Artificial Intelligence in Educational Research

Abstract

The proliferation of textual data in academic literature necessitates accelerating qualitative research methodologies. Text mining, underpinned by artificial intelligence and natural language processing, emerges as a transformative solution. This study analyzes how AI-integrated qualitative data analysis software such as ATLAS.ti and MAXQDA have streamlined processes like automatic coding and summarization since early 2023. These tools now facilitate rapid preliminary reviews through summarization features and obviate programming expertise through intuitive interfaces. Key advantages include drastic reductions in manual coding time through AI coding, enrichment of inductive coding systems via semantic analysis-based sub-code suggestions, and insights-driving code commenting summaries. Deep learning models unlocked by such tools will enable discernment of increasingly intricate patterns, improving educational interventions through real-time strategies informed by empirical findings. However, responsible use requires human oversight to refine coding and interpret nuanced results. While propelling qualitative research to unprecedented scales and depths, text mining also poses challenges around potential oversight neglect and lack of ethical guidelines. Optimizing these tools ensures accurate, responsible analyses that revolutionize understanding complex educational processes. AI ultimately enhances social science and education research outcomes through large-scale textual data analysis.

1. Introduction

The world’s information is growing at an exponential rate, with estimates indicating that 80% of all information is in text form [1]. The academic landscape is undergoing a significant increase in the volume of scholarly publications [2, 3]. This proliferation is particularly impactful in the field of data science, as artificial intelligence can assist in generating novel research methodologies, especially when it comes to analyzing large volumes of textual information. This is further facilitated by the continual advancements in Natural Language Processing (NLP).

Text mining, which has its roots in artificial intelligence (AI), significantly reduces the time required for data analysis in qualitative research that relies on text analysis. It employs statistical techniques and leverages algorithms to scrutinize written information [4, 5]. This technological advancement not only streamlines the research process but also enhances the depth and breadth of insights that can be gleaned from large textual datasets, given that it is impractical to manually analyze large volumes of text, text mining can uncover patterns that may not be immediately apparent to the researcher. The utilization of artificial intelligence has the potential to guide education towards the discovery of new curricula, teaching methods, and novel research avenues [6].

Text mining is a suite of processes that employs Natural Language Processing to facilitate human-machine communication, mediated by artificial intelligence; thus, a computer is essential for this process [7]. Text mining has found diverse applications throughout history, ranging from politics to medicine. For instance, it has been used to analyze behavioral patterns in patients through alcohol-related forums, employing Latent Dirichlet allocation (LDA) techniques [8]. Although its applications date back to the 1980s, in the educational field, text mining can be utilized to enhance teaching and learning processes, as well as to analyze issues related to educational underperformance or success [9]. By employing Quantitative Text Analysis (QTA), one can systematically analyze various collections of text in an automated manner, identifying underlying structures or patterns within the text. It is important to note that this approach is not intended to replace careful reading. Rather, its aim is to augment the understanding of the information, thereby enhancing the depth and rigor of textual analysis. This method serves as a complementary tool that can facilitate more nuanced interpretations and insights into the subject matter under investigation [10].

2. Exploring educational data through text mining

Furthermore, text mining can be employed to analyze student feedback, aiming for a more comprehensive understanding of their needs and experiences. It can also be used to review scholarly articles, with the objective of constructing clear maps of prevailing trends in the educational field. This capability not only enriches our understanding of the educational landscape but also provides actionable insights for targeted improvements [11]. Therefore, educational research should seize the new opportunities offered by AI-supported text mining to analyze large volumes of data, a task that was previously unattainable for individuals without expertise in programming. This opens a new frontier for educational innovation and data-driven policy formulation.

In the study titled “Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century” [12] an increase in publications related to AI was observed. These publications show a clear concentration of such research endeavors in the United States and China, falling into three major clusters: AI, pedagogical issues, and technological matters. However, the study also notes that ethics, despite its importance, is a topic often overlooked in research involving artificial intelligence.

