Analysis of Emerging Trends in Artificial Intelligence in Education in Nigeria

Analysis of Emerging Trends in Artificial Intelligence in Education in Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Analysis of Emerging Trends in Artificial Intelligence in Education in Nigeria Bulus Bali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3819828/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the domain of education, the integration of Artificial Intelligence (AI) has ushered in a paradigm shift towards a more technologically-driven landscape, demonstrating its efficacy as an emergency strategy. The pervasive influence of computer technology has catalyzed a surge in online learning within the country, yielding positive educational outcomes. Despite these advancements, a considerable number of educational institutions in Nigeria have yet to leverage AI technologies. Recognizing the expanding significance of AI in education, this study seeks to align with this trajectory by aggregating instances of AI implementation in education from developed countries. The methodology employed involves a comprehensive review of current advancements in AI applications within the Nigerian educational context. The review process, spanning papers retrieved from four digital libraries published between 2008 and 2022, culminated in the inclusion of 73 papers. These selected papers demonstrated the utilization of AI software tools and technologies, adhering to predefined exclusion and inclusion criteria. The findings of the study reveal a prevalent use of AI technologies in education in Nigeria, encompassing evolutionary software modelling, student performance prediction, multimedia e-learning platforms and frameworks, and the incorporation of Moodle learning. This discernible trend indicates a growing demand for the application of AI technology in the educational landscape of Nigeria. However, the study also highlights a discrepancy wherein more sophisticated AI techniques, such as intelligent tutoring systems, learnable robots or agents, web-based educational systems, and learning management systems explored extensively in other nations were infrequently applied in the Nigerian context. In light of these observations, the study proposes that educational institutions in Nigeria should consider adopting AI practices from more advanced nations. This strategic alignment is posited as a means to augment student learning opportunities and bridge the existing gap between the current state of AI integration in Nigerian education and the more advanced applications witnessed globally. Artificial Intelligence and Machine Learning Artificial Intelligence E-learning Education E-learning Teaching and learning Trend analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction In ancient times, traditional classrooms served as the primary arena for student education, employing uniform teaching methods and consistent teacher guidance. The requirement for simultaneous student presence posed challenges in addressing individual learning needs (Sampayo-Vargas et al., 2013 ). Recent decades have witnessed rapid technological advancements, leading to a proliferation of digitally enabled tools and services (Canton, 2021 ). Societal progress hinges on the acquisition and dissemination of valuable knowledge (Odegbesan et al., 2019 ). The swift evolution of computer technology has significantly influenced the learning environment, with educational resources increasingly embracing computerization. These technologies enhance learning experiences, foster skill development, and promote classroom collaboration, reflecting the transformative impact of technological progress. The ability to swiftly access the expertise of top professionals on specific subjects has become feasible, facilitated by intelligent tutoring systems replicating teachers' knowledge to offer personalized assistance (Pai et al., 2021 ). Artificial Intelligence (AI) emerges as a dynamic force capable of reshaping social interactions, particularly in education. AI-driven teaching and learning solutions are undergoing testing to prepare students for an AI-driven future (Pedro et al., 2019 ). The adoption of AI technologies in education seeks to enhance knowledge acquisition, leading to a surge in online learning. However, many developing nations, Nigeria included, face challenges in fully harnessing AI benefits due to infrastructural limitations and limited access to the Internet. The escalating demand for education worldwide strains existing institutional infrastructure and human resources. Developing nations grapple with operational and technological challenges, impeding the integration of AI-backed learning despite its recognized advantages. This is exacerbated by financial constraints, hindering the establishment of necessary infrastructure and internet access. The disparity between education demand and institutional capacity results in the rejection of numerous qualified candidates, limiting access to education and potential income. In Nigeria alone, where millions apply for admission, the available universities cannot accommodate the influx due to technological deficiencies (Adesulu, 2018 ). Institutions offering distance learning face challenges in providing robust AI e-learning platforms, with manual processes predominating due to technological limitations. The economic risks associated with manual operations underscore the efficacy of AI in streamlining tasks, thereby enhancing productivity and technology integration (Robinson, 2018 ). In advanced technologies, Artificial Intelligence (AI) stands at the forefront, widely adopted by well-funded universities. While Nigeria claims reputable academic institutions, the lack of financial support impedes their ability to keep pace with the latest AI advancements. Consequently, scholars in the field of AI exhibit diminishing commitment to their work. Analogously, the application of AI in Nigeria is akin to requesting a fish to climb a tree or comparing a small knife to a machete. Despite the availability of online learning resources in numerous institutions, only a handful actively cultivate AI capabilities (Liverpool et al., 2009 ; Robinson, 2018 ; Adejo & Misau, 2021; Enang, 2022 ). Afolabi (2014) notes that, despite the prevalence of online learning, learners face computer literacy challenges, hindering their engagement with technology-centric education. This predicament arises from the failure of institutions to innovate teaching and learning methods through AI. Nigeria's educational landscape lags behind in AI integration, despite the pressing need for expansion. The application of technology in research, education, and learning is crucial for growth, yet the impetus for change in the Nigerian teaching and learning sector remains limited (Liverpool et al., 2009 ). Efforts to introduce AI e-learning models in Nigerian educational institutions primarily revolve around collaborative learning frameworks. The educational goals set by the Seventh National Development Plan and Vision 2020 align with the United Nations' Millennium Development Goals, emphasizing the use of AI technologies in education (Eneh, 2011 ). Evaluating the suitability of these platforms in fostering a conducive learning environment for technology personnel is imperative. The integration of AI-enabled learning into the university system necessitates the education of teacher educators, demanding professional development and support. However, existing workshops and training have proven inadequate. AI-based learning emerges as a potential solution to the challenge of limited physical space on university campuses (Ndzibah & Ofori, 2017 ). The research aims to address specific objectives. Firstly, it seeks to identify AI technology studies in education reported in Nigeria from 2008 to 2022, as documented in high-impact scientific journals. Secondly, the research aims to identify the software tools essential for sustained AI-supported learning. Thirdly, it endeavors to comprehensively explore global trends in AI-supported learning across educational institutions. The research questions include inquiries into the publication of research articles on AI technologies in Nigerian education between 2008 and 2022, the utilization of AI technologies in supporting Nigerian education, and the software tools requisite for long-term AI-supported learning in the Nigerian educational context. 2. Literature Review According to Castaeda and Selwyn (2018), AI is a growing trend, it is time for the precise revolution that we anticipate in the educational sector. Almost all industries have adopted it, including the educational sector, and some of its components are currently being automated. According to a study by Agarry et al. ( 2022 ), Nigerian education must transition from analogue to digital, and AI technology is a key component of this process. Students are aware of and prepared to use AI-based learning systems, even at the secondary school level, according to Adelana and Akinyemi's (2021) research. Currently, social media and the internet are used by most Nigerian institutions. According to Adelana and Akinyemi ( 2021 ), since students are aware of and willing to accept AI-based learning systems, it is necessary to design, develop, and apply them in secondary schools. A study by Agarry et al. ( 2022 ) looked at how proficient elementary school pupils were at using AI to learn. The findings of the study revealed that the majority of primary education students are not skilled and incompetent in the use of AI for learning. Students' ability to explore digital resources such as AI depends on their access to digital technologies. Onyema et al. ( 2019 ) evaluated the undergraduate students' perceptions of how effective and successful ICT use is in promoting learning in Nigeria. It was found that mobile devices have features built in that can spur learners' interest in learning. This offers several features, like simple access to forums with interactive content that encourages teamwork, among others. During the pandemic in Nigeria, learning through the Google Classroom platform was seen as an efficient strategy to positively influence learners' academic progress (Oyarinde & Komolafe, ( 2020 ). Ajadi et al. ( 2008 ) talked about the theoretical underpinnings and the applicability of AI e-learning in the context of distance education at Open University. Although Nigeria Open University uses e-learning to conduct lectures and provide students with homework, this digitization has not been fully tapped into in many institutions around the nation. Nigeria joined other industrialized nations and used e-learning in the educational system to avert brain drain and the complete collapse of the nation's educational system (Oyediran et al., 2020 ). The world of AI technology, as we know it today, will be completely transformed once AI systems begin operating at their full potential, and Nigeria will not be an exception if it gives the development of AI more priority. Robots will eventually be able to serve humans or take over all of their tasks as AI advances (Robinson, 2018 ). Due to its growing significance, researchers have recently adopted AI technologies to offer personalized learning guidance and support for individual students in a variety of courses, including engineering, computer science, and informatics (Kose et al., 2015 ) and many more. Because AI technologies are constantly evolving, the educational community is addressing the promise and challenges they present. These technologies profoundly alter the design, administration, and governance of educational institutions (Popenici & Kerr, 2017 ). A variety of tools and applications, including intelligent tutoring systems, teaching robots, adaptive learning systems, and other emerging technologies being integrated with AI for various learning supports, are currently used by students and educators in institutions of higher learning. AI technologies with their flexibility, effectiveness, and realization of individualized learning for students to fulfil their specific needs. AI research focuses on cognitive issues that are frequently associated with human intelligence (Ventura, 2017 ). Techniques from AI and soft computing are being used in various aspects of our lives to address pressing issues (Khan, et al., 2018 ). Salem ( 2015 ) identified seven key areas of AI in education, including intelligent educational systems, teaching aspects, learning aspects, cognitive science, knowledge structure, intelligent tools, shells, and interfaces. Intelligent tutoring systems, educational robotics, and multimedia systems make up the Intelligent Educational Systems. The summary of AI-based educational research includes sections on intelligent tutoring systems, intelligent e-learning systems, and intelligent writing shells and tools as presented in Fig. 1 . The major evolutionary process of AI covers initial AI, machine learning, and the recent Deep Learning. One of the major challenges of educational institutions is the accumulated amount of data and how it can be utilized to boost the academic programs' quality (Abunasser & AL-Hiealy, 2022). The most common AI data mining techniques; C4.5 algorithms, k-mean algorithms, support vector machines, a priori algorithms, and expectation-maximization algorithms, are commonly used. According to Oguine, Oguine, and Bisallah ( 2022 ) classification algorithms like Decision Trees, Ada Boost, Support Vector Regression, Naive Bayes, and Stochastic, gradient Descent can modestly predict a student's academic success and, in particular, model the difference between high, low, and failed performances. Since the advent of AI technologies like Artificial Neural Networks (ANN) and Deep Learning (DL), educators and