Journal of Technology and Science Education

Figure 2. PRISMA Protocol

Before proceeding to the analysis and extraction of results, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol was applied (Figure 2), which is one of the most appropriate tools for selecting and filtering the quality of the publications (Rodríguez, Moreno & López, 2020; Sánchez-Serrano, Pedraza-Navarro & Donoso-González, 2022) with different inclusion and exclusion criteria in the variables that show the results obtained (Martínez et al., 2014).

The first results ascertained were 2,927 papers from 220 distinct WoS categories. Subsequently, documents whose categories were related to education (n=325) such as Education Educational Research and Education Scientific Disciplines were included. Next, the plain text file was downloaded from the WoS and articles without abstracts were identified for exclusion (n=15). Finally, the abstracts were reviewed to exclude those papers not related to the field of education (n=132) for various reasons such as biochemistry, tourism, nursing, molecular biology, dentistry, psychiatry, anatomy, architecture, industry, business, etc. No records of duplicate documents were identified. In total, articles related to education were included in the study (n=178).

In order to extract the results corresponding to Objective 1, the search for IA-Gen terms is limited to the periods between 1 January 1998, the date on which Google first appears (Romero, San Román & Oceja, 2018), and 31 December 2023. Subsequently, other inclusion criteria have likewise been established to ascertain the performance of scientific production related to years of publication, bibliographic citations, research areas, types of documents, categories, publishers, journals, authors, languages and countries (Table 1).

Indicators	Total entries	Inclusion criteria
Year of publication	n= 178	1998-2023
Languages	n= 2	All
Countries	n= 51	x ≥ 12 publications
Affiliations	n= 268	x ≥ 5 publications
Citations	n= 178	Top three most cited documents
Authors	n= 460	x ≥ 3 publications
Type of documents	n= 6	All

Table 1. Production indicators and inclusion criteria

For Objective 2, the keyword network map in Vosviewer, the following procedure is carried out. First, the ‘creation of a bibliographic network map’ option and its reading of data from the WoS in ‘.txt’ file format was selected. Next, the type of co-occurrence analysis was selected, using a unit of analysis consisting of all keywords and a total count method. In this step, an Excel document called Thesaurus is introduced, which is used to group similar keywords for various reasons such as synonyms, similar words written in singular and plural or a set of compound words written with and without hyphens. In this fashion, a more meaningful and thorough keyword network and keyword clusters are extracted. Finally, from the total number of keywords (n=575), a minimum number of co-occurrence (x>2) and total link strength (x≥6) is filtered out and a total of 36 keywords are selected for the study.

4. Results

4.1. Results in Relation to the First Objective of the Study

It is noteworthy that the entire scientific production of the AI-Gen in the field of education in 2023 stands at 178 papers. Therefore, 100% of this analysis is undertaken during 2023. Only two languages are used in the publications, English being by far the most commonly used language with 174 papers (97.75%) and Spanish with 4 papers (2.25%).

Among the most influential countries with IA-Gen contributions in education, the USA (x=45) stands out, followed by Australia (x=26), China (x=22) and England (x=12). These 4 countries account for 57% of the total scientific production, the rest being distributed among 47 countries. Figure 3 shows the production percentages of these countries.

Figure 3. Countries with most productions

Furthermore, the universities and affiliations which have undertaken the highest volume of research on AI-Gen in education belong to Deakin University (Australia) with 6 papers (3.37%). This is followed by 4 universities with 4 publications representing 2.08% each. These are; Monash University of Melbourne, the State University System of Florida, the University of Hong Kong and the University of Tasmania. Despite the fact that all papers are published in 2023, certain articles have a considerable number of citations considering their brief publication timeframe. In Table 2, the three most cited papers are highlighted.

