The Presence of Artificial Intelligence in the Educational Field

Evelyn Parker¹

[1] Universidad de Teesside - Inglaterra, evelynparker3339@gmail.com, https://orcid.org/0009-0006-2181-1701

Received:

28 March, 2022

Accepted for publication:

12 May, 2022

ABSTRACT

Urgent transformations are required in global educational systems to meet the demands of the knowledge society. These changes must align with contemporary technologies and intangible services. We are currently in the era of Artificial Intelligence (AI), where various sectors, including transportation, healthcare, financial services, entertainment platforms, robotics, and manufacturing, heavily rely on this cutting-edge technology. The objective of this projective research with a bibliographic design is to suggest a methodology for incorporating AI into the educational sector. The theoretical framework for this research includes Tascón and Collaut (2020), Yan-Tak (2019), Dark (2018), and organizations such as ISO/IEC (2019) and UNESCO (2018). The proposed outcomes are categorized into key areas: supervision processes, university admission and retention processes, early detection of behavioral issues, and methodological strategies for the learning of individuals with disabilities. The primary conclusion drawn is that AI holds immeasurable value in the market, both presently and in the future. However, this value extends beyond mere monetary considerations and lies in its potential to optimize non-commercial processes, particularly within the educational sector. AI stands as a pivotal force in reshaping traditional educational paradigms.

Keywords: Machine intelligence; extensive data; automated learning; profound learning

La Presencia de la Inteligencia Artificial en el Ámbito Educativo

RESUMEN

Se requieren transformaciones urgentes en los sistemas educativos globales para satisfacer las demandas de la sociedad del conocimiento. Estos cambios deben alinearse con las tecnologías contemporáneas y los servicios intangibles. Actualmente, nos encontramos en la era de la Inteligencia Artificial (IA), donde diversos sectores, incluyendo transporte, atención médica, servicios financieros, plataformas de entretenimiento, robótica y manufactura, dependen en gran medida de esta tecnología de vanguardia. El objetivo de esta investigación proyectiva con un diseño bibliográfico es sugerir una metodología para incorporar la IA en el sector educativo. El marco teórico para esta investigación incluye a Tascón y Collaut (2020), Yan-Tak (2019), Dark (2018) y organizaciones como ISO/IEC (2019) y UNESCO (2018). Los resultados propuestos se categorizan en áreas clave: procesos de supervisión, procesos de admisión y retención universitaria, detección temprana de problemas de conducta y estrategias metodológicas para el aprendizaje de personas con discapacidad. La principal conclusión es que la IA tiene un valor incalculable en el mercado, tanto en el presente como en el futuro. Sin embargo, este valor va más allá de simples consideraciones monetarias y radica en su potencial para optimizar procesos no comerciales, particularmente en el sector educativo. La IA se presenta como una fuerza fundamental en la reconfiguración de paradigmas educativos tradicionales.

Palabras clave: Inteligencia artificial; macrodatos; aprendizaje automatizado; aprendizaje profundo

INTRODUCTION

Society has transitioned from an industrial era to a knowledge era, where intangible services play a leading role. These services are identified as non-physical value structures that have the potential to produce and transform into other value structures. Intangible assets are non-material agents that can be indirectly valued, contributing to the progress of organizations in the production of goods and services that create future economic benefits for entities or individuals implementing them. In the future, intangible services are projected to constitute over 90% of a company's value. The increasing demand of the socio-economic context emphasizes the need for the development of human capital, with education playing a crucial role.

The three fundamental pillars of any educational system are reading, writing, and arithmetic, serving as the foundations of the learning process. However, in the knowledge society, students require additional competencies, including non-cognitive skills. The new connectivist pedagogical model proposes the necessary skills for individuals in the knowledge society. According to Siemens (2006), these skills include anchoring, information filtering, connecting with others, being human together, evaluating the value of knowledge, constant critical thinking, pattern and trend recognition, resilience, and adaptability.

New technologies can assist in optimizing the teaching-learning process, recognizing that education is not a product but a continuous process where learning goes beyond mere knowledge acquisition. Artificial Intelligence (AI) as a novel technology holds significant potential in education. AI-based systems have the capacity to facilitate personalized learning based on the specific needs and interests of students.

The use of Artificial Intelligence (AI) has the potential to help humanity overcome many serious social problems it faces, while simultaneously posing a series of complex challenges, particularly in the realms of ethics, human rights, and security. Organizations and institutions, such as ISO/IEC JTC/1 SC/42—a subcommittee of the International Organization for Standardization (ISO)—aim to develop and implement a standardization program for AI usage. Various institutions and organizations worldwide should regulate artificial intelligence, not only concerning its scope and applications but also addressing ethical and social considerations.

