Does Artificial Intelligence help Teachers with Student Engagement?

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Jose C. Hinojosa Jr. This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7257652/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This qualitative case study explores the perceptions and experiences of three middle school educators regarding the integration of artificial intelligence (AI) in the classroom to enhance student engagement. Grounded in cognitive load theory, the study investigates how AI tools can support differentiated instruction, provide personalized learning experiences, and reduce teacher workload through adaptive scaffolding and real-time feedback. Semi-structured interviews with teachers of varying experience levels were conducted to identify common themes. Thematic analysis revealed four key areas: student engagement and personalized learning, teacher integration of AI technology, ethical considerations, and the need for professional development. While participants recognized the potential of AI to promote student motivation and success, they also expressed uncertainty about ethical boundaries and a lack of formal training. Findings underscore the importance of equipping educators with structured, ethical, and pedagogically sound professional development to ensure effective AI implementation in classrooms. Educational Philosophy and Theory Artificial Intelligence Student Engagement Personalized Learning Educational Technology Teacher Perspectives Cognitive Load Theory AI Integration Professional Development Ethical Use of AI K–12 Education Objectives During the age of education, there have been many different phases. Following the COVID-19 pandemic, the educational system was compelled to adopt technology, enabling online classes for all grade levels. Educators were forced to expand their pedagogical skills, which included the use of educational technology. Kamalov et al. ( 2023 ) state, "the recent high performance of ChatGPT on several standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education" (p.1). For some teachers, this was a stifling time as they were forced to learn educational technologies to adapt to the situation. Chiu et al. (2020) state, "AI K-12 education is new to academia and schools" (p. 5). A new phase in education is being led by a new and emerging technology known as artificial intelligence. Teachers are now faced with the question of using an emerging technology in the classroom. Although artificial intelligence is not a new technology by any standards, it has become a trending topic in the world of education. According to Adiguzel et al. ( 2023 ), "for students, a primary result of implementing AI is increased motivation and engagement" (p. 5). Does AI, an emerging technology, engage students in the classroom? Does it help teachers promote student engagement? This is the topic of interest in today's world of education. Kearsley et al. (1998) state, "by engaged learning, we mean that all student activities involve active cognitive processes such as creating, problem solving, reasoning, decision making, and evaluation" (p. 20). Does artificial intelligence help educators engage their students with their lessons? Does the incorporation of different mediums of artificial intelligence engage students in such a way that it helps them become academically successful across all content? Can artificial intelligence be leveraged in such a way that its power can be harnessed and projected towards helping teachers with experience and teachers with little to no experience create a personalized learning experience for individual students? Is it possible to create customized learning plans with the help of artificial intelligence that will be more engaging to a particular student because it has specific differentiated instruction geared toward the specific student? According to Huang et al. ( 2023 ), they state, "AI-enabled personalized recommendations had a significant and positive effect on the online learning engagement of students with a moderate level of motivation" (p. 11). Are educators equipped with the skills to implement different types of artificial intelligence in their classrooms? Have they even attended any professional development training to help guide them with structured steps and protocols? Are there statutes that help guide educators with the ethical use of artificial intelligence in the classroom? These are all valid questions that relate to the incorporation of artificial intelligence in the classroom, along with ethical use of AI in the classroom and discovering if educators are equipped with the proper professional development to facilitate their educational content in conjunction with AI to help better engage students. Theoretical Framework In this research study, cognitive load theory was used as the framework to help guide the study. Chauncey et al. (2023), states, "cognitive load theory is concerned with how information is presented to support optimal learning" (p. 3). It was chosen because the theory explores how the cognitive load imposed by learning tasks, such as AI integration in the classroom, for student engagement. AI can help manage cognitive load by providing adaptive scaffolding, feedback, and resources tailored to students' individual needs, optimizing their engagement and learning. Adaptive scaffolding could help students with support by analyzing students' performance and understanding their needs. According to Sun