In the study titled “Text Mining Undergraduate Engineering Programs’ Applications: The Role of Gender, Nationality, and Socio-economic Status” [13], the aim was to identify user behavior patterns related to Massive Open Online Courses (MOOCs). For this purpose, topic models with Latent Dirichlet allocation were employed. This approach assists in uncovering the hidden semantic structure within the analyzed texts:

The process begins by drawing a K-dimensional Dirichlet vector θd that captures the expected proportion of words in document d that can be attributed to each topic. Then for each position (or, equivalently, for each word) in the document, indexed by n, it proceeds by sampling an indicator zd, n from a Multinomial K (θd, 1) whose positive component denotes which topic such position is associated with. The process ends by sampling the actual word indicator wd,n from a Multinomial V(Bzd,n, 1), where the matrix B = [β1|. . . |βK], encodes the distributions over terms in the vocabulary associated with the K topics [14].

Topic modeling leverages machine learning techniques, wherein Natural Language Processing capitalizes on the presence of textual data to enable computers to understand words and learn data patterns [15]. This sophisticated approach not only facilitates the extraction of meaningful insights but also enhances the computational capabilities for analyzing large and complex textual datasets.

In the study titled “Data Mining Analysis of the 2022 Curriculum Framework and Study Plan for Basic Education in Mexico” [16] a comparison was made between the 2011, 2017, and 2022 Mexican Study Plans using text mining with packages such as Quanteda, Tidyverse, and word2vec. The study indicates that the 2022 plan promotes inclusion and well-being for populations facing inequality.

In the study titled “Learner-Centered Analysis in Educational Metaverse Environments: Exploring Value Exchange Systems through Natural Interaction and Text Mining” [17] text mining techniques were applied to a compilation of comments, discussions, and reflections. Utilizing term extraction, co-occurrence analysis, and network modeling, valuable information was gleaned regarding student learning outcomes. In conclusion, text mining proved to be a powerful tool for exploring self-directed learning within the ever-changing landscape of education in the digital age.

The study titled “Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach” [18] examines the use of Artificial Neural Networks (ANN) through a text mining lens, utilizing a corpus of 10,661 articles and 35,973 keywords from scientific journals. The results indicate that research has evolved from optimization and machine learning towards deep learning and artificial intelligence. This progression has led to improved predictive models that are capable of understanding and modeling complex systems, including educational processes and teaching-learning mechanisms. This study serves as a comprehensive overview, shedding light on the transformative impact of ANN in various domains.

Educational research must embrace the challenges associated with utilizing artificial intelligence for data analysis, particularly for text-based data, to gain a deeper understanding of teaching and learning processes. The proliferation of technologies that leverage artificial intelligence consequently offers innovative opportunities for educational research. Previously, conducting such analyses required a researcher or a team member to possess expertise in programming and statistical methods for text mining. However, with the advent of deep learning models and neural networks, such specialized knowledge is no longer essential. This shift signifies the democratization of new research techniques, which are readily accessible in contemporary research software platforms.

3. Possibilities of using research software with artificial intelligence

Educational research must embrace the challenges associated with utilizing artificial intelligence for data analysis, particularly for text-based data, to gain a deeper understanding of teaching and learning processes. The proliferation of technologies that leverage artificial intelligence consequently offers innovative opportunities for educational research. Previously, conducting such analyses required a researcher or a team member to possess expertise in programming and statistical methods for text mining. However, with the advent of deep learning models and neural networks, such specialized knowledge is no longer essential. This shift signifies the democratization of new research techniques [19], which are readily accessible in contemporary research software platforms.

With natural language processing, research software designed to identify patterns in text is evolving to a new level of analysis. NLP allows for the examination of large volumes of textual data without the need for manual coding [20], a practice to which researchers using tools like MAXQDA, ATLAS.ti, or Quirkos had previously become accustomed.

