Impact of Artificial Intelligence in Achieving Quality Education

Abstract

This chapter highlighted the impact of artificial intelligence (AI) in achieving quality education. The purpose of this chapter was clearly articulated by investigating the intersection of AI and education by considering its role in educational technology through AI-powered learning management system, personalized learning, virtual and augmented reality among others. The various ways of enhancing teaching methods using AI-driven tutoring systems, adaptive learning platforms, intelligent classroom assistants were constructively considered. Literature review showed the various ways that artificial intelligence have been used in addressing inequality in education by helping to mitigate learning disparities in diverse student populations, overcoming language barriers through AI translation and bridging the digital divide. The challenges and ethical considerations in educational AI system were identified as Bias in AI algorithms and privacy concerns. The various strategies mitigating these challenges relating to AI use in education to achieve quality education were also captured. However, the policy implications and governance regarding AI use in education including international collaboration for standardization to assist in enhancing responsible AI implementation to achieve quality education were also properly presented. The outcome of the investigations showed that with the active collaboration of the major stakeholders in education in its implementation, artificial intelligence has improved the quality of education globally.

1. Introduction

Artificial intelligence has been a topic of interest and has raised curiosity in various disciplines of which education is one of the prime spaces of its applications. Artificial intelligence (AI) is described as the designing and building of intelligent agents which depend on recognition by the senses from the environment and acts in a way that affects the environment. It is a branch of computer science that use algorithms and machine learning techniques to replicate or simulate human intelligence [1]. The Oxford dictionary of phrase and fable further defined artificial intelligence as the theory and development of computer systems which has the ability to carry out tasks that normally require human intelligence, decision making as well as language translations. There are three types of artificial intelligence namely: Narrow AI, General AI and Artificial superintelligence. Narrow AI is the most common and realized form of artificial intelligence. It is goal-oriented and employs machine learning techniques to actualize a task. General AI is also known as deep AI, it is one that is deemed on par with human capabilities which can discern the needs or emotions of other intelligent beings. While the Artificial Super intelligent is AI which is more capable than humans [2]. AI is considered the most admirable among all other technological revolutions in the world. Quality education describes a holistic educational approach which provides learners with academic knowledge, critical thinking, skills for solving daily problems, creativity and provide them with a sense of social responsibility. Any form of education which is considered quality must be all inclusive, equitable and accessible to all learners, must promote lifelong learning and should be able to prepare its recipients for active participation in the society. Quality education is the basis for individual growth and societal development globally. It provides knowledge for individuals empowering them with critical thinking skills and creativity, encouraging personal developments and learning throughout the individual’s life. It helps to create economic opportunities and employment for individuals thereby promoting their income potentials. Quality education speeds up societal progress by promoting social integration, bringing down societal inequality while enhancing the creation of an informed citizenry who are properly engaged. Nations whose citizens have received quality education are better prepared and equipped to address the challenges of poverty, health and environment. Such nations have been known to develop better innovations and adapted favorably to the demands of a rapidly evolving global economy. Quality education has served as the driving force for sustainable development and progress in the society while it shapes and empowers individuals as informed citizens. UNESCO [3] emphasized the global significance of quality education for individual and societal development using the SDG4—Education 2030, Incheon Declaration (ID) and framework for action which states that “For the implementation of sustainable Development Goal 4, ensure inclusive and Equitable Quality Education and Promote Lifelong Learning Opportunities for All”. Hence, the purpose of this chapter, is to explore the impact of artificial intelligence (AI) in achieving quality education.

2. Main body

2.1 Impact of artificial intelligence in achieving quality education

Artificial intelligence has transformed the entire globe, it is important that this chapter provides a brief discussion of the many applications of artificial intelligence across various industries and domains that showcase its versatility in improving efficiency and decisions before giving a detailed description of its impact in achieving quality education. The domains are as briefly discussed below:

1. Education: artificial intelligence has been employed in the education sector to support personalized learning, instructional system, educational analytics, among others.

2. Health care: AI has been found very assistive in disease diagnosis, personalized medicine and drug delivery.

3. Finance: AI is used by financial institutions for detection of fraud, algorithmic trading as well as enhancing services rendered to customers in banks.

4. Retail services: AI has improved retail services by its ability to enable recommendation systems, demand forecasting and supply chain optimization.

5. Autonomous vehicles: the emergence of self-driving cars and drones for navigation and decision making has been traced back to Artificial Intelligence which powers them.

6. Manufacturing: AI is used in manufacturing for predictive maintenance procedures, quality control as well as process optimization.

7. Customer services: customer support interactions have been greatly improved by the use of Chatbots and virtual assistants.

8. Marketing: AI has improved marketing strategies by assisting in targeted advertising, social media analysis and in customer segmentation.

9. Cyber security: AI is used to detect threats, anomaly detection and to secure digital systems.

10. Human resources: AI helps employers in engaging employees, talent acquisition and workforce analytics.

11. National language processing: AI has been used in chatbots, language translations and in sentiment analysis.

12. Entertainment: AI has been employed in games, virtual reality experiences and content recommendation.

13. Agriculture: precision farming, monitoring of crops and prediction of yields has been improved by Artificial intelligence.

14. Energy: AI is employed for smart grid management, preventive maintenance and optimization for energy consumption.

15. Legal: AI assist legal practitioners to review documents, analyze contracts and carry out legal research.

However, in exploring the impact of AI in achieving quality education. It is necessary to look at various areas where AI and education intersect by considering the following subsections:

2.2 The role of AI in educational technology

2.2.1 AI powered learning management system

According to [4], learning analytics can substantially increase the way the field of learning science understands learning both theoretically and practically. Hence artificial intelligence in learning management system through data analysis can optimize content delivery by personalizing learning paths, access learner’s performance and improve engagement with adaptive learning techniques.

