The presence of artificial intelligence (AI) in all areas of our lives has grown significantly in recent years. This growth provided the impetus for our investigation into how AI currently provides opportunities to extend and enhance learning in K-12 (from kindergarten to 12th grade) settings. To better understand how AI is currently used in K-12 schools, we reviewed 169 studies published from 2011-2021 to examine the educational affordances AI can provide in K-12 settings. In addition, we also examined the challenges regarding the use of AI. Finally, we made recommendations for future research to ensure that the use of AI in K-12 settings is effective. Our research revealed that educational benefits occurred in three major categories: pedagogy, administration, and subject matter content.
Pedagogy
Pedagogical uses of AI involve using AI for teaching and learning methods (Figure 1). Some examples of current pedagogical AI benefits include (1) The use of games with an automatic feedback system. (2) The use of intelligent tutors in which a 3D computer character is always on screen, speaking with a recorded human voice acting as a human tutor. (3) Mentoring in which AI provides students with career mentoring, as they work with a real scientist. (4) Personalised learning, where students receive individualised remedial learning materials generated by an expert system based on students’ pre-test scores of concepts. (5) Simulations in which students learn about gravity and planetary motion in an immersive, whole-body interactive simulation. (6) Remote laboratories which enable students to use distant laboratories where students can evaluate phenomena while studying at home and performing hands-on lab experiments. These are examples of AI extending and enhancing well-known best-practice pedagogies. Our hope for the future is that educators will go beyond past practices, examine alternative pedagogical approaches that AI has now made available, and move beyond using 21st Century technologies, with 20th Century teaching practices.
Credit: Kris Nario at ODUGlobal
Administration
The second category of educational benefits in K-12 was administration. This involves using AI in educational management systems (Figure 2). Some examples of administrative benefits include: (1) Using a diagnostic tool to provide timely recognition of speech impairments in primary school students. (2) AI can help to predict the potential difficulties in writing skills development and clarify the causes and character of these difficulties. (3) Student tracking systems can assist teachers in monitoring students’ activities, informing them of real-time interventions, and reviewing students’ achievements to assist in future lesson planning. (4) Assessment tools can provide automated essay evaluation systems that combine shallow and deep semantic attributes of essays. (5) Comprehensive managerial use of AI enables the analysis of student data to identify the factors influencing student success or failure. These applications of AI have the potential to improve K–12 education.
Credit: Kris Nario at ODUGlobal
Subject Matter Content
The third category of educational benefits in K-12 was in subject matter content. This use of AI provides teaching and learning support directly connected to a subject matter discipline. Most of the studies investigated the use of AI in developing writing skills and acquiring foreign languages (Figure 3). The widespread use of AI in writing and foreign languages may be explained by the technology’s capacity to interact effectively with routine knowledge and systems found in written text and languages. Additionally, writing and language proficiency are abilities and knowledge applicable across academic fields. As a result, these tools’ growth has more widespread application.
Credit: Kris Nario at ODUGlobal
Challenges
Although AI has the capacity to extend and enhance K-12 education, there are many challenges that need to be considered. Our research identified three main challenges. These include (1) the technology itself, both hardware and software, (2) the actual use of the technology, and (3) ethics. Challenges in technology included technology requirements and limitations, ease of use, and program design. The actual use of the technology provided challenges regarding the lack of technology skills on the part of either or both teacher and students, an inability or difficulty to troubleshoot problems as they arise and a lack of understanding about how to use technology effectively. Ethical concerns include privacy issues and potential bias built into AI.
Future Research
Our research findings led us to propose four areas that could help to make the use of AI in K-12 settings more effective. First, to better understand AI and how it can be integrated into teaching and learning, educators must expand their professional development to examine the use of AI in education. It would benefit researchers to create frameworks for AI integration and look at best practices to use as examples for teachers. Second, many topic areas, including art, geography, and history, were underrepresented in the distribution of AI studies. Researchers could investigate the possibilities of AI in these areas. Third, data indicate that teachers and students are the main focus of AI research. Future researchers could examine how AI can assist administrators, especially in examining large datasets. Finally, the most common research approach to AI in K–12 was quantitative. The scholarly community would benefit from qualitative investigations to offer other types of research on the use of AI in K-12 settings.
Our research has provided information on the possibilities and challenges of using AI in K-12 education. This research provides researchers, educators, and administrators with valuable information on the use of AI in education and a springboard for further examination of AI in this context.
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Journal reference
Crompton, H., Jones, M. V., & Burke, D. (2022). Affordances and challenges of artificial intelligence in K-12 education: a systematic review. Journal of Research on Technology in Education, 1-21. https://doi.org/10.1080/15391523.2022.2121344