Title	Authors	Year	Citation
Chatting and Cheating: Ensuring academic integrity in the era of ChatGPT	Cotton, Cotton & Shipway	2023	89
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education	Tlili, Shehata, Adarkwah, Bozkurt, Hickey, Huang et al.	2023	83
What it the impact of ChatGPT on Education? A rapid review of the Literature	Lo	2023	72

Table 2. Most cited papers

A total of 460 authors are included in the results among the 178 papers analysed, all corresponding to 2023. Crawford, J. stands out with 4 articles, that is, 2.25%. This is followed by 6 researchers with 3 papers each (Chan, C.K.Y.; Cowling, M., Denny, P., Henriksen, D., Leinonen, J. and Mishra, P.), each representing 1.685% of the total scientific production.

The types of documents most commonly used by researchers are articles (n=139), which represent 78.09%. This is followed by Early Access (n=61), Review Article (n=20), Editorial material (n=12), Proceeding paper (n=6) and Correction (n=1).

1. 1. 4.2. Results in Relation to the Second Objective of the Study

The results show that AI-Gen in education specifies 7 distinct clusters in Table 3. Of the 36 items representing the five keywords with the highest level of co-occurrence are ChatGPT, AI, higher education, AI-Gen and large language models (LLM).

Clusters	Keywords	Co-occurrence	Total Link strength
1	ChatGPT	105	318
1	Academic integrity	16	57
1	Automated written assessment	12	37
1	Students	11	54
1	Plagiarism	7	19
1	Knowledge	4	10
1	Ethics	2	7
1	Human-machine collaboration	2	10
2	Large Language Models (LLM)	26	127
2	OpenAI	7	32
2	Machine Learning	4	24
2	Computer Programmer	2	20
2	Computer Science Education	2	8
2	Copilot	2	20
2	Deep learning	2	20
3	Digital learning	5	34
3	Teaching	4	23
3	Competence	3	19
3	Automation	2	6
3	Awareness	2	14
3	4th Industrial Revolution	2	6
4	Artificial Intelligence	94	280
4	Higher Education	34	129
4	Chatbots	17	65
4	Innovation in education	5	27
4	Critical thinking	4	12
4	Human-computer interaction	2	7
5	Generative AI	33	124
5	Challenges	3	12
5	Risks	3	17
5	Opportunities	2	12
6	Education	20	73
6	Prompt	5	12
7	Education technology	6	27
7	Trends	2	9

Table 3. Keyword clusters

The results with the various keyword cluster relationships and their correlation strength are represented in the following network map (Figure 4).

Figure 4. Keyword network map

5. Discussion

If 2022 is remembered as the year of the emergence of AI-based digital tools in society with a behaviour and evolution difficult to predict, garnering the attention of social networks, traditional and digital media according to García (2023), and Prendes-Espinosa (2023), 2023 is remembered as the starting point of the production of papers on AI-Gen prompting educational innovations and with the first publications in the scientific field in the fourth industrial revolution as pointed out by the studies of Li et al. (2023) and Yang and Kim (2023), where US and Australian universities head this research. This fourth digital stage is associated in the study results with terms such as digital learning, teaching, competences, automation and awareness because the use of digital technologies and devices are transforming our reality.

This technology in education has become an innovative and promising tool that can enhance the learning experience and foster greater interaction between students and teachers according to the results of this study and the research of Montenegro-Rueda et al. (2023) where the term ‘human-machine collaboration’ is included in several clusters. In education there is a need to ensure and uphold academic integrity, prevent plagiarism and maintain the ethics of text work production as noted by Ellis and Slade (2023). This ethics is considered in academia as a policy concern related to ethical writing in basic education, higher education, even in university research itself with the limitations and integrity of authorship as noted in Nam and Bai’s study (2023).

Among the studies with the highest citation rate of AI-Gen in education, the term ChatGPT stands out (Cotton et al., 2023; Lo, 2023; Tlili et al., 2023). A technology that, due to its growing popularity and accessibility, requires a significant transformation in pedagogical methods. In this regard, the cluster and network map results of this study demonstrate that the term ChatGPT is related to in the educational scientific literature with academic integrity, automated writing assessment, students, plagiarism, knowledge, ethics and human-machine collaboration. Therefore, when considering who is the author of a text, image, video or music created with AI-Gen, is it the user or the machine?

