Review on AI Online Learning Platform

Abstract

Thisresearch offersa systematic overview of artificial intelligence adoption in open and distancelearningfrom 2007to2021,mapping the sector\'s fast development. Artificial intelligence is defined as machines that can conduct human-like thought patterns—like learning, reasoning, and problem-solving— through means such as machine learning, neural networks, deep learning, and expert systems. In education, AI applications are intelligent tutoring systems, chatbots, recommender platforms, and automated assessment engines that facilitate personalized learning, learner profiling, predictiveanalytics,anddata-drivendecision support.Thereviewdrawsattentiontoan increase in research articles after the pandemic-induced global shift to remote learning but indicates a fragmented terrain with research being spread over different subdomains. Through the integration of publication and authorship trends, trends in methodology, core thematic topics, and forthcomingchallenges,thispaperpresentsa detailed overview of the position of AI in distance education and maps directions for future research in AI-facilitated education.

Introduction

Artificial intelligence (AI), defined by John McCarthy as machines acting intelligently like humans, includes technologies such as machine learning, neural networks, deep learning, and expert systems. AI is increasingly seen as central to Industry 4.0 and is predicted to play a transformative role in education, with tools like intelligent tutoring systems, chatbots, automated assessment, and learning analytics driving personalized and adaptive learning. These systems support students through tailored content, feedback, tutoring, and assist educators by automating tasks, providing predictive insights, and reducing workload—benefits that expanded rapidly during the COVID-19 shift to online learning.

The literature shows strong global interest in AI in Education (AIEd), especially in distance learning. Major reports (e.g., Stanford’s AI100) highlight education as a key domain for AI impact. AIEd draws from multiple disciplines—psychology, neuroscience, linguistics, sociology, and computer science—and includes applications like smart learning environments, intelligent learning management systems, adaptive platforms, and pedagogical agents. AI improves access for learners with disabilities, supports teachers through professional development, and enhances learning in low-resource environments.

Despite progress, gaps remain: limited critical review studies, insufficient focus on sustainability, cost, ethics, and long-term implementation. Existing reviews have highlighted trends such as profiling and prediction, adaptive learning, deep learning, machine learning, chatbots, and educational human–AI interaction.

The current study conducts a systematic review of 171 articles (2007–2021) on AI in open and distance education. The review follows strict inclusion criteria and content analysis methods, with a high inter-rater reliability (Cohen’s Kappa = 0.72). Data was collected from major academic databases, filtered from an initial 3,007 records down to 171 relevant empirical studies.

Key Findings

• Publication Trends: Major growth after 2018, peaking during COVID-19 due to online learning demands.

• Geographical Distribution: Highest contributions from China, the U.S., Spain, India, and Turkey.

• Journal Sources: Most studies appear in technology-focused journals like Computers & Education, Expert Systems with Applications, and Education and Information Technologies.

• Methodological Trends:

  • 47% quantitative

  • 19% mixed-methods

  • 18% design-based research

  • 16% qualitative

• Main Application Areas:

  • Intelligent Tutoring Systems (30%)

  • Adaptive systems and personalization (26%)

  • Profiling, prediction, and learning analytics (22%)

  • Assessment and evaluation (17%)

  • Affect recognition (10%)

  • Virtual learning environments (9%)

• Emerging Themes: Deep learning, NLP, learning analytics, ethics, and human–AI collaboration.

• Research Gaps: Sustainability, cost-effectiveness, real-world implementation challenges, and long-term adoption studies (only 7% of research).

Conclusion

This systematic review investigated patterns of publication and authorship in AI research for distance and open education (AIODEd). It emerges from the analysis that most articles (n = 116) were published subsequent to 2018, indicating newer scholarly momentum. Authors fromChina,Spain,Turkey,theUnitedStates,and India—comprising43%ofauthors—dominated the landscape, hailing mainly from Computer Engineering, Information Sciences, and other STEM disciplines (79%). Major publication outlets are the International Journal of Emerging Technologies in Learning (IJET), Computers & Education, the International Journal of Artificial Intelligence in Education, and Complexity. By content coding, six main research areas were uncovered:intelligenttutoringsystems,adaptive systems and personalization, assessment and evaluation, learner analytics, affect recognition, and virtual learning environments, which were further categorized into 28 subcategories. In general, the results prove thatAI can facilitate pedagogical as well as system-wide solutions to distance education. Pedagogical use includes intelligent agents and tutoring systems to assist teaching and learning; expert systems to provide adaptive, tailored e-learning environments; and chatbots or conversational agents to promote collaboration and participation. System-wide applications involve data analytics within educational management information systems to act as decision-support and recommendation systems to inform educational authorities on the assessment and improvement of teaching and learningprocesses.Suchinsightsarealignedwith du Boulay\'s (2022) classification of intelligent educationaltoolstobeusedbylearners,teachers, and administrators. Recent research (after 2018) has been more concerned with creating affect-sensitive e- learning platforms to enhance interaction and participation, as well as with providing exam security through plagiarism detection and user authentication using machine learning algorithms. This trend towards affective computing is supported by research by Aljarrah et al. (2021) andindicatestheimperativeforrobustassessment security and immersive virtual training environments—especially in the health sector— fosteredbythesuddengrowthofe-learninginthe COVID-19 pandemic (2020–2021). Therefore, thefutureofopenanddistancelearningseemsset to beAI-driven, in a position to provide resilient educational solutions even in the face of disruptive crises. Despite these opportunities, several challenges may hinder sustainable AI adoption. Zhai et al. (2021) categorize these challenges into technical limitations, redefined roles for teachers and learners, and ethical concerns. UNESCO\'s report (Pedro et al., 2019) also identifies six policy imperatives: making strategic AI policies for sustainable development; making equity and inclusionareality;educatingeducatorswhile empoweringAItolearnabouteducationcontexts; developing high-quality, inclusive data systems; raisingtheprofileofAIresearchineducation;and maintaining ethical standards and transparency arounddatapractices.Inaddition,themajorityof empiricalresearchstressesAItooldesignwithout theoretical foundationsin educational or learning theories, which detract from the long-term sustainability of these resource-hungry technologies. Sincethemajorityofresearchreportshavemostly positive findings, subsequent research should explore potential limitations and challenges. Priority areas for future research are: • Ethical and Human Factors: Examine transparency procedures, data management, and privacy protection in AI-based education, as called for by Sharma, Kawachi, and Bozkurt (2019). • Institutional Readiness: Evaluate organizationalcapacitiesandreadinessfor incorporating AI technologies, thus forestalling deployment issues. • AI-Powered Curriculum Design: Create curricula that provide educators and learners with expertise for an AI-infused educational environment. In conclusion, futureAIODEd research needs to gobeyondtechnologicalbreakthroughstoinclude institutional readiness evaluations, pedagogical modelsforAIincorporation,andcoursesofaction for preparing stakeholders—educators and students alike—for the challenges of an AI- powered future.

References

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Copyright

Copyright © 2025 Dr. Nikita R Hatwar, Mayur S Burale, Yash D Ukare, Rupesh L Rahangdale, Vrushabh S Hedaoo, Vaibhav D Bhoyar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.