INTRODUCTION

In recent years, technology has transformed the way in which modern-day teaching and learning takes place. Nevertheless, education has evolved to incorporate an emerging disruptive technology: Artificial Intelligence (hereinafter ‘AI’), understood as ‘a field of study that combines the applications of machine learning, algorithm productions, and natural language processing’ (Akgun & Greenhow, 2022, p.1). It is, therefore, crucial to adapt to these changes in the field of education and consider how we can effectively integrate AI in education. A clear example of AI inclusion is that which is sparked by ChatGPT (Cooper, 2023; Duha, 2023); recent studies suggest that the proper use of such a tool could boost teaching and learning (Baidoo-Anu & Owusu, 2023; Skavronskaya et al., 2023).

In line with Huang et al. (2021), AI technology has the potential to improve students’ cognitive ability and learning, as well as the efficiency of the teaching and learning process. It is therefore essential to effectively consider AI integration in education in order to leverage its transforming power with regard to improving the quality of education and student development.

At present, AI integration in education is regarded as a highly relevant tool for improving the learning process, given its ability to personalise teaching and learning, provide automated feedback and offer more objective, accurate assessments ( Castaneda, 2023). According to Chen et al. (2020), AI could revolutionise the education sector by providing new opportunities for personalised learning, student assessment and educational research.

It is fundamental to highlight that, although AI offers many opportunities for education, it is not a blanket solution for all educational issues. Therefore, it is essential for it to be used consciously and reflectively in line with the educational context in question, in order to make the most of its potential benefits. Accordingly, it is necessary to reflect critically and thoroughly in order to ensure the effective and responsible use of AI in education. In this regard, teachers play a fundamental role in the integration of AI in education. Celik (2023) suggests that teachers should have specific knowledge of AI-related technology and pedagogy in order for it to be effectively integrated in education. They should also have an understanding of ethics to assess AI-based decisions and ensure they use it responsibly and fairly.

This idea has already been developed by Carrington (2016) through his Pedagogy Wheel (Padagogy Wheel) for technology integration, a tool he designed to help teachers integrate technology in their pedagogical practice, with a focus on pedagogy as opposed to technological applications. The reason this wheel works as a tool for improving the design and assessment of student-centred learning is because all the sections of the wheel are interconnected, thus indicating that learning is not a linear process, but rather a process in constant evolution (Carrington, 2015).

In this regard, Allan Carrington’s Pedagogy Wheel features several concentric rings: 1) a central ring featuring the six cognitive levels of Bloom’s Taxonomy, 2) a subsequent ring with the four levels of technology integration according to the SAMR Model and 3) outer rings containing examples of technology tools and apps that may be used for each cognitive and technology integration level.

Based on these ideas, the Pedagogy Wheel proposed by Carrington (2017) combines the two theoretical frameworks mentioned above (Bloom’s Taxonomy and SAMR Model) in a visual representation (see Figure 1) that allows for the selection of appropriate technology tools and strategies for each level of cognitive skill and technology integration.

Allan Carrington’s Pedagogy Wheel has proven to be an effective tool for improving the quality of teaching (Zhang et al., 2018) and learning (Matta et al., 2016) in the context of technology. However, the wheel does not consider AI as a technology that can be integrated in teaching and learning.

Therefore, in light of the changes being triggered by AI in the field of education, this study proposes an adaptation of Allan Carrington’s Pedagogy Wheel (2017) in the form of a new AI Pedagogy Wheel that allows for the effective AI integration in education at all stages. This proposal is based on the premise that AI has the potential to substantially transform and improve education, so long as it is used in a responsible and conscious manner (Wiley, 2023). For this reason, the creation of an AI Pedagogy Wheel, as adapted from Allan Carrington’s original model, is crucial in order to harness the benefits of this emerging technology in the teaching and learning process, as it facilitates the selection of appropriate technological AI tools and strategies for each level of cognitive skill and AI technology integration. The aim is to create a more personalised, interactive and effective educational environment in line with the needs and expectations of students in today’s digital and technological era.

METHODOLOGY

To ensure scientific rigour in the article and justify the need for the adapted AI Pedagogy Wheel, a research method based on a systematic mapping review was followed in order to answer the following mapping questions (MQ), together with a bibliometric study involving co-occurrence analysis of terms in order to answer the following research questions (RQ) shown in Figure 2.