et al. ( 2019 ) they state, "Learning analytics could capture student behaviors to build a behavior model and give feedback" (p. 1295). Feedback can also be provided by and AI program to help monitor their progress in any given content area. AI can analyze students' responses and provide a tailored experience. Resources tailored to students' needs will enhance student engagement because they are specifically customized for each individual student. Methods In this case study teachers are exploring the idea of artificial intelligence in the classroom and if it increases student engagement. This research study was conducted in Mission, Texas at a small charter school named Excellence in Leadership Academy. Three willing and able educators in a middle school setting volunteered their time and expertise for this research study. All three teachers were male, taught grade levels 6th -8th grade, taught different contents ranging from science and CTE and had various levels of experience in their career in education. One of the teachers was brand new to the profession and had no prior experience. Another had 1 year of experience and the third participant had 10 years of experience as an educator. Each participant was interviewed from a phenomenological point of view because of their lived experiences as teachers practicing the integration of artificial intelligence in some form or facet in their daily facilitation of their curriculum. The interview process consisted of a set of 10 questions for each participant. Each interview was facilitated after the instructional day beginning at 4:30 PM in room 101. Each participant set aside 45–50 minutes of their time to complete the interview process with no interruptions in between. The process began with a conversational tone, which started with a couple of general questions about the participant and their background. Once an established rapport was established with the participant, a conversational tone was set, and the actual interview, which included 10 questions, began. Each participant was interviewed separately to prevent mutual influence on their answers and to maintain confidentiality. All ten questions pertained to the integration of artificial intelligence in the classroom to promote student engagement. Data Analysis Process For the data analysis, an inductive and deductive thematic analysis was used to analyze the data. Chui et al. (2020) states, that "a hybrid inductive and deductive thematic analysis to identify themes related to our theoretical framework" (p.7). According to Chui et al. (2020), they state, "Accordingly, we adopted the thematic analysis using four phases, guided by the theoretical constructs, to analyze the data" (p. 7). This serves an excellent blueprint for the processes and structure for the data analysis. The data analysis consisted of 4 carefully planned phases, each with a specific function that culminates in the gathered evidence. Phase 1 : In this phase, the participants' responses are transcribed from an audio recording into individual Microsoft documents, each with a pseudonym to protect their identity. The transcription of the participants' responses, highlighted in red, is now ready for the next phase. Phase 2 : During this phase, the initial codes will be generated using the participants' responses to the 10 interview questions. A series of two read throughs will take place. During the first read-through, specific ideas and key phrases will be highlighted, and a code will be attached to each phrase or sentence from the participants' responses. After the initial coding read-through has been completed, a second round of coding will be conducted to identify any missing codes or until saturation is achieved. The codes generated from all three participants' responses will be placed in a code bank in the first column of the thematic analysis bank. Once this has been done then the raw data is ready for the next phase. Phase 3 : In phase three the raw data will then be grouped with similar codes in the second column of the thematic analysis bank. During this phase is when general categories start to emerge as the initial codes begin to form into categories. The number of initial codes determines the number of categories that have formed. There should be fewer categories versus codes. Phase 4: During the final phase, the codes have been grouped into categories, and now the categories will be grouped into overall themes. In this phase, the overall themes of the research study are generated and finalized. Results After gathering the participants' data and following the four phases according to Chui et al. (2020), the data revealed four clear-cut themes. Student Engagement and Personalized Learning All three teachers were aware that artificial intelligence may have the ability to pick up on a student's patterns and create personalized learning experiences based on a student's interactions with educational technology programs. A central theme in all three teachers' approaches was the emphasis on student engagement and personalized learning. Baker ( 2021 ) states, "modern educational technologies in many cases can recognize when students are using ineffective or inefficient strategies and provide them recommendations or nudges to get back onto a more effective trajectory" (p. 47). The use of AI to facilitate differentiated learning methods, individualized learning plans, and immediate feedback aligns with contemporary educational goals centered on catering