Enterprise AI Analysis
A Systematic Review of Artificial Intelligence in Higher Education Institutions (HEIs): Functionalities, Challenges, and Best Practices
Authors: Neema Florence Vincent Mosha, Josiline Chigwada, Gaelle Fitong Ketchiwou, and Patrick Ngulube
Published: 24 January 2026
The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed teaching, learning, and research practices within higher education institutions (HEIs). Although a growing body of literature has examined the application of AI in higher education, existing studies remain fragmented, often focusing on isolated tools or outcomes, with limited synthesis of best practices, core functionalities, and implementation challenges across diverse contexts. To address this gap, this systematic review aims to comprehensively examine the best practices, functionalities, and challenges associated with the integration of AI in HEIs. A comprehensive literature search was conducted across major academic databases, including Google Scholar, Scopus, Taylor & Francis, and Web of Science, resulting in the inclusion of 35 peer-reviewed studies published between 2014 and 2024. The findings suggest that effective AI integration is supported by best practices, including promoting student engagement and interaction, providing language support, facilitating collaborative projects, and fostering creativity and idea generation. Key AI functionalities identified include adaptive learning systems that personalize educational experiences, predictive analytics for identifying at-risk students, and automated grading tools that improve assessment efficiency and accuracy. Despite these benefits, significant challenges persist, including limited knowledge and skills, ethical concerns, inadequate infrastructure, insufficient institutional and management support, data privacy risks, inequitable access to technology, and the absence of standardized evaluation metrics. This review provides evidence-based insights to inform educators, institutional leaders, and policymakers on strategies for leveraging AI to enhance teaching, learning, and research in higher education.
Executive Impact: Key Findings at a Glance
Our deep dive into AI in Higher Education reveals critical insights for strategic adoption and challenges to navigate.
35
Total Studies Reviewed
10 Years
Publication Period (2014-2024)
11
Key AI Functionalities Identified
11
Primary Challenges Highlighted
11
Best Practices Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI Functionalities
AI facilitates teaching, learning, and research in HEIs. AI tools facilitate learning activities through adaptive learning platforms and interactive learning tools. ChatGPT can be utilized as a pedagogical tool for exercising, practicing, preparing for exams, summarizing, and analyzing educational content. ChatGPT also assists educators in assessment and evaluation, as well as in refining existing assessment methods. Students can utilize ChatGPT to obtain assistance with completing learning assignments, clarifying doubts, and reinforcing their learning. They are also used to help teachers automate and track student progress and to provide tutoring services utilizing adaptive algorithms, intelligent tutoring systems, and adjustable difficulty levels.
22
Personalised learning
20
Research practices
11
Data analysis and management
9
Curriculum development
7
Interactive learning
26
Assessment and grading
14
Tutoring and educational practices
9
Smart content creation
8
Knowledge management
5
Students' self efficacy
3
Examinations and evaluations
Challenges
AI integration faces several challenges, including limited skills and institutional readiness, inadequate policies, and risks to academic integrity. Generative AI intensifies ethical concerns regarding data privacy, algorithmic bias, transparency, and fairness. Evidence suggests demographic bias in AI systems, which may exacerbate inequalities if deployed without critical evaluation. The digital divide further limits equitable access, and over-reliance on AI can reduce critical thinking and initiative. Technical issues, such as system instability, can also negatively affect motivation and engagement. Most AI tools encourage laziness, diminishing the quality of learning experience. Lastly, the current review noted that most AI tools encourage laziness. As students increasingly rely on AI tools, there is a risk of fostering laziness, which can diminish the quality of their learning experience. Nikolopoulou (2024) added that AI tools foster laziness in learning and teaching processes.
15
Limited knowledge in HEIs
8
Not detected by plagiarism software
13
Threatens academic integrity
11
Lack HEIs management support
9
Ethical challenges
22
Lack of privacy and security
19
Losing professional jobs
17
Costs implications
5
Lack of infrastructure
10
Encouragement of laziness
9
Lack of student-teacher interactions
Best Practices
AI enhances supplementary resources and facilitates collaborative projects and peer review. AI tools are used to assist users in learning new languages and support student learning across various contexts, including language acquisition, research, writing, and general academic inquiry. They are also used to promote language learning and editing, enhance language proficiency, provide grammar assistance, and develop language translation skills. AI enhances language processing for language learning. Students' engagement and interaction when using AI were also highlighted, underscoring that Chatbots facilitate communication by allowing students to ask questions and receive timely feedback from instructors, thereby enhancing the learning experience. AI enhances student engagement, supports gamification, and promotes interactive learning by engaging and motivating students.
17
Feedback provision
14
Student engagement and interaction
9
Create interactive learning experiences
8
Group discussion
2
Supplementary learning resources
3
Distance learning support
9
Generate new ideas
7
Information retrieval and organisation
5
Critical thinking
4
Problem-solving
2
Cognitive engagement
The Rise of AI in Higher Education Publications
52 Studies in 2023, reflecting rapid adoption & interestEnterprise Process Flow
Initial Database Search (n = 220,387)
→
Duplicate & Non-English Records Removed (n = 197,211)
→
Title Screening (n = 22,900)
→
Abstract Screening (n = 230)
→
Full-text Screening (n = 72)
→
Studies Included (n = 35)
| AI Functionalities | Associated Challenges |
|---|---|
| Personalized Learning & Adaptive Systems | Risk of over-reliance leading to academic laziness |
| Automated Grading & Assessment | Difficulty detecting AI-generated plagiarism, ethical concerns |
| Research Support & Data Analysis | Lack of domain expertise, potential for unreliable information |
| Language Learning & Translation | Lack of institutional support & resources for effective integration |
| Improved Engagement & Interaction | Loss of human-technology relationship, reduced student-teacher interaction |
Case Study: Georgia State University's AI Intervention
Addressing 'Summer Melt' with AI
Georgia State University implemented AI-driven systems to combat 'summer melt', a phenomenon where admitted students fail to enroll due to administrative hurdles. AI-enabled interventions identified and addressed these barriers early, significantly improving student matriculation and retention outcomes. This demonstrates AI's potential for optimizing institutional efficiency and student support.
Outcome: Improved student matriculation and retention.
Calculate Your Potential AI Impact
Estimate the efficiency gains and cost savings your institution could achieve with strategic AI integration.
Estimated Annual Savings
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Your AI Implementation Roadmap
A structured approach to integrating AI, ensuring ethical deployment and maximum educational impact.
Phase 1: Strategic Assessment & Policy Development
Conduct a comprehensive needs assessment. Develop clear AI policies, ethical guidelines, and data governance frameworks. Establish an AI steering committee with representation from faculty, administration, and students.
Phase 2: Infrastructure & Capacity Building
Invest in robust digital infrastructure and AI-compatible systems. Provide extensive training for educators and students on AI tools, literacy, and responsible use. Foster collaborations with AI developers and researchers.
Phase 3: Pilot Programs & Pedagogical Integration
Implement pilot AI programs in diverse disciplines and contexts. Focus on integrating AI within constructivist and self-regulated learning frameworks, ensuring appropriate scaffolding and feedback mechanisms. Continuously evaluate and refine pedagogical designs.
Phase 4: Scaled Deployment & Continuous Improvement
Scale up successful pilot programs across the institution. Establish ongoing monitoring and evaluation systems for AI effectiveness, equity, and ethical implications. Adapt strategies based on feedback and emerging AI trends.
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