A systematic review of the literature was carried out in line with the guidelines set out in the PRISMA Statement. An exhaustive search of academic databases (Web of Science/WoS and Scopus) was conducted using the following search phrase: ‘artificial intelligence’ AND ‘education’ AND ‘integration’ AND ‘learning’, taking into account the title, summary, author keywords and KeyWords Plus. The studies were collected selected according to the eligibility criteria shown in Table 1:

Out of the 280 articles found on WoS, 262 were excluded following the application of the relevant filters, leaving 18 articles to be analysed. Meanwhile, of the 861 articles found on Scopus, 818 were excluded according to the established criteria, resulting in the analysis of 43 studies. Of the 61 articles (18 studies from WoS and 43 from Scopus), 17 duplicates were eliminated, leaving a total of 44 articles to be analysed that provide us with a current view of the state of research published in this area in recent years (2021–2023).

For the bibliometric study based on co-occurrence analysis of keywords, the programme VOSviewer was used. The analysis was simplified by taking into account only those keywords with a frequency of occurrence of at least 2. Thus, a square matrix of N x N elements representing the co-occurrence between pairs of keywords was formed and a bibliometric network of the relationships between the keywords through nodes and links was created in graph form. This allowed for the visualisation and analysis of the connections between keywords.

RESULTS

Results of the systematic mapping review

The mapping questions (MQ) set out in this study are answered below. With regard to the first question MQ1. ‘How many studies have been published from 2021 up to now?’, Table 2 shows a summary of the 44 references analysed in this review, together with an identifier used for referencing throughout the analyses.

Next, an answer is provided for question MQ2. ‘What is the production rate of the studies in chronological order (2021–2023)?’; the results are shown in Figure 3. Out of the three years under analysis, the greatest proportion of articles (48%) corresponds to the year 2022. It is important to bear in mind that the year 2023 has not yet concluded at the time of writing, yet it already represents 36% of the total number of articles. This suggests a clear trend of increased interest in the field, evidenced by the significant increase in publications since 2021.

With regard to questions MQ3. ‘Who are the most active authors?’ and MQ4. ‘What are the countries of origin of the most active authors?’, the results are shown in Figure 4.

Within the group of 173 authors of the 44 works selected for the systematic literature review (see Table 2), 4 authors stand out as they have each contributed 2 publications, thus establishing themselves as the most active contributors. The countries of origin of these authors are Indonesia, Finland and Sweden.

Results of the keyword co-occurrence analysis

The main objective of keyword co-occurrence analysis is to identify the relevant research topics related to AI and education. To achieve this, co-occurrence analysis was carried out for the keywords extracted from the 43 articles under analysis. A frequency of occurrence threshold of a minimum of 2 times was set in order to select the most significant keywords. From the 332 initial keywords, a total of 36 keywords met this criterion and will be analysed in detail.

To ensure consistency and coherence in the topic groups, a minimum number of 5 keywords was set to form a cluster. Based on these settings, the resulting co-occurrence network is shown in Figure 5. This Figure shows how many times the keywords appear together in the texts analysed and how they are grouped into four clusters corresponding to the colours red, green, blue and yellow. These clusters were formed based on the similarity between keywords, allowing us to identify the research topics related to AI and education addressed in the articles under analysis.

The research questions (RQ) set out in this study are answered below. Specifically, question RQ1. ‘What relevant approaches or trends have been dealt with in the studies selected from this period?’ is answered in Table 3, which shows a summary of the network, the topic categories created based on the keywords and the references related to each topic category (some articles may fall under more than one cluster, but they have been grouped according to the specific area dealt with in the article).

Adapting Carrington’s Pedagogy Wheel to an AI Pedagogy Wheel is justified based on the topic clusters identified in the analysis. These clusters highlight the importance of technology, knowledge integration and innovation in the field of education.

Taking into account the topic clusters identified in the analysis, we may now answer the following question: RQ2. Do these approaches or trends support the need for adapting Carrington’s Pedagogy Wheel to create a new version that integrates AI in education?