It is crucial not to fear the implications of AI; the answer is a resounding NO. We should embrace and fully leverage the benefits that artificial intelligence offers while dispelling myths and phobias associated with this technology. This research project follows a projective approach with a documentary design, proposing a methodology for the use of AI in the education system. The educational sectors covered include supervision processes, university admission and retention processes, early detection of behavioral issues, and methodological strategies in the learning of individuals with disabilities. This is just a drop in the ocean of potential applications and benefits that AI has in the field of education, contributing to the enhancement of the knowledge society. The objective of this research is to propose a methodology for the use of AI in the educational sector.

THEORETICAL FOUNDATIONS

Artificial Intelligence

On a daily basis, we consciously or unconsciously benefit from Artificial Intelligence (AI). Whenever we conduct web research through search engines of various types (hierarchical, metasearch engines, vertical, or specialized), they present a multitude of results because their Machine Learning software has assimilated how to index pages. When various social networks or mobile technology applications recognize facial features in photos of contacts, they apply machine learning. The world's major email platforms use spam filters and redirect emails to specific folders to prevent users from having to read dozens or hundreds of unwanted emails. This is because through machine learning, programs can distinguish between desired and undesired emails (Norman 2019).

The entertainment giant Netflix utilizes its "Recommendation Algorithm" to suggest programs, movies, or series based on user profiles, even offering a compatibility percentage. This algorithm, powered by Artificial Intelligence (AI), enriches its databases through the incorporation of Big Data. In tracing the historical evolution, we find that the field of Artificial Intelligence (AI) was publicly initiated in 1956 at Dartmouth College in Hanover, USA, during a summer course led by four American researchers: John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon (Ganascia, 2018). From a scientific perspective, AI involves emulating human intellectual processes using algorithms integrated into a dynamic, data-driven environment (ISO/IEC, 2019).

Despite AI's roots dating back over 60 years, its current advancements and foundational principles remain relatively unknown. Recognizing this, institutions such as UNESCO (2018) and ISO/IEC (2019) advocate for a glossary of fundamental terms to foster "essential literacy" in comprehending topics related to this discipline and its associated sciences. Key terms within this glossary include Algorithm, Machine Learning, Deep Learning, Strong Artificial Intelligence, Weak Artificial Intelligence, Big Data, and Neural Network. The ensuing descriptions provide clarity on some of these pivotal terms crucial for a comprehensive understanding of this discipline.

Machine Learning: This scientific field empowers computers to learn without explicit programming. Many scientists advocate for advancing through the use of learning algorithms, particularly neural networks that emulate the human brain (Yan-Tak, 2019). The current acceleration in Artificial Intelligence (AI) owes much to the substantial progress in understanding how our brains function, as revealed by Neuroscience.

Deep Learning: Falling under machine learning, this subset enables machines to independently recognize intricate concepts like faces or human bodies by analyzing millions of unlabeled internet images (Jones, 2018). Combining advanced machine learning algorithms, formal neural networks, and leveraging big data, deep learning has significantly expedited the evolution of artificial intelligence (Dark, 2018).

Weak AI/Strong AI: Weak AI mimics human cognition, proving highly advantageous for automating tasks, jobs, and processes where substantial time investment or tasks beyond human capability are involved. Strong AI, a futuristic concept with consciousness and problem-solving autonomy, remains purely theoretical for now (Rouhiainen, 2018).

Big Data: A compilation of digital data that, due to its volume, surpasses human analytical capacities. On the internet, approximately 2.5 trillion bytes of data are generated daily, encompassing emails, videos, weather information, GPS signals, online transactions, and more (Tascón y Collaut, 2020). According to ISO/IEC (2019), 90% of stored data pertains to the most recent 2 years.y Collaut, 2020). Según la Organización ISO/IEC (2019) el 90% de los datos almacenados corresponden a los últimos 2 años.

Transformation of Artificial Intelligence (AI) Over Time

Artificial Intelligence (AI) has undergone profound changes throughout its historical evolution, encompassing both theoretical foundations and practical applications. Technological advancements and a deeper understanding of human cognition have led to remarkably significant and accelerated progress in this field. Over the past two years, this development has experienced exponential growth, driven by findings in Neuroscience, Neural Networks, and Big Data (Russel, 2018).

Ganascia (2018) encapsulates the stages of AI transformation within six phases:

1. Era of the Prophets: Initially marked by optimism and early projections of potential achievements, researchers allowed their imaginations to soar with some hasty declarations and unrealistic forecasts.