students have increasingly used tools and applications powered by AI, such as intelligent robots and adaptive learning systems (Chan & Zary, 2019 ). Using conventional educational methods can be challenging because every learner is independent and has different learning preferences, needs, and talents. Yet with AI, teachers may individually adapt their instruction to each student's needs, and students can learn with greater motivation, engagement, and independence (Ventura, 2017 ). Educational institutions greatly benefit from providing variables that raise success rates and lower student failure rates. The best method for identifying hidden patterns and making recommendations that improve student performance is data mining (Hamoud, Hashim and Awadh, 2018 ) The performance level of the instructor and student can be predicted using a variety of machine-learning algorithms. According to Abunasser and AL-Hiealy (2022) machine learning categories include a: Decision tree, Ensemble (Gradient Boosting Classifier, Gradient Boosting Regressor, AdaBoost Regressor, Extra Trees Regressor, Nave Bayes (GaussianNB), Neighbors (Nearest Centroid, KNeighbors Class. Machine learning techniques provide computers with the ability to learn from data and further anticipate the future. Jordan and Mitchell ( 2015 ) claim that within AI, ML has become a viable option for creating useful tools to address a variety of problems. For example, forecasting student enrollment, teacher and student performance evaluation, forecasting student grade point average, and other aspects of education management use popular soft computing techniques including fuzzy logic, neural networks, and genetic algorithms (Khan et al., 2018 ). The use of computers in education to give students instructions is known as computer-based education. Before the advent of AI, Computer Based Education (CBE) systems were standalone tools that ran on a local computer to address concerns like student modelling, adaption, and personalization. With the widespread use of the Internet, new web-based educational tools like e-learning platforms have appeared. Moreover, new types of adaptive and intelligent systems for educational purposes have been driven by the growing usage of AI techniques. As a result, there are similarities between CBE and AI in education. For example, learning management systems (Romero, et al., 2008). Test and quiz systems are all major forms of CBE systems that are currently implemented (Romero et al., 2013 ). Learning analytics (LA) is the collecting, analyzing, and reporting of data on learners and their surroundings to comprehend and improve learning and the environments in which it takes place (Romero et al., 2013 ). Educational Data Mining (EDM) and Learning Analytics share characteristics, passions, and objectives. Nonetheless, there are significant distinctions between them, primarily in the approaches and emphasis (Siemens & Baker, 2012 ). Statistics, visualization, discourse analysis, social network analysis, and sense-making models are the approaches that are most widely used in LA. Nevertheless, clustering, classification, Bayesian modelling, relationship mining, and model-based discovery are the most widely used algorithms in EDM. Another difference between LA and EDM is that LA places more emphasis on presenting data and results, while EDM places more emphasis on discussing and contrasting DM technology. The adoption of AI skills in education plays a vital role in the struggles to make the prospective labour force AI-ready (Pedro et al., 2019 ). AI tools have served as a vital tool in battling COVID-19 as the epidemic continues to affect people and industries (Egielewa et al., 2022 ). Regardless of the various benefits that would be acquired by others in the sector, according to Garc'a-Gorrostieta et al. (2018) and Pise et al. ( 2020 ), students would have a tutor who would instruct them at their pace while maintaining the same feeling throughout. Education is the most significant industry in Nigeria since it affects everyone's lives, no matter their age or where they live. If the classrooms are outfitted with AI technological solutions to provide pupils with the finest learning environment, AI technology can make Nigeria's overcrowded educational system smarter. It can also operate along with virtual networks to provide the ideal learning environment for both teachers and students (Robinson, 2018 ). Therefore, to incorporate 21st -century educational programmes, Nigeria's curriculum reform initiatives must define "AI competencies" beyond fundamental ICT competencies, as many other nations have done. Instead, they should focus on skills that would enable students to address challenges utilizing AI technology (Pedro et al., 2019 ). Advancement in educational institutions has an impact on higher education and plays a key role in the growth of a nation, which has a reciprocal and dynamic impact on the social environment. All learning experiences can be learned with just one click to AI-assisted learning. Distance, time, space, availability of current learning materials, cost of trip, and risk of travel, just but few are no longer problems. Fast technological advancement is a sign that AI e-learning will inescapably play a part in raising the standard of higher education (Angib et al., 2022 ). As for the downsides, AI technologies come at a great cost, not every nation can meet the expense (Iqbal et al., 2020 ). Another bottleneck is that applying AI in a system can create a lot of difficulties (Lexcellent, 2019 ). Lexcellent ( 2019 ) asserts that before moving toward artificial intelligence, challenges like a lack of monetary resources, a lack of interest on the part of institution administrations, a lack of knowledge about successful involvements in the field, and a lack of studies addressing assertions must be deliberated and resolved. Considering the significance of developing technology in tutoring and learning, Nigeria is still far from adopting it. The effective integration of AI technologies into teaching and learning is hampered by several issues. Epileptic power supply, a lack of knowledge or skills, problems with availability and accessibility, funding, inadequate professional development, disinclination to change, unreliable internet connections, and so on to name a few. Actors and stakeholders in the education sector like educators, policymakers, curriculum planners, and students can all work together to overcome these obstacles (Enang, 2022 ). AI algorithms and systems have been increasingly popular and well-known in recent times, particularly in the field of education. At many educational installations, tablets have largely taken the role of books in the learning process and internet-based learning has become more common (Kairouz et al., 2021). Digital tools have transformed education and made learning more active and independent. New technology has also changed the way that people communicate, so educators must find new ways to inspire learners and adapt to this new century (Egielewa et al., 2022 ). 3. Methodology The study used the literature review research method to retrieve research articles from four reliable online databases to examine recent research publications on AI in higher education (Springer Link, Science Direct, IEEE, and WoS). The selected research was published between January 2008 and December 2022. This is characterized by looking for pertinent material to help identify, evaluate, and analyze the studies that are already available to help with the study's research demands. The rationale for using Springer Link, Science Direct, IEEE, and (WoS) databases for this research on trends in AI application in education in Nigeria is: that these databases are known for hosting peer-reviewed, comprehensive, credible, and high-quality AI research articles, conference papers, and journals in contrast with the Google Scholar search engine, which is unrestricted and also indexes low-quality or non-peer reviewed articles. When researching AI in education, it is essential to rely on credible sources to ensure the accuracy and validity of the information. To collect relevant data for the study, the researchers in December 2022, performed electronic searches using four reliable electronic databases. The source databases were searched several times using various keyword combinations and search techniques. It was done by combining the search terms ("artificial intelligence" OR "Machine learning" OR “Data mining” OR “intelligent tutoring system" OR "robotics" OR “learning management system” OR “e-learning”) AND ("Nigerian education system" OR “Nigerian institutions”) from 2008 to 2022 in the selected database. The inclusion requirements were limited to papers directly related to AI in education in Nigeria, and popular AI technologies in education in other nations published between the years 2008 and 2022. They include proceedings papers, or peer-reviewed journal articles, and were defined to assure the paper's relevance and accuracy. Included were empirical and theoretical works that used quantitative or qualitative approaches and had as their major subject research pertinent to AI in education. After that, duplicate articles removal was completed. Animal and non-human studies, non-English language, non-empirical data, incorrect study design, incorrect exposure, incorrect outcome, and no useable data are all excluded from the exclusion criteria. Books, dissertations, editorials, and other writings without empirical data were also excluded. As a result, the criteria include papers with empirical data that were pertinent to the study's goals. A total number of 874 papers were found using the four electronic databases, from which duplicates were excluded in the initial screening. The final selection of reviewed studies is made up of 73 papers as in (Fig. 2 ) after applying inclusion and exclusion criteria. All abstracts were reviewed to ensure that the studies were relevant to the purpose of this study. The included studies have several things in common based on the article selection framework. They all have a learning environment as their primary focus, study the beneficial effects of applied AI technologies on students' learning processes, apply AI technical tools, and produce empirical proof in learning institutions. The study's sample size was chosen to meet the research objectives by adhering to the inclusion and exclusion criteria. Only papers that were published in English and connected to the use of AI in education met the linguistic criterion. Articles that give pertinent evidence for the use of AI technologies by students, or by students and teachers, to improve educational processes were chosen after the initial screenings. The proportion of articles by year is shown in Fig. 3 . 4. Results and Discussions This paper examines the trends in research on AI in education in Nigeria between 2008 and 2022 years. The analysis provides answers to the research questions as follows: RQ1: What is the distribution of research articles on AI technologies in education in Nigeria published in high-impact scientific journals between 2008 and 2022? Early AI Tool Adoption in Nigerian Education The first phase of AI adoption in Nigeria, which tended towards rather slow development, spanned from 2008 to 2013. To accommodate a range of learning styles and abilities, adaptive learning technologies were implemented in addition to the initial focus on fundamental e-learning platforms and online platform introduction. AI algorithms were used by adaptive learning systems to assess student performance and customize course materials to meet each student's needs (Liverpool et al., 2009 ; Ayanda et al., 2011 ; Falaye et al., 2014 ). Liverpool et al. ( 2009 ) and Ayanda et al. ( 2011 ) suggested an e-learning programme at a specific Nigerian higher education institution to investigate its efficacy in enhancing learning. They concluded that the case study institution and other institutions interested in implementing the scheme would no longer be subject to the constraints of the conventional educational system once the scheme is completely integrated. Using the local intranet networks that are already available in the majority of Nigerian tertiary institutions, Falaye et al. ( 2014 ) created and deployed an intranet-based communication and e-learning system as a unified system to deliver seamless, low-cost institution-wide communication and distant learning. In addition to many other features, the system offers affordable customized SMS, audio, video, file sharing, e-classrooms, high-quality VoIP calls, search FM radio utility, news, and entertainment. Very few studies have looked into the application of artificial intelligence in education. However, challenges such as limited internet access in some regions, the adoption of AI systems such as Teachable robots, Web-based Educational systems, and Learning Management Systems (Thomaz and Breazeal, 2008 ; Bittencourt et al., 2009 ; Cavus, 2010 ) was slower due to infrastructure challenges and limited awareness. Also, efforts made to train educators and professionals in AI applications in education to effectively integrate AI into teaching practices were ineffective. RQ2: Which AI technologies are used and how do existing AI technologies support Nigerian education? Increased Integration of AI Tools in Nigerian Education From 2013 to 2017, the growth trend accelerated. There was a global trend towards integrating AI tools into educational systems. This includes the use of AI for content delivery, personalized learning experiences, and adaptive learning platforms, to tailor educational content to individual students' needs, helping them learn at their own pace (hybrid models Gbolagade et al., 2015 ). The