In this regard, the author agrees with Nacipucha, Benites, Montaño and Corrales (2023) on the teaching role in the educational context and the use of AI-Gen is crucial to improve educational practice and foster new teaching methodologies, beyond requesting the delivery of texts that may be plagiarised, texts learned by heart, or the creation of deepfake videos using AI or voice cloning as specified in the study by Franganillo (2022). On the one hand, teaching the risks that these behaviours pose for humans through education in values and criticism. And on the other, it is essential to consider replacing conventional assessment activities and methods that require automation with those tasks that are competency-based and creative, such as the creation of infographics, the relationship of the content with its context or the use of portfolios to comprehensively record the entire learning process.

Focusing on the results of this study where the term education is associated with prompts, the authors agree with Evans’ study (2023) that the majority of people do not understand the process by which AI‑Gen derives its conclusions and even believe that AI has a mind of its own (sentient). Added to this is the worrying fact that teachers themselves, with little or no knowledge or understanding of what goes on in the AI black box, allow themselves to make educational recommendations on the use of prompts as if these were magic formulas in AI-Gen-based programmes, without using critical thinking and verifying the machine’s responses.

These concerns can be addressed from initial educational stages and by attending to the cluster of this research related to the term IA-Gen with terms such as risks, opportunities and challenges. These concepts and knowledge can be learned from a programmer’s perspective with educational ML software based on the creation of educational projects from the Primary Education stage. Through data trained with software such as LearningML by the students themselves, students can perceive through cross‑curricular projects why a machine has predictions which are erroneous for humans, that is, biases. This method of learning AI-Gen follows the line of research of Rodríguez-García et al., (2020b) and is consistent with the results of this study, where the term ML is associated with specific terms in order to understand AI-Gen such as computer science, programmer or large language models (LLM).

These AI challenges are synonymous with assuming a responsible role on the part of educational institutions and centres to effectively meet the educational demands of a constantly evolving and increasingly digitised society in line with Camacho et al. (2022). Notwithstanding the fact that knowledge as regards AI is generally subject to the computer-technological field due to the complexity of its systems, in the educational field teachers and students must have basic knowledge as regards its operation to know how AI-Gen-based programmes respond, becoming producers and consumers of AI-Gen with a critical and responsible attitude.

6. Conclusions

The conclusions of this study are that 2023 is considered as the starting point of research on AI-Gen in education with the first 178 papers where Australia, China and England head the ranking of studies as regards this subject matter. Within AI-Gen in education, the ChatGPT phenomenon materialises as a focus of study in the most cited research. In the study, 7 keyword clusters are identified, highlighting the term IA-Gen, which is the subject matter of this study and is related to terms such as challenges, risks and opportunities. Furthermore, ChatGPT has included related terms such as academic integration, students, plagiarism or ethics, among others. Finally, the term education is related to prompts. Unquestionably, we are facing a scenario of uncertainty that is difficult to manage due to the exponential growth of AI, where anyone with a device connected to the Internet can use AI-Gen. Therefore, if these tools are available to children and adults in society, schools through an innovative methodology should be the first link to educate and teach with and in relation to AI-Gen in a critical and creative manner.

Following the results of this study, we have a comprehensive research of AI-Gen in education that represents the creation of an educational technological tool. This fact represents the basis for future lines of research by universities, administrations and educational policies in order to optimise the benefits and reduce the risks of the use of AI-Gen under responsible ethics by all the people who make up the educational community.

As a limitation of the study, only the WoS database was used; therefore, this study could be more concrete and consistent if other databases were incorporated. Finally, as a result of the discussion and conclusions of this study, as future lines of research it is necessary to conduct research on the use of AI-Gen in the teaching-learning processes in various educational stages, curriculum areas and evaluation methods used by teachers when using these tools to prevent plagiarism in order to obtain a comprehensive understanding of this technology.

Conflict of Interest Declaration

The author declare that they have no potential conflict of interest with respect to the research, authorship and/or publication of this article.

Funding

This study has not received financial support for the research, authorship and/or publication of this article.

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