The significant roles played by AI-powered LMS centers around the improvement of content generation and transformation, personalized learning, intelligent insights, real-time assistance, and auto translation as shown in Figure 1. Artificial intelligence easily adapts to learners needs and provides contents and feedbacks which are tailored to actualize learning outcomes in the best possible ways. With AI, the teachers are assisted to create more effective lesson plans and assessments.

Figure 1.

Roles of AI-powered LMS.

Artificial intelligence can become a student companion and assistant. This is so because AI understands the strength and weakness of an individual learner. AI has the capacity of analyzing learner’s pace and can identify the latent skills in learners which can be used to guide them during the teaching and learning process.

On the part of the teacher, AI can assist the teacher to properly guide the learners, to know the right time for evaluation of their teaching, the type of questions to be chosen for evaluation on a particular topic, the rate of lecture delivery and the choice of topic that will benefit redundant learners. With AI, teachers can quickly outsource their targets and actualize the outcomes set for each learner’s performance. There are many examples of AI-powered LMS but a few of them has been described in this chapter.

• Zavvy: this is an AI powered LMS which provides features that can be used to create engaging training courses in a very short duration. It assists in course creation, launches workshops and on-demand courses faster than traditional learning platforms. It can handle repetitive basics while allowing more time for the improvement of course content. Zavvy platform is used in translating training contents into multiple languages and can help its users manage their courses into one centralized location there by enhancing a course library management, thus bridging knowledge gaps.

• Paradiso: this is an AI-powered authoring tool which creates e-learning courses. Its templates help learners generate content, videos, images and presentations to be transformed into training materials. This technology help learners manage instruction design, graphic design, voice-overs, videos and assessments which can be customized for individual learners to enhance their specific development goals.

• Docebo: this provides a variety of efficient and customized learning programs with special features which permits learners to create audience specific pages using drag and drop functionality. Docebo encourages customized learning experiences with virtual coaching, content suggestion as well as auto-tagging thereby encouraging personalized learning. This LMS offers gamification, auto assignment of training depending on the required skills of the learners. It also helps learners to streamline non-formal learning and automates learning management. With this platform, various business systems are brought within one learning environment using its over four hundred integrations with the inclusion of web conferencing, payment gateways among others.

• Cipher learning: this AI-LMS helps learners create courses and assessments. It encourages personalized learning. Learners can build course outlines, descriptions and content in very many languages. Learner’s progress can easily be tracked.

• Moodle: this is an online learning management system built on an open-source platform. This is best suited for organizing distance learning in which the learner is privileged to independently choose the sequence and schedule of learning.

• EdAPP: this is a mobile learning platform with efficient AI features that can be used for training. With this platform users have their personalized course content, course creation and generation of whole lesson highly simplified. Learners can easily analyze online resources and obtain relevant information within a possible short time frame. Schools and organization employers can use EdAPP to manage training of their employees anytime and from various locations.

• Absorb LMS: this is designed for universal learning. With this system, organizations are empowered to train their staff with modern skills to keep them updated and relevant in modern societies. It enables organizations maximize staff efficiency by inspiring learning and increasing business productivity. It is a cloud based LMS which engages learners, enhances content retention and increases training programs.

2.2.2 Examples of AI-powered intelligent classroom assistants

• Squirrel AI: this is an adaptive learning platform that uses AI algorithms to personalize learning for each learner. It provides tailored recommendations and feedback to improve learners understanding.

• Edmodo: this uses AI to provide learners with personalized learning experiences. It offers features such as quizzes, assignments and collaborative tools to enhance student engagement and performance.

• Brainy: this is a community-driven assistant that can help learners ask and answer academic question. It uses AI to match questions with relevant answers helping them find solutions quickly and efficiently.

• Cognii: this is a virtual learning assistant that relies on conversational technology to assist learners from open resource format. They are designed to provide instant feedback with written responses. It utilizes Natural Language Processing algorithm to evaluate essays, giving personalized feedback thereby improving learners writing skills.

• IBM Watson Education: this assistant provides AI powered solutions for classrooms. Its tools leverage AI to enhance individualized learning experiences, enhancing learning outcomes and optimizing teaching techniques. It provides virtual tutors, adaptive learning systems as well as analytic tools.

2.2.3 AI enhances individualized or personalized learning

This is so because AI can adapt to individual learner’s related requirements there by reducing the unproductive works of the teachers and help to increase the quality of teaching and learning.

AI has also improved the grading system used in the educational sector. This has been possible by automating the grading system in multiple choice questions. This has reduced to a great extent time wastage and stress on the part of the teachers.

AI promotes global access to quality education by providing global classroom for learners in every part of the world. The gap of communication between learners and instructors can be comfortably filled by the use of artificial intelligence. Real time subtitles in AI can help blind learners with sight, hearing difficulties and those with different languages to understand and assimilate the learning process effectively. Moreover, AI assists learners from all over the world to connect themselves for brainstorming and further interaction thereby increasing knowledge spread among the students. Vanlehn [5], in an experiment to compare the effectiveness of human tutoring and intelligent tutoring system with other tutoring systems, observed that intelligent tutoring system possess step-based form of interaction while other tutoring systems have answer-based user interfaces. The experiment concluded that the effective size of intelligent tutoring system was as nearly as effective as that of the human tutoring. Hence AI algorithms can be used to customized educational content and learning paths thereby enhancing personalized learning which in turn improves the quality of learning.

AI algorithms analyze large amounts of data to ensure that learning experiences are learner centered and are tailored to the learners needs and preferences.

