Technologies are transforming teaching and learning worldwide—from digital platforms, LMS, and e‑portfolios to adaptive systems, AI, and immersive environments. Across sectors, innovations enable personalization, collaboration, and inclusion while raising pedagogical, ethical, and cultural questions (Zawacki‑Richter & Qayyum, 2018; Bond et al., 2020).

Digital tools support flexible, data‑informed, learner‑centered instruction. Learners benefit from adaptive feedback, simulations, multimedia, and collaborative projects via social media and cloud services. In global and local contexts, MOOCs and open educational resources (OER) broaden access and participation (Wong et al., 2019). For educators, learning analytics support continuous monitoring, and AI increasingly automates routine tasks, freeing time for individualized guidance (Ifenthaler & Yau, 2020).

This presentation synthesizes current research and practice from higher and secondary education. It examines how AI‑supported systems provide individualized, data‑driven feedback and adaptive pathways, how cloud/social tools foster collaboration, and how OER/MOOCs open international learning spaces (Khalil & Ebner, 2014). We particularly consider co‑design scenarios that enhance participation and motivation (Castañeda & Selwyn, 2018).

Concurrently, we address key challenges: integrating technologies requires ongoing pedagogical development and teacher professionalization, plus a critical appraisal of educational added value (Redecker, 2017; Bond et al., 2020). Issues of data protection, algorithmic transparency, copyright, and the digital divide—unequal access to infrastructure and skills—are scrutinized (van Dijk, 2020). Evidence suggests effectiveness when instructional quality, constructive integration, active interaction, and critical media literacy guide design (Wong et al., 2019).

We conducted a systematic review using standardized, reproducible procedures to compile and appraise empirical implementations in higher and secondary education (Boland et al., 2017; Newman & Gough, 2020). Search strategies and inclusion/exclusion criteria guided screening; eligible studies were structured via the PICO model (population, intervention, comparison, outcome) (Higgins et al., 2024). A narrative synthesis integrated qualitative and quantitative evidence with thematic coding (pedagogical integration; feedback/adaptation; collaboration; inclusion/ethics) (Popay et al., 2006; Lisy & Poritt, 2016). Triangulated practice cases drew on platform logs, learner artifacts, and interviews.

Among other things, the results show that AI-supported feedback and adaptive learning paths improve the quality of formative assessment and the time needed to complete tasks when they are aligned with clear goals and teaching by teachers, and that automation reduces routine corrections and shifts the focus to planning and curation.

The presentation will introduce proven strategies and practice-based recommendations from research and development. These are designed to support teachers, schools, and higher education institutions in leveraging the potential of digital technologies for quality, flexibility, and equity, and in strategically shaping innovation processes. The aim is to contribute to evidence-based and inclusive future scenarios in digital learning environments and to identify key factors for the sustainable development of international, just education systems.

Keywords: Educational Technology, Personalization, Artificial Intelligence, Digital Equity.