Category: EDCI 515: Technology Mediated-Research Methods

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This project explores the research question: ‘How can AI-based tools be integrated into K-12 education to enhance teaching and learning experiences.’ Through a literature review and an annotated bibliography, this study examines the impact of AI-based tools in K-12 education while investigating the steps to ensure these technologies’ meaningful, appropriate, and ethical implementation. The literature reveals opportunities for integrating AI-based tools in K-12 education to enhance student learning and decrease teacher workloads. The benefits of AI-based tools for teachers include planning, implementation, and assessment support, and for students, these include immediate feedback, personalized tutoring, and metacognitive development. Although the literature review demonstrates the benefits of integrating AI-based tools in K-12 education, there are challenges, concerns, and considerations that educators and AI developers need to address. These include the technical integrity, privacy and security, and ethical concerns of AI-based tools. Although these are valid considerations, depending on the program used, they can be addressed through teachers’ professional development, refined AI development processes, and future research opportunities. When these concerns are addressed, the research shows that AI-based tools can effectively integrate into K-12 education, enhancing student learning and decreasing teacher workloads.

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Chiu, T. K. F. (2021). A holistic approach to the design of artificial intelligence (AI) education for K-12 schools. TechTrends, 65(5), 796–807. https://doi.org/10.1007/s11528-021-00637-1

This article provides a holistic framework for integrating K-12 Artificial Intelligence (AI) in education, focusing on curriculum design’s content, product, process, and praxis. Like Celik et al. (2022), this study is published in TechTrends, a reputable and peer-reviewed journal for educational technology. Chiu (2021) states that few studies have addressed how to design effective and valuable AI educational curricula, and this study aimed to develop a model of AI curriculum that incorporates teachers’ thoughts, perspectives, and ideas. Chiu (2021) argued that many studies of AI in K-12 education focus on the tools and skills needed to utilize AI. However, curricular design is a dynamic process involving personalized learning for teachers’ classrooms of diverse learners. Through the author’s study, he used a qualitative research method which incorporated interviews, document analysis, and meetings with 24 teachers from 12 Hong Kong middle schools. He interviewed the educators about their perspectives on effective AI curricular design regarding what content, learning outcomes, and strategies should be implemented in K-12 education. The participants identified knowledge, process, impact, student relevance, communication, and flexibility as aspects of a holistic approach to designing AI curricula. A strength of this study is the diverse teaching perspectives, attitudes, and thoughts throughout the research and data. Limitations of Chiu’s (2021) article for this project include its context in Hong Kong, data collected from a small geographic area, and the fact that its curricula were not tested. Although this study is focused on Hong Kong middle schools, future research would be beneficial in increasing the generalizability of Chiu’s study and framework. This article is relevant and applicable to how AI can be implemented in K-12 education. It offers a structured approach to integrating AI into K-12 education and states the importance of teacher input in curricular design. Chiu (2021) has provided a foundation for the multitude of aspects regarding AI in K-12 education and highlighted the importance of flexible, adaptable, and teacher-informed curricula. (320 words) 

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Zhang, P., & Tur, G. (2023). A systematic review of ChatGPT use in K‐12 education. European Journal of Education, 1–22. https://doi.org/10.1111/ejed.12599

Zhang and Tur (2023), in affiliation with the University de les Illes Balears in Spain, recognized a gap in the research regarding how ChatGPT is implemented in K-12 education. This article was published in the European Journal of Education and communicates the use of ChatGPT in educational settings. This study utilized a systematic review of existing literature following PRISMA guidelines and focused on 13 papers that met the authors’ criteria. This article aimed to identify the strengths, weaknesses, opportunities, and threats (SWOT) of implementing ChatGPT in K-12 education. Zhang and Tur (2023) state that the strengths of ChatGPT in education are personalized learning, student achievement, and the ability of AI programs to improve teacher efficiency. This has potential opportunities for differentiated instruction, AI-supported curriculum development, and teacher support. However, the weaknesses of ChatGPT include low task specificity and content output, while the threats of AI programs include data privacy and ethical concerns. These themes of the positives and challenges of teacher support, ethical considerations, and personalized learning have also been identified in the studies of Celik et al. (2022), Hays et al. (2023), and Murphy (2019). Through Zhang and Tur’s (2023) data analysis, the authors recommend developing policies for implementing AI in K-12 education and ensuring teachers are appropriately educated on ChatGPT uses. This study provides reliable data analyses and information regarding how ChatGPT can be utilized in K-12 education. The authors focus on gaps in existing literature, which supports this research question in acknowledging how AI programs, specifically ChatGPT, can positively impact student learning outcomes and teacher workload when done meaningfully, appropriately, and ethically. Although this study was conducted in Spain, the data reflects similar studies completed in other areas worldwide, especially in Canada. Therefore, the data is applicable, reliable, and valid for this research question. This study will be utilized in this research question through further understanding and critical thinking about how educators can adopt AI-based tools in their educational practices. (323 words)