However, personalization in education using artificial intelligent are reflected in the following aspects which are described below:

• Adaptive learning platforms: this provides opportunity for the learner’s strengths and weaknesses to be assessed by AI algorithms and the content delivery are adapted to focus on the areas that need to be improved. This ensures that learning pace and style proceeds based on individual learner. Dam [6] showed from his work that the integration of AI methods into adaptive learning system enables it to dynamically adapt feedback, pacing, content and teaching methods to the learners need. According to him, advances in multimodal learning analytics, Natural Language Processing (NLP) and affective computing can improve personalization and adaptability of AI powered learning system. The adaptation is achieved in AI system by analyzing learner’s performance, preferences and behavior to dynamically adjust the experience in order to promote an effective learning path. AI performs an important role in adaptive navigation support within educational hypermedia systems. These systems utilize AI algorithms to individualize the learning experience in the following specific ways:

  1. User modeling: by analyzing learners’ preferences, behavior and style of learning, AI creates accurate user models. The knowledge obtained from such models will then be learning used to tailor the navigation suggestions to the learners need.

  2. Adaptive content delivery: AI helps to evaluate learner’s proficiency and adaptive learners’ content at a level where the learner finds difficult and makes sure that the instructional materials suit the current understanding of the learner; this ensures the optimization of learning outcome.

  3. Use of AI-powered recommendation system: this ensures the provision of personalized suggestions for learning paths. This in turn assist learners to navigate through the entire learning content more efficiently thereby improving learning comprehension and engagement of the learning process.

  4. Natural Language Processing (NLP): using its NPL ability, AI is able to understand and answer learner’s questions correctly. This provides learners with an effective interactive and conversational learning experience.

  5. Provision of instant feedback: AI provides real-time feedback on the progress of the learner pointing out the learner’s area of strength and weakness. Such feedbacks affect the adaptive navigation by moving the learner to the needed instructional content which will address the identified gaps in learning.

  6. Dynamic pathways: AI has the ability to dynamically adjust the learning path according to the learner’s performances. The system can suggest a more challenging material if the learner’s performance becomes excellent in a particular area while providing additional support in areas where help is needed.

  7. Continuous learning improvement: the continuous interaction of the learner with AI system makes it possible for the system to identify the learner and to continuously refine his model, making specific recommendations for him. This creates opportunities for interactive learning which ensures accuracy of adaptive navigation support that is tailored specifically to individual learner.

  8. Intelligent classroom assistants: AI assistants are used by teachers for routine administrative tasks and classroom management. AI intelligent assistants help by automating administrative tasks, streamlining workflows and enhancing efficiency. These assistants can manage data entry, schedule appointments and routine communications thus allowing human professionals to focus their attention on more complex roles. Thus, saving meaningful time and minimizing human errors associated with manual tasks. In classroom management, AI assistants provide educators with teaching tools to improve their teaching strategies. They can assist with grading and analysis of the learners thereby providing the instructor with the proper insight into the learner’s performance. However, it is important to strike a balance in order to prevent over reliance on AI assistance as this may diminish the human touch in education and administration which is extremely needed in order to produce learners with exceptional outcomes.

• Learning analytics: artificial intelligence has been used to track the learner’s progress. This helps to provide insights as to the learning pattern of each student. This knowledge can be used by instructors to pinpoint learner’s challenges, refine the instructional materials/contents and hence can provide adequate and timely help where it is needed to improve the entire learning experience.

• Automated feedback: AI algorithms helps to generate immediate and automated feedbacks on assignments, quizzes and examinations to enable the learners see and understand where they have made mistakes and correct them. This in turn reinforces the learning objectives.

• Accessibility and inclusivity: the various learning needs of the students can be taken care of easily by artificial intelligence which can customize the learning content. The diverse group of learners are considered including disabled learners and those with learning challenges. This enhances inclusivity of all learners in the learning process.

• Adjustments of learning paths: artificial intelligence helps to adjust the learning path of the learner depending on his/her progress. This helps to prevent boredom and improves learner’s ability to learn.

• Prediction of learners needs: based on the historical data of the learner, AI has the ability to predict the future learning needs of the student. This helps the instructor to be very proactive in addressing the learning challenges. This approach helps to fill the gaps in learning and enhances quality education.

• Cognitive tutoring system: AI tutoring system encourages individualized interaction with learners. This enables tutors to adapt their teaching strategies to fit individuals learning styles. The cognitive tutoring system powered by AI provides real-time assistance and better comprehension and retention by the learners.

2.2.4 Virtual and augmented reality in education

In describing the role of virtual and augmented reality in creating immersive learning experiences, Dede [7] pointed out that interactive media enable various degrees of digital immersion and that the more a virtual immersive experience is based on design strategies that combine actional symbolic and sensory factors, the greater the participants suspension of disbelief of being inside an enhanced digital setting. He emphasized that emersion in a digital environment enhances quality education by allowing multiple perspectives situated in learning and transfer. Virtual and augmented reality contribute in developing quality education by making available immersive individualized and accessible learning experiences. This is made possible by enabling remote learning and removal of geographical barriers. The availability of virtual classrooms makes possible for experiences to be shared among learners as well as encourage collaborative projects among them. This in effect fosters a sense of global connection among students. Augmented reality offers contextual insights in education by overlaying digital information onto the real world. It has the immense ability of fostering deeper understanding of learners by enriching textbooks with 3D models, audiovisual aids and supplementary information. Virtual reality uses simulated environments to immerse learners into a world of experimental learning. This is a powerful tool used in producing quality and realistic learning in science subjects or in exploring historical events.

2.2.5 Enhancement of teaching methods by artificial intelligence

Teaching methods have been greatly enhanced by using AI-driven tutoring systems. The effectiveness of AI tutors in providing personalized assistance to learners cannot be over emphasized. Anderson et al. [8] observed from their experiments using a computer based instructional technology called Cognitive Tutor that the best tutorial interaction style for learners was the one in which the tutor provided immediate feedback with short and direct error messages. They pointed out that tutors worked better when presented as non-human tools. Hence, AI-driven tutor system through the provision of personalized assistance to learners is playing a pivotal role in achieving quality education.