implementation of AI-powered chatbots and virtual assistants in education started gaining traction during this period. These tools were used for answering student queries, providing information, and facilitating communication within educational institutions (Idris, 2020 ; Kelechi, 2021 ). Data Analytics for Decision-Making Educational institutions began leveraging AI for automated data analytics to make informed decisions in teaching processes. AI technologies, such as machine learning algorithms Gbolagade et al. ( 2015 ); Oguine et al. ( 2022 ) were applied to analyzing student performance data, assessment processes, and consistent evaluation in a short time. The last stage was characterized by a rapid growth phase from 2017 to 2022, during which the number of research publications increased in comparison to earlier stages as a result of the increased use of AI technology. Most of their notable AI studies related to the study have increased significantly during the past few years. The trend analysis demonstrates that there is a growing body of knowledge about the use of AI technologies in education. The examination of AI technology trends in publications in the education sector reflects changes in interest and production as well as advancements in science on a worldwide scale and sector development patterns. In general, an increase in publications typically indicates that the field's global science system is growing. The trend for publications on AI technology in education from 2008 to 2022 by sources is shown in Fig. 4 . Across the examined sources, there was an increase in the number of publications on AI technologies in education. As presented in Fig. 4 , 874 articles were found from the four databases related to AI in education in Nigeria that have been published between 2008 and 2022. As shown in Fig. 4 , there was a rise in interest in artificial intelligence (AI) in education research between 2008 and 2013, especially in publications on SpringerLink when compared to other sources. From 2014 to 2022, there was a greater trend in AI research in education. According to Springer Link, WoS, IEEE, and Nigerian publications released between 2014 and 2022 had the most papers on AI in education. This indicates that there is a growing demand in Nigeria for the application of AI technology in education. Table 1 Research on common AI technologies in education platforms in Nigeria Author(s) and year Title Name of Application AI Technologies Type Educational Process Institution Facet Liverpool et al., ( 2009 ) Towards a Model for E-learning in Nigeria HEIs: Lessons from the University of Jos ICT Maths Initiative Proposed Model for E-learning Web 2.0 tools Planning Higher institution in Nigeria Ayanda et al., (2011) Towards a Model of E-Learning in Nigerian Higher Institutions: An Evolutionary Software Modelling Approach evolutionary software modelling Web 2.0 tools online Planning Higher institution in Nigeria Falaye et al., ( 2014 ) Cost Effective Multimedia E-Learning Application for Nigerian Higher Institutions Multimedia E-Learning Microsoft Visual Basic 6.0, ASP.NET Visual Studio2010 offline virtual E-Classrooms FUT Minna intranet-based communication and e-learning system Gbolagade, et al, ( 2015 ) Predicting Postgraduate Performance Using Resample Preprocess Algorithm and Artificial Neural Network Prediction model hybrid of Resample and ANN Offline Performance prediction University of Ilorin Predict a student’s performance Mamudu & Oyewo ( 2015 ) Use of Mobile Phones for Academic Purposes by Law Students of Igbinedion University mobile phones Online academic activities Igbinedion University Nwachukwu and Onyenankeya ( 2017 ) Use of Smartphones among College Students in Nigeria: Revelations and Reflections GSM protocol Smartphones Online academic activities Caleb University Saheed et al., (2017) Fingerprint-based approach for examination clearance in higher institutions Biometric Examination Clearance SecuGen fingerprint scanner academic activities Federal University Wukari Adekitan et al., (2019) Data mining approach for predicting the daily Internet data traffic of a smart university KNIME and Orange platforms Naive Bayes, Neural Network, Random Forest, kNN Online Internet traffic prediction Covenant University predictive KNIME and Orange-based models Hambali, et al., ( 2020 ) Automated university lecture timetable using heuristic approach Automated timetable generator Genetic algorithm and simulated annealing desktop application timetable generation Federal University Wukari Idris ( 2020 ) The Challenges and Prospects of E-learning in Nigerian Polytechnic Education: A Case Study of Kaduna Polytechnic, Kaduna State, Nigeria E-learning technologies Virtual Classroom, Digital Learning, Computer Based Test offline Learning and assessment Kaduna Polytechnic Student Engagement Matthew et al., (2020) Adoption of smart and disruptive technologies for educational development and automation of industry 4.0 in Nigeria Federated Multimedia Digital Library ecosystem Data Mining Implementation Online Learning selected tertiary institutions in the South-Eastern Nigeria Student Engagement Kelechi ( 2021 ) The E-Learning on World Epidemic (COVID-19) in Tertiary Institutions in the North East States of Nigeria Framework of eLearning Google Classroom, Zoom, WhatsApp and Blog. Online teaching and learning North-East Nigeria Adaptive learning environment Adeyeye et al., ( 2022 ) Online Learning Platforms and Covenant University Students Academic Performance in Practical-Related Courses during COVID-19 Pandemic Moodle Learning Management System Open Source or Proprietary online learning learning and lecture delivery Covenant University Predict a student’s performance Adeyeye et al., ( 2022 ) Online Learning Platforms and Covenant University Students Academic Performance in Practical-Related Courses during COVID-19 Pandemic Zoom Open Source or Proprietary online learning and lecture delivery Covenant University Predict a student’s performance Ogolodom et al., (2022) Online Learning in Nigerian Universities During COVID-19 Pandemic: The Experiences of Nursing and Radiography Undergraduate Students Internet connection Laptop and Smartphone Online learning and lecture UNN, BUK, Nnamdi Azikiwe UniLag, Lead City University, UniCal, UniMaid, FUT Owerri, ABU, University of Benin, University of Port Harcourt) Adaptive learning Environment Oguine et al., ( 2022 ) Big Data and Analytics Implementation in Tertiary Institutions to Predict Students’ Performance in Nigeria Big Data and Analytics Google Forms, Jupyter Notebook, and Python offline Prediction of Students Performance University of Abuja prediction model The broad summaries of a few chosen studies on the use of AI technology in education in Nigeria are shown in Table 1 . The parameters of the study included the author(s) and year, the name of the research, the name of the application, the type of platform, the educational process, the institution, and the aspect of learning. The authors are based in Nigeria and come from education departments and computer science departments at various institutions of higher learning. The majority of the titles include boosting online learning, e-learning, automating the examination process, and making learning more affordable through the use of AI technologies in institutions. The applications that have been found largely involve artificial intelligence (AI) models for online learning, evolutionary software modelling, student performance prediction, multimedia e-learning platforms and frameworks, and Moodle learning. The majority of studies indicated interest in e-learning. There is not a lot of educational software available that uses AI technologies. For forecasting student performance, machine learning and data mining algorithms such as support vector machines, decision trees, Naive Bayes, and K-nearest neighbours were used. Adekitan et al. ( 2019 ) carried out a data mining analysis utilizing four learning algorithms: The Decision Tree, the Tree Ensemble, the Random Forest, and the Naive Bayes Algorithm. Comparative performance analyses of the models' accuracy, Kappa values, and areas under ROC curves were reported. According to Matthew et al., (2020), the current curriculum of education needs to incorporate technological innovations and inventions into their mainstream educational program through the early adoption of scientific methodology, to hybridize the current teaching procedures and practices in addition to several other scientific approaches. The early adoption of Artificial Intelligence (AI) technologies will provide potential leverage in the creation of scientific knowledge in the 21st century using mobile Android computing devices. Genetic algorithms and simulated annealing techniques were only employed in one study that utilized automation. Backend web design technologies include MySQL, Apache Wamp Server, and PHP web design tools. In contrast, web designs utilized Web 2.0, HTML, CSS, and JavaScript. Virtual E-Classrooms learning, lecture delivery, planning, and performance prediction are all part of the educational process. The majority of studies focused on developing AI via data mining and machine learning approaches. This shows that numerous academics are paying attention to the sub-field of artificial intelligence (AI) that deals with academic achievement in higher education, such as predicting students' performance and increasing student involvement through data mining. This shows that efforts are being made by researchers to employ AI technologies to improve learning in Nigeria. Nigerian higher education institutions, on the other hand, are lagging in terms of the use of AI technologies when compared to other industrialized countries. One cause could be that they don't receive adequate funds and training to keep up with the most recent advancements in AI. Researchers in the field of artificial intelligence consequently grew less dedicated to their work. Analyzing research topics reveals the past, present, and upcoming areas of focus for publications based on research in a given discipline. RQ3: What software tools are required for long-term AI-supported learning in Nigerian education? Table 2 Software tools needed for sustainable AI-supported learning in education Authors and year Journal Title Name of Application AI Technologies Type Thomaz and Breazeal ( 2008 ) Artificial Intelligence Teachable robots: Understanding human teaching behaviour to build more effective robot learners. Teachable robots RL Web Bittencourt et al., ( 2009 ) Knowledge-Based Systems A computational model for developing semantic web-based educational systems Web-based educational systems Semantic web Web Lykourentzou et al. (2009) Computers & Education Dropout prediction in e-learning courses through the combination of machine learning techniques. Dropout Prediction Feed-forward neural networks, SVM Online Moridis and Economides (2009) Computers & Education Prediction of student’s mood during an online test using formula-based and neural network-based methods. Prediction of student’s mood Neural networks Online Calvo et al. (2010) IEEE Transactions on Learning Technologies Collaborative Writing Support Tools on the Cloud Collaborative Writing ML; NLP Online Cavus ( 2010 ) Advances in Engineering Software The evaluation of Learning Management Systems using an artificial intelligence fuzzy logic algorithm Learning management systems Fuzzy logic Web Chen et al. ( 2010 ) Etr & educational Technology Research and Development Engaging online learners: The impact of Web-based learning technology on college student engagement Teachable agents hierarchical linear model and multiple regressions Web-based Delen ( 2010 ) Decision Support Systems A comparative analysis of machine learning techniques for student retention management Student retention management ANN, decision trees, logistic regression, SVM, ensembles/ Bagging (random Forest) NS Gamalel-Din ( 2010 ) Egyptian Informatics Journal Smart e-Learning: A Greater Perspective; From the Fourth to the Fifth Generation E-learning Smart e-Learning XML Web Kotsiantis et al. ( 2010 ) Knowledge-Based Systems A combinational incremental ensemble of classifiers as a technique for predicting students’ performance in distance education Predict a student’s performance Naive Bayes, nearest neighbour, linear online algorithm Web Al-Hmouz et al. 2011 IEEE Transactions on Learning Technologies Modelling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning Mobile Learning system ANFIS tool Web Chi et al. ( 2011 ) Interaction Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive Pedagogical strategies. User Modelling and User-Adapted Intelligent tutoring systems RL Web Kotsiantis ( 2012 ) Artificial Intelligence Review Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades Forecasting students’ grades Regression Web Tan et al. (2012) IEEE Transactions on Education Intelligent Computer-Aided Instruction Modelling and a Method to Optimize Study Strategies for Parallel Robot Instruction Parallel robot Instruction ANN Web Tassopoulos and Beligiannis (2012) Expert Systems with Applications A hybrid particle swarm optimization based algorithm for high school timetabling problems School timetabling problem Particle swarm Optimization Web Verdú et al. ( 2012 ) Expert Systems with Applications A genetic fuzzy expert system for automatic question classification in a competitive learning environment Adaptive question sequencing system Classification; genetic algorithms; fuzzy systems Web Romero et al. ( 2013 ) Expert Systems Predicting students' final performance from participation in online discussion forums Computer-based Testing Association rule mining; genetic programming Web Lan et al. ( 2014 ) Journal of Machine Learning