to diverse learning styles and needs. Neji et al. ( 2023 ) states, "personalized learning as an approach that tailors instruction and learning experiences to meet the individual needs of learners, while adaptive learning uses technology to adjust the learning experience to the learner's abilities and progress" (p. 97). One teacher highlights his commitment to student engagement and personalized learning. Wang et al. ( 2022 ) mentions, "engaging students is seen as crucial for student learning in various environmental settings" (p. 2). He encompasses the use of AI for student engagement and comprehension, the encouragement to use AI in the classroom, and the recognition that students will embrace technology when familiar with it. Another teacher includes their focus on AI and test generation with auto-grading, as well as the belief in AI's potential to positively impact student success and engagement. Teachers' integration of AI technology in their classroom Lee et al. ( 2021 ) mention, "emerging efforts are beginning to explore how to incorporate AI more intentionally at the K-12 levels" (p. 15592). One of the participants, a first-year teacher, takes a more holistic approach to implementing AI in their classroom due to their limited experience. The participant uses AI to fill in his gaps of inexperience, thereby expediting the generation of topics and quiz questions he might not have otherwise considered. According to Alshahrani ( 2023 ), he states, "aims to identify best practices and highlight recommended strategies for integrating AI to enhance the learning experience and improve the sustainability of education delivery" (p. 2030). The student engagement also led to teacher engagement, thanks to the ease of use of AI programs like ChatGPT and Google Gemini. These tools helped teachers with everyday tasks, such as generating journal topics, bellringers, and exit tickets based on the lessons they were covering. One participant used ChatGPT to generate exit ticket assignments based on the lesson objectives, concepts, and ideas. Ethical dimensions of AI integration in the classroom All three participants understood the ethical usage of artificial intelligence in their classroom. Kooli ( 2023 ) states, "the application of these new systems is associated with several challenges and limitations, mainly related to ethics" (p. 1). The participant with the least amount of teaching experience was aware of ethical usage but was unclear about the boundaries of its usage. He was vague about the amount of usage and whether it was allowable by his school due to the emergence of AI in education. The participant with the most years of teaching experience was hesitant to use AI in the classroom due to concerns about the ethical implications of AI integration. The teacher, with some years of teaching experience, found a happy medium by being proactive with his professional development and his eagerness to study the topic of AI integration in the classroom. This participant used AI with certain ethics. He used AI in his classroom as a tool and not the sole driving force of his lesson or activity. He used his better judgment and only integrated AI if he thought it was appropriate. If he were unsure about using it in certain aspects, he would not do so. Professional Development Needs in AI The fourth and final theme was the need for professional development from all three participants. Lin et al. (2021) mention "little research has investigated how AI curriculum and tools can be designed to be more accessible to all teachers and learners" (p. 1). One participant immediately realized that he lacked the skills to integrate artificial intelligence into his everyday pedagogical practices, even if he had wanted to. Luckin et al. (2016) states, "AI researchers are exploring novel user interfaces, such as natural language processing, speech and gesture recognition" (p.24). This participant attempted to integrate small applications of AI into his class, utilizing educational technologies like Summit K12, a program designed to help English language learners practice their listening, speaking, and writing skills in English. The program analyzes their entries and provides recommendations on the skills they need to improve. Another participant knew that he needed professional development but was unaware that he was able to receive it. The third participant, with some years of teaching experience, sought out any type of professional development that he could find, whether it was research from scholarly articles or YouTube videos from credentialed professionals or subject matter experts. This theme also includes the teacher's need for professional development in AI integration, as well as their belief in using AI as a tool, not a crutch. Conclusion In today's world of education, the emergence of artificial intelligence has become a trending topic. Many emerging questions come along with the use of artificial intelligence in the classroom. Educators are still wrestling with the idea of integrating artificial intelligence in their classrooms and their pedagogical practices. Participants in this study were unaware of the different types of AI that could be integrated into their classroom to engage students. Their understanding of AI was limited due to a lack of professional development in this area. They were also limited because their understanding of AI was limited to ChatGPT and Google Bard. They only associated AI with chatbots, overlooking other