Education is inherently more qualitative than quantitative in nature; thus, qualitative text analysis is most employed for this type of data [21, 22, 23, 24]. Since the beginning of 2023, some software platforms like MAXQDA and ATLAS.ti have started to incorporate artificial intelligence to assist researchers in the processes of systematization and analysis. These platforms enable researchers to label text through coding, creating a comprehensive coding system that offers deep insights. This facilitates the construction of categories, which can be developed either from a theoretical perspective that relies more on human judgment or through an empirical approach [25]. The former represents a deductive analytical framework, while the latter adopts a more inductive stance, which is particularly well-suited for information management using AI.

3.1 ATLAS.ti and its possibilities with artificial intelligence for data analysis

ATLAS.ti is a Qualitative Data Analysis (QDA) software designed to manage and analyze large volumes of qualitative data, ranging from interview transcripts and researcher notes to documents, images, and videos. One of the earliest studies to utilize ATLAS.ti was “ATLAS/ti — A Prototype for the Support of Text Interpretation” [26], which described how researchers could create maps linking concepts in a network-like fashion. The software also allows for the writing of memos, thereby assisting researchers in maintaining organization through its graphical user interface. This comprehensive toolset not only streamlines the research process but also enhances the depth and quality of qualitative analyses.

Currently, ATLAS.ti leverages OpenAI technology to extract qualitative insights in a matter of minutes, transforming the landscape of qualitative data analysis. Whereas the traditional coding process could take weeks to complete, the software now promises to expedite research projects by up to tenfold [27] with the aid of OpenAI. It offers an array of advanced features such as automated coding, real-time code suggestions, automatic summarization, and data visualization tools, thereby revolutionizing the efficiency and effectiveness of qualitative research in both academic and professional settings.

As can be seen in Figure 1 (screenshot captures), from the moment the user imports a document, ATLAS.ti enables the use of artificial intelligence to initiate document analysis. For demonstration purposes, Mexico’s 2022 Curriculum Plan was used as a sample. The process is as straightforward as selecting the document to be analyzed and clicking the “Summarize” button, which then yields the following results. This streamlined approach significantly simplifies the qualitative analysis process, making it more accessible and efficient for researchers.

Figure 1.

Screenshot captures of ATLAS.ti.

ATLAS.ti results: “In conclusion, Mexico’s Secretariat of Public Education (SEP) is implementing strategies to strengthen inclusive education, gender equity, and respect for human rights. The aim is to improve the management of educational resources and encourage community participation in enhancing education. The curricular proposal is grounded in respect for diversity and aims to cultivate citizens committed to social transformation. The importance of strengthening ties between schools and communities is recognized, and the professional autonomy of teachers is emphasized. The approach also seeks to foster holistic skills in students and underscores the importance of their involvement in shaping the educational environment. The New Mexican School promotes well-being and respectful treatment, acknowledges the importance of family participation, and aims to address digital inequalities while fostering comprehensive education in digital culture. During the pandemic, teachers adapted to working from home, and the importance of mathematics and other disciplines in everyday life was acknowledged. However, the curriculum has also been criticized for historically promoting a mestizo vision, thereby neglecting the country’s ethnic and cultural diversity. Each educational reform is a political-pedagogical project that defines the curriculum and teaching methods based on a vision for the nation and the diverse groups within society.”

With ATLAS.ti, researchers have the capability to upload various research documents and obtain summaries. As demonstrated in the previous example, a summary was generated for a document exceeding 200 pages in length. This feature significantly streamlines the research process, allowing for quick and efficient overviews of extensive materials, thereby enhancing the researcher’s ability to focus on key insights and findings.

Similarly, the most significant advancement that accelerates the coding process for research is the implementation of automatic coding through artificial intelligence. In ATLAS.ti, this can be easily accomplished by following the “Search & Code → IA Code” pathway, as shown in Figure 2. Researchers simply need to select the document to be coded and then wait for the computer to complete the task. This innovation greatly enhances the efficiency and accuracy of the qualitative analysis process.

Figure 2.