Research Sparse Factor Analysis for Learning and Content Analytics Assignments Grading regression; Bayesian latent factor analysis NS Rodrigues and Oliveira (2014) Computers & Education A system for formative assessment and monitoring of student's progress Formative assessment and monitoring of Web Luna et al. (2015) Applied Intelligence An evolutionary algorithm for the discovery of rare class association rules in learning management systems Mining rare class association rules Association rules; genetic programming; evolutionary computation Web Kose et al. ( 2015 ) GLOKALde E-learning experience with artificial intelligence supported software: An international application on English language courses. AI-based e-learning software ANN and cognitive development optimization algorithm Eguchi ( 2016 ) Robotics and Autonomous Systems RoboCupJunior for promoting STEM education, 21st-century skills, and technological advancement through robotics competition Educational Robotics RoboCupJunior Web Chih-Ming & Ying-You, (2020) Computers and Education: Artificial Intelligence , Developing a computer-mediated communication competence forecasting model based on learning behaviour features computer-mediated communication competence forecasting model WEKA, machine learning algorithms Hwang et al. ( 2020 ) Computers and Education: Artificial Intelligence A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors Adaptive learning system fuzzy expert system Latif et al., ( 2020 ). Heliyon Computer-assisted learning using the Cabri 3D for improving spatial ability and self-regulated learning Computer-assisted learning 3D Cabri program Chen et al. ( 2021 ). Computers and Education: Artificial Intelligence An interactive test dashboard with diagnosis and feedback mechanisms to facilitate learning performance Interactive test dashboard HTML, PHP, JavaScript programming languages, and MySQL database Web Hemachandran et al. (2022) Computational Intelligence and Neuroscience Artificial Intelligence: A Universal Virtual Tool to Augment Tutoring in Higher Education Universal Virtual Tool AI modelling; GAN’s and Machine learning algorithms Web Wanichsan, et al., (2021) Computers and Education: Artificial Intelligence Enhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems Testing and diagnostic learning problem system PHP programming language, MySQL database and Apache webserver Web Dhyani & Kumar ( 2021 ) Materials today: proceedings An intelligent Chatbot using deep learning with Bidirectional RNN and attention model Virtual Assistant Neural network and deep learning Web Table 2 shows the general summary of the selected studies on AI applications in higher learning in other nations for educational purposes. The findings in Table 2 were intended to demonstrate how AI tools can be used to enhance learning quality and introduce an interactive learning system. Retaining the students' AI learning step while enhancing interactive communication, information contextualization, and adaptability. Several pieces of instructional software have been created that include AI technologies. Thomaz and Breazeal's (2008) work highlights the significance of comprehending the relationship between humans, robots, and learners in order to create algorithms that both support human teaching preferences and enhance robot learning behaviour. A web-based system that may be used at any time, wherever in the world, “thanks to the Internet” was created by Cavus ( 2010 ). It is a web-based decision support system that assesses learning management systems using a flexible and clever algorithm created from fuzzy logic values and artificially intelligent notions. To translate from English to English, Dhyani and Kumar ( 2021 ) created a model using bidirectional recurrent neural networks. The major goals of the work were to make the model more complex, accelerate learning, and determine the Bleu Score for translation in the same language. The average time per 1000 steps was 4.5, perplexity was 56.10, the learning rate was 0.0001, elegance score was 30.16, and time per 1000 steps attained was 0.0001. The length of an epoch is 23,000 steps. Consistent with Table 2 's analysis of AI software, learnable robots or agents, intelligent tutoring systems, web-based educational systems, and learning management were found to be among the most influential AI studies. Eguchi ( 2016 ) offers a case study of RoboCupJunior and examines how well its methodology enhances students' understanding of STEM subjects as well as their capacity for innovation and creativity. There is evidence that instructional robotics encourages pupils' interest in Science, Technology Engineering, and Mathematics (STEM) fields. An educational robotics competition gives learners a hands-on, project-based, and goal-oriented learning experience that has a lasting positive effect on their learning and drive to pursue STEM or fields that are closely related to it. With the aid of artificial intelligence, Hemachandran et al. (2022) foresaw the future of higher education. Regarding AI methodology, earlier studies used ANN, decision trees, logistic regression, SVM, ensembles/bagging (random forest), fuzzy logic, and many more AI algorithms of data mining and machine learning methods. The majority of the articles that were chosen for publication appeared in the journal Computers and Education: Artificial Intelligence and Expert Systems with Applications. A select handful were printed in other periodicals, however rare. Studies that used two or more AI algorithms frequently sought to create models that could forecast students' learning patterns or performance (Lykourentzou et al., 2009 ; Delen, 2010 ; Kotsiantis, et al., 2010 ; Lan et al., 2014 ; Dhyani & Kumar, 2021 ). This shows that using AI technology in research is becoming increasingly popular as a way to improve the efficiency of educational institutions. This study's selection of papers from other nations is significant because it gives Nigeria a global perspective on the issue's current situation and enables it to draw lessons from it. Following "profiling and prediction," "adaptive systems with personalization," and intelligent tutoring systems as the most significant contributors to AI. This is in line with the findings of earlier studies on research-related difficulties (Zawacki-Richter et al., 2019 ). While simulation annealing and search and optimization are less frequently used, most research utilized two or more AI algorithms to create models of learners' online learning to forecast their learning success. 5. Conclusion and Recommendations Analyzing publication trends in a given field of science can reveal changes in output and interest, as well as trends in the discipline's development and advancement on a worldwide scale. In terms of software tools, AI technology, and the learning process, this study looks at the developments in AI application in higher learning. According to the study's findings, there is a favorable correlation between the usage of AI technologies in higher education and positive learning outcomes in terms of technology use, student engagement, and learning outcomes. Students who use AI technology in their education not only do better academically, study more actively and collaboratively, and learn in a supportive atmosphere, but they also have access to higher-order thinking, integrative learning, and personal and social growth. This study makes a compelling case for the importance of AI research in higher education as well as for several other key developments. On the other hand, research on learning management systems, mobile learning systems, parallel robots, training, interactive test dashboards, and virtual assistants is still pending in Nigeria. The paper makes recommendations for structuring AI research and creating policies to support AI in the future based on the findings. It suggests that educational institutions in Nigeria adopt some of the best practices of some of the world's most advanced nations and regions that are rarely applied to encourage the use of AI in higher education, enhance student learning opportunities, and boost student engagement. Focus more on implementing educational software using AI techniques like Data mining techniques and machine learning approaches as in other nations. Contribution to Knowledge Several of ways, our work adds to the body of knowledge in this area. It helps academics and educators comprehend the status and advancement of pertinent AI applications in Nigerian higher education. It assists in locating the institutes and researchers engaged in current AI in education research. Finding outlets to contribute to the community has been made easier by being able to identify pertinent papers that deal with AI in education. 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Introduction","content":"\u003cp\u003eIn ancient times, traditional classrooms served as the primary arena for student education, employing uniform teaching methods and consistent teacher guidance. The requirement for simultaneous student presence posed challenges in addressing individual learning needs (Sampayo-Vargas et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Recent decades have witnessed rapid technological advancements, leading to a proliferation of digitally enabled tools and services (Canton, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Societal progress hinges on the acquisition and dissemination of valuable knowledge (Odegbesan et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The swift evolution of computer technology has significantly influenced the learning environment, with educational resources increasingly embracing computerization. These technologies enhance learning experiences, foster skill development, and promote classroom collaboration, reflecting the transformative impact of technological progress. The ability to swiftly access the expertise of top professionals on specific subjects has become feasible, facilitated by intelligent tutoring systems replicating teachers' knowledge to offer personalized assistance (Pai et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eArtificial Intelligence (AI) emerges as a dynamic force capable of reshaping social interactions, particularly in education. AI-driven teaching and learning solutions are undergoing testing to prepare students for an AI-driven future (Pedro et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The adoption of AI technologies in education seeks to enhance knowledge acquisition, leading to a surge in online learning. However, many developing nations, Nigeria included, face challenges in fully harnessing AI benefits due to infrastructural limitations and limited access to the Internet. The escalating demand for education worldwide strains existing institutional infrastructure and human resources. Developing nations grapple with operational and technological challenges, impeding the integration of AI-backed learning despite its recognized advantages. This is exacerbated by financial constraints, hindering the establishment of necessary infrastructure and internet access. The disparity between education demand and institutional capacity results in the rejection of numerous qualified candidates, limiting access to education and potential income. In Nigeria alone, where millions apply for admission, the available universities cannot accommodate the influx due to technological deficiencies (Adesulu, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInstitutions offering distance learning face challenges in providing robust AI e-learning platforms, with manual processes predominating due to technological limitations. The economic risks associated with manual operations underscore the efficacy of AI in streamlining tasks, thereby enhancing productivity and technology integration (Robinson, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn advanced technologies, Artificial Intelligence (AI) stands at the forefront, widely adopted by well-funded universities. While Nigeria claims reputable academic institutions, the lack of financial support impedes their ability to keep pace with the latest AI advancements. Consequently, scholars in the field of AI exhibit diminishing commitment to their work. Analogously, the application of AI in Nigeria is akin to requesting a fish to climb a tree or comparing a small knife to a machete. Despite the availability of online learning resources in numerous institutions, only a handful actively cultivate AI capabilities (Liverpool et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Robinson, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Adejo \u0026amp; Misau, 2021; Enang, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Afolabi (2014) notes that, despite the prevalence of online learning, learners face computer literacy challenges, hindering their engagement with technology-centric education. This predicament arises from the failure of institutions to innovate teaching and learning methods through AI. Nigeria's educational landscape lags behind in AI integration, despite the pressing need for expansion. The application of technology in research, education, and learning is crucial for growth, yet the impetus for change in the Nigerian teaching and learning sector remains limited (Liverpool et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEfforts to introduce AI e-learning models in Nigerian educational institutions primarily revolve around collaborative learning frameworks. The educational goals set by the Seventh National Development Plan and Vision 2020 align with the United Nations' Millennium Development Goals, emphasizing the use of AI technologies in education (Eneh, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Evaluating the suitability of these platforms in fostering a conducive learning environment for technology personnel is imperative. The integration of AI-enabled learning into the university system necessitates the education of teacher educators, demanding professional development and support. However, existing workshops and training have proven inadequate. AI-based learning emerges as a potential solution to the challenge of limited physical space on university campuses (Ndzibah \u0026amp; Ofori, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe research aims to address specific objectives. Firstly, it seeks to identify AI technology studies in education reported in Nigeria from 2008 to 2022, as documented in high-impact scientific journals. Secondly, the research aims to identify the software tools essential for sustained AI-supported learning. Thirdly, it endeavors to comprehensively explore global trends in AI-supported learning across educational institutions. The research questions include inquiries into the publication of research articles on AI technologies in Nigerian education between 2008 and 2022, the utilization of AI technologies in supporting Nigerian education, and the software tools requisite for long-term AI-supported learning in the Nigerian educational context.