educational programs that could engage students more effectively due to the program's ability to personalize its understanding of each student through interaction and usage. The participants' experiences highlight the potential benefits of AI in enhancing student engagement, supporting personalized learning, and addressing the diverse needs of students in a modern educational landscape. As the participants continue to navigate the challenges and opportunities presented by AI, their story serves as an inspiration for educators seeking to embrace technology while maintaining a student-centric and ethically responsible approach to teaching. This research study focuses on the topic of AI in education, specifically its integration by teachers to engage their students. The research study provides evidence of teachers' willingness to integrate AI into their classrooms, aiming to engage students across different experience levels, despite their lack of professional development, as highlighted in this study. Participants acknowledged that using educational technologies and AI in their classroom had a positive effect on student engagement. The research study also allowed participants to self-reflect and recognize the need for professional development to better understand how to integrate AI in their classrooms, thereby enhancing student engagement. Declarations Consent to Participate Declaration All participants provided written informed consent to participate in this study. For participants under the age of 18, consent was also obtained from a parent or legal guardian. Clinical Trial Number Clinical trial number: not applicable Ethical Approval This study was conducted in accordance with the ethical standards outlined by the IRB, the Institutional Review Board (IRB) at the University of Texas Rio Grande Valley (UTRGV). References Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology , 15 (3), ep429 https://doi.org/10.30935/cedtech/13152 Alshahrani, A. (2023). The impact of ChatGPT on blended learning: Current trends and future research directions. International Journal of Data and Network Science , 7 (4), 2029-2040. Baker, R. S. (2021). Artificial intelligence in education: Bringing it all together. Digital education outlook: Pushing the frontiers with AI, blockchain, and robots , 43-54. Chauncey, S. A., & McKenna, H. P. (2023). A framework and exemplars for ethical and responsible use of AI Chatbot technology to support teaching and learning. Computers and Education: Artificial Intelligence , 100182. Chiu, T. K., & Chai, C. S. (2020). Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability , 12 (14), 5568. https://doi.org/10.3390/su12145568 Huang, A. Y., Lu, O. H., & Yang, S. J. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners' learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education , 194 , 104684. Kamalov, F., Santandreu Calonge, D., Gurrib, I. (2023). New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability , 15 (16), 12451. Kearsley, G., & Shneiderman, B. (1998). Engagement theory: A framework for technology-based teaching and learning. Educational technology , 38 (5), 20-23. Kooli, C. (2023). Chatbots in education and research: A critical examination of ethical implications and solutions. Sustainability , 15 (7), 5614. Lee, S., Mott, B., Ottenbreit-Leftwich, A., Scribner, A., Taylor, S., Park, K., ... & Lester, J. (2021, May). AI-infused collaborative inquiry in upper elementary school: A game-based learning approach. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 15591-15599). https://doi.org/10.1609/aaai.v35i17.17836 Lin, P., & Van Brummelen, J. (2021, May). Engaging teachers to co-design integrated AI curriculum for K-12 classrooms. In Proceedings of the 2021 CHI conference on human factors in computing systems (pp. 1-12). Luckin, R., & Holmes, W. (2016). Intelligence unleashed: An argument for AI in education. Neji, W., Boughattas, N., & Ziadi, F. (2023). Exploring New AI-based technologies to enhance student motivation. Issues in Informing Science & Information Technology , 20 . Sun, J. C. Y., Yu, S. J., & Chao, C. H. (2019). Effects of intelligent feedback on online learners' engagement and cognitive load: The case of research ethics education. Educational Psychology , 39 (10), 1293-1310. Wang, J., Tigelaar, D. E., Luo, J., & Admiraal, W. (2022). Teacher beliefs, classroom process quality, and student engagement in the smart classroom learning environment: A multilevel analysis. Computers & Education , 183 , 104501. https://doi.org/10.1016/j.compedu.2022.104501 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7257652","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493481709,"identity":"691bbe19-6394-440d-9a2f-77f429d3e03a","order_by":0,"name":"Jose C. Hinojosa Jr.","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACxgYehgMMDGwgZuODjw1gscYDRGphbjac2cAgARLDq4WBgQfGYG+T5gVrYWDAq4W5/ezBwwUVfHLy7QfbpG132NTpth8G2lJjE43TYT15CYdnnGEzNjiT2GydeyZNwuxMIlDLsbTcBlxaZvAYHOZtY0vcwJDYeDu37bCE2QGgFsaGw4S1zO9/2CBtCdJy/iGRWhpuJDZJM4K03CBkS0+OwWEekF9uPGw27G1Lk9x2A2hLAh6/GLafMf7MU3FMTr4//eGDn202/GbngYwPNTa4tUAkjqEJJ+BQDgLyEKoGj5JRMApGwSgY8QAAID5k3a9bIqoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0009-8372-9369","institution":"The University of Texas Rio Grande Valley","correspondingAuthor":true,"prefix":"","firstName":"Jose","middleName":"C.","lastName":"Hinojosa","suffix":"Jr."