The first cluster focuses on AI integration and therefore provides opportunities for exploring how this innovative technology can optimise education and improve students’ learning at various levels in different contexts. Therefore, this cluster is key for the adapted wheel, since it offers a theoretical basis for AI integration in the context of pedagogy. The second cluster focuses on the integration of digital technologies in education with the aim of improving and enriching the teaching and learning process. This group of keywords covers different areas, with the use of technologies in the educational context as their common denominator. Adapting the wheel to an AI version would involve integrating pedagogical approaches based on emerging technologies, fostering efficiency, personalised learning, responsible use of technological resources and a solid digital infrastructure to implement AI in the teaching and learning process. The third cluster, ‘pedagogical design and innovation’, highlights the value of adapting educational approaches and curriculums to new technologies and emerging trends. The adapted wheel would integrate AI and emerging technologies in pedagogical design and innovation, with the aim of developing more efficient and up-to-date educational approaches in line with the latest trends in technology to improve teaching and learning experiences. Finally, the cluster ‘sustainable and ethical education’ places emphasis on sustainability and ethics in education. In this regard, the wheel adapted to include AI would underline the importance of taking into account questions of ethics and sustainability in education by means of reflective, metacognitive approaches that allow for critical reflection on the use of AI in education.

In summary, the adapted AI Pedagogy Wheel is justified because these topic clusters show how AI can influence and transform different aspects of education, from teaching and learning to curriculum design. The outcome of the analysis underlines the importance of integrating educational technology in the teaching and learning process, harnessing the potential of AI to improve education, promoting the ethical and sustainable use of AI and fostering pedagogical design and innovation. This will enable the development of a more up-to-date and relevant perspective of pedagogy in the age of AI and digital technologies.

Allan Carrington’s Pedagogy Wheel adapted to the Artificial Intelligence Pedagogy Wheel

As mentioned above, Allan Carrington’s Pedagogy Wheel does not consider AI as a technology that can be integrated in teaching and learning. For this reason, the creation of an AI Pedagogy Wheel, as adapted from Allan Carrington’s original model, is necessary because it allows for the effective integration of AI in the teaching and learning process by providing a solid, well-structured pedagogical framework. This would provide a response to the clusters obtained previously, which reflect the relevance and necessity of integrating AI into the teaching and learning process.

In the case of the AI Pedagogy Wheel, like the original, it is composed of several concentric rings showing how AI can be gradually integrated into teaching and learning at different cognitive and technology integration levels.

The central ring shows Bloom’s Taxonomy (Bloom & Krathwohl, 1956), a hierarchical structure of six cognitive levels used to design and classify learning objectives. In ascending order of complexity, the levels are: remember, understand, apply, analyze, evaluate and create.

The AI Pedagogy Wheel proposed by the authors in this paper adapts the cognitive skill levels of Bloom’s Taxonomy to AI, thus providing a structured and clear approach that allows teachers and learners to work towards more advanced and in-depth learning goals. Along these lines, Lameras and Arnab (2021) propose a taxonomy of AI applications in education linked to teaching and learning practice. This taxonomy is divided into four categories: (1) support for teaching and learning, (2) evaluation and feedback, (3) personalised learning and (4) classroom management. However, although there is no AI-specific Bloom’s Taxonomy, the authors have chosen to adapt Bloom’s Taxonomy and to create an AI Pedagogy Wheel with the same theoretical underpinning as Carrington’s Pedagogy Wheel, in order to make the adaptation as accurate as possible.

Table 4 shows some examples of how to use Bloom’s Taxonomy as a guide for both the teaching process and the learning process through activities that effectively integrate AI.

The following ring represents the SAMR Model proposed by Puentedura (2014). This model is a reference framework for technology integration in teaching and learning, i.e. it focuses on how technology is used in teaching and learning, from simple substitutions to redefinition of the way in which the educational process is carried out. In ascending order of complexity, the levels are: Substitution, Augmentation, Modification and Redefinition.

This model provides a guide for the effective integration of technology in the classroom and for improving the quality of learning according to these four levels of technology integration in teaching and learning (Garcia-Utrera et al., 2014), which emerged as a relevant topic in the four clusters obtained from the keyword co-occurrence analysis.

In terms of AI, Lameras and Arnab (2021) use this model as a framework for understanding how AI can be used at different levels to improve education. However, this study goes beyond by exploring this approach further, with Table 5 showing a detailed description of the five levels of integration, as well as some examples of how the SAMR Model can be used as a guide for AI integration in the teaching and learning process.

The outer rings of the AI Pedagogy Wheel contain examples of AI tools and apps that can be used for each cognitive and AI technology integration level. These suggested AI tools and applications available for each cognitive level are just a sample and are by no means an exhaustive list, given the growing number of applications that are currently emerging.