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eAccording to Castaeda and Selwyn (2018), AI is a growing trend, it is time for the precise revolution that we anticipate in the educational sector. Almost all industries have adopted it, including the educational sector, and some of its components are currently being automated. According to a study by Agarry et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Nigerian education must transition from analogue to digital, and AI technology is a key component of this process. Students are aware of and prepared to use AI-based learning systems, even at the secondary school level, according to Adelana and Akinyemi's (2021) research. Currently, social media and the internet are used by most Nigerian institutions. According to Adelana and Akinyemi (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), since students are aware of and willing to accept AI-based learning systems, it is necessary to design, develop, and apply them in secondary schools. A study by Agarry et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) looked at how proficient elementary school pupils were at using AI to learn. The findings of the study revealed that the majority of primary education students are not skilled and incompetent in the use of AI for learning. Students' ability to explore digital resources such as AI depends on their access to digital technologies. Onyema et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) evaluated the undergraduate students' perceptions of how effective and successful ICT use is in promoting learning in Nigeria. It was found that mobile devices have features built in that can spur learners' interest in learning. This offers several features, like simple access to forums with interactive content that encourages teamwork, among others. During the pandemic in Nigeria, learning through the Google Classroom platform was seen as an efficient strategy to positively influence learners' academic progress (Oyarinde \u0026amp; Komolafe, (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAjadi et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) talked about the theoretical underpinnings and the applicability of AI e-learning in the context of distance education at Open University. Although Nigeria Open University uses e-learning to conduct lectures and provide students with homework, this digitization has not been fully tapped into in many institutions around the nation. Nigeria joined other industrialized nations and used e-learning in the educational system to avert brain drain and the complete collapse of the nation's educational system (Oyediran et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The world of AI technology, as we know it today, will be completely transformed once AI systems begin operating at their full potential, and Nigeria will not be an exception if it gives the development of AI more priority. Robots will eventually be able to serve humans or take over all of their tasks as AI advances (Robinson, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDue to its growing significance, researchers have recently adopted AI technologies to offer personalized learning guidance and support for individual students in a variety of courses, including engineering, computer science, and informatics (Kose et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and many more. Because AI technologies are constantly evolving, the educational community is addressing the promise and challenges they present. These technologies profoundly alter the design, administration, and governance of educational institutions (Popenici \u0026amp; Kerr, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A variety of tools and applications, including intelligent tutoring systems, teaching robots, adaptive learning systems, and other emerging technologies being integrated with AI for various learning supports, are currently used by students and educators in institutions of higher learning. AI technologies with their flexibility, effectiveness, and realization of individualized learning for students to fulfil their specific needs. AI research focuses on cognitive issues that are frequently associated with human intelligence (Ventura, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Techniques from AI and soft computing are being used in various aspects of our lives to address pressing issues (Khan, et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Salem (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) identified seven key areas of AI in education, including intelligent educational systems, teaching aspects, learning aspects, cognitive science, knowledge structure, intelligent tools, shells, and interfaces. Intelligent tutoring systems, educational robotics, and multimedia systems make up the Intelligent Educational Systems. The summary of AI-based educational research includes sections on intelligent tutoring systems, intelligent e-learning systems, and intelligent writing shells and tools as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe major evolutionary process of AI covers initial AI, machine learning, and the recent Deep Learning. One of the major challenges of educational institutions is the accumulated amount of data and how it can be utilized to boost the academic programs' quality (Abunasser \u0026amp; AL-Hiealy, 2022). The most common AI data mining techniques; C4.5 algorithms, k-mean algorithms, support vector machines, a priori algorithms, and expectation-maximization algorithms, are commonly used. According to Oguine, Oguine, and Bisallah (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) classification algorithms like Decision Trees, Ada Boost, Support Vector Regression, Naive Bayes, and Stochastic, gradient Descent can modestly predict a student's academic success and, in particular, model the difference between high, low, and failed performances. Since the advent of AI technologies like Artificial Neural Networks (ANN) and Deep Learning (DL), educators and students have increasingly used tools and applications powered by AI, such as intelligent robots and adaptive learning systems (Chan \u0026amp; Zary, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Using conventional educational methods can be challenging because every learner is independent and has different learning preferences, needs, and talents. Yet with AI, teachers may individually adapt their instruction to each student's needs, and students can learn with greater motivation, engagement, and independence (Ventura, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Educational institutions greatly benefit from providing variables that raise success rates and lower student failure rates. The best method for identifying hidden patterns and making recommendations that improve student performance is data mining (Hamoud, Hashim and Awadh, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe performance level of the instructor and student can be predicted using a variety of machine-learning algorithms. According to Abunasser and AL-Hiealy (2022) machine learning categories include a: Decision tree, Ensemble (Gradient Boosting Classifier, Gradient Boosting Regressor, AdaBoost Regressor, Extra Trees Regressor, Nave Bayes (GaussianNB), Neighbors (Nearest Centroid, KNeighbors Class. Machine learning techniques provide computers with the ability to learn from data and further anticipate the future. Jordan and Mitchell (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) claim that within AI, ML has become a viable option for creating useful tools to address a variety of problems. For example, forecasting student enrollment, teacher and student performance evaluation, forecasting student grade point average, and other aspects of education management use popular soft computing techniques including fuzzy logic, neural networks, and genetic algorithms (Khan et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe use of computers in education to give students instructions is known as computer-based education. Before the advent of AI, Computer Based Education (CBE) systems were standalone tools that ran on a local computer to address concerns like student modelling, adaption, and personalization. With the widespread use of the Internet, new web-based educational tools like e-learning platforms have appeared. Moreover, new types of adaptive and intelligent systems for educational purposes have been driven by the growing usage of AI techniques. As a result, there are similarities between CBE and AI in education. For example, learning management systems (Romero, et al., 2008). Test and quiz systems are all major forms of CBE systems that are currently implemented (Romero et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLearning analytics (LA) is the collecting, analyzing, and reporting of data on learners and their surroundings to comprehend and improve learning and the environments in which it takes place (Romero et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Educational Data Mining (EDM) and Learning Analytics share characteristics, passions, and objectives. Nonetheless, there are significant distinctions between them, primarily in the approaches and emphasis (Siemens \u0026amp; Baker, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Statistics, visualization, discourse analysis, social network analysis, and sense-making models are the approaches that are most widely used in LA. Nevertheless, clustering, classification, Bayesian modelling, relationship mining, and model-based discovery are the most widely used algorithms in EDM. Another difference between LA and EDM is that LA places more emphasis on presenting data and results, while EDM places more emphasis on discussing and contrasting DM technology.\u003c/p\u003e \u003cp\u003eThe adoption of AI skills in education plays a vital role in the struggles to make the prospective labour force AI-ready (Pedro et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). AI tools have served as a vital tool in battling COVID-19 as the epidemic continues to affect people and industries (Egielewa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Regardless of the various benefits that would be acquired by others in the sector, according to Garc'a-Gorrostieta et al. (2018) and Pise et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), students would have a tutor who would instruct them at their pace while maintaining the same feeling throughout. Education is the most significant industry in Nigeria since it affects everyone's lives, no matter their age or where they live. If the classrooms are outfitted with AI technological solutions to provide pupils with the finest learning environment, AI technology can make Nigeria's overcrowded educational system smarter. It can also operate along with virtual networks to provide the ideal learning environment for both teachers and students (Robinson, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, to incorporate 21st -century educational programmes, Nigeria's curriculum reform initiatives must define \"AI competencies\" beyond fundamental ICT competencies, as many other nations have done. Instead, they should focus on skills that would enable students to address challenges utilizing AI technology (Pedro et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdvancement in educational institutions has an impact on higher education and plays a key role in the growth of a nation, which has a reciprocal and dynamic impact on the social environment. All learning experiences can be learned with just one click to AI-assisted learning. Distance, time, space, availability of current learning materials, cost of trip, and risk of travel, just but few are no longer problems. Fast technological advancement is a sign that AI e-learning will inescapably play a part in raising the standard of higher education (Angib et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs for the downsides, AI technologies come at a great cost, not every nation can meet the expense (Iqbal et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Another bottleneck is that applying AI in a system can create a lot of difficulties (Lexcellent, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Lexcellent (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) asserts that before moving toward artificial intelligence, challenges like a lack of monetary resources, a lack of interest on the part of institution administrations, a lack of knowledge about successful involvements in the field, and a lack of studies addressing assertions must be deliberated and resolved.