2.2.6 Addressing educational inequality with artificial intelligence

AI has been employed to mitigate learning disparities in diverse student populations. Warschaver and Matuchniak [9] showed that new technologies and digital world can be powerful tools for transforming learning and can be used in several ways to promote equality and justice in the society by providing access to knowledge and improved communication to every one irrespective of their geographical location and socio-economic status. AI has been classified as one of the latest technologies and the most admired among them which can be used to address educational inequality globally. By providing personalized and adaptive learning experiences which are tailored to individual learners, by analyzing diverse learner’s data, discovering learning gaps and offering targeted exercises, assisting instructors in creating inclusive learning contents that takes care of varied learning styles, diverse cultures and languages, AI provides an equitable educational environment for learners which in turn helps to achieve quality education.

2.2.7 Overcoming language barriers through AI translation

AI has played vital roles in breaking language barriers and promoting inclusivity in an attempt to improve the quality of education. For instance, AI-driven translation tools facilitate multilingual communication, empowering both teachers and students to easily access teaching and learning resources in their native languages. AI powered language learning platform personalize instructions adapting to the learner’s proficiency levels and styles thereby contributing to a more inclusive and effective educational experiences. AI-driven language translation tools enable learners and instructors from diverse nations of the world and languages to collaborate on different projects and learn from each other. Agbonika [10], in his findings, reaffirmed that by harnessing the power of AI, the status quo of Northern Nigeria can be disrupted and her educational system transformed, empowering her students to reach their full potential thereby contributing to a more equitable and prosperous future for the region.

2.2.8 Bridging the digital divide with artificial intelligence

AI initiatives are used to improve digital accessibility in underserved communities. The AI-driven mobile apps are used to provide educational resources, bridging the digital divide by offering learning materials to learners who have limited access to traditional schooling. For areas with limited medical resources, AI-powered Chatbots have been used to make available healthcare information and support. All these initiatives aim at leveraging AI technology to address inequality and improve access to essential services.

2.3 Real-world applications and case study success stories of AI in achieving quality of education

There have been notable instances showing how AI has impacted positively and improved the quality of educational outcomes. Seo et al. [11] conducted a study to determine the impact of artificial intelligence on learner-instructor interaction in online learning by using speed Dating with storyboards to analyze the authentic voices of 12 students and 11 instructors on diverse use of possible AI systems in online learning. Their findings showed that participants envisioned that adopting AI system in online learning can enable personalized learner-instructor interaction at scale though at the risk of violating social boundaries. Their findings have implications for the design of AI systems to ensure explainability, human-in-the-loop, careful data collection and presentation.

Chen et al. [12] conducted a study to access the impact of AI on education with the scope limited to AI application and effects on administration, instruction and learning, leveraging the use of literature review as a research design. Their study discovered that using AI, instructors were able to review and grade students more effectively and efficiently and achieved higher quality in their teaching activities. The curriculum and learning contents became customized and adapted to the learners needs which improved their learning experiences and overall quality of learning.

In an attempt to explore some real-life examples of how AI was used to improve the quality of education and how students’ successes were achieved, [13], described how an AI program developed by a Stanford researcher was used to provide students with assistance when they get stuck in self-paced digital learning. The study tested a machine-learning program that would predict when a student was likely to get stuck and start wheel-spinning as well as the point it would recommend a relevant solution. The training was done by analyzing the performance data from over a hundred school children who had used tablets to learn English reading skills using videos and mini-games. The result showed that the program clearly predicted when a learner would fell into wheel-spinning before the start of a new lesson. His conclusion showed that AI can be used to identify learner’s problems and make it easier for a limited number of human instructors to assist a large number of learners.

Hwang et al. [14] proposed an intelligent tutoring system to assist fifth-grade students learn multiplication and division of mathematical units. They found out that students mathematical learning performance and learning motivation was greatly improved by the intelligent tutoring system (ITS) and that AI improved students grades by 30% while reducing their anxiety by 20%.

According to Verma [13], an AI-powered Chatbot called Jill Watson developed by IBM’s Watson at the Georgia institute of technology which was employed as a teaching assistant for a course with 300 students produced a 97% success rate in answering ten thousand students’ inquiries each semester with a very remarkable human-like efficiency.

The result of the study conducted by [15], with the objective of analyzing the impact of AI components and computational sciences on student performance using web of science (WOS) and Scopus databases showed the positive impact that AI and computational sciences have on student performance. They found a rise in the students’ attitude towards learning and their motivation especially in the Science, Technology, Engineering and Mathematics.

Camesaria et al. [16] conducted a systematic review of literature which focused on the analysis of the application of AI in the assessment of primary and secondary school students through collections of published articles in the most popular databases from 2010 onwards. They concluded that despite the complexity of AI, the potential of AI-related tools to improve the quality of teaching particularly student assessment at lower levels is very high.

2.4 Challenges and ethical considerations facing use of AI in achieving quality education

2.4.1 Bias in AI algorithms

Bias in AI algorithm can stem from biased training data or the algorithm design itself. It is important to identify and address these to ensure that AI applications produce fair and equitable outcomes.

However, the essential factors for mitigating this challenge include the following factors

• Ethical considerations

• Regular audits

• Diverse dataset representation

Bias in educational artificial intelligence algorithms can lead to several challenges. If there is any form of bias in the training data employed in developing the AI algorithm, then it is certain that it will perpetuate and increase the existing inequality in education. This may affect certain groups and lead to unfair advantages and disadvantages. Biased AI algorithms can limit opportunities for certain groups preventing their access to quality education. This can lead to a lack of diversity in educational resources and content. Biased AI algorithms is related to transparency and accountability. However, to mitigate this challenge, a thorough understanding of how AI algorithms make decisions and efforts to ensure that they are fair and unbiased will help to build trust in this educational technology. Moreover, collaborative efforts from educators, policymakers, and developers to mitigate this challenge remains very essential to creating inclusive and equitable educational technologies.

