Biography
Dr Lamprini Seremeti is an Assistant Professor in the Department of Agricultural Economics & Rural Development at the School of Applied Economics and Social Science of the Agricultural University of Athens. She holds a PhD in Computer Science with a focus on propagating knowledge in networks of aligned ontologies, and a PhD in Artificial Intelligence Regulation; a Master in Special Education and a Master in Pure Mathematics; a Bachelor in Mathematics, a Bachelor in Informatics, and a Bachelor in Law. She has also participated as a researcher in EU-funded projects, in which, her tasks involved mathematical modeling, data processing, knowledge representation and management, and conceptualization of heterogeneous environments (socio-economic, educational, biomedical, ambient intelligent). She has published her research work in peer-reviewed international journals and book chapters, and has presented her work in international peer-reviewed conferences. Her research interests focus on theories applied in inclusive education, interpretation of Law in ambient intelligent environments, knowledge representation and management through ontologies, mathematical modeling.
Conference contributions 4
INTRODUCING STUDENTS TO PROMPT ENGINEERING
How can students be effectively introduced to the basic principles of prompt engineering in the age of generative artificial intelligence (GenAI)? This study approaches the question through an experiential learning design, using the analogy of the Pythia as a pedagogical entry point. The educational intervention was implemented as part of workshops conducted during the Panhellenic Student Conference entitled "Bring Your Own Prophecy to the Pythia". It was designed as a two-phase interactive learning experience, combining audiovisual material, short presentations, and participatory digital tools (live polling, word clouds, quizzes, and open-ended responses via mobile devices). In the first phase, students, aged 8–18, explored two heterogeneous "answer-generating systems", namely, the Pythia and GenAI, by examining key dimensions such as the role of the question, the nature of the response, the need for interpretation, the presence of uncertainty, and the "black-box of intermediator between question and answer". This comparative process enabled students to recognize that, while the Pythia requires interpretation and GenAI requires validation, both systems fundamentally depend on the formulation of the question. In the second phase, the workshop shifted toward the use of GenAI for content generation, where prompt engineering was introduced as a key practice for optimizing outputs. Through guided experimentation, students recognized that more precise and well-structured questions produce more relevant responses, thereby reducing the need for extensive validation. Findings indicate a clear cognitive shift, that is, students moved from focusing on evaluating answers to reflecting on how questions must be formulated. This shift highlights prompt engineering as a core cognitive competence of contemporary digital literacy.
THE SEMANTICS OF EDUCATIONAL DATA ANALYTICS
Educational decision-making is increasingly relying on the processing of large volumes of heterogeneous data generated by multiple actors within the educational ecosystem, including students, teachers, administrators, policymakers, families, and other stakeholders. Such data may take both explicit and tacit forms, depending on their source, as they may originate from learning management systems, classroom interaction, institutional records, teacher observations, and policy-oriented databases. To process, interpret, and use these data effectively, several data-driven fields have emerged, including Educational Data Analytics, Learning Analytics, Educational Data Mining, Academic Analytics, Big Data in Education, Predictive Analytics, and Data-Driven Decision-Making. The insights generated from these fields are mainly used to support personalized learning, monitor student progress, improve curriculum design, and evaluate educational interventions. Nevertheless, the terminology used remains conceptually ambiguous. The relevant terms are often used interchangeably, although they differ in scope, technical approach, and pedagogical objective. This lack of semantic clarity may lead to inconsistent educational design. In this paper, we aim to clarify the conceptual landscape of data analytics in education, by reviewing the main research areas, defining the key terms used, and identifying the meaningful relationships among them.
BUSINESS INTELLIGENCE IN EDUCATION:
Opportunities and challenges
Emerging technologies have radically transformed the modern educational ecosystem. Among these technological innovations, Business Intelligence (Business Intelligence, BI) is emerging as an increasingly important driver of educational transformation, supporting stakeholders in decision-making processes related to teaching, planning, quality assurance policies, and the administration of educational institutions. This study examines the role of BI in education through a synthesis of the existing literature. It focuses on the main arguments for and against the use of BI in educational settings, highlighting both its potential benefits and limitations. On the one hand, from a theoretical perspective, Business Intelligence is expected to support evidence-based decision-making, improve the monitoring of student performance, and enhance the effectiveness of educational institutions. On the other hand, in practice, its implementation in the educational routine raises concerns regarding data protection issues, the assessment of data quality, and the suitability of quantitative indicators for measuring complex educational effectiveness. By combining theoretical expectations with practical challenges, this study aims to provide a balanced understanding of how Business Intelligence can contribute to education.
INTEGRATING ESG PRINCIPLES IN K-12 SCHOOLS:
A case study of a primary school sustainable-oriented activity
The integration of Environmental, Social and Governance (ESG) principles into education represents a paradigm shift in preparing educational stakeholders to design sustainability-oriented activities. This study discusses the motivation behind the adoption of ESG principles in K-12 schools, the categories of educational activities in which K-12 schools engaged, and the institutional mechanisms that affect their implementation through the prism of ESG. The aim is to highlight how K-12 schools adapt to global norms and expectations concerning sustainable development. To this purpose, the study focuses on a specific educational activity that has been implemented in a primary school. The activity concerns the investigation of the history of traditional foods within the school's local territory, with the aim of connecting students with local cultural heritage, collective memory and community identity. Through interviews with older residents, the collection of traditional recipes, the study of local agricultural products and food reservation practices, students investigated how food reflects the history, geography, economy and social life of the region. From an ESG perspective, this activity is described by using the Social, Environmental and Governance dimensions, as well as the institutional mechanisms that affect its implementation under each dimension. By examining this school-based activity through the ESG lens, the study demonstrates how sustainability can be assessed not only through quantitative indicators but also through qualitative evidence derived from everyday educational practices.