Conference Agenda

The emerging practices of application of artificial intelligence (AI) in education create not only challenges for teachers and teacher trainers, but also a paradoxical situation: the scope of AI tools suggested to schools exceeds the institutional and teacher professional capacity to select tools that would serve the purpose of digital didactics and would enhance teaching and learning practices. Digitally competent schools, teachers and students as foreseen in EU DigCompOrg (Kampylis et al., 2015) and DigCompEdu (Redecker, 2017) frameworks are supposed to be competent to select digital technologies (including AI supported solutions) which enhance digital didactics. However, the advent of AI highlighted new challenges which are not exactly related with the didactics as such. Safety and data protection issues along with the European AI Act risk categories establish new areas of responsibilities that can hardly align with the school or teacher competences. These new areas are interdisciplinary, requesting contributions from collaborative efforts from education, law, media literacy, ethics, informatics and more. This combination of expertise and research experience may be found in research and academic institutions.

The aim of this analytic research is to establish methodological guidelines which would facilitate the schools and the teachers in informed decision – making during the process of selecting AI tools for digital didactics. The research focuses on the question which didactical, legal, ethical and technological criteria should be taken into account before selecting the proper AI tools for school education.

International research and practice show that AI tools can be used for multiple purposes in education, such as being used as assistants in preparing teaching materials, monitoring learners’ progress, and creating individualised learning (Bagherimajd & Khajedad, 2025). However, the “black box” nature of AI means that traditional criteria for evaluating and selecting digital technologies for learning, teaching and assessment are insufficient and get even more complicated. Research confirms that despite of advances in research of AI in education, there are still significant gaps related to defining clear and well-founded criteria that would go beyond the assessment of technical parameters (Luckin & Cukurova, 2019), include ethical aspect, responsibility for using the obtained data (Pedro et al., 2019) and clear legal regulations regarding data privacy (Holmes et al., 2022). Foster et al. (2023) highlight that school leaders are basing their selection of EdTech solutions (also running on AI) on colleague recommendation, internet searches, and education websites, but the tendency is that a significant number are also turning to research. Thus, researchers (Madanchain & Taherdoost, 2025) suggest a new decision - making criteria for AI tools in digital education which are supposed to leave schools and teachers well-informed about the technical, legal, ethical and didactical characteristics of the AI tools. Along with concerns about educational equity and AI-generated content for tailored learning experiences, transparency in AI operations is found to be essential for acceptability. Moreover, the authors claim that systematic re-evaluation is required for AI tools used in education, which may be a very heavy or unbearable workload for schools and teachers. However, if these decision - making criteria are underestimated, the authors predict the challenges stemming from the lack of standardised criteria for selecting AI tools.

Having a set of criteria identified and emphasised by different researchers, this paper introduces research-based guidelines for the AI tool selection that is being introduced by Vytautas Magnus University in Lithuania, serving as an instrument and support system for teachers and school administration when it comes to the critical and targeted tool selection.The paper invites teacher trainers at universities and researchers in education to discuss how schools and teachers should be supported in making informed decisions on the selection of AI tools for digital didactics.

In the context of artificial intelligence and information technology, computational thinking (CT) has become a core competence necessary for adapting teaching and learning. CT can be seen as a thinking process that is essentially about using knowledge, methods, and attitudes to solve problems in life and find solutions through computer science concepts and digital tools. The development of CT in education shows a shift from its adoption in information technology-related education toward discipline-related education (Kafai & Burke, 2013; Palop et al., 2025). This indicates that CT education in primary schools extends beyond IT teachers and should be undertaken by teachers across disciplines. Moreover, the cognition level of educators’ CT competence has a direct impact on the quality of education and the subject matter they teach (Yadav et al., 2014). Therefore, understanding teachers' CT competence and their ability to teach CT is crucial not only for improving subject teaching effectiveness but also for informing European and international educational reforms aimed at fostering sustainable, future-oriented education systems. Some European teachers are already engaged in classroom activities and have covered several competencies that show high potential for introducing CT components with less burden on teachers (Mannila et al., 2014). Previous research has established that there are few empirical studies with quantitative methods on the cognition level of CT educators’ assessment in this field, while quantitative studies have problems such as small sample size and insufficient reliability and validity of scales (Kong et al., 2020; Tang et al.,2020). However, available research about the assessment of subject teachers’ CT competence from teacher and school characteristics remains limited. Research gaps are related to the operational assessment of the current state and influencing factors of teachers’ CT cognitions and their CT integration into their classrooms when focusing on a wide range of school subjects (STEM, social sciences, and humanities). This research mainly solves the problem of in-service primary school teachers’ CT competence assessment. To address these problems, a reliable and valid scale has been developed and used for the purpose of determining the teachers’ cognition levels of CT competence from life and professional applications. The questionnaire developed in this study is designed to gather statistical information about the characteristics of schools and in-service primary teachers’ cognitions about CT aspects in China. Our main hypothesis is that teacher and school characteristics variables affect teachers’ CT competence. Specifically, the following questions need to be solved: (1) What is the level of cognitive CT competence among Chinese in-service teachers in primary education? (2) To what extent are teachers’ CT competence related to teacher characteristics variables (i.e., gender, age, job title, final degree, teaching subjects, teaching experience, interdisciplinary cooperation teaching experience)? (3) To what extent are teachers’ CT competence related to school characteristics variables (i.e., location and socioeconomic status of schools, school type, instructional materials and resources, professional development training)? By doing so, this study expects to help understand teachers' cognitive CT competence from teacher and school characteristics. The theory in this research is mainly based on the new taxonomy of educational objectives to assess teachers’ CT competence from life and professional application in the Chinese context. This study incorporated the definition of CT from previous studies (Brennan & Resnick, 2012; CSTA & ISTE, 2011) and built a conceptual framework for its evaluation based on the categorization framework of Marzano’s educational goals (Marzano & Kendall, 2007). Finally, the key dimensions to assess in-service primary school teachers’ CT competence include cognitive knowledge of CT, practices and skills of CT, attitudes and perspectives of CT, CT teaching design, and CT teaching implementation.

