Introduction
This volume examines the dynamic convergence of Artificial Intelligence (AI), digital pedagogy, cybersecurity, Human–Computer Interaction (HCI), and User Experience (UX). With the growing integration of AI-powered tools into pedagogical and learning environments, students are increasingly uploading significant personal, academic, biometric, and institutional data to AI platforms, frequently without complete awareness of the associated privacy, security, usability, and ethical implications.
The scope of this includes textual submissions, behavioural data, voice recordings, and images, which consequently elevates concerns regarding the misuse of data, intrusive surveillance, user profiling, and the indefinite retention of personal information. Furthermore, the increasing reliance on AI-powered educational technologies raises important questions regarding user trust, transparency, accessibility, and the design of human-centred digital learning experiences.
Furthermore, the lack of standardized provenance and timestamping for AI-generated content leaves institutions without dependable methods to ascertain the origin, timing, or platform of any given output, thereby impacting academic integrity and institutional responsibility.
Another aspect pertains to the issue of proprietary rights. Work generated by students with AI assistance could unintentionally result in the creation of intellectual property for commercial entities. Concurrently, the inclusion of materials from other sources, including but not limited to peer-authored works, institutional documents, and proprietary content, introduces unresolved issues concerning consent and the permissible scope of data submission.
Accordingly, the volume provides a critical analysis of the impact of AI-powered educational tools on digital learning settings, alongside an escalation of cybersecurity risks. The education sector is presently a principal global target, enduring continuous threats from malware, phishing schemes, ransomware, and data breaches.
Recent threat vectors incorporate AI-driven social engineering, where tools mimicking legitimate services illicitly obtain sensitive credentials or account access by offering financial, administrative, or academic aid, a risk category that remains insufficiently theorized and underreported within educational cybersecurity literature. Educational institutions are repeatedly identified as a primary target by live cyber threat intelligence dashboards, such as those provided by Check Point, highlighting the critical need to integrate cybersecurity awareness and data protection into educational frameworks.