2. Dark Ages: In the mid-1960s, progress was slow to materialize, encountering the harsh realities of limitations. Anecdotes, such as a computer's defeat in a chess match by a ten-year-old in 1965, illustrate the setbacks of the discipline during this period.

3. Semantic AI: Focused on the psychology of memory and comprehension processes, seeking to understand the meaning of things, their relationships, and formal representations. Practices of semantic knowledge representation gained substantial development in the 1970s.

4. Neoconnectionism and Machine Learning: Advances in programming languages led to the design of machine learning algorithms, enabling computers to accumulate knowledge and reprogram themselves automatically based on experiences.

5. From AI to Human-Machine Interfaces: In the late 1990s, AI converged with robotics and human-machine interfaces, aiming to design intelligent technology that not only simulated human learning processes but also incorporated emotions and affect, giving rise to emotional computing (Montero, 2018).

6. AI Renaissance: Since 2010, the computational power of machines has allowed the processing and utilization of big data with deep learning practices, relying on the application of neural networks. The term "big data" refers to sets of information whose size, complexity, and processing speed exceed the capabilities of conventional computer systems (Monleon, Vega, and Reverter, 2017).

As in any innovative process, there are divergent opinions about the future of artificial intelligence and its impact on the knowledge society. Influential personalities and researchers share their reflections and thoughts on the subject. Fei-Fei Li, a computer science professor at Stanford University (2019), notes, "As a technologist, I observe how AI and the fourth industrial revolution will affect all aspects of people's lives." In contrast, Yoshua Bengio, a teacher and researcher at the University of Montreal in Canada, argues in the UNESCO Courier (2018), "We must promote greater diversity in the economic sphere related to AI and avoid a monopoly situation." On the other hand, renowned theoretical physicist Stephen Hawking (2014) warns in an interview with the BBC in London, "Artificial intelligence heralds the end of the human race."

Faced with this diversity of opinions, including apocalyptic perspectives on AI, we can consider another reflection from entrepreneur and visionary Bill Gates (1997): "People always fear change. They feared electricity when it was invented, didn't they?" The reality is that researchers, institutions, and organizations directly related to the topic emphasize the need to regulate the use of this powerful technology, which currently and in the future will be a significant influencing factor in all sectors of our society, in the information and knowledge society.

Currently, the research community converges on an English acronym that synthesizes the appropriate use of artificial intelligence, called F.A.T.E. (fairness, accountability, transparency, and ethics). This acronym encapsulates key ideas that should be considered when designing, creating, and regulating this technology. The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) are developing technical reports on the assessment of neural network robustness (ISO/IEC TR 24029-1) and bias in AI systems and AI-based decision-making (ISO/IEC TR 24027). All these elements contribute to the upcoming ISO/IEC TR 24368, designed to address ethical and social concerns arising from AI.

MATERIALS AND METHOD

The research type employed is projective, involving the development of a proposal as a solution to a practical problem or need within a social group or institution (Hurtado, 2012). Such investigations aim at inventing programs or creations directed towards addressing specific needs. The objective of this research is to propose a methodology for the use of AI in the educational sector from various perspectives. The design is bibliographic, utilizing secondary data obtained and processed by others in accordance with their original purposes (Tamayo and Tamayo, 2004). The units of information for this research consist of books and specialized journals, both physical and digital. While the opinions of several specialists in the field, such as Andrew Yang Tak (2019), Yoshua Bengio (2018), Leslie Loble (2018), among others, were considered for the proposal construction, their views were gathered, processed, and edited by the personnel (journalists and editors) of the associated magazines. Consequently, it constitutes indirect or second-hand information.

DISCUSSION OF RESULTS

Artificial Intelligence in Education

The fundamental pillars of any educational system—reading, writing, and arithmetic—now, in the information and knowledge society, must be expanded to include non-cognitive skills such as empathy, creativity, and critical thinking (Lobles, 2018). Artificial Intelligence (AI) can significantly assist in acquiring these necessary skills by utilizing pedagogical applications grounded in Big Data, machine learning, and deep learning. This can decentralize and personalize the teaching-learning process, providing guidance to students on curricula and even facilitating distance learning. Below, a series of proposals are presented, which can be designed and implemented for the benefit of the educational sector.