Screenshot capture of ATLAS.ti (IA codig).

Thus, utilizing this process, 44 parent codes were generated from the document in just 30 minutes using a PC equipped with an Intel(R) Core(TM) i5-7400 CPU @ 3.00GHz and 8GB of RAM. These parent codes were accompanied by various child codes or sub-codes, amounting to a total of 1210 codings. Although the software is still in its beta stage, this number of codes can be overwhelming when the goal is to generate clear labels (codes) that identify thematic patterns. Therefore, human oversight is essential to refine the coding system by merging similar codes, deleting unnecessary ones, and thereby reducing the dataset to facilitate clearer categorization of information. Nevertheless, the use of artificial intelligence undoubtedly serves to dramatically reduce the amount of time required for this labor-intensive process, making it a valuable tool in modern qualitative research.

3.2 MAXQDA and its possibilities with artificial intelligence for data analysis

MAXQDA is another software platform that has embraced text mining using Natural Language Processing. Initially developed in Germany in 1998 to facilitate qualitative data analysis, the software was originally named WINMAX. It was designed following the methodologies of Max Weber and Alfred Schutz [28]. Over the years, MAXQDA has evolved to incorporate advanced text mining capabilities, thereby expanding its utility, and making it a versatile tool for researchers in various disciplines seeking to analyze complex textual data.

Currently, MAXQDA is designed for managing and analyzing qualitative data, but it also supports mixed-methods research. The software allows for the handling of interview transcriptions, field notes, documents, images, and videos. In its 2022 version, a new virtual research assistant feature, known as AI Assist, has been incorporated. This feature enables researchers to streamline their work by automatically analyzing and summarizing various elements of the research project. Notably, it integrates with OpenAI’s AI technology, the creator of ChatGPT, and is currently in its beta stage.

The three primary tools available in MAXQDA are: (A) “Summarize Selected Text Passages,” which condenses portions of text selected by the researcher and saves them directly as notes; (B) “Suggest Subcodes,” where the software analyzes text portions based on codes developed by the researcher and offers suggestions for possible new sub-codes; and (C) “Summarize a Coded Segment to a Code Comment,” which allows for the creation of summaries of coded segments that are saved as code comments. These features collectively enhance the software’s utility, making it a comprehensive and versatile tool for academic research in various disciplines (Figure 3).

Figure 3.

Screenshots capture of MAXQDA (IA Assit – Summarize coded segments).

To utilize the AI Assist feature, researchers must first establish their own code system, underscoring the continued necessity for human oversight to achieve optimal results. This step allows for a more logical linkage of text segments with the corresponding codes. For illustrative purposes, the 2022 Mexican Study Plan was used as an example. The process involves the following steps: select code → right-click → AI Assist → Summarize Code Segments → choose language → accept policies and confirm. After a variable amount of time, depending on the length of the coded text segments, the software generates the results. This comprehensive approach enhances the reliability and depth of the analysis, making it a valuable tool for academic research. In this specific example, the code “learning” was used, yielding the following summary.

Learning is fundamental in the New Mexican School, which aims to guarantee the right to education for children and youth. The community serves as the integrating core of teaching and learning processes, emphasizing the need to contextualize content to address the regional, local, contextual, and situational nature of the teaching and learning process. Additionally, there is criticism of the focus on cognitive capitalism and the knowledge society in education.

The assessment of learning should be formative and centered on tracking the learning process of students. Accreditation should substantiate the granting of both grades and certificates based on the teachers’ judgment of the entire teaching and learning process. The learning outcome profile is based on capacities, values, knowledge, and skills gradually acquired in formative fields. Articulating axes connect the content of different disciplines and teaching and learning actions with the students’ reality.

The curricular proposal emphasizes the importance of the community as a social, cultural, political, productive, and symbolic space in which the school is situated. The socially constructed knowledge and wisdom of community life by the students should be considered to discuss and even problematize the content of the Study Plan and Programs. The community introduces into the school a living dialog between the present and the past, allowing students to see themselves as historical subjects.