}],"badges":[],"createdAt":"2025-07-31 03:40:18","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7257652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7257652/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88115571,"identity":"20b04a0a-a1e6-40cc-bcd3-0213cbb416da","added_by":"auto","created_at":"2025-08-01 14:32:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":291043,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7257652/v1/f69931e6-e610-48e3-a195-31f3cf110828.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDoes Artificial Intelligence help Teachers with Student Engagement?\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Objectives","content":"\u003cp\u003eDuring the age of education, there have been many different phases. Following the COVID-19 pandemic, the educational system was compelled to adopt technology, enabling online classes for all grade levels. Educators were forced to expand their pedagogical skills, which included the use of educational technology. Kamalov et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) state, \"the recent high performance of ChatGPT on several standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education\" (p.1). For some teachers, this was a stifling time as they were forced to learn educational technologies to adapt to the situation. Chiu et al. (2020) state, \"AI K-12 education is new to academia and schools\" (p. 5). A new phase in education is being led by a new and emerging technology known as artificial intelligence. Teachers are now faced with the question of using an emerging technology in the classroom.\u003c/p\u003e\u003cp\u003eAlthough artificial intelligence is not a new technology by any standards, it has become a trending topic in the world of education. According to Adiguzel et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \"for students, a primary result of implementing AI is increased motivation and engagement\" (p. 5). Does AI, an emerging technology, engage students in the classroom? Does it help teachers promote student engagement? This is the topic of interest in today's world of education. Kearsley et al. (1998) state, \"by engaged learning, we mean that all student activities involve active cognitive processes such as creating, problem solving, reasoning, decision making, and evaluation\" (p. 20). Does artificial intelligence help educators engage their students with their lessons? Does the incorporation of different mediums of artificial intelligence engage students in such a way that it helps them become academically successful across all content?\u003c/p\u003e\u003cp\u003eCan artificial intelligence be leveraged in such a way that its power can be harnessed and projected towards helping teachers with experience and teachers with little to no experience create a personalized learning experience for individual students? Is it possible to create customized learning plans with the help of artificial intelligence that will be more engaging to a particular student because it has specific differentiated instruction geared toward the specific student? According to Huang et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), they state, \"AI-enabled personalized recommendations had a significant and positive effect on the online learning engagement of students with a moderate level of motivation\" (p. 11).\u003c/p\u003e\u003cp\u003eAre educators equipped with the skills to implement different types of artificial intelligence in their classrooms? Have they even attended any professional development training to help guide them with structured steps and protocols? Are there statutes that help guide educators with the ethical use of artificial intelligence in the classroom? These are all valid questions that relate to the incorporation of artificial intelligence in the classroom, along with\u003c/p\u003e\u003cp\u003eethical use of AI in the classroom and discovering if educators are equipped with the proper professional development to facilitate their educational content in conjunction with AI to help better engage students.\u003c/p\u003e"},{"header":"Theoretical Framework","content":"\u003cp\u003eIn this research study, cognitive load theory was used as the framework to help guide the study. Chauncey et al. (2023), states, \"cognitive load theory is concerned with how information is presented to support optimal learning\" (p. 3). It was chosen because the theory explores how the cognitive load imposed by learning tasks, such as AI integration in the classroom, for student engagement. AI can help manage cognitive load by providing adaptive scaffolding, feedback, and resources tailored to students' individual needs, optimizing their engagement and learning. Adaptive scaffolding could help students with support by analyzing students' performance and understanding their needs. According to Sun et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) they state, \"Learning analytics could capture student behaviors to build a behavior model and give feedback\" (p. 1295). Feedback can also be provided by and AI program to help monitor their progress in any given content area. AI can analyze students' responses and provide a tailored experience. Resources tailored to students' needs will enhance student engagement because they are specifically customized for each individual student.