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Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3

Roll and Wylie (2016) conducted this literature review and analysis, published in the International Journal of Artificial Intelligence in Education (IJAIED). As the authors stated, Artificial Intelligence in Education (AIED) has evolved and revolutionized education over the past 25 years. To explore the trends, evolution, and potential directions of AIED, the authors reviewed and analyzed 47 papers in the IJAIED and published in 1994, 2004, and 2014. The study aimed to analyze the AIED field, identify shifts in focus, and explore new opportunities for evolution and revolution in AI in education. Through the literature review, Roll and Wylie (2016) discovered that AIED research has been primarily composed of personalized tutoring systems, research trends regarding an increase in empirical research on AIED, and focuses on STEM subjects. The authors argue that AIED research balances evolving existing frameworks and revolutionizes interactive learning environments (ILE). Through this, AIED’s frameworks must expand to incorporate informal learning environments, cultural norms, and new technologies. The study’s comprehensive analysis of information has created a reliable and valid overview of AIED evolution, and it uniquely describes the trends, evolution, and revolution of AI in education. However, although Roll and Wylie (2016) only focused on papers in the IJAIEDjournal, which may have limited the scope of the study, the authors have identified the growth of AIED implementation and research. This article positively contributes to our understanding of AI in K -12 education, and the authors’ findings apply to the research question of how AI-based tools can be effectively implemented in education through needing to revolutionize how AI is implemented to enhance teaching, increase student learning experiences, and address teacher workload. (272 words) 

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Ottenbreit-Leftwich, A., Glazewski, K., Jeon, M., Jantaraweragul, K., Hmelo-Silver, C. E., Scribner, A., Lee, S., Mott, B., & Lester, J. (2023). Lessons learned for AI education with elementary students and teachers. International Journal of Artificial Intelligence in Education, 33(2), 267–289. https://doi.org/10.1007/s40593-022-00304-3

Ottenbreit et al. (2023), affiliating with Indiana University and North Carolina State University, investigated how to introduce Artificial Intelligence (AI) effectively and meaningfully to K-12 students. Like Roll and Wylie (2016), this study was published in the International Journal of Artificial Intelligence in Education, a reputable and reliable source for educational technology research. The authors state that introducing AI to K-12 students must ensure they are well-prepared and successful as workers, creators, and innovators. However, the framework of successful AI curricula for K-12 students is multifaceted and complex. Therefore, through a qualitative study, Ottenbreit et al. (2023) aimed to investigate introductory AI concepts to implement in education and co-design AI curricula with support from teachers. Many limitations and gaps in previous frameworks were due to the need for K -12 educators who designed AI curricula. Through this study, the authors utilized a two-phased qualitative research design method, including student interviews and collaborative meetings with teachers. The authors’ interviews with students examined their understanding of AI, while the meetings with teachers explored how they would teach AI to those students. Ottenbreit et al.’s (2023) data analysis show that students associate AI with programming or coding, and teachers recognize the importance of educating students on AI technology. Students demonstrated that they have a gap in understanding AI’s decision-making processes but were able to identify the positive and negative implications of using AI in education. Ottenbreit et al. (2023) argue that designing AI curricula that connect to students’ existing knowledge must be meaningful to students, accessible to teachers, and co-designed with educators. This study provides essential insights into successfully designing an AI curriculum for K-12 education. Although this study focuses on a small sample group of 10 students and three teachers, the information is reliable and provides essential information for successful curriculum development. As this is a more recent field of research, it allows for further studies using Ottenbreit et al.’s (2023) approach with larger sample sizes regarding AI curricular design. This study completed by Ottenbreit et al. (2023) is practical for this research question as it identifies introductory AI concepts, emphasizes the importance of teacher involvement, and acknowledges ethical considerations. (358 words)