2.4.2 Privacy concerns in educational AI

As AI technology expands across the diverse industries, it opens up a great many privacy concerns thereby putting the traditional norms of personal data protection to many challenges. AI privacy dilemma involves the following main issues:

• The insatiable appetite of AI for extensive personal data to supply its machine learning algorithm has brought about serious concerns regarding data storage, usage and access. These have raised questions regarding data sources, storage and accessibility which the traditional data protection cannot provide answers to. The remarkable ability of AI to analyze and make complex analysis within the received data poses privacy concerns. AI’s potential to infer sensitive information relating to an individual’s location, habits and personal preferences, poses risks of unauthorized data dissemination. The fact that AI also has the potential for identity theft and unwarranted surveillance poses a set of worries and challenges that requires proactive attentions. AI developments have given rise to ethical guidelines and best practices to reduce privacy risks and several leaders in industries have risen to address these concerns. Several esteemed bodies have proposed ethical benchmark to which the partnerships on AI (PAI) which is a coalition of leading companies, organizations and persons impacted by AI stand out a cornerstone. By bringing different stakeholders from tech-giants to AI users together, PAI creates a platform that share collaboration between entities that might not typically interact. The aim is to establish a common ground with PAI as a unifying agent for positive change among AI users. Collecting student’s data with artificial intelligence has raised several ethical concerns related to privacy, consent and potential misuse of information. Therefore, it is necessary to ensure transparent communication regarding data collection purposes, obtain users consent and implement robust sensitive measures to protect sensitive information. Addressing the challenges of biases in AI algorithms is important to mitigate unfair treatment or discrimination based on student data. Therefore, a balance should be maintained between leveraging on AI for educational advantages while respecting the user’s privacy.

2.5 Ensuring fair access to AI-enhanced education

To ensure fair access to AI-enhanced education, the following strategies must be considered in combination:

Digital inclusion: access to the necessary hardware and internet connectivity must be provided for all learners.

Integration into educational content: AI educational tools must be integrated into the curriculum of the learners in order to expose them to the modern educational technology.

Training educators: the educators must be trained on AI tools and methods of use to ensure efficient and effective implementation.

Equitable distribution of AI tools: AI tools and resources must be distributed evenly among schools and communities.

Accessibility features: AI applications and tools must be developed with accessibility features that can accommodate and sustain the different learning needs of the students.

Community engagement: communities must be carried along and included in decision-making to help in addressing local needs and ensure inclusivity.

Regular assessment: the need for regular monitoring and evaluation of the impact of AI enhanced education on both the teachers and learners within different geographical locations will help to identify and address disparities.

Addressing privacy and security concerns: there must be measures put in place to respect the learner’s privacy and security when using AI tools.

2.6 Future trends and innovation in educational AI

2.6.1 Emerging technologies in educational AI

There are many cutting-edge technologies likely to shape the future of AI in education. These cutting-edge technologies possess the potentials of reshaping education by making a more personalized, efficient and adaptive learning experiences. This in turn enhances a highly advanced era in AI education. The following technologies described below hold the potential to significantly shape the educational AI.

Brain-computer interface: commonly abbreviated as (BCIs), can enable direct thought-based interaction with the educational curriculum which in turn could revolutionize learning when there is constant direct communication between the brain and computers.

Edge AI: this involves bringing AI processing nearer to the source of data enabling real-time and localized AI applications which is advantageous in constrained environments.

Neuro-informed learning systems: this educational approach draws insight from neuroscience to inform the design and implementation of learning experiences. It leverages an understanding of the brains ability to learn and process data to optimize educational strategies and techniques to enhance individualize instruction, cognitive load management, feedback mechanism, metacognition development among other key elements. AI system could incorporate neuroscientific principles to understand and care for learner’s cognitive processes in a much better way.

Explainable AI (XAI): this is the capability of an AI system to provide explanations that are understandable and clear regarding its decisions and actions. The aim of XAI is to make AI systems more accessible and interpretable enabling users to comprehend reasons behind its specific outputs. This applies more in the domains of healthcare and finance where trust is highly needed.

Block chain for credential verification: this is an emerging technology in educational AI which can secure and streamline the verification of academic credentials to enhance the authenticity of educational records and to prevent frauds.

Emotion recognition: this can be incorporated in the AI systems to help recognize and respond to learners’ emotions. This will enhance the learning experience by adapting the curriculum based on the learner’s emotional state and provide support when needed.

Computer vision: when computer vision is integrated into education, remote learning experiences are encouraged and enhanced. This can be achieved by enabling the AI systems to analyze learners’ facial expressions and engagement levels, while providing necessary feedback to the teachers.

Educational robots: robots which are designed with AI has the potentials of providing interactive learning experiences and they can help with important learning tasks such as language learning, coding and problem solving.

Federated learning: this is a decentralized educational approach which allows AI models to be trained across multiple servers or devices without the exchange of raw data. This helps to improve security and privacy by localizing student’s sensitive data.

Quantum Computing: this computing system has the potential of solving complex problems in machine learning and optimization. It is believed that the quantum computing technology can revolutionize the AI application in education.

AI wearable technologies: incorporating AI with human wearable devices or other technologies can improve human capacity. This can produce tools or devices that can provide assistants to handicapped or disabled learners and provide additional support in education.

