Artificial Intelligence (AI) technologies are rapidly advancing and increasingly integrated into both professional and daily life. AI plays a transformative role in various sectors such as education, healthcare, and economics, reshaping social structures and practices. Therefore, it is essential for individuals to develop AI literacy in order to understand AI technologies, use them ethically and effectively, and avoid potential disadvantages. AI literacy encompasses understanding fundamental AI concepts, knowledge of its working principles and techniques, and the ability to critically evaluate AI-generated information and transform these technologies into tools for social benefit. High school students constitute a critical group in acquiring AI literacy, as they will shape the future workforce influenced by AI technologies. This study aims to evaluate the AI literacy levels of high school students, examine their perceptions of AI, and raise awareness regarding the use of AI in education. The research was designed using a quantitative cross-sectional survey model. Data were collected through an online scale from high school students enrolled in four different school types in Eskişehir, Turkey: one vocational high school, two project-based public high schools, and one private high school. Preliminary findings indicate that AI literacy levels differ significantly according to school type, gender, and grade level. Vocational high school students were found to require greater support in AI awareness, while 12th-grade students should be further supported in the context of university preparation. The findings are expected to contribute to the safe, ethical, and effective use of AI applications among students and to promote AI literacy awareness.

Supporting learners with Special Educational Needs and Disabilities is a critical global challenge exacerbated by a systemic shortage of specialists. This paper introduces the MIKKO framework — an integrated ecosystem for inclusive education and the world's first patented, full-cycle inclusive education solution. Built on 10 years of research and development and over 100,000 hours of clinical practice, MIKKO integrates a three-pillar model: Spaces, Knowledge, and Innovation. By utilizing Artificial Intelligence-driven screening and automated Individual Education Plan generation based on the "6 Spheres of Development" methodology, the framework enables national-scale educational reforms. Evidence from large-scale implementations in Kazakhstan demonstrates significant improvements in specialist training and student support standards.

The rapid emergence of generative artificial intelligence (GenAI) is reshaping higher education by transforming how students produce knowledge and complete assessment tasks. While AI technologies create new opportunities for learning, feedback, and problem solving, they also raise significant concerns regarding academic integrity, authorship, and the validity of assessment. Educational institutions therefore face the challenge of designing assessment systems that both safeguard reliable measurement of learning outcomes and support the development of AI-related competencies. This short article situates the implementation of on-campus digital examinations using Chromebooks within the Two-Lane assessment framework, which distinguishes between secure, controlled environments for summative assessment (Lane 1) and development-oriented contexts in which AI can be integrated to support learning (Lane 2). The study analyses the institutional rollout of Chromebook-based digital examinations between 2022 and 2026 at a European university of applied sciences. Institutional data, student surveys, and teacher feedback are used to examine the scaling of the infrastructure and its impact on assessment practices. Results show a rapid increase in adoption, with the number of on-campus digital exams rising from 295 during the pilot year to 4,822 in 2025-2026. Teachers report improved exam logistics, reliable technical performance, and stronger safeguards against academic misconduct through kiosk-mode devices. Students generally rate the system highly in terms of usability and gradually show a preference for digital examinations over paper-based formats. The findings indicate that a secure digital examination infrastructure can contribute to safeguarding the integrity of high-stakes assessment by reducing opportunities for unauthorized resource use and standardizing testing conditions.

Organizations worldwide are grappling with a dual challenge: “Hunger”: employees are simultaneously feeling an urgent need to understand Artificial Intelligence and how to adopt it; And “Fear”: people also increasingly fear being displaced by Artificial Intelligence. The traditional corporate Data and Analytics training programs, when targeting entire organizations, have struggled with low participation rates, poor content relevance, and limited scalability. This paper presents a practitioner-driven case study from a large international manufacturing organization (10,000+ employees) that addresses this challenge by leveraging Artificial Intelligence itself as the medium for delivering Artificial Intelligence literacy. Our solution combines two complementary Artificial Intelligence-powered mechanisms: a suite of Artificial Intelligence-driven learning games and a personalized learning path navigator. Drawing on early pilot results, related work in Artificial Intelligence literacy frameworks, and organizational learning theory, we argue that this self-referential knowledge transferring approach - democratizing Artificial Intelligence via Artificial Intelligence - offers a scalable, contextually relevant, and engagement-sustaining model for organizational Artificial Intelligence literacy. We discuss outcomes, limitations, and a roadmap for evolving the solution toward a lifelong learning ecosystem.

Digital competencies of higher-education teachers are a prerequisite for high-quality, future-ready learning. Yet institutional measurement practices still rely predominantly on self-assessment instruments whose subjectivity limits their value for data-informed professional development and policy. This work-in-progress paper presents the design of a skills-based, performance-oriented assessment aligned with DigCompEdu and contextualized for higher education (HE). We outline the competence matrix, task design principles, scoring and proficiency modelling, and the planned pilot at a Belgian University of Applied Sciences. We argue that observing demonstrated competencies through authentic tasks produces more trustworthy, actionable evidence than self-perception data, while acknowledging the implications for validity, fairness, acceptance, and ethical data governance. The paper concludes with a validation roadmap and institutional embedding strategy that connects measurement to targeted professional learning.