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Murphy, R. F. (2019). Artificial intelligence applications to support K–12 teachers and teaching. Perspective: Expert Insights on a Timely Policy Issue, 1–20. https://doi.org/10.7249/PE315

This literature review by Murphy (2019) examines potential Artificial Intelligence (AI) programs and applications that positively impact K-12 education. Through this article, Murphy (2019) recommends resources, aims to identify AI-based tools that can help teachers address challenges in the classroom, and reviews AI applications used in schools to support instruction. Through Murphy’s (2019) review of existing literature, he found that intelligent tutoring systems (ITS), automated essay scoring (AES), and early warning systems can support teachers and student learning. These current uses of AI programs can personalize learning, provide feedback on writing, and identify at-risk students. Although Murphy (2019) identified positive aspects of AI-based tools in education, he acknowledges that ITS has limitations on student learning, there are concerns regarding learned bias in AI systems, and it is essential for the transparency of limitations of AI models. Although the author has acknowledged a limited influence and lack of research on AI applications in education, it is essential to note that this article was written in 2019, before the release of major AI programs, such as ChatGPT. However, its information, data, and analysis are still relevant in AI education research. Murphy (2019) also identifies that AI has the potential to positively impact teachers’ capabilities and allow teachers to deliver more effective classroom instruction. The author argues that the most effective AI-based tools in education will support teachers rather than replace them. In addition, Murphy (2019) states that AI developers need to address data privacy regulations, algorithmic bias, and transparency. This article is relevant to this research question as Murphy (2019) identifies promising AI programs, evaluates AI’s impact on education, and discusses AI limitations. The author acknowledges how AI-based tools can support teachers, enhance student learning, and decrease teacher workload. (287 words) 

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Hays, L., Jurkowski, O., & Sims, S. K. (2023). ChatGPT in K-12 Education. TechTrends, 68(2), 281–294. https://doi.org/10.1007/s11528-023-00924-z

 This quantitative study, conducted by Hays et al. (2023), explores ChatGPT in K-12 education. The authors acknowledge that although there are news articles, opinions, and research on the potential impact of ChatGPT in education, there needs to be more research on how K-12 educators implement ChatGPT in school systems. The study aims to close that research gap and understand teachers’ perceptions and uses of ChatGPT in Missouri. A survey-based study by Hays et al. (2023) distributed a questionnaire to all Missouri K-12 schools, and 89 educators responded with a consensus of neutral feelings regarding the integration of ChatGPT in education. More teachers were unfavourable towards implementing ChatGPT in education despite over 40% of educators not being knowledgeable about ChatGPT and only 18% of participants actively integrating ChatGPT into their work as educators. However, the participants were interested in professional development opportunities for integrating ChatGPT into education. Through the survey, participants identified concerns regarding the ethical use, academic integrity, and effective implementation of ChatGPT in education. Participants gave various responses, and there were valuable statistics and insights regarding this survey. Since this study by Hays et al. (2023) was conducted in Missouri, there are limitations regarding the generalizability of their results. Still, the authors provide a framework for expanding their survey into different geographic areas and building on their findings. Overall, Hays et al. (2023) add to the study completed by Akgun and Greenhow (2022) regarding the ethical use of AI in K-12 education. The authors argue for increased guidelines and professional development to implement ChatGPT in K-12 education successfully. This study is relevant and applicable to this research question as the data from Hays et al. (2023) is diverse, reliable, and informative. In addition, the study provides a foundation for integrating ChatGPT as an AI-based tool in education, and the authors advocate for the balanced, informed, and ethical implementation of ChatGPT in K-12 education. The authors state that there is a need for additional research with larger participant populations and that this survey should be conducted again after one year for a continued study. (344 words)

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Crompton, H., & Burke, D. (2022). Artificial intelligence in K-12 education. SN Social Sciences, 2(7), 1–14. https://doi.org/10.1007/s43545-022-00425-5