2.6.2 Potential disruptions and transformations by artificial intelligence

AI may disrupt traditional educational models and foster innovative approaches. AI has the potential of revolutionizing education by personalizing learning, automating educational administrative tasks and enhancing innovative pedagogical techniques. AI powered adaptive learning platforms can tailor educational curriculum to individual learners needs promoting better learning. It can automate grading, creating more free time for instructors to concentrate on personal mentorship. AI-driven simulation environments and virtual reality provides immersive learning environments for improving practical skills. However, the challenges associated with AI use relating to ethical considerations, privacy of data and digital divide must be adequately managed to ensure equal accessibility and responsible implementation. Christenson et al. [17] explored the concept of destructive innovation in education. They emphasized the potential of technology instruction to transform traditional educational models. They argued that customizable student-centric approaches can meet diverse learning needs better and challenge established norms. They also described the impact of disruptive technologies on K-12 education and suggested that accepting innovations is the key to addressing the limitations of conventional education.

2.7 Anticipated challenges of educational AI and preparation for the future

Future AI developments may pose several challenges such as described in this chapter. However, addressing these challenges will entail collaborative efforts from researchers, government, leaders in industries and the entire society to ensure that AI technologies are deployed beneficially and responsibly. Some of the anticipated challenges of AI developments for educational use include the following:

1. Ethical concerns: AI usage ethical concerns are usually related to bias, transparency and accountability. Experience has shown that ensuring fairness and avoidance of discrimination in AI systems has remained critical challenges.

2. Redundancy: AI has the potential of automating the grading system and other administrative tasks in education. However, AI-driven automation could replace and displace jobs leading to unemployment in certain sectors thereby preparing the workforce for new skill requirements which is not very convenient.

3. Lack of standards and regulations: the rapid pace of AI development can outstrip regulatory frameworks leading to gap creation in oversight. Therefore, it is important to establish global standards for AI deployment to ensure responsible practices.

4. Privacy and security risks: as the vast amount of personal data handled by AI system increases, an equal increase in risk of privacy breaches arises. With this, the possibility of safeguarding sensitive information and preventing unauthorized access becomes an increasing risk.

5. Dependency and bias: prolonged and continuous usage of AI system can lead to over reliance and dependency on the system. This factor alongside the bias embedded in AI system can bring about errors in decision making as well as reinforce already existing social inequalities. Nevertheless, there have been ongoing efforts to manage these challenges.

6. Unintended consequences: AI systems produces unintended behaviors resulting in consequences that are unintended especially in complex and dynamic environments. Anticipating and preventing these unintended outcomes has posed significant challenges.

7. Human-AI collaboration: it has been a huge challenge to create an effective collaboration between humans and the AI system. This is because all the efforts to do so have resulted in diminishing human autonomy and creating over-dependence on the AI-system. Hence, careful design and implementation is required in order to mitigate this challenge.

8. Energy consumption: developing energy efficient AI solutions has been a very big challenge and this has impacted negatively on the environment. This is so because substantial computing power require large amount of energy in training sophisticated AI models. This can be prevented by developing energy efficient AI solutions which are yet to be achieved.

9. Public understanding and perception of AI: there has been widespread fear and resistance to AI implementation by the public due to their perceptions and misunderstanding regarding AI and its usage. This has affected the responsible adoption of AI both in education and other sectors. Hence enhancing public perception through proper public awareness campaigns and education regarding AI capabilities and limitations can increase its adoption by the public.

10. International cooperation: the global nature of AI development requires that international standards and norm be established to manage all its challenges which are related to ethical standards, data sharing and security issues.

2.8 Strategies for addressing potential challenges arising from future AI developments

In view of the anticipated potential challenges arising from future AI developments; the following strategies have been suggested for educators in order to prepare themselves ahead of these challenges

• Ethical frameworks and guidelines: ethical guidelines should be developed and enforced in order to address challenges of bias, transparency and accountability. The industrial sector should be encouraged to embrace self-regulation and adhere to ethical standards.

• Training and education of the workforce: public education awareness campaigns should be launched to enlighten the public on AI technologies and thereby impact on the society. Misconceptions regarding AI should also be cleared by fostering open dialogs with the public.

  Educational and training programs should be organized regularly in order to equip the workforce with the required skills relevant to face the AI driven future labor market.

• Privacy and security measures: the use of privacy-preserving technologies and encryption in AI applications should be strengthened with strict privacy measures properly enforced.

• Regulatory policies: it is important to form clear regulatory policies that can adapt to the rapid pace of AI advancements. Harmonized standards for AI developments and implementations should be created by international cooperation.

• Transparency: transparency in AI algorithms should be advocated and standards for providing explanations for AI driven decisions should also be put in place especially in the critical domains such as in the health care and finance sectors where trust is highly needed

• Diversity and inclusion: this can be used to reduce bias in AI algorithms when diversity is promoted among the AI development teams. When proper representations from different and varied backgrounds are included in decision making, then the challenge related to decision-making processes in AI will be addressed.

• Energy efficiency: researches geared towards the development of energy efficient AI models and algorithms should be encouraged and developed to address the challenge of energy consumption and its consequent environmental impact. The use of renewable sources of energy in AI data centers should also be promoted.

• International collaborations: by fostering international collaboration and global cooperation in the policies and standards of AI, the challenges can be properly managed. Establishing forums or platforms that are internationally coordinated will help to share ideas on the best practices globally.

2.9 Need for regulatory frameworks for artificial intelligence in education

These is a critical need for establishing regulatory frameworks for AI in education to ensure its responsible and ethical use in education in order to achieve quality education. These frameworks provide protection against misuse. This is because regulatory frameworks establish guidelines for ethical development of AI algorithms, usage and data handling. Thus, they foster responsible learning environments and help to prevent bias in education AI.