\u003c/p\u003e \u003cp\u003eConsidering the significance of developing technology in tutoring and learning, Nigeria is still far from adopting it. The effective integration of AI technologies into teaching and learning is hampered by several issues. Epileptic power supply, a lack of knowledge or skills, problems with availability and accessibility, funding, inadequate professional development, disinclination to change, unreliable internet connections, and so on to name a few. Actors and stakeholders in the education sector like educators, policymakers, curriculum planners, and students can all work together to overcome these obstacles (Enang, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). AI algorithms and systems have been increasingly popular and well-known in recent times, particularly in the field of education. At many educational installations, tablets have largely taken the role of books in the learning process and internet-based learning has become more common (Kairouz et al., 2021). Digital tools have transformed education and made learning more active and independent. New technology has also changed the way that people communicate, so educators must find new ways to inspire learners and adapt to this new century (Egielewa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe study used the literature review research method to retrieve research articles from four reliable online databases to examine recent research publications on AI in higher education (Springer Link, Science Direct, IEEE, and WoS). The selected research was published between January 2008 and December 2022. This is characterized by looking for pertinent material to help identify, evaluate, and analyze the studies that are already available to help with the study's research demands. The rationale for using Springer Link, Science Direct, IEEE, and (WoS) databases for this research on trends in AI application in education in Nigeria is: that these databases are known for hosting peer-reviewed, comprehensive, credible, and high-quality AI research articles, conference papers, and journals in contrast with the Google Scholar search engine, which is unrestricted and also indexes low-quality or non-peer reviewed articles.\u003c/p\u003e \u003cp\u003eWhen researching AI in education, it is essential to rely on credible sources to ensure the accuracy and validity of the information.\u003c/p\u003e \u003cp\u003eTo collect relevant data for the study, the researchers in December 2022, performed electronic searches using four reliable electronic databases. The source databases were searched several times using various keyword combinations and search techniques. It was done by combining the search terms (\"artificial intelligence\" OR \"Machine learning\" OR \u0026ldquo;Data mining\u0026rdquo; OR \u0026ldquo;intelligent tutoring system\" OR \"robotics\" OR \u0026ldquo;learning management system\u0026rdquo; OR \u0026ldquo;e-learning\u0026rdquo;) AND (\"Nigerian education system\" OR \u0026ldquo;Nigerian institutions\u0026rdquo;) from 2008 to 2022 in the selected database.\u003c/p\u003e \u003cp\u003eThe inclusion requirements were limited to papers directly related to AI in education in Nigeria, and popular AI technologies in education in other nations published between the years 2008 and 2022. They include proceedings papers, or peer-reviewed journal articles, and were defined to assure the paper's relevance and accuracy. Included were empirical and theoretical works that used quantitative or qualitative approaches and had as their major subject research pertinent to AI in education. After that, duplicate articles removal was completed. Animal and non-human studies, non-English language, non-empirical data, incorrect study design, incorrect exposure, incorrect outcome, and no useable data are all excluded from the exclusion criteria. Books, dissertations, editorials, and other writings without empirical data were also excluded.\u003c/p\u003e \u003cp\u003eAs a result, the criteria include papers with empirical data that were pertinent to the study's goals. A total number of 874 papers were found using the four electronic databases, from which duplicates were excluded in the initial screening. The final selection of reviewed studies is made up of 73 papers as in (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) after applying inclusion and exclusion criteria. All abstracts were reviewed to ensure that the studies were relevant to the purpose of this study.\u003c/p\u003e \u003cp\u003eThe included studies have several things in common based on the article selection framework. They all have a learning environment as their primary focus, study the beneficial effects of applied AI technologies on students' learning processes, apply AI technical tools, and produce empirical proof in learning institutions. The study's sample size was chosen to meet the research objectives by adhering to the inclusion and exclusion criteria. Only papers that were published in English and connected to the use of AI in education met the linguistic criterion. Articles that give pertinent evidence for the use of AI technologies by students, or by students and teachers, to improve educational processes were chosen after the initial screenings. The proportion of articles by year is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Results and Discussions","content":"\u003cp\u003eThis paper examines the trends in research on AI in education in Nigeria between 2008 and 2022 years. The analysis provides answers to the research questions as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ1: What is the distribution of research articles on AI technologies in education in Nigeria published in high-impact scientific journals between 2008 and 2022?\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEarly AI Tool Adoption in Nigerian Education\u003c/strong\u003e \u003cp\u003eThe first phase of AI adoption in Nigeria, which tended towards rather slow development, spanned from 2008 to 2013. To accommodate a range of learning styles and abilities, adaptive learning technologies were implemented in addition to the initial focus on fundamental e-learning platforms and online platform introduction. AI algorithms were used by adaptive learning systems to assess student performance and customize course materials to meet each student's needs (Liverpool et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ayanda et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Falaye et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Liverpool et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Ayanda et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) suggested an e-learning programme at a specific Nigerian higher education institution to investigate its efficacy in enhancing learning. They concluded that the case study institution and other institutions interested in implementing the scheme would no longer be subject to the constraints of the conventional educational system once the scheme is completely integrated. Using the local intranet networks that are already available in the majority of Nigerian tertiary institutions, Falaye et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) created and deployed an intranet-based communication and e-learning system as a unified system to deliver seamless, low-cost institution-wide communication and distant learning. In addition to many other features, the system offers affordable customized SMS, audio, video, file sharing, e-classrooms, high-quality VoIP calls, search FM radio utility, news, and entertainment. Very few studies have looked into the application of artificial intelligence in education.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eHowever, challenges such as limited internet access in some regions, the adoption of AI systems such as Teachable robots, Web-based Educational systems, and Learning Management Systems (Thomaz and Breazeal, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Bittencourt et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cavus, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) was slower due to infrastructure challenges and limited awareness. Also, efforts made to train educators and professionals in AI applications in education to effectively integrate AI into teaching practices were ineffective.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ2: Which AI technologies are used and how do existing AI technologies support Nigerian education?\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIncreased Integration of AI Tools in Nigerian Education\u003c/strong\u003e \u003cp\u003eFrom 2013 to 2017, the growth trend accelerated. There was a global trend towards integrating AI tools into educational systems. This includes the use of AI for content delivery, personalized learning experiences, and adaptive learning platforms, to tailor educational content to individual students' needs, helping them learn at their own pace (hybrid models Gbolagade et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The implementation of AI-powered chatbots and virtual assistants in education started gaining traction during this period. These tools were used for answering student queries, providing information, and facilitating communication within educational institutions (Idris, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kelechi, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Analytics for Decision-Making\u003c/strong\u003e \u003cp\u003eEducational institutions began leveraging AI for automated data analytics to make informed decisions in teaching processes. AI technologies, such as machine learning algorithms Gbolagade et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); Oguine et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were applied to analyzing student performance data, assessment processes, and consistent evaluation in a short time. The last stage was characterized by a rapid growth phase from 2017 to 2022, during which the number of research publications increased in comparison to earlier stages as a result of the increased use of AI technology. Most of their notable AI studies related to the study have increased significantly during the past few years. The trend analysis demonstrates that there is a growing body of knowledge about the use of AI technologies in education. The examination of AI technology trends in publications in the education sector reflects changes in interest and production as well as advancements in science on a worldwide scale and sector development patterns. In general, an increase in publications typically indicates that the field's global science system is growing.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe trend for publications on AI technology in education from 2008 to 2022 by sources is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Across the examined sources, there was an increase in the number of publications on AI technologies in education. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, 874 articles were found from the four databases related to AI in education in Nigeria that have been published between 2008 and 2022. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, there was a rise in interest in artificial intelligence (AI) in education research between 2008 and 2013, especially in publications on SpringerLink when compared to other sources. From 2014 to 2022, there was a greater trend in AI research in education. According to Springer Link, WoS, IEEE, and Nigerian publications released between 2014 and 2022 had the most papers on AI in education. This indicates that there is a growing demand in Nigeria for the application of AI technology in education.