Crompton and Burke (2022) completed this study, published in the Springer Nature Social Sciences journal, focusing on how Artificial Intelligence (AI) is used in K-12 education. The authors have stated a gap in teachers’ and students’ understanding of the educational benefits of using AI-based tools. Their study examines how AI-based tools support teaching and learning in K-12 education environments. Through a thematic systematic review of 204 studies utilizing PRISMA principles, Crompton and Burke (2022) answered their research questions on how AI-based tools can support K-12 educators and students. They discovered that AI could support teachers in student monitoring, group management, automatic grading, and data-driven decisions. In addition, Crompton and Burke (2022) found that AI can help students through personalized, individualized, and differentiated learning. This article is reliable and unbiased, as demonstrated through the study’s methodology, and it highlights that future research is required to understand the ethical implications of integrating AI-based tools in K-12 education, which connects to the research completed by Akgun and Greenhow (2022). Crompton and Burke (2022) provide a comprehensive summary of teachers’ and students’ utilization of AI-based tools in education, and the authors show the potential of using AI programs to decrease teacher workload and enhance the personalization of student learning. This article emphasizes the importance of integrating AI thoughtfully, authentically, and meaningfully into education. Their data and information provide an approach for implementing technology in K-12 education. This article applies to this research question as it discusses the benefits of AI in education and the ethical considerations of implementing AI in K-12 education. The study’s findings support the integration of AI in K-12 education, demonstrating positive effects on teacher workload and student learning outcomes. (279 words)

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Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y

This systematic review of research study, conducted by Celik et al. (2022), explores the use of Artificial Intelligence (AI) in education. It is published in TechTrends, a peer-reviewed journal focusing on technology in education. This article aims to explore the promises and challenges of AI for teachers. The authors state that most AI programs are developed for profit, and since teachers do not develop these programs, Celik et al. (2022) argue that there needs to be more understanding and research on AI-based tools from the teachers’ perspective. Therefore, this study attempts to address this gap. Through a review of research, the authors found that AI-based tools can positively impact teachers’ planning, implementation, and assessment in education. It was discovered that the challenges of implementing AI-based tools in education for teachers include the limited reliability, technical capacity, and infrastructure of these AI-based tools and a lack of technological knowledge and interest from teachers regarding AI. Through this study, the researchers reviewed 44 studies published since 2004, 22 of which were published since 2018. Although the researchers used reliable and valid sources, this discrepancy in the year of publication shows that research on AI in education is increasing, and the amount of trustworthy, usable, and applicable sources on AI in K-12 education is accumulating. This study offers valuable insights and reliable information on the research topic of how AI-based tools can be utilized in education. It emphasizes the importance of teacher involvement in AI program development, showcases the promises of AI-based tools for teachers, and provides a foundation of understanding for the AI landscape in education. (264 words) 

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Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7

This article by Akgun and Greenhow (2022) focuses on the ethical considerations and challenges of integrating Artificial Intelligence (AI) in K-12 environments. Through this synthesis study, although Akgun and Greenhow (2022) have stated that utilizing AI can support student learning and teachers’ practices, they argue that AI’s ethical and societal implications are rarely considered. The article aims to explore the utilization of AI programs in K-12 education by providing a framework and resources for the ethical implementation of AI-based programs. Through this synthesis study, the authors stated that the benefits of AI include personalized learning, automated assessment systems, and facial recognition. In contrast, the ethical challenges of integrating AI into K-12 education include privacy, surveillance, autonomy, bias, and discrimination. It was interesting that Akgun and Greenhow (2022) assessed automated assessment systems as a benefit, not an ethical consideration, as assessments should be utilized to personalize learning, guide instruction, and provide meaningful feedback. While automated assessment systems are efficient, they can negatively impact meaningful evaluations to improve student learning. Regardless, the authors presented resources and tools from MIT Media Lab and Code.org to ensure that AI-based tools are authentically and ethically implemented in K-12 education. This research was completed in affiliation with Michigan State University, contributes to research regarding AI in K-12 education, and addresses the research gap in the ethical implications of AI education. Akgun and Greenhow (2022) provide reliable information regarding AI in education to further support teachers, students, and administrators in engaging with AI technologies. This article is beneficial for this research question as it is situated in K-12 education, shows the benefits of AI-based tools for teachers, and addresses ethical and responsible use challenges. Although AI has significant opportunities to improve teacher practices and enhance student learning outcomes, the ethical implications of AI-based tools must be discussed, researched, and evaluated. As noted by Akgun and Greenhow (2022), more research is essential for designing future resources that address AI privacy and bias-related ethical concerns. (326 words)