite these challenges, AI in education holds great potential for enhancing student engagement and improving reading skills. To overcome existing barriers, several measures are needed. First, teachers should receive comprehensive professional development programs that focus on the effective use of AI tools in reading instruction. Policymakers must ensure that schools are equipped with the necessary infrastructure and resources to implement AI technologies. Clear ethical guidelines should be established to protect student data and maintain privacy. Additionally, policies aimed at bridging the digital divide must be prioritized to ensure equitable access to AI-powered educational resources. From a practical standpoint, educators should begin integrating AI-based reading programs gradually. Start by experimenting with gamified programs or interactive storytelling to gauge student interest and adapt the tools to the needs of the class. Teachers should also monitor the impact of AI on students' motivation and comprehension, adjusting their strategies as needed. Furthermore, ongoing professional development in AI should be prioritized for educators, ensuring they remain equipped to adapt to rapid technological advancements. Looking forward, AI has the potential to continue evolving, addressing some of its current limitations. Advances in natural language processing (NLP) could enable AI systems to provide more sophisticated feedback on students' reading progress, considering both emotional engagement and comprehension. Future developments in AI might also allow for deeper customization of learning experiences, tailoring educational content to not just academic level, but also emotional and motivational needs, creating a truly holistic learning experience. Despite its promising potential, this review has its own limitations. While we have discussed various AI tools and their benefits, the review itself may overlook certain nuances, particularly those relating to different cultural contexts and educational settings. Further research should consider a broader range of geographic and demographic contexts to provide a more comprehensive view of AI’s impact on reading education. Additionally, much of the research cited here is focused on specific AI applications, without considering the long-term effects or the cumulative impact of these technologies over time. IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3937 Conclusion AI presents promising opportunities to improve elementary students' reading interest by offering personalized and engaging learning experiences that cater to individual needs, fostering intrinsic motivation and enhancing comprehension through interactive storytelling, gamified reading programs, and AI-powered tutors. Research shows that these AI-driven applications can significantly boost reading engagement, providing immediate feedback and immersive environments that motivate students to read more. However, successful implementation of AI in education requires addressing key challenges, such as ensuring equitable access to technology, preparing teachers with the necessary skills and training, and addressing ethical concerns related to data privacy. In particular, issues like socioeconomic disparities in access to technology must be mitigated to prevent widening educational inequalities. Moreover, teacher readiness plays a crucial role in the adoption of AI tools in the classroom, as their attitudes and preparedness directly influence how effectively these tools are integrated into the curriculum. Ethical concerns surrounding the collection and usage of student data must also be carefully managed to maintain trust between educators, students, and parents. Future research should focus on long-term studies to assess the sustained impact of AI on reading motivation, and the development of inclusive AI tools that are adaptable to the diverse needs of students, ensuring that these innovations are accessible to all learners and contribute to improving educational outcomes across different References Brunner, M. (2017). The role of interactivity in digital stories: Enhancing engagement through interactive narratives. Journal of Educational Technology, 19(3), 200-214. Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284. Guri-Rosenblit, S. (2005). E-learning: The key challenges. International Journal of Educational Management, 19(4), 320-334. Hamari, J., Koivisto, J., & Sarsa, H. (2016). Does gamification work? A literature review of empirical studies on gamification. 2014 47th Hawaii International Conference on System Sciences, 3025–3034. Ismayanti, D., & Syam, A. T. (2022). The Community Manipulation through Big Brother’ s Tyranny in George Orwell’ s Nineteen Eighty-Four. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 10(2), 1556â-1569. Ismayanti, D., Said, Y. R., Usman, N., & Nur, M. I. (2024). The Students Ability in Translating Newspaper Headlines into English A Case Study. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 12(1), 108-131. Nurila Sari Ray, Yundi Fitrah, Adiopenta The Role of Artificial Intelligence in Enhancing Elementary School Students' Reading Interest: A Systematic Literature Review 3938 Jackson, C., Mohr-Schroeder, M., Bush, S., Maiorca, C., Roberts, T., Yost, C., … & Fowler, A. (2021). Equity-oriented conceptual framework for k-12 stem literacy. International Journal of Stem Education, 8(1). https://doi.org/10.1186/s40594-021-00294-z Kukulska-Hulme, A. (2020). Mobile language learning: Strategies for a new learning environment. Open Learning: The Journal of Open, Distance and e-Learning, 35(3), 213-224. Lindgren, R., & McDaniel, R. (2012). Fueling engagement through an interactive storytelling experience. Computers and Education, 59(2), 1186-1194. Mouza, C., Yang, H., Pan, Y. C., & Pollock, L. (2019). The role of teacher professional development in shaping the integration of technology in classrooms. Journal of Educational Technology & Society, 22(2), 46-58. Penuel, W. R., Sherer, D., Hill, H. C., & Sabelli, D. (2017). Creating a framework for designing technology-rich learning environments. Educational Researcher, 46(7), 391-405. Regan, P. M., & Jesse, S. (2019). The ethical implications of AI in education: A critical review. Journal of Educational Technology, 20(1), 32-47. Wang, F., Zheng, F., & Wang, S. (2021). AI tutoring systems: Improving learning performance through personalized interventions based on learning analytics. Journal of Educational Technology & Society, 24(3), 132-145. Zhao, X. and Yan-jun, Y. (2021). A study on the application of blended teaching to english reading course under the background of artificial intelligence. Iop Conference Series Earth and Environmental Science, 693(1), 012019. https://doi.org/10.1088/1755-1315/693/1/012019 Zhao, Y., Yusof, S., Hou, M., & Li, Z. (2024). How can generative artificial intelligence help teachers in early childhood education with their teaching? analyses from the perspective of teaching methods. International Journal of Academic Research in Progressive Education and Development, 13(1). https://doi.org/10.6007/ijarped/v13-i1/20958