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResearch on common AI technologies in education platforms in Nigeria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor(s) and year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName of Application\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAI Technologies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEducational\u003c/p\u003e \u003cp\u003eProcess\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInstitution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFacet\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiverpool et al., (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTowards a Model for E-learning in Nigeria\u003c/p\u003e \u003cp\u003eHEIs: Lessons from the University of Jos ICT Maths Initiative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProposed Model for E-learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeb 2.0 tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePlanning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003cp\u003einstitution in Nigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAyanda et al., (2011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTowards a Model of E-Learning in Nigerian Higher Institutions:\u003c/p\u003e \u003cp\u003eAn Evolutionary Software Modelling Approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eevolutionary software modelling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeb 2.0 tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eonline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePlanning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigher institution in Nigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalaye et al., (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCost Effective Multimedia E-Learning Application for Nigerian Higher Institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultimedia E-Learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMicrosoft Visual Basic 6.0, ASP.NET Visual Studio2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eoffline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003evirtual E-Classrooms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFUT Minna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eintranet-based communication and e-learning system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGbolagade, et al, (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicting Postgraduate Performance Using Resample Preprocess Algorithm and Artificial Neural Network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrediction model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehybrid of Resample\u003c/p\u003e \u003cp\u003eand ANN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOffline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePerformance prediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUniversity of Ilorin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePredict a student\u0026rsquo;s performance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMamudu \u0026amp; Oyewo (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUse of Mobile Phones for Academic Purposes by Law Students of Igbinedion University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emobile phones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eacademic activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIgbinedion University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNwachukwu and Onyenankeya (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUse of Smartphones among College Students in Nigeria:\u003c/p\u003e \u003cp\u003eRevelations and Reflections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSM protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmartphones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eacademic activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCaleb University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaheed et al., (2017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFingerprint-based approach for examination clearance in higher institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiometric Examination Clearance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecuGen fingerprint scanner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eacademic activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFederal University Wukari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdekitan et al., (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData mining approach for predicting\u003c/p\u003e \u003cp\u003ethe daily Internet data traffic of a smart\u003c/p\u003e \u003cp\u003euniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKNIME and Orange platforms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNaive Bayes, Neural Network, Random Forest, kNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInternet traffic prediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCovenant University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003epredictive KNIME and Orange-based\u003c/p\u003e \u003cp\u003emodels\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHambali, et al., (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutomated university lecture timetable using\u003c/p\u003e \u003cp\u003eheuristic approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutomated\u003c/p\u003e \u003cp\u003etimetable generator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenetic algorithm and simulated annealing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edesktop application\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003etimetable generation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFederal University Wukari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdris (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Challenges and Prospects of E-learning in Nigerian Polytechnic\u003c/p\u003e \u003cp\u003eEducation: A Case Study of Kaduna Polytechnic, Kaduna State, Nigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE-learning technologies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVirtual Classroom, Digital Learning, Computer Based Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eoffline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLearning and assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKaduna Polytechnic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatthew et al., (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdoption of smart and disruptive technologies\u003c/p\u003e \u003cp\u003efor educational development and automation of\u003c/p\u003e \u003cp\u003eindustry 4.0 in Nigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFederated\u003c/p\u003e \u003cp\u003eMultimedia Digital Library ecosystem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData Mining\u003c/p\u003e \u003cp\u003eImplementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLearning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eselected tertiary institutions in the\u003c/p\u003e \u003cp\u003eSouth-Eastern Nigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003cp\u003eEngagement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKelechi (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe E-Learning on World Epidemic (COVID-19) in\u003c/p\u003e \u003cp\u003eTertiary Institutions in the North East States of Nigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFramework of eLearning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGoogle Classroom, Zoom, WhatsApp and Blog.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eteaching and learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNorth-East Nigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdaptive learning\u003c/p\u003e \u003cp\u003eenvironment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdeyeye et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnline Learning Platforms and Covenant University Students\u003c/p\u003e \u003cp\u003eAcademic Performance in Practical-Related Courses during\u003c/p\u003e \u003cp\u003eCOVID-19 Pandemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoodle\u003c/p\u003e \u003cp\u003eLearning Management System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpen Source or Proprietary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eonline learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elearning and lecture delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCovenant University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePredict a student\u0026rsquo;s performance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdeyeye et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnline Learning Platforms and Covenant University Students\u003c/p\u003e \u003cp\u003eAcademic Performance in Practical-Related Courses during\u003c/p\u003e \u003cp\u003eCOVID-19 Pandemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZoom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpen Source or Proprietary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eonline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elearning and lecture delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCovenant University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePredict a student\u0026rsquo;s performance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOgolodom et al., (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnline Learning in Nigerian Universities During COVID-19 Pandemic: The Experiences of Nursing and Radiography Undergraduate Students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternet connection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLaptop and Smartphone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003elearning and lecture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUNN, BUK, Nnamdi Azikiwe\u003c/p\u003e \u003cp\u003eUniLag, Lead City University, UniCal, UniMaid, FUT Owerri, ABU, University of Benin, University of Port Harcourt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdaptive learning\u003c/p\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOguine et al., (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBig Data and Analytics Implementation in Tertiary Institutions to Predict Students\u0026rsquo; Performance in Nigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBig Data and Analytics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGoogle Forms, Jupyter Notebook, and Python\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eoffline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrediction of Students Performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUniversity of Abuja\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eprediction model\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe broad summaries of a few chosen studies on the use of AI technology in education in Nigeria are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The parameters of the study included the author(s) and year, the name of the research, the name of the application, the type of platform, the educational process, the institution, and the aspect of learning. The authors are based in Nigeria and come from education departments and computer science departments at various institutions of higher learning. The majority of the titles include boosting online learning, e-learning, automating the examination process, and making learning more affordable through the use of AI technologies in institutions. The applications that have been found largely involve artificial intelligence (AI) models for online learning, evolutionary software modelling, student performance prediction, multimedia e-learning platforms and frameworks, and Moodle learning. The majority of studies indicated interest in e-learning. There is not a lot of educational software available that uses AI technologies. For forecasting student performance, machine learning and data mining algorithms such as support vector machines, decision trees, Naive Bayes, and K-nearest neighbours were used. Adekitan et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) carried out a data mining analysis utilizing four learning algorithms: The Decision Tree, the Tree Ensemble, the Random Forest, and the Naive Bayes Algorithm. Comparative performance analyses of the models' accuracy, Kappa values, and areas under ROC curves were reported. According to Matthew et al., (2020), the current curriculum of education needs to incorporate technological innovations and inventions into their mainstream educational program through the early adoption of scientific methodology, to hybridize the current teaching procedures and practices in addition to several other scientific approaches. The early adoption of Artificial Intelligence (AI) technologies will provide potential leverage in the creation of scientific knowledge in the 21st century using mobile Android computing devices. Genetic algorithms and simulated annealing techniques were only employed in one study that utilized automation. Backend web design technologies include MySQL, Apache Wamp Server, and PHP web design tools. In contrast, web designs utilized Web 2.0, HTML, CSS, and JavaScript. Virtual E-Classrooms learning, lecture delivery, planning, and performance prediction are all part of the educational process. The majority of studies focused on developing AI via data mining and machine learning approaches. This shows that numerous academics are paying attention to the sub-field of artificial intelligence (AI) that deals with academic achievement in higher education, such as predicting students' performance and increasing student involvement through data mining. This shows that efforts are being made by researchers to employ AI technologies to improve learning in Nigeria. Nigerian higher education institutions, on the other hand, are lagging in terms of the use of AI technologies when compared to other industrialized countries. One cause could be that they don't receive adequate funds and training to keep up with the most recent advancements in AI. Researchers in the field of artificial intelligence consequently grew less dedicated to their work. Analyzing research topics reveals the past, present, and upcoming areas of focus for publications based on research in a given discipline.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ3: What software tools are required for long-term AI-supported learning in Nigerian education?\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSoftware tools needed for sustainable AI-supported learning in education\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthors and year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eName of Application\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAI Technologies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThomaz and Breazeal (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial\u003c/p\u003e \u003cp\u003eIntelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTeachable robots: Understanding human teaching behaviour to build more effective robot learners.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTeachable robots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBittencourt et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge-Based\u003c/p\u003e \u003cp\u003eSystems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA computational model for developing semantic web-based educational systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeb-based\u003c/p\u003e \u003cp\u003eeducational\u003c/p\u003e \u003cp\u003esystems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSemantic web\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLykourentzou\u003c/p\u003e \u003cp\u003eet al. 