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn this case study teachers are exploring the idea of artificial intelligence in the classroom and if it increases student engagement. This research study was conducted in Mission, Texas at a small charter school named Excellence in Leadership Academy. Three willing and able educators in a middle school setting volunteered their time and expertise for this research study. All three teachers were male, taught grade levels 6th -8th grade, taught different contents ranging from science and CTE and had various levels of experience in their career in education. One of the teachers was brand new to the profession and had no prior experience. Another had 1 year of experience and the third participant had 10 years of experience as an educator.\u003c/p\u003e\u003cp\u003eEach participant was interviewed from a phenomenological point of view because of their lived experiences as teachers practicing the integration of artificial intelligence in some form or facet in their daily facilitation of their curriculum. The interview process consisted of a set of 10 questions for each participant. Each interview was facilitated after the instructional day beginning at 4:30 PM in room 101. Each participant set aside 45–50 minutes of their time to complete the interview process with no interruptions in between. The process began with a conversational tone, which started with a couple of general questions about the participant and their background. Once an established rapport was established with the participant, a conversational tone was set, and the actual interview, which included 10 questions, began. Each participant was interviewed separately to prevent mutual influence on their answers and to maintain confidentiality. All ten questions pertained to the integration of artificial intelligence in the classroom to promote student engagement.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Analysis Process\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor the data analysis, an inductive and deductive thematic analysis was used to analyze the data. Chui et al. (2020) states, that \"a hybrid inductive and deductive thematic analysis to identify themes related to our theoretical framework\" (p.7). According to Chui et al. (2020), they state, \"Accordingly, we adopted the thematic analysis using four phases, guided by the theoretical constructs, to analyze the data\" (p. 7). This serves an excellent blueprint for the processes and structure for the data analysis. The data analysis consisted of 4 carefully planned phases, each with a specific function that culminates in the gathered evidence.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePhase 1\u003c/em\u003e:\u003c/p\u003e\u003cp\u003e In this phase, the participants' responses are transcribed from an audio recording into individual Microsoft documents, each with a pseudonym to protect their identity. The transcription of the participants' responses, highlighted in red, is now ready for the next phase.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePhase 2\u003c/em\u003e:\u003c/p\u003e\u003cp\u003eDuring this phase, the initial codes will be generated using the participants' responses to the 10 interview questions. A series of two read throughs will take place. During the first read-through, specific ideas and key phrases will be highlighted, and a code will be attached to each phrase or sentence from the participants' responses. After the initial coding read-through has been completed, a second round of coding will be conducted to identify any missing codes or until saturation is achieved. The codes generated from all three participants' responses will be placed in a code bank in the first column of the thematic analysis bank. Once this has been done then the raw data is ready for the next phase.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePhase 3\u003c/em\u003e:\u003c/p\u003e\u003cp\u003eIn phase three the raw data will then be grouped with similar codes in the second column of the thematic analysis bank. During this phase is when general categories start to emerge as the initial codes begin to form into categories. The number of initial codes determines the number of categories that have formed. There should be fewer categories versus codes.\u003c/p\u003e\u003cp\u003ePhase 4:\u003c/p\u003e\u003cp\u003eDuring the final phase, the codes have been grouped into categories, and now the categories will be grouped into overall themes. In this phase, the overall themes of the research study are generated and finalized.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAfter gathering the participants' data and following the four phases according to Chui et al. (2020), the data revealed four clear-cut themes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStudent Engagement and Personalized Learning\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAll three teachers were aware that artificial intelligence may have the ability to pick up on a student's patterns and create personalized learning experiences based on a student's interactions with educational technology programs. A central theme in all three teachers' approaches was the emphasis on student engagement and personalized learning. Baker (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) states, \"modern educational technologies in many cases can recognize when students are using ineffective or inefficient strategies and provide them recommendations or nudges to get back onto a more effective trajectory\" (p. 47). The use of AI to facilitate differentiated learning methods, individualized learning plans, and immediate feedback aligns with contemporary educational goals centered