Finally, the proposed AI Pedagogy Wheel includes an additional ring featuring a new aspect: the Reflective-Metacognitive level (covered by the fourth cluster: sustainable and ethical education). The inclusion of the Reflective-Metacognitive level in the wheel is important because it allows students and teachers to reflect critically on the use of AI and how it affects their learning and teaching. With regard to the teaching process, teachers at this level can assess how they are integrating technology in their teaching, reflect on the impact this is having on students and make changes where necessary. This implies a reflective and metacognitive approach towards technology, specifically AI, that is more critical and analytical, rather than simply using it as a tool.

With regard to the learning process, students can also reflect critically on how they use it and the impact it is having on their learning. They can assess how they are using AI to learn, identify what is effective and what is not, and make changes to their learning approach where necessary. Here, reflection refers to the process through which students can critically assess and analyse their own learning, while metacognition refers to students’ ability to monitor their own learning and understand the learning strategies they use. Both skills are essential for successful lifelong learning, and are particularly important in the context of AI-focused education.

Along these lines, there are several studies that justify the need for reflection and metacognition in the use of AI in education. Luckin (2018) believes that AI can be a valuable tool for improving education, but it is important to reflect on how it is used and what impact it has on students and learning overall. Therefore, she highlights the importance of reflecting on how AI can be used effectively in education, and how it can work alongside human intelligence to improve learning.

Furthermore, Selwyn (2019) underlines how important it is for teachers to carefully consider how AI is integrated in the classroom and how they can use it to improve students’ learning. They must also reflect on the potential consequences of AI for teaching and the relationship between humans and machines in the classroom. Siemens and Baker (2012) analyse how AI and data mining can be used in education and the importance of reflecting on the ethical and pedagogical implications of its use. In this regard, reflection on the use of AI for educational data analysis and the collaboration between the actors involved may contribute towards the more effective and responsible use of technology in education.

With respect to metacognition, according to Cerchiaro et al. (2021), this refers to the “knowledge that a person has about their own cognitive processes and products, as well as the monitoring and regulation of these processes in terms of the achievement of an objective or goal” (p. 3). Another perspective is that offered by Porayska-Pomsta (2016), who identifies the use of AI as a methodology for supporting educational praxis and teacher metacognition, as it provides personalised, adaptive, real-time feedback to students, while helping teachers to reflect on their pedagogical practice and make informed decisions.

Nevertheless, the proposed inclusion of this Reflective-Metacognitive level refers more to the ethical and responsible use of AI, where educators can discuss these issues with students and foster the responsible and ethical use of AI. In this way, AI allows teachers to design and implement pedagogical strategies that foster reflection and metacognition in their students as key skills for their future learning.

Therefore, including this Reflective-Metacognitive level in the wheel allows for the identification of areas for improvement and opportunities for improving AI integration in the educational process, ensuring the effective and responsible use of technology.

Figure 6 shows the AI Pedagogy Wheel together with Bloom’s Taxonomy and the SAMR Model, plus the Reflective-Metacognitive ring featuring a selection of appropriate AI tools and technological strategies for each cognitive skill and AI technology integration level.

Link to download the wheel with active links for each of the apps: https://drive.google.com/drive/folders/1ALL3OxidlQiZrGakICCNOjHMFdZOdqFF?usp=sharing

It must be noted that these are just some examples of AI tools and apps that may be used for each cognitive and integration level. There are lots of other tools and apps available that may be adapted and modified according to the specific needs and objectives of the learning activity in line with students’ level of skill and knowledge in terms of AI.

DISCUSSION AND CONCLUSIONS

The relationship between AI and education is a topic that has gained increasing attention in recent years, given the great potential of AI to improve the quality and efficiency of learning. The systematic review of the 43 relevant articles has demonstrated the growing trend in research about AI in education. Specifically, the fact that in 2023 a significant proportion of articles have already been published, despite the year not yet being over, indicates the possibility that research in this field will continue to grow, thus signalling the importance and relevance of the topic.

Furthermore, regarding the authors who have contributed the most (Järvelä, Sanna; Oyelere, Solomon Sunday; Prahani, Binar Kurnia and Suprapto, Nadi) and the geographical diversity of the countries of origin of these authors (Indonesia, Finland and Sweden) demonstrates the international, globalised nature of research in this field.