Formative fields address the relationship of human beings with society and nature, the construction of participatory and responsible citizenship, and the building of identity and a sense of belonging. Study programs must be adapted to the concrete conditions of teaching and learning. Phases and grades of learning indicate the indispensable sequences between disciplines.

In summary, public education in Mexico focuses on student development through formative processes centered on the community and contextualized at the regional and local levels. The assessment is formative and based on tracking the students’ learning process, and the exit profile is grounded in capacities, values, knowledge, and skills gradually acquired in formative fields. The curricular proposal underscores the importance of the community as a multifaceted space where the school is embedded, and formative fields explore human relationships with society and nature, participatory citizenship, and identity construction.

To illustrate the “Suggest Subcodes” feature, the same document was used, specifically focusing on the “learning” code from the 2022 Mexican Study Plan. As depicted in Figure 4, the following procedure is followed: select code → right-click → AI Assist → Suggest Subcodes → choose language → accept policies and confirm. In this manner, MAXQDA, in collaboration with OpenAI, analyzes the information within the code and its associated text segments to provide suggestions. The process culminates in generating the following results, thereby offering a nuanced and data-driven approach to qualitative research.

1. Curriculum design and community integration.

• Designing programs of study to address regional, local, contextual, and situational aspects of teaching and learning.

• Community as the integrative core of teaching and learning processes.

• Contextualization of content for diverse learners and changing social, cultural, environmental, and health contexts.

1. Equity, excellence, and continuous improvement.

• Prioritizing maximum learning achievement for all students.

• Developing integral human development of learners.

• Promoting a culture of educational responsibility and social transformation.

1. Diversity and inclusion.

• Recognizing diversity as a condition and starting point for learning processes.

• Valuing and incorporating cultural, social, digital, economic, and linguistic expressions of learners.

• Fostering autonomy and professional decision-making of teachers.

1. Challenges and opportunities of technology.

• Addressing digital inequalities and promoting digital literacy.

• Critiquing the capitalist and cognitive exploitation of learners.

• Redefining the role of technology as a tool for teaching and learning, not a substitute for teachers, schools, or pedagogy.

1. Redefining the role of schools and teachers.

• Problem-solving and critical thinking as the core of teaching and learning.

• Resignifying the role of schools as spaces for problematizing reality.

• Redimensioning the role of teachers as facilitators of learning, not just transmitters of knowledge.

Figure 4.

Screenshot capture of MAXQDA (IA Assit – Suggest subcodes).

Note: Due to the amount of data, AI Assist had to limit the analysis to 26 segments from 1 documents.

4. Impact of artificial intelligence on academic research

In the academic panorama, artificial intelligence (AI) thus not only represents a technological advancement that will facilitate processes but also acts as a catalyst propelling academic research forward. The integration of AI into educational research will enable the transcendence of boundaries, moving from a few textual data to massive corpora, thereby enhancing collaborative research. Consequently, the impact of AI on research methodology cannot be underestimated. Although the robustness of quantitative and qualitative approaches with software was already significant, patterns that the human eye cannot detect immediately will be discerned by AI vision.

However, the integration of AI into academia is not without challenges. Ethical considerations emerge to the forefront, particularly regarding data privacy, bias, and authenticity. A mere test of how ChatGPT or Claude invents non-existent sources for citation reveals that their current algorithms are not immune to bias and could fall into pseudo-analysis. Therefore, the ethical landscape also requires development to ensure that the use of AI in research adheres to clear guidelines and strict ethical principles.

In academia, AI can also support the regulation of publications by streamlining peer analysis, identifying potential reviewers according to their publication history, and classifying similarities through modeling of themes [8], as well as accelerating plagiarism detection. Perhaps in the future, it will evaluate the validity and reliability of findings, improving publication processes and generating new standards of quality and authenticity.