on catering to diverse learning styles and needs. Neji et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) states, \"personalized learning as an approach that tailors instruction and learning experiences to meet the individual needs of learners, while adaptive learning uses technology to adjust the learning experience to the learner's abilities and progress\" (p. 97). One teacher highlights his commitment to student engagement and personalized learning. Wang et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) mentions, \"engaging students is seen as crucial for student learning in various environmental settings\" (p. 2). He encompasses the use of AI for student engagement and comprehension, the encouragement to use AI in the classroom, and the recognition that students will embrace technology when familiar with it. Another teacher includes their focus on AI and test generation with auto-grading, as well as the belief in AI's potential to positively impact student success and engagement.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTeachers' integration of AI technology in their classroom\u003c/em\u003e\u003c/p\u003e\u003cp\u003eLee et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) mention, \"emerging efforts are beginning to explore how to incorporate AI more intentionally at the K-12 levels\" (p. 15592). One of the participants, a first-year teacher, takes a more holistic approach to implementing AI in their classroom due to their limited experience. The participant uses AI to fill in his gaps of inexperience, thereby expediting the generation of topics and quiz questions he might not have otherwise considered.\u003c/p\u003e\u003cp\u003eAccording to Alshahrani (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), he states, \"aims to identify best practices and highlight recommended strategies for integrating AI to enhance the learning experience and improve the sustainability of education delivery\" (p. 2030). The student engagement also led to teacher engagement, thanks to the ease of use of AI programs like ChatGPT and Google Gemini. These tools helped teachers with everyday tasks, such as generating journal topics, bellringers, and exit tickets based on the lessons they were covering. One participant used ChatGPT to generate exit ticket assignments based on the lesson objectives, concepts, and ideas.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEthical dimensions of AI integration in the classroom\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAll three participants understood the ethical usage of artificial intelligence in their classroom. Kooli (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) states, \"the application of these new systems is associated with several challenges and limitations, mainly related to ethics\" (p. 1). The participant with the least amount of teaching experience was aware of ethical usage but was unclear about the boundaries of its usage. He was vague about the amount of usage and whether it was allowable by his school due to the emergence of AI in education. The participant with the most years of teaching experience was hesitant to use AI in the classroom due to concerns about the ethical implications of AI integration. The teacher, with some years of teaching experience, found a happy medium by being proactive with his professional development and his eagerness to study the topic of AI integration in the classroom. This participant used AI with certain ethics. He used AI in his classroom as a tool and not the sole driving force of his lesson or activity. He used his better judgment and only integrated AI if he thought it was appropriate. If he were unsure about using it in certain aspects, he would not do so.\u003c/p\u003e\u003cp\u003e\u003cem\u003eProfessional Development Needs in AI\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe fourth and final theme was the need for professional development from all three participants. Lin et al. (2021) mention \"little research has investigated how AI curriculum and tools can be designed to be more accessible to all teachers and learners\" (p. 1). One participant immediately realized that he lacked the skills to integrate artificial intelligence into his everyday pedagogical practices, even if he had wanted to. Luckin et al. (2016) states, \"AI researchers are exploring novel user interfaces, such as natural language processing, speech and gesture recognition\" (p.24). This participant attempted to integrate small applications of AI into his class, utilizing educational technologies like Summit K12, a program designed to help English language learners practice their listening, speaking, and writing skills in English. The program analyzes their entries and provides recommendations on the skills they need to improve. Another participant knew that he needed professional development but was unaware that he was able to receive it. The third participant, with some years of teaching experience, sought out any type of professional development that he could find, whether it was research from scholarly articles or YouTube videos from credentialed professionals or subject matter experts. This theme also includes the teacher's need for professional development in AI integration, as well as their belief in using AI as a tool, not a crutch.