The purpose of this study is to identify both situational (within-lesson) and student-level profiles of academic emotions, and to examine how these emotion profiles vary across different ICT related-practices (or technology-enhanced classroom situations) characterised by ICT device use and purposes of ICT use.

Emotions are not peripheral to learning, rather they dynamically influence the regulatory processes that underpin knowledge construction and understanding (Ketonen, 2017; Pekrun, 2006). Within the control–value theory of academic emotions (Pekrun, 2006, 2024), such emotions are understood to arise from students’ appraisals of their control in a learning situation and the subjective value they attribute to it. Recent theoretical developments (Pekrun, 2024) extend this framework to encompass epistemic emotions, which arise during cognitive activities, such as when learners confront uncertainty, identify knowledge gaps, reconcile conflictinginformation or evaluate explanations, rather than solely from judgements of success or failure.

Epistemic emotions, such as curiosity, confusion, frustration, boredom, and surprise, are directed toward the epistemic aspects of learning, including how students process, evaluate, and make sense of information.These emotions play a central role in regulating cognitive and motivational processes. Curiosity, for example, typically arises when learners detect a knowledge gap or identify an intellectually meaningful target that they value and feel capable of mastering (high perceived control) (Pekrun, 2019; Peterson & Cohen, 2019). In contrast, confusion and frustration arise when learners appraise a situation as involving substantial cognitive incongruity, such as contradictory information, combined with low perceived control over resolving it, despite high task importance. Surprise is elicited when incoming information violates prior expectations (Pekrun, 2024).

Pekrun (2024) argues that classroom instruction plays a central role in explaining students’ emotions. In contemporary education, the use of ICT has become inevitable. However, there is little consensus on how it can be used effectively to support learning, particularly given substantial variability among students in prior knowledge, motivation, and self-regulation skills (Alvarez-Garcia et al., 2024; Borgonovi & Pokropek, 2021; Chen et al., 2024; Consoli et al., 2025). Teachers’ instructional practices inherently involve decisions about whether and how ICT is integrated, and ICT-based learning activities interact with students’ varying levels of ICT proficiency (Blundell et al., 2022; Consoli et al., 2025). These interactions can influence students’ cognitive load and may introduce cognitive incongruity during learning. Such cognitively demanding learning situations are particularly relevant for the emergence of epistemic emotions—such as confusion, surprise, and curiosity—which are directly tied to knowledge construction and meaning-making processes (D’Mello & Graesser, 2012).Since epistemic emotions often co-occur and fluctuate dynamically during learning processes, adopting a profile-based approach that captures patterns of co-existing emotions provides a richer understanding of students’emotional experiences than single-variable approaches, especially in ICT-supported learning environments. Therefore, the present study addresses the following research questions:

1. What patterns of epistemic emotions can be identified across situations?

1. How do epistemic emotions vary across different technology-enhanced situations, as characterised by ICT device use and purposes of ICT use at the situational level?

1. What patterns of epistemic emotions can be identified across students?

Topic and Rationale

Across Europe and internationally, primary education systems are undergoing profound transformation driven by curriculum reforms, competence-based education, and digitalisation. Project-Based Learning (PBL) has gained strong policy and research support as a pedagogical approach capable of fostering deeper learning, learner agency, interdisciplinary integration, and key 21st-century competences. At the same time, digital learning platforms are increasingly embedded in everyday school practice. However, despite their growing prevalence, the pedagogical integration of digital platforms into PBL, particularly in primary education, remains theoretically fragmented and unevenly supported by research-based models.

This proposal addresses a critical gap in European and international educational research: the lack of coherent, theoretically grounded models that support teachers in designing, implementing, and sustaining project-based learning through digital platforms in primary education. The study responds to growing concerns that digital platforms are often used instrumentally rather than pedagogically, without sufficient alignment to learning design principles, learner development, and sustainability of educational practice.

Research Questions and Objectives

The contribution is guided by the following overarching research question:

How can project-based learning in primary education be systematically designed and implemented through digital learning platforms in a pedagogically meaningful and sustainable way?

This question is addressed through three interrelated objectives:

• To conceptualise project-based learning in digital platforms as a learning design challenge rather than a technological implementation task.
• To develop a theoretically and empirically grounded model for designing PBL in digital platforms tailored to the specific characteristics of primary education.
• To examine how such a model can support teachers’ pedagogical decision-making, learner agency, and the sustainable use of digital platforms across diverse educational contexts.

Theoretical and Conceptual Framework

The study is grounded in a multi-layered theoretical framework integrating:

• Project-Based Learning theory, understood as a synthesis of project-based, problem-based, inquiry-based, and experiential learning traditions;
• Constructivist, socio-constructivist, and connectivist learning theories, emphasising active knowledge construction, collaboration, and learning in networks;
• Learning design and instructional design theory, with particular attention to design-oriented models such as ADDIE and contemporary learning design perspectives;
• Deeper learning and learner agency frameworks, highlighting autonomy, reflection, collaboration, and meaningful engagement;
• Digital pedagogy and platform-based learning, focusing on the pedagogical affordances of digital learning platforms beyond content delivery.

Within this framework, digital platforms are conceptualised not merely as tools, but as pedagogical ecosystems that can scaffold planning