While the capabilities of platforms integrated with AI, such as MAXQDA or ATLAS.ti, in data analysis are fundamental, the impact of AI in academia is expansive, permeating various facets of research, ethics, pedagogy, and publication. Navigating through the opportunities and challenges presented by AI necessitates a balanced approach. The future of academia, underpinned by AI, heralds an era of enhanced research capabilities, data-driven pedagogical strategies, and streamlined publication processes, propelling academic research towards a future that is not only technologically advanced but also ethically aware and methodologically robust.

5. Conclusions

Although [12] asserts that the innovative approaches brought forth by artificial intelligence liberate research from human bias, thereby enhancing the reliability and validity of investigations, it is imperative to acknowledge that entrusting total responsibility to artificial intelligence and its artificial neural network is not yet feasible. This is because artificial intelligence does not obviate the necessity for researchers to engage in reflective thinking to attain a profound understanding of the texts analyzed. Consequently, AI does not supplant humans in research; rather, it transforms into a supportive tool designed to enhance and expedite the processes of data analysis and systematization, ensuring a meticulous and efficient exploration of information.

Undoubtedly, the emerging trends in research within the social sciences and education, as articulated by Kariri [18] will necessitate processes wherein information analysis employs machine and deep learning. This is already exemplified in sentiment analysis [29] and text classification, where these advanced computational techniques facilitate a more nuanced and efficient exploration of data, thereby enriching the analytical depth and breadth of scholarly investigations in these domains. The exponential growth of textual information in the academic sphere necessitates a paradigm shift towards more efficient and rapid data analysis methodologies. Text mining, underpinned by artificial intelligence and Natural Language Processing, emerges as a transformative tool, particularly in the realm of social sciences and education. This is especially pertinent in educational contexts where qualitative elements often outweigh quantitative ones. The study focuses on analyzing texts generated by key stakeholders in education, such as students and educators, to explore the transformative potential of text mining in qualitative methodologies and to pave new avenues for text-based qualitative research.

Text mining has evolved to become an accessible tool for researchers, obviating the need for programming expertise. The advent of AI-integrated qualitative data analysis software like ATLAS.ti and MAXQDA, particularly since the beginning of 2023, has significantly expedited the coding and analysis processes.

The integration of deep learning models and neural networks into text mining portends a future wherein even more complex patterns can be discerned from large data sets. However, it is also crucial to acknowledge that the ability to recognize the context of sentences or documents [30] remains a limitation that must be overcome, considering regional dialects that can lead to varying meanings and, consequently, to interpretative biases. Nevertheless, it is vital to recognize that what was once unattainable in traditional qualitative research, namely the massive analysis of data, is now within reach. These advancements are poised to revolutionize educational interventions; coupled with findings from empirical research, they will contribute to a more nuanced understanding of complex educational processes, thereby enhancing the depth and efficacy of scholarly inquiries.

AI, bolstered by NLP, will facilitate the analysis of large volumes of textual data in both educational and social science contexts, thereby enhancing the outcomes of qualitative research in unprecedented ways. The key advantages include:

1. Automatic summarization features that expedite the preliminary understanding of extensive documents, significantly accelerating material review.

2. Incorporation of automatic coding functions through NLP, drastically reducing the time traditionally spent on manual coding—one of the most time-consuming aspects of qualitative research.

3. Semantic analysis-based sub-code suggestions that enrich the inductive construction of the coding system.

4. Generation of concise summaries of coded segments, serving as code comments and facilitating insights that guide researchers towards conclusions.

5. The ability to tackle larger-scale projects by streamlining analysis through AI, thereby expanding the frontiers of qualitative research.

While AI-assisted text mining offers numerous advantages, it also poses challenges, such as the potential to overlook the need for human oversight and the urgent requirement for clear ethical guidelines in scientific production. Future research should focus on optimizing these tools for more accurate and nuanced analyses, thereby ensuring the responsible and effective utilization of AI in academic research.

Conflict of interest

“The author declares no conflict of interest.”