Artificial Intelligence (AI) and Quality Assurance Processes in Education

Technological mechanisms can be implemented to automatically monitor the quality management in both administrative and teaching-learning processes within an institution. Considering various regulations, models, and standards that safeguard educational quality, such as ISO 9000 (2015), ISO 9001 (2015), EFQM model (2012), Virtual Education Regulations and Models like ISO/IEC 19796-1 (2005), ISO/IEC 19796-3 Standard (2009), Virtual Education Evaluation Model/Marciniak (2018), among others. Their criteria and characteristics can be loaded into the database (Big Data) of an AI-based evaluation program. Consequently, entities responsible for supervising educational quality at different levels (countries, regions, municipalities, depending on each country's territorial policy) can adopt elements from one or multiple models according to their educational policies. They can then conduct periodic evaluations of educational institutions. The evaluations carried out through the AI-based program will generate a report on the evaluation process for the relevant authorities. This report will propose a set of measures to improve educational practices both administratively and pedagogically. The report will assist in the supervision practices conducted in person by specialists (supervisors). Consequently, members of the entire educational community and the entire country can access the institution's general report, and even establish a performance score or coefficient.

University Admission and Student Retention Processes

Presently, the escalating dropout rates within the university sector underscore a multifaceted issue. While the reasons for this trend are diverse, a primary contributing factor is the deficiency in vocational guidance during the selection of a university major. Approaches to understanding dropout and retention, as outlined by Pineda (2010), can be distilled into five categories. These perspectives encompass individual, institutional, or familial considerations, spanning psychological, economic, sociological, organizational, and interactional facets. Notably, a more recent integrated approach has been incorporated into the analysis.

Consequently, there is a need to devise a proposal leveraging Artificial Intelligence (AI) as a comprehensive service system. This system aims to empower prospective university students to partake in a vocational guidance process. Through regionally validated psychological assessments, this process identifies aptitudes, interests, and potential career paths. Simultaneously, it offers recommendations for universities with programs aligned with the individual's outcomes. The program is designed to encompass a wealth of data, theories, and procedures facilitating student retention. It entails continuous support to address social, pedagogical, and psychological needs.

In this context, integrating AI into university admission processes transcends the mere acceptance of students into different institutions or specific programs. It involves a nuanced consideration of vocational interests and sociodemographic factors within the student's context. This consideration determines the level of attention and support required for each applicant. Importantly, this process must uphold principles of justice, responsibility, transparency, and ethics to safeguard societal rights.

In our contemporary society, grappling with pervasive violence, we are witness to alarming instances of extreme violence within educational institutions globally. A poignant and unfortunately recurring example is the dire scenario in the United States, where a student armed with a firearm enters a school, indiscriminately targeting classmates, teachers, and anyone in their vicinity. The tragic Stoneman Douglas High School shooting in Parkland, resulting in 17 fatalities, epitomizes this distressing reality. As reported by BBC News (2018), the assailant, 19-year-old Nikolas Cruz, a former student of the aforementioned school, utilized an AR-15 assault rifle and ample ammunition, leading to loss of lives and numerous injuries.

Artificial Intelligence (AI), informed by insights from specialists in the fields of Psychology and Counseling, including Ortega and Plancarte (2017), Murueta and Orozco (2014), among others, holds promise in collaboration with families to mitigate such heightened levels of violence across diverse societal sectors, particularly educational institutions. The envisioned program's design unfolds as follows: The AI-driven system will collate information from three pivotal dimensions of an individual: genetics, environment, and attitude.

In the genetic realm, a meticulous examination of the student's family lineage is undertaken, with a specific emphasis on grandparents and parents. This involves gathering insights into factors such as gestation period, nutritional considerations, substance usage, pregnancy complexities, medication history, physical activities, occupational details, hobbies, temperament, character traits, and any psychological or physical challenges.

Regarding the student's environment, considerations encompass economic stability, suitable and hygienic housing with essential amenities, access to healthcare facilities, daycare services, and academic resources, alongside evaluations of marital dynamics, family cohesion, mutual support, and communication. This extends to shared household responsibilities, a connection to the living environment, religious and philosophical beliefs, and cultural background.

The third and final facet that the software addresses pertain to attitude, scrutinizing the affection, attention, and encouragement extended to the student. This involves an assessment of social reinforcements such as commendations, expressions of affection, positive affirmations in public, considerations for sleep and rest, maintaining organizational structures in terms of schedules and spaces, participation in shared recreational and educational activities, family vacations, attention to each child's specific needs, involvement in tutoring sessions, meeting teacher requirements, establishment of rules and boundaries, instillation of values, encouragement of dialogue and negotiation, adherence to coexistence norms, role modeling in education, and ensuring that behaviors and attitudes align seamlessly with verbal discourse.

Once the required information has been gathered from the father, mother, and student through various tests, an activity that can be carried out from the comfort of their home, a report with relevant recommendations will be promptly generated for the representative. As an example, consider a case where a child may need to be admitted; in such cases, the presence of professionals and educational institution authorities will be required. These personnel will assist in the intervention, collaborating with the family to achieve understanding and an action plan.

