Keynotes ETLTC-ETSS-ICAIH-ICES 2027 SERIES

Prof. Dr. Wolfgang Ziegler 

Professor,  Department of Information Management & Media, 

Karlsruhe University of Applied Sciences, Germany

AI-Powered Analytics in Technical Communication: Advancing Information Management toward Content Data Science

Abstract - Technical Communication (TC), as an interdisciplinary field within information management, is shaped by applied linguistics, computer science, and user-centered media design. Its primary goal is to create, manage, and deliver concise information tailored to the needs of its recipients. In modern settings, this is typically supported by topic-based content enriched with semantic metadata, such as PI classifications. 

So far, analytics in TC has received limited academic attention and has been applied in industry mainly through a few specialized tools. These tools focus on metrics and KPIs, such as linguistic content quality parameters or content reuse performance within content management processes. On the delivery side, user experience has been explored mainly through usability studies and web analytics of user behavior, often with an emphasis on sales-oriented contexts. 

In our recent approach, we aim to expand analytics in TC by leveraging AI—particularly language models—within an interactive environment called the PIAI-Lab. In collaborative business–academic projects, our goal is to provide easier access to analytical insights derived from content ensembles originating in content management systems (CMS) and used in various delivery scenarios. More broadly, our research focuses on three use cases: Content engineering in the pre-CMS phase, Content analytics within content management processes, and KPIs for content delivery in AI-driven environments such as chat systems and retrieval-augmented generation (RAG). 

We present typical approaches for these use cases in academic studies and derive characteristic properties of topic ensembles explored within the PIAI-Lab.

Ravi Teja Reddy Mandala 

Senior Site Reliability Engineer, Oracle Cloud Infrastructure

IEEE Senior Member | ACM Member, 

Cloud Reliability, AI Systems, and Large-Scale Infrastructure Engineering, USA

LinkedIn: https://www.linkedin.com/in/ravi-teja-reddy-mandala/

Trustworthy AI Systems at Scale: Governance, Decision Intelligence, and Reliable Cloud Infrastructure 

Abstract: Artificial intelligence is rapidly moving from experimental pilots to real-world decision-making systems across education, healthcare, finance, public services, and enterprise operations. As adoption increases, the central question is no longer only how powerful AI can become but how trustworthy, governable, reliable, and human-aligned these systems can be when deployed at scale. This keynote will discuss the practical foundations for building trustworthy AI systems, focusing on responsible AI adoption, governance frameworks, decision accountability, and the critical role of reliable cloud infrastructure. 

The session will examine how organizations can move beyond model accuracy and evaluate AI systems through broader dimensions such as transparency, fairness, privacy, explainability, security, resilience, observability, and operational reliability. It will also highlight the importance of human-in-the-loop decision-making, auditability, risk controls, and policy alignment in ensuring that AI supports rather than replaces responsible judgment. We'll pay special attention to the infrastructure layer, including scalable cloud platforms, monitoring, incident response, deployment governance, and reliability engineering practices that enable AI applications to operate safely in production environments. 

Drawing from industry experience in cloud infrastructure, site reliability engineering, AI-enabled systems, and large-scale operational governance, the keynote will provide a practical roadmap for institutions and enterprises seeking to adopt AI responsibly. The talk will also connect technical reliability with institutional trust by showing how governance policies, engineering controls, and continuous monitoring must work together. The talk will conclude with future directions for trustworthy AI, including AI governance maturity, continuous validation, ethical automation, and the need for collaboration among educators, researchers, engineers, policymakers, and industry leaders.

Prof. Dr. Lazaros Moysis

Assistant Professor, University of Nova Gorica, Slovenia

Chaotic Navigation - Guiding Autonomous Agents Using Chaos

Abstract

Chaos theory is a prominent field of physics and mathematics that studies the behavior of nonlinear phenomena in all aspects of life. Apart from their use in describing natural phenomena, chaotic systems have also been used as lightweight deterministic sources of randomness in various engineering and security applications. Among these applications is chaotic navigation, also known as chaotic path planning.

The goal in chaotic navigation is to guide an autonomous agent to explore or patrol a given area but do so while moving unpredictably. Moving in unpredictable patterns can improve security in scenarios where either the area or the agent itself is under the threat of intruders or adversaries. There are several ways of utilizing a chaotic system to introduce unpredictability in the motion of an autonomous agent, each having its advantages and disadvantages. The motion can then be further improved by introducing several optimization techniques to improve the agent’s area coverage, without weakening the unpredictability of the motion.

