ChatGenerative AI swept into university lecture halls with disruptive speed, leaving academic governance struggling to adapt; bibliometric analysis maps global patterns of AI adoption and pedagogical transformation across higher education.
The sudden mainstream proliferation of large language models and automated coding assistants created an institutional crisis in global higher education, challenging traditional assessment and academic integrity.
University administrators and faculty lacked a comprehensive evidence base regarding how AI is actually being deployed across diverse academic disciplines—from the humanities to medical sciences.
This study conducts an extensive bibliometric analysis of international scientific literature on AI in higher education. The authors map co-citation networks, research hot spots, and emerging themes, revealing a dramatic shift from initial concerns over plagiarism toward proactive integration in personalized tutoring and curriculum design.
This global bibliometric map provides university leaders with a strategic foundation to formulate balanced institutional AI policies that uphold academic integrity while preparing students for an AI-integrated workforce.
Uso de la inteligencia artificial en la educación universitaria
This study aims to conduct a bibliometric analysis to investigate the current state of artificial intelligence (AI) adoption and utilization in higher education. A quantitative methodology was employed, analyzing 1476 scientific articles from renowned databases such as Scopus and Web of Science. Data was processed using the digital tools R and VOSviewer. The findings reveal an exponential growth in publications, with a growing focus on personalized learning, automated assessment, and the use of tools like ChatGPT. Significant international collaborations were identified; however, ethical challenges and the need for appropriate policies to ensure equitable and effective AI implementation in education were also highlighted. This study provides a global overview of research trends in AI in higher education, examining its applications, opportunities, and challenges for the teaching-learning process.
Smart city initiatives frequently imported glossy foreign technological blueprints that failed in regional municipal governments; empirical ICT auditing maps local technological capabilities to design realistic e-government roadmaps.
University educators traditionally identified struggling students only after midterm exams were failed; predictive machine learning models analyze continuous digital engagement to trigger early proactive student counseling.
Architectural floor planning for elder-care facilities required weeks of manual drafting to balance accessibility with regulatory safety codes; Conditional Generative Adversarial Networks (cGANs) autonomously generate optimized retirement home layouts.
Online digital classrooms were accused of isolating learners and degrading human social skills; mining learning management system interactions reveals that big data analytics can actively track and foster student collaborative social competencies.
Modern workforces demand complex, multi-dimensional problem-solving skills that traditional standardized tests fail to quantify; structural equation modeling validates the eComplexity instrument to measure real-world complex thinking competencies.
Mathematics students frequently faced uniform lecture pacing that alienated struggling learners and bored advanced pupils; intelligent tutoring systems leverage predictive diagnostic algorithms to deliver personalized mathematical instruction.
Emerging AI educational tools are predominantly engineered for high-bandwidth, resource-rich institutions; this empirical analysis explores the opportunities and infrastructural bottlenecks of deploying AI learning platforms within Cuban schools.
Artificial intelligence effortlessly solves historical substitution ciphers; modern military-grade encryption remains completely immune to large language model attacks. By evaluating top commercial models on an extensive cryptanalysis benchmark, cybersecurity researchers proved that modern mathematical encryption cannot be cracked by pattern-matching AI.
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