AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Based on the results of all evaluations performed, it has been found that AI-based adaptive learning systems provide greater motivation, more successfully comprehend course content, and a higher level of academic performance compared to the use of traditional mobile learning apps.
This article explores how continual learning systems can support human-AI collaboration by functioning as intelligent instructional partners rather than replacements for educators and proposes a foundation for sustainable, transparent and relevant AI-enhanced learning ecosystems.
The study contributes user-derived design requirements that can guide the development of trustworthy and context-appropriate AI-supported learning platforms for undergraduate ICT students in programming-related courses at the two participating universities; broader generalization to other higher education fields requir...
A faculty-centered AI-based personalized learning framework for undergraduate (UG) and postgraduate (PG) students is proposed and the research gap is defined, framework architecture, methodology, data requirements, evaluation plan, and ethical safeguards are defined.
It is argued that AI systems improve academics by offering individualized instruction, real-time feedback, and self-paced learning methods while enabling teachers to monitor progress with real-time data.
A reinforcement-learning-based pedagogical agent then dynamically adapts instructional strategies and learning materials based on the predicted learner state. In comparison to static instructional models and traditional classifiers, experimental assessment on a university-level Learning Management System (LMS) dataset...
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