According to European commission [18], “a framework for trustworthy AI provide guidelines which articulates a framework for achieving trustworthy AI based on fundamental right as enshrined in the charter of fundamental Rights of the European union (EU charter) and in relevant international human rights law”. The commission emphasized that any trustworthy AI is composed of three components which include the following:

1. It should be lawful, complying with all laws and regulations which are applicable.

2. It should be ethical, ensuring adherence to all ethical principles and values.

3. It should be robust both from a technical and social perspective. Indicating that such AI systems should perform in a safe, secure and reliable manner and safeguards should be foreseen to avoid unintended adverse effects.

The following guidelines and best practices among others have been Suggested for maintaining standards in AI applications:

1. Ethical committee formation: establishment of ethical review boards or committees is a good practice which will assess the impact of AI and its ethical implications before applying in teaching and learning.

2. Regular monitoring and evaluation: regularly monitoring and evaluating of the effects of AI applications on the learners, instructors and the entire educational system will help to predict and identify possible unintended consequences and enable stakeholders to effect corrective measures.

3. Involvement of stakeholders: all the major stakeholders in education such as students, parents, educators and the community must be actively involved in the decision-making process in AI education application as this will foster collaborative cooperation and decision making.

4. Continuous training of stakeholders: educators and administrators should be provided with continuous training and upgrading to remind them of the ethical considerations, responsible use of AI and potential biases associated with its applications.

5. Regular audit: auditing of the AI system should be performed at regular intervals to identify and correct any challenges and concerns. Such regular auditing process will promote any ongoing improvement in AI technology.

6. Improving accessibility: there is need to design AI system with more accessibility to learners. This will provide an AI system that is adapted to the personalized needs and ability of the learner.

7. Incorporation of human oversight: this will ensure that decisions are made in line with the required ethical standards thus enhancing interventions when needed.

8. Transparency and explainability: the AI system when designed and made explainable, enable stakeholders to understand how decisions are made. This in turn promotes accountability and trust.

9. Informed consent: it is very important that user’s consent be sorted especially regarding sensitive data. It is also very necessary to let the users know and understand how data obtained from them will be handled. This is important for building trust in the AI system.

2.10 Stakeholders perspectives on the impact of AI in achieving quality education

Byers [19] reported on some interviews conducted among educators on their embrace of the usefulness of AI in achieving quality education and its potential problems when used in the classroom. He quoted a high school principal saying that the focus is driven towards how to use AI to elicit better educational experience for the students. The report further described the use of AI in education as an evolving phenomenon at every level of the academia which has made educators and administrators to seek ways of determining program’s potential use in the classroom and whether its use could lead to possible forms of cheating, plagiarism or other forms of academic misconducts which are detrimental to students.

The U.S department of Educator’s AI main focus has been on how AI permits the educators and their students to experience new forms of interaction, loops of feedback enhancement and how the technology has been making easier the work of the teachers [19]. Commenting further on the positive impacts of AI use in the classroom, the high school principal acknowledged that learning software embedded in AI such as Grammarly, provides help for writing and that other programs such as Magic eraser for image alteration and Tetra can assist in note taking during virtual meetings. He stated that the technology is making people more efficient especially as it applies to teachers creating lesson plans and scoring tests leading to more time to focus on students’ school experience.

An interview with a professor of humanities reported by the same author, exposed the fear and notion nurtured by some educators regarding AI technology. The professor questioned information gathered by AI system and who has access to them. He used the phrase “garbage in, garbage out” implying that if the source material for AI is not quality, then the content will not yield quality, and that the education system is running the risk of over trusting in the AI system. Another educator expressed his concern on students use of AI programs such as Chat GPT to complete an assignment, emphasizing that such works often lack the personal touch and author’s voice which makes the work original, and that disciplinary measures are not yet in place on how to manage AI use in assignments. He concluded that the inclusion of AI in education may cause him to rethink some of his assignments to ensure the originality of the work.

Ryu and Han [20] studied Korean school teachers’ perception of AI education and reported that teachers with experience in leadership recognized that AI would help to improve creativity.

The study carried out by Marrone et al. [21], highlighted an analysis on the students view on the relationship between AI and creativity as four key concepts captured as social, affective, technological and learning factors. According to them, the students with a higher understanding of AI gave more positive thoughts about integrating AI into their classrooms, while those with low understanding of the system expressed fear of AI technology inclusion in education and hence explained that AI could never match human creativity. Nevertheless, the majority of the learners expressed satisfaction for the benefits they enjoyed from AI enhanced and quality education through personalized learning, adaptive assessments and real-time feedback. AI system also facilitated collaborative learning among learners from varied geographical locations, promoted critical thinking and offered diverse educational resources which contributed to a better engagement and holistic environment.

Parental and community involvement in AI educational initiative ensures that AI education is made relevant and accessible to a wide range of learners. Parental involvement plays a crucial role in a child’s academic achievement and overall well-being. Parental involvement is a powerful force which shapes the student’s academic success, social development and the entire well-being. It creates a supportive and enriching educational environment which empowers the students to reach their full potentials. Recent reports from Tech novation on family interest on artificial intelligence revealed that 86% of parents want new ways to learn critical computing skills outside the traditional classroom in order to provide their children with more guidance on at-home education. With immersive AI curriculum for children and their families, parents learn with their children and help to create AI-based products that solve problems in their communities. Hence, involving parents in the AI education initiatives helps to create a holistic learning environment for the learners. It also helps in bridging the generational gap in understanding technology as well as fosters parental support for their children. Community involvement in AI education initiatives encourages collaboration, knowledge sharing, diversity and supportive network for the learners. Diverse perceptions from members of the community provides grounds for more initiatives and helps to identify and address local challenges associated with AI initiatives.

2.11 Use of AI in addressing language barriers in education

AI has been a vital tool for addressing language barriers in education by making available the following relevant language resources and services available to both learners and educators.

