Integrating AI in Online Learning in Higher Education: A Literature Review

Synthesis: Lock, Arteaga & Johnson (2025) conduct a two-pronged critical literature review of how AI technologies are integrated into online learning environments in higher education, narrowing 207 initial citations to 63 for the final review (spanning 32 countries). Their thematic analysis yields four interconnected themes: (1) the types and purposes of AI integration; (2) pedagogical approaches, centered on AI literacy and self-regulated learning; (3) benefits of using AI in online learning; and (4) challenges spanning academic integrity, equity and bias, ethics/privacy/surveillance, and institutional policy gaps. The authors position AI integration as a complex, sociotechnical undertaking that must be anchored in pedagogy and human relationships rather than technology adoption.

Key Findings

• Types of AI integration. Fifty-four of the 63 reviewed citations reported specific AI uses in online learning, classifiable into: AI teaching assistants/machine teachers; automated AI monitoring (e.g., remote proctored exams); AI-driven surveillance (e.g., Proctorio, Turnitin, Perusall); algorithms and learning analytics; generative AI (intelligent tutoring, content creation, automated grading); language-support models (translators, voice assistants); large language models and chatbots (the most common, e.g., ChatGPT, Q-Module-Bot); machine learning; and predictive/adaptive early-warning systems.
• Pedagogical approaches cluster around AI literacy, strategy, and self-AI Regulation in Education. The review distinguishes AI literacy from digital literacy, framing the former as the skills and competencies to use AI effectively and the critical capacity to question design and implementation. Pedagogical strategies include reducing transactional distance, providing personalized and adaptive learning, role-play and Simulation (e.g., chatbots embodying patients for anamnesis practice), and supporting a heutagogical, learner-directed shift in teaching philosophy. AI was found useful for metacognitive, cognitive, and behavioral regulation but not for motivational regulation, where learner identity, activeness, and position were necessary complements.
• Benefits center on personalization, analytics, and instructor workload. AI tools personalize learning, provide automatic assessment and timely feedback, run predictive analytics, and reduce instructor time on repetitive tasks so instructors can focus on higher-order needs. Notably, students who used ChatGPT alongside teacher tutoring perceived greater learning and skill improvement than those using ChatGPT alone — a design-contingent finding consistent with hybrid human–AI collaboration.
• Challenges are multidimensional and interconnected. (i) Academic integrity: pandemic-era remote proctored exams and plagiarism software proved inadequate against unsupervised AI access; students criticized punitive surveillance and memory-based assessments displacing authentic learning, while content generation without prior learning "masked" learning and obstructed cognitive development. (ii) Equity and bias: AI nudges rarely benefit all learners, algorithmic scoring can penalize non-native and culturally diverse input, unfamiliar complex systems add cognitive load, and biased metrics risk unfair practice. (iii) Ethics, security, privacy: heavy data collection normalizes surveillance, threatens autonomy, and raises privacy/security risks, especially for vulnerable students. (iv) Institutional support and policy gaps: institutions often lack frameworks for accountable AI deployment, and ambiguous data-ownership/retention policies enable misuse.
• The automation-without-human review critique is vivid. The authors quote a teaching-assistant vignette of "click[ing] release scores" without reading AI comments, arguing such automation without human-in-the-loop undervalues instructor–student interaction and the positive role of human engagement.

Implications

The authors frame implications across practice, policy, and research. For practice: develop AI literacy for both students and instructors, teach responsible use and ethical guidelines, and design assignments engaging students in critical thinking — while never letting technology displace the human dynamic ("never let the robots take over"). Educational development should cover AI for course development, assessment, teaching, image generation, and student-facing training. For policy: institutions need transparent frameworks governing data collection, storage, surveillance, and ownership, plus student-facing policies. For research: shift focus from creating/deploying AI to how learning is designed, facilitated, and assessed within AI-enhanced environments, including how the instructor's role and the student–instructor–technology relationship change.

The article is a critical (narrative) literature review rather than a PRISMA systematic review; its authors recommend a future systematic or scoping framework and broader database coverage as next steps. It complements the knowledge base's other online-learning syntheses by emphasizing the overlapping, interconnected nature of AI-integration themes in online higher education.

Connected Concepts

• Online Teaching and Learning
• Higher Education
• AI Literacy
• Generative AI
• Personalized Learning
• Adaptive Learning
• Self-Regulated Learning
• Equity
• Privacy
• Remote Proctoring
• AI Ed Evaluation

Connected Articles

• Artificial Intelligence and Student Engagement in Online Learning: A Literature Review — AI and student engagement in online learning: literature review
• Artificial Intelligence in Online Education: A Systematic Review of Its Impact on Learner Engagement and Satisfaction — Systematic review of AI in online education: engagement and satisfaction
• Artificial Intelligence in Distance Education: A Systematic Review of Emerging Pedagogical, Cognitive and Institutional Dynamics — Systematic review of AI in distance education
• Generative AI in Higher Education: A Systematic Review of Opportunities, Challenges, and Pedagogical Innovations (2022–2025) — Systematic review of GenAI in higher education
• Architecting an AI-Driven Decision Support System for Enhanced Online Learning and Assessment — AI decision support in online learning assessment

Citation

Lock, J., Arteaga, S., & Johnson, C. (2025). Integrating AI in Online Learning in Higher Education: A Literature Review. International Journal on Innovations in Online Education, 9(1), 59–79.