In other cases, representatives will receive a report tailored to the student's profile. This report will consist of a set of recommendations for their educational process, suggesting subjects that could help the student overcome challenges and develop their potential. Another crucial aspect is the home environment, for which the representative will receive guidance on proper supervision, nutrition, recreation, among other things. Naturally, the educational institution also needs recommendations to avoid potential dangers within the educational community. Therefore, reports will also be provided for different hierarchies, including authorities, student counseling departments, teachers, and other students.

METHODOLOGY

Approaches Utilizing Artificial Intelligence to Facilitate the Learning of Individuals with Disabilities

The imperative need to effectively integrate individuals with disabilities into society goes beyond mere statements or international documents. Unfortunately, according to the "United Nations Disability and Development Report (2018)" cited by the Secretary-General of the United Nations, Antonio Guterres, people with disabilities continue to be at a disadvantage compared to most Sustainable Development Goals.

Despite the notable advances that Artificial Intelligence (AI) has achieved in the medical field, the question arises of whether it can play a fundamental role in strengthening the techniques, methods, strategies, and educational tools used by teachers. This specifically focuses on supporting students with disabilities to acquire the necessary skills and achieve full integration into the knowledge society.

The methodological approach of the proposal should begin with a comprehensive diagnosis, led by a team of specialists including doctors, psychologists, counselors, and teachers from various disciplines. When applying the AI-based program, this diagnosis should provide the educational community, especially teachers and representatives, with specific strategies, methods, and tools to work with students facing various disabilities. The goal is for these students to reach the competencies established in educational systems.

The AI-based program should not only recommend a variety of tools, with an emphasis on technological tools, but also highlight their potential to facilitate collaborative learning. These technological tools could include specialized software for adding text to videos and subtitling images, as well as the use of optical character recognition, a valuable alternative for transforming scanned documents into accessible text for individuals with visual impairments. Additionally, the incorporation of the Azahar Project could be considered, specifically designed for students with autism or intellectual disabilities, offering free applications that improve interaction and planning both in the classroom and in the social life of these students.

CONCLUSION

Artificial Intelligence (AI) represents an invaluable technology in the market, not only in monetary terms but also in optimizing non-commercial processes, such as the educational sector. AI serves as a turning point in transforming traditional educational paradigms, especially in a context where virtual education modalities are gaining ground in the educational policies of developed countries. Its potential lies in optimizing the use of valuable resources, addressing the current issue of underutilization or isolated and contextually irrelevant use of technological tools.

As reflected in the presented proposals, artificial intelligence can be a significant benefit in the educational sector, providing alternatives to address the current challenges faced by educational systems. As the social and economic model, as well as communication and information forms, evolve rapidly, it is crucial for the educational sector to move away from its traditional pedagogical paradigms. If it does not adapt to the new models and competencies demanded by society, especially the technologies offered by AI, it risks becoming obsolete and having limited influence on future social and economic dynamics.

The integration of AI in the roles of the educational system, whether in administrative, guidance, pedagogical, or research contexts, allows for adapting and enhancing the new pedagogical paradigm, such as Siemens' Connectivism (2009). With the help of AI, it is possible to organize dynamic networks and ecologies that adjust and react to changes, which are fundamental in this new pedagogical approach. Learning is conceived as a network formation process, where nodes represent external entities used to build a network. In this context, AI plays a crucial role in suggesting information nodes based on the needs and interests of students, similar to how major service and advertising companies like Google and Netflix operate.

The use of artificial intelligence (AI) can contribute to improving educational supervision activities, which currently generate uncertainty for various reasons. In this regard, supervision tasks become significantly relevant by providing feedback and refining educational processes. Criteria for comparison with international standards and quality models can be established, issuing reports with recommendations to address identified weaknesses.

The use of AI to guide future higher education students is deemed an essential step. Given that states allocate substantial portions of their Gross Domestic Product (GDP) to the university sector (in countries where higher education is free), this government investment in citizens' academic training not only justifies guidance when choosing a career but also necessitates proper monitoring to identify potential risk factors that could lead to dropout.

The use of AI in guiding activities and strategies for teachers and representatives of students (or anyone) facing disabilities or cognitive issues constitutes an indispensable human value. Society has the responsibility to implement policies and practices that facilitate the true integration of individuals with special needs. AI-backed assistance will not only collaborate with treatments recommended by specialists but also ease their integration into society by recognizing and leveraging their exceptional skills and diverse potentials.

We should not fear the capabilities and potentials of AI, as emphasized by UNESCO (2018): artificial intelligence is "at our service and not at our expense."

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