• Provision of language translation services: AI translation tools can assist language translators to point out and correct mistakes, make suggestions, recommend alternatives and provide translation in real-time. AI provides enabling environment for communication among people with diverse languages to understand each other. AI translation tools can augment human translators by providing instant translations of spoken or written content. AI translators work by ensuring that contents are broken down into smaller segments and algorithms are then applied to each segment. This analyzes the content to generate a translation depending on patterns and set rules. AI translators can also improve future translations accuracy and consistency by learning from previous translations. Example of AI translators include: Google Translate, Microsoft Translator and DeepL.

• Provision of Natural Language Processing resources: AI provides natural language tools such as google Cloud Natural Language API, Gensim, SpaCy, IBM Watson, Natural language Toolkit, chatbots, search engines among others to help resolve ambiguities in language. The language APP provided by AI can help learners with diverse languages with list of vocabularies, listening exercises, gramma lessons, explanations and interactive language exercises.

• Language teaching: AI language Apps are used by teachers to translate texts, generate summaries at various levels. Multilingual learners also use AI tools and platforms to interpret words, construct sentences, write essays, generate communications and learn different language skills. AI enhance language evaluation by providing automated grading and feedback. AI language processing tools and Chatbots provide enhanced immersive language practice. Learners’ speech can be evaluated and real-time feedbacks on punctuation, intonation and rhythm can be provided for him/her and the feedback in turn gives the learner the opportunity to improve the speaking skills accurately within the shortest time.

2.12 Current policies and regulations regarding the use of AI in education

Policies and regulations of AI in education differ from country to country and regions, however, irrespective of how these policies differ, some common considerations have been identified which play vital roles in teaching and learning. They are as follows:

Policies on ethical considerations: all stakeholders in education must be educated on the ethical use of AI in education. This is important because maintaining student privacy must be considered a primary ethical issue. This is supported by the fact that AI requires data to function and educational AI collects and analyzes sensitive data related to the learner’s personal information which must be kept confidential and must not be shared without their consent. Even with consent, it is not right to prompt public models with data which can be traced to the learner.

Policies on bias and fairness: all stakeholders in education must be trained to identify and respond to bias associated with AI algorithms appropriately. This is because AI is usually trained using existing database which can lead to bias. Hence educators and learners must be prepared to recognize inaccurate information and racism as well as how to manage them.

Accessibility and equity: AI technology must be universal and accessible to all categories of educators and learners irrespective of their location, culture, ability, disability and social-economic status.

Instructor-learner relationship: AI technology must not be allowed to replace the human emotional, social and moral components which cannot be exhibited by AI. This relationship is very essential to enhance improve quality of learning in education.

Policy on balance use of AI: all stakeholders in education must be trained on the responsible use of AI in teaching and learning. This is because AI technologies use over time can pose the risk of dependency which can lead to hindered critical thinking skills which will affect the cognitive ability of the learners. This will also affect their problem-solving ability which can hinder learning. Hence the policy on maintenance of balance in use of educational AI.

2.13 Impact of generative AI on the quality of education

Generative AI includes all AI systems which has the potential of generating new contents such as written text, images and videos. Examples of Generative AIs include GPT, Pix 2 Pix, Cycle GAN, Style GAN, Text to image synthesis among others. Generative AI has significantly impacted the quality of education in the following ways.

Analyzing data: AI algorithms has the potential of analyzing large amount of educational data to point out areas that require improvement, identify patterns and various trends thereby educating stakeholders in education to reach useful decisions and help educators refine their pedagogical strategies.

Easy accessibility: generative AI makes it easy for learners to access learning resources irrespective of the category they belong. They help learners with various disabilities with alternative formats in form of text-to-speech features-, audio- and audio-visual descriptors for enhanced quality of learning, this on the other hand makes teaching and learning more inclusive.

Bridging language barriers: generative AI provides immersive experiences, language translation and language acquisition for all learners globally. These has helped in bridging the language gap in teaching and learning thereby making teaching and learning experience more qualitative.

Provision of automated feedbacks: generative AI provides a tutoring system that enable instant or automated feedback for learners. This in effect provide corrections for errors and provide guidance and support. These tutoring has the potential of augmenting the conventional pedagogy technologies and providing a better understanding of the learning process.

Individualized learning/instruction: generative AI enhances education quality by helping the learners learn at their own pace, according to their specific needs and circumstance, preferences and learning style. This helps to encourage engagement and better comprehension.

Task management: generative AI platforms can help instructors to manage their administrative tasks by automating assignment and quiz gradings, managing learners records thereby permitting the teachers to concentrate more on teaching and mentoring of the learners.

Creation of educational content: generative AI helps educators and instructors in generating assignments, quizzes, exercises and lesson content preparations and textbooks. This in turn helps him/her to present a very high-quality content to the learners.

3. Conclusion

The emergence and inclusion of artificial intelligence in education has contributed immensely in the achievement of quality education globally and this has benefited all the stakeholders. With AI system integration in teaching and learning content, the learners are better equipped mentally and psychologically with material resources to face learning challenges. The educators are assisted with administrative tasks which affords them the opportunity to attend to other important pedagogical activities. AI in education has given rise to automated grading system, real-time feedback, personalized learning, intelligent classroom assistants, intelligent Tutoring system among other advantages and these have enhanced the quality of education. However, AI system, has posed several challenges related to biases in AI algorithms, privacy concerns, redundancy, over dependency among others. These challenges have been addressed and strategies are being put in place to prepare for future challenges. Hence, AI in education has impacted positively in achieving quality education on a global scale.

Acknowledgments

The authors sincerely appreciate the contributions of the ICT staff of the Federal College of Dental Technology and Therapy Enugu Nigeria for their support during the full chapter development. We appreciate the useful contributions of some of our colleagues in the Biomedical Engineering department of the college in providing basic resources for the chapter. Special thanks to all the staff of IntechOpen publishers especially Dr. Ivana Barac for continuous effort to contact and encourage the authors. Thank you all.