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn today's world of education, the emergence of artificial intelligence has become a trending topic. Many emerging questions come along with the use of artificial intelligence in the classroom. Educators are still wrestling with the idea of integrating artificial intelligence in their classrooms and their pedagogical practices.\u003c/p\u003e\u003cp\u003eParticipants in this study were unaware of the different types of AI that could be integrated into their classroom to engage students. Their understanding of AI was limited due to a lack of professional development in this area. They were also limited because their understanding of AI was limited to ChatGPT and Google Bard. They only associated AI with chatbots, overlooking other educational programs that could engage students more effectively due to the program's ability to personalize its understanding of each student through interaction and usage. The participants' experiences highlight the potential benefits of AI in enhancing student engagement, supporting personalized learning, and addressing the diverse needs of students in a modern educational landscape.\u003c/p\u003e\u003cp\u003eAs the participants continue to navigate the challenges and opportunities presented by AI, their story serves as an inspiration for educators seeking to embrace technology while maintaining a student-centric and ethically responsible approach to teaching. This research study focuses on the topic of AI in education, specifically its integration by teachers to engage their students. The research study provides evidence of teachers' willingness to integrate AI into their classrooms, aiming to engage students across different experience levels, despite their lack of professional development, as highlighted in this study. Participants acknowledged that using educational technologies and AI in their classroom had a positive effect on student engagement. The research study also allowed participants to self-reflect and recognize the need for professional development to better understand how to integrate AI in their classrooms, thereby enhancing student engagement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent to Participate Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent to participate in this study. For participants under the age of 18, consent was also obtained from a parent or legal guardian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical standards outlined by the IRB, the Institutional Review Board (IRB) at the University of Texas Rio Grande Valley (UTRGV).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdiguzel, T., Kaya, M. H., \u0026amp; Cansu, F. K. (2023). Revolutionizing education with AI: Exploring the transformative potential of ChatGPT. \u003cem\u003eContemporary Educational Technology\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), ep429 https://doi.org/10.30935/cedtech/13152\u003c/li\u003e\n\u003cli\u003eAlshahrani, A. (2023). 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Teacher beliefs, classroom process quality, and student engagement in the smart classroom learning environment: A multilevel analysis. \u003cem\u003eComputers \u0026amp; Education\u003c/em\u003e, \u003cem\u003e183\u003c/em\u003e, 104501. https://doi.org/10.1016/j.compedu.2022.104501\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The University of Texas Rio Grande Valley","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Student Engagement, Personalized Learning, Educational Technology, Teacher Perspectives, Cognitive Load Theory, AI Integration, Professional Development, Ethical Use of AI, K–12 Education","lastPublishedDoi":"10.21203/rs.3.rs-7257652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7257652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis qualitative case study explores the perceptions and experiences of three middle school educators regarding the integration of artificial intelligence (AI) in the classroom to enhance student engagement. Grounded in cognitive load theory, the study investigates how AI tools can support differentiated instruction, provide personalized learning experiences, and reduce teacher workload through adaptive scaffolding and real-time feedback. Semi-structured interviews with teachers of varying experience levels were conducted to identify common themes. Thematic analysis revealed four key areas: student engagement and personalized learning, teacher integration of AI technology, ethical considerations, and the need for professional development. While participants recognized the potential of AI to promote student motivation and success, they also expressed uncertainty about ethical boundaries and a lack of formal training. Findings underscore the importance of equipping educators with structured, ethical, and pedagogically sound professional development to ensure effective AI implementation in classrooms.\u003c/p\u003e","manuscriptTitle":"Does Artificial Intelligence help Teachers with Student Engagement?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-01 14:24:14","doi":"10.21203/rs.3.rs-7257652/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2da9e8e7-f00d-4cf9-9aa7-08169ce684a8","owner":[],"postedDate":"August 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52404589,"name":"Educational Philosophy and Theory"}],"tags":[],"updatedAt":"2025-08-01T14:24:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-01 14:24:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7257652","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7257652","identity":"rs-7257652","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}