Digital Artificial Intelligence (AI), exemplified by Large Language Models (LLMs) such as ChatGPT, has achieved remarkable progress across a wide range of applications, driven not only by advances in algorithms but also by the emergence of a shared research ecosystem built upon commodity computing platforms, standardized software frameworks, open-source models, benchmark datasets, cloud infrastructure, and broadly accessible educational resources. In contrast, Autonomous Vehicles (AV), AI systems that perceive, reason, and act in the physical world, have advanced more slowly despite substantial public and private investment. Progress remains constrained by fragmented research and educational infrastructure that limits reproducibility, interoperability, scalable validation, and workforce development. This paper surveys the current state of the AV research ecosystem, including hardware platforms, autonomy software stacks, datasets, simulation environments, digital twins, testing and validation frameworks, and educational programs. Drawing lessons from the evolution of Digital AI, the paper identifies key gaps in accessibility, standardization, integration, and openness across the AV technology stack and outlines opportunities to develop shared research testbeds, modular open platforms, interoperable software and data ecosystems, common benchmarks, and interdisciplinary educational programs that can accelerate autonomous vehicle innovation. Finally, the paper provides a framework for evaluating AV research and educational infrastructure which identifies priorities for future investment.
Rahul Razdan, Dmitri Mironov, Janika Leoste et al.· Applied Informatics· 0 citations
Rapidly expanding use of Generative Artificial Intelligence (GenAI) in higher education creates both opportunities and challenges for learning assessment. While GenAI can provide adaptive feedback and personalization, its pedagogical integration remains underdeveloped and often disconnected from established theories of learning and participatory design processes. This paper addresses this gap by proposing an integrative conceptual model of Hybrid Intelligent Assessment Systems (HIAS), which combines AI capabilities with human oversight to enable transparent, ethical, and pedagogically aligned assessment. HIAS is structured through three interdependent layers of adoption: a pedagogical layer, aligning AI-supported assessment with self-regulated learning and the development of knowledge, skills, and attitudes; a governance layer, ensuring transparency, fairness, and human-in-the-loop validation; and a technological layer, enabling scalable integration within digital learning environments. The study is situated in Estonia, a digitally advanced context with system-level AI integration through the national AI Leap initiative. To complement the conceptual model, an empirical study was conducted across three major Estonian universities, involving 66 professors and researchers and 153 students. In addition, a small-scale pilot implementation was conducted in a design thinking course to explore the practical feasibility of a course-specific HIAS-based AI assistant. The findings reveal a consistent pattern: while both groups demonstrate a broadly positive orientation toward AI, students approach AI primarily as an efficiency-driven learning tool, whereas academic staff emphasize pedagogical control, ethical considerations, and responsible use. Across both groups, AI literacy remains uneven, particularly in critical evaluation and structured application. These findings expose a critical gap between rapid AI adoption and insufficient pedagogical integration. In response, HIAS is proposed as a structured, human-centered framework that supports teachers in designing AI-enhanced learning environments and students in developing critical, self-regulated, and responsible use of AI.
S. Rakić, Janika Leoste, Einar Kivisalu et al.· Education sciences· 0 citations
Analysis of reflective essay-feedback-appraisal instances from 283 Estonian bachelor students across one semester contributes descriptive classroom evidence on integration of AI feedback - a fast and scalable way to provide immediate writing advice, but not a self-contained route to better reflection.
Andres Karjus, Janika Leoste, Tiia Õun· arXiv.org· 0 citations
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