In this talk, the problem of chaotic navigation will be explained. Its design approaches, as well as its challenges, applications, integration with AI systems, and future topics of interest, will be discussed.

Prof. Dr. Adriano Fagiolini

PhD in Robotics, Associate Professor

Programme Coordinator of Automation and Systems Engineering

Head of ARTES 4.0 UNIPA Macro-Node | ARTES 4.0 ARB Coordinator 

Head of MIRPALab | Università degli Studi di Palermo

Learning Through Physical Interaction: Adaptive Estimation and Control in Autonomous Robotic Systems

Abstract

Autonomous robotic systems increasingly operate in uncertain and dynamic environments, where effective interaction with the physical world requires more than accurate motion control. The ability to acquire information from the environment, interpret it, and adapt system behavior in real time is becoming a fundamental requirement for achieving reliable autonomy. This keynote presents a control-theoretic perspective on adaptive interaction in robotic systems, drawing on research in adaptive control, state and parameter estimation, unknown input observers, and interaction control. The talk illustrates how adaptive control can be interpreted as a mathematically grounded mechanism for online learning, allowing robotic systems to continuously adjust their behavior in the presence of uncertainties. Similarly, observer-based estimation techniques provide a principled framework for reconstructing information that is not directly measurable, including unknown inputs and physical properties of the environment such as stiffness. These estimated quantities can then be exploited to achieve safe and effective physical interaction through integrated position and stiffness control strategies. 

Mr. Shuaib Ahmed

DevSecOps Engineer | PhD Researcher, Applied AI

CKS Certified | IEEE Member | US Patent Holder

Governing the Intelligent Cloud: A Zero-Trust Framework for Trustworthy AI Deployment in Critical Infrastructure

The rapid integration of artificial intelligence into enterprise and government systems has introduced unprecedented capabilities—and equally unprecedented risks. As organizations increasingly rely on AI-driven decision-making for critical operations, the question is no longer whether to adopt AI, but how to adopt it responsibly, securely, and at scale.

This keynote presents a practitioner-informed framework for trustworthy AI governance built on three foundational pillars: zero-trust cloud architecture, machine-readable compliance evidence, and policy-governed AI lifecycle management. Drawing from real-world deployments in federal cloud environments and critical infrastructure, the talk explores how organizations can move beyond checkbox compliance toward continuous, automated assurance of AI systems.

Central to this discussion is the role of reliable cloud infrastructure as the backbone of scalable AI adoption. Without a security-hardened, policy-enforced cloud foundation — one that integrates identity governance, workload isolation, and cryptographic evidence generation — even the most sophisticated AI models become liabilities rather than assets.

The keynote further examines the emerging challenge of AI governance in post-quantum environments, where classical cryptographic dependencies in AI pipelines must be proactively migrated to quantum-resistant standards before adversarial exploitation becomes viable.

Attendees will leave with actionable insights on designing AI systems that are not only intelligent and performant, but auditable, explainable, and inherently trustworthy—qualities that regulators, enterprise stakeholders, and the public increasingly demand.

This session is relevant to researchers, policymakers, cloud architects, and anyone navigating the intersection of AI adoption, governance, and secure infrastructure at scale.

Prof. Dr. Peter Ilic

Center for Language Research, School of Computer Science and Engineering, 

The University of Aizu, Japan

Dialogic Education and Artificial Intelligence: Exploring Synergies in Learning 

Abstract - This keynote examines synergies between dialogic education and artificial intelligence in education amid advancements in large language models that reshape learning paradigms. The address covers three areas: how artificial intelligence facilitates dialogic interactions by simulating conversational partners and supporting shared meaning construction; how artificial intelligence expands dialogic spaces through access to diverse perspectives and global connections; and how artificial intelligence fosters creativity, empathy, and ongoing inquiry while requiring attention to risks such as limited viewpoint diversity. Where relevant, aspects related to language learning will be discussed to illustrate these correlations, including how artificial intelligence enables immersive conversational practice that promotes reciprocity and perspective switching, and how it supports engagement with cultural and linguistic diversity in ways that align with dialogic principles. 

The presentation aims to prompt attendees to explore correlations between dialogic principles and artificial intelligence tools, reconsider teaching approaches in technology-mediated environments, and stimulate broader discussion on emerging learning paradigms. It offers conceptual frameworks for human artificial intelligence collaborative dialogues while encouraging reflection through a forward-looking perspective. 

The address highlights the value of continued exploration of these synergies to shape the future of education and urges commitment to educational excellence and impact.