Meanwhile, turning to AI integration in education, Pedro et al. (2019) explored this relationship in their study and underlined the opportunities and challenges that arise when implementing AI in the area of education to achieve sustainable development. Personalised learning is one of the key opportunities that AI has to offer, as it adapts educational content to the specific needs of each student, allows for the early detection of learning difficulties and offers personalised feedback. Bhutoria (2022) found similar results, where AI is able to take into account the learning requirements and habits of students and optimise learning trajectories.

However, it is important to underline that teachers should be considered a fundamental part of the planning and execution of personalised education with AI. The active involvement of teachers in this process is essential to ensure learning objectives are achieved and that students’ needs are observed. Moreover, as already mentioned, this wheel can be a support tool for teachers, but it is important that teachers are trained to use AI appropriately, to understand its limitations and to make the most of these emerging technologies (Salas-Pilco et al., 2022).

In this sense, AI integration in education must be done carefully, and must be part of a broader, more balanced approach that combines technology with pedagogy and teaching expertise. Lameras and Arnab (2021) and Cope et al. (2021) emphasise that AI should be used as a tool for supporting and improving the teaching and learning process, rather than completely replacing teachers or fully automating the educational process. It is therefore important to understand that AI cannot replace the human relationship between teacher and student, which is fundamental to successful learning.

In line with this idea, the clusters identified in the topic analysis (1. AI integration to improve education, 2. use of educational technologies in the teaching and learning process, 3. pedagogical design and innovation and 4. sustainable and ethical education) reveal the key areas in which AI can have a meaningful impact on education. Thus, adapting the Pedagogy Wheel for AI according to these clusters provides an up-to-date, relevant framework that enables educators to effectively consider how to harness AI capabilities in curriculum planning, teaching methodology, assessment and other key areas of the teaching and learning process.

In addition, one of the contributions made is the inclusion of a new outer ring in the AI Pedagogy Wheel: the Reflective-Metacognitive level. The inclusion of such a level could be an important step towards ensuring the effective and responsible use of AI in education. This is due to the need to understand the crucial role of reflection and metacognition in the educational process. Authors such as Bostrom (2014) emphasise the importance of reflecting on the potential impacts of AI on society and the need to develop strategies to ensure that these impacts are positive. In this regard, he addresses the potential risks and benefits of AI and how its development should be handled with caution and reflection. Drigas et al. (2023) propose a meta-learning model that combines metacognition with smart technologies to improve learning outcomes. The model consists of nine layers, with the metacognition layer referring to the learner’s ability to reflect on their own learning process. It is therefore essential that students learn to reflect on their own learning process and develop effective metacognitive strategies to enhance their learning, especially in situations where technology tools such as AI are used.

Therefore, the adaptation of this wheel is necessary because the authors seek to ensure the pedagogical use of AI in education. However, it is important to note that AI has limitations and disadvantages that need to be considered. While this technology may improve efficiency and accuracy in several areas, its misuse can have negative effects on society. In education, it is important for ethical, moral and privacy issues to be addressed when it comes to handling personal data, as underlined by Renz and Vladova (2021) and Yuskovych-Zhukovska et al. (2022). It is therefore essential to establish clear policies and protective measures to avoid any discrimination or harm to students. As such, the potential benefits of AI in education can be harnessed without compromising privacy and ethics. Furthermore, it is crucial that students and teachers develop competency for the age of AI, as highlighted by UNESCO (2021). This involves understanding the ethical and social aspects of AI, such as data privacy and security, as well as the impact it may have on society (Arrieta et al., 2020).

Consequently, it is essential that AI is applied responsibly and ethically in education, as this tool has a direct impact on the development and education of students. For this reason, it is crucial to have a theoretical framework supported by pedagogy that enables teachers and students to understand the scope and limitations of AI, as well as to acquire critical skills that allow them to assess the accuracy and reliability of the results it provides.

In this regard, the proposal set out in this article stands as a valuable tool for education. It should be noted that the inclusion of the AI Pedagogy Wheel in this theoretical framework is particularly relevant, as this tool is centred around the effective integration of technology and pedagogy. As such, it seeks to ensure that teaching and learning methods are appropriate for the specific context in which AI is used.

Finally, it is worth highlighting that this AI wheel is just an initial approach towards the proper integration of this technology in the age of AI. However, it is important to consider that given the constant emergence of numerous new AI apps, this wheel should be regularly updated to ensure it remains relevant and useful.

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