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User Modelling and User-Adapted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntelligent\u003c/p\u003e \u003cp\u003etutoring systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKotsiantis (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial\u003c/p\u003e \u003cp\u003eIntelligence Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUse of machine learning techniques for educational proposes: a decision support system for forecasting students\u0026rsquo; grades\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForecasting\u003c/p\u003e \u003cp\u003estudents\u0026rsquo; grades\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTan et al. 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(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eComputers and Education: Artificial Intelligence\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAn interactive test dashboard with diagnosis and feedback mechanisms to facilitate learning performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInteractive test dashboard\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHTML, PHP, JavaScript\u003c/p\u003e \u003cp\u003eprogramming languages, and MySQL database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemachandran et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputational Intelligence and Neuroscience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArtificial Intelligence: A Universal Virtual Tool to Augment\u003c/p\u003e \u003cp\u003eTutoring in Higher Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUniversal Virtual Tool\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAI modelling; GAN\u0026rsquo;s and Machine learning\u003c/p\u003e \u003cp\u003ealgorithms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWanichsan, et al., (2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eComputers and Education: Artificial Intelligence\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTesting and diagnostic learning problem system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePHP programming language, MySQL\u003c/p\u003e \u003cp\u003edatabase and Apache webserver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhyani \u0026amp; Kumar (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMaterials today: proceedings\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAn intelligent Chatbot using deep learning with Bidirectional RNN and attention model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVirtual Assistant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNeural network and deep learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeb\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the general summary of the selected studies on AI applications in higher learning in other nations for educational purposes. The findings in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e were intended to demonstrate how AI tools can be used to enhance learning quality and introduce an interactive learning system. Retaining the students' AI learning step while enhancing interactive communication, information contextualization, and adaptability. Several pieces of instructional software have been created that include AI technologies. Thomaz and Breazeal's (2008) work highlights the significance of comprehending the relationship between humans, robots, and learners in order to create algorithms that both support human teaching preferences and enhance robot learning behaviour. A web-based system that may be used at any time, wherever in the world, \u0026ldquo;thanks to the Internet\u0026rdquo; was created by Cavus (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It is a web-based decision support system that assesses learning management systems using a flexible and clever algorithm created from fuzzy logic values and artificially intelligent notions. To translate from English to English, Dhyani and Kumar (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) created a model using bidirectional recurrent neural networks. The major goals of the work were to make the model more complex, accelerate learning, and determine the Bleu Score for translation in the same language. The average time per 1000 steps was 4.5, perplexity was 56.10, the learning rate was 0.0001, elegance score was 30.16, and time per 1000 steps attained was 0.0001. The length of an epoch is 23,000 steps.\u003c/p\u003e \u003cp\u003eConsistent with Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e's analysis of AI software, learnable robots or agents, intelligent tutoring systems, web-based educational systems, and learning management were found to be among the most influential AI studies. Eguchi (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) offers a case study of RoboCupJunior and examines how well its methodology enhances students' understanding of STEM subjects as well as their capacity for innovation and creativity. There is evidence that instructional robotics encourages pupils' interest in Science, Technology Engineering, and Mathematics (STEM) fields. An educational robotics competition gives learners a hands-on, project-based, and goal-oriented learning experience that has a lasting positive effect on their learning and drive to pursue STEM or fields that are closely related to it. With the aid of artificial intelligence, Hemachandran et al. (2022) foresaw the future of higher education. Regarding AI methodology, earlier studies used ANN, decision trees, logistic regression, SVM, ensembles/bagging (random forest), fuzzy logic, and many more AI algorithms of data mining and machine learning methods. The majority of the articles that were chosen for publication appeared in the journal Computers and Education: Artificial Intelligence and Expert Systems with Applications. A select handful were printed in other periodicals, however rare. Studies that used two or more AI algorithms frequently sought to create models that could forecast students' learning patterns or performance (Lykourentzou et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Delen, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kotsiantis, et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lan et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dhyani \u0026amp; Kumar, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This shows that using AI technology in research is becoming increasingly popular as a way to improve the efficiency of educational institutions. This study's selection of papers from other nations is significant because it gives Nigeria a global perspective on the issue's current situation and enables it to draw lessons from it. Following \"profiling and prediction,\" \"adaptive systems with personalization,\" and intelligent tutoring systems as the most significant contributors to AI. This is in line with the findings of earlier studies on research-related difficulties (Zawacki-Richter et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While simulation annealing and search and optimization are less frequently used, most research utilized two or more AI algorithms to create models of learners' online learning to forecast their learning success.\u003c/p\u003e"},{"header":"5. Conclusion and Recommendations","content":"\u003cp\u003eAnalyzing publication trends in a given field of science can reveal changes in output and interest, as well as trends in the discipline's development and advancement on a worldwide scale. In terms of software tools, AI technology, and the learning process, this study looks at the developments in AI application in higher learning. According to the study's findings, there is a favorable correlation between the usage of AI technologies in higher education and positive learning outcomes in terms of technology use, student engagement, and learning outcomes. Students who use AI technology in their education not only do better academically, study more actively and collaboratively, and learn in a supportive atmosphere, but they also have access to higher-order thinking, integrative learning, and personal and social growth. This study makes a compelling case for the importance of AI research in higher education as well as for several other key developments. On the other hand, research on learning management systems, mobile learning systems, parallel robots, training, interactive test dashboards, and virtual assistants is still pending in Nigeria. The paper makes recommendations for structuring AI research and creating policies to support AI in the future based on the findings. It suggests that educational institutions in Nigeria adopt some of the best practices of some of the world's most advanced nations and regions that are rarely applied to encourage the use of AI in higher education, enhance student learning opportunities, and boost student engagement. Focus more on implementing educational software using AI techniques like Data mining techniques and machine learning approaches as in other nations.\u003c/p\u003e "},{"header":"Contribution to Knowledge","content":"\u003cp\u003eSeveral of ways, our work adds to the body of knowledge in this area. It helps academics and educators comprehend the status and advancement of pertinent AI applications in Nigerian higher education. It assists in locating the institutes and researchers engaged in current AI in education research. Finding outlets to contribute to the community has been made easier by being able to identify pertinent papers that deal with AI in education. This research also aids in the identification of pertinent software applications for the use of AI in education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDeclaration of competing interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo specific grant was given to this research by funding organizations in the public, private, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbunasser, B. S., AL-Hiealy, M. R. J., Barhoom, A. M., Almasri, A. R., \u0026amp; Abu-Naser, S. S. (2022). 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Systematic review of research on artificial intelligence applications in higher education\u0026ndash;where are the educators? \u003cem\u003eInternational Journal of Educational Technology in Higher Education\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 1-27.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Adamawa State University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, E-learning, Education, E-learning, Teaching and learning, Trend analysis","lastPublishedDoi":"10.21203/rs.3.rs-3819828/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3819828/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the domain of education, the integration of Artificial Intelligence (AI) has ushered in a paradigm shift towards a more technologically-driven landscape, demonstrating its efficacy as an emergency strategy. The pervasive influence of computer technology has catalyzed a surge in online learning within the country, yielding positive educational outcomes. Despite these advancements, a considerable number of educational institutions in Nigeria have yet to leverage AI technologies. Recognizing the expanding significance of AI in education, this study seeks to align with this trajectory by aggregating instances of AI implementation in education from developed countries. The methodology employed involves a comprehensive review of current advancements in AI applications within the Nigerian educational context. The review process, spanning papers retrieved from four digital libraries published between 2008 and 2022, culminated in the inclusion of 73 papers. These selected papers demonstrated the utilization of AI software tools and technologies, adhering to predefined exclusion and inclusion criteria. The findings of the study reveal a prevalent use of AI technologies in education in Nigeria, encompassing evolutionary software modelling, student performance prediction, multimedia e-learning platforms and frameworks, and the incorporation of Moodle learning. This discernible trend indicates a growing demand for the application of AI technology in the educational landscape of Nigeria. However, the study also highlights a discrepancy wherein more sophisticated AI techniques, such as intelligent tutoring systems, learnable robots or agents, web-based educational systems, and learning management systems explored extensively in other nations were infrequently applied in the Nigerian context. In light of these observations, the study proposes that educational institutions in Nigeria should consider adopting AI practices from more advanced nations. This strategic alignment is posited as a means to augment student learning opportunities and bridge the existing gap between the current state of AI integration in Nigerian education and the more advanced applications witnessed globally.\u003c/p\u003e","manuscriptTitle":"Analysis of Emerging Trends in Artificial Intelligence in Education in Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-01 05:20:22","doi":"10.21203/rs.3.rs-3819828/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7ad76f6-98ee-4d1f-a079-5ff172cd2d9f","owner":[],"postedDate":"January 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":27856950,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2024-01-01T05:20:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-01 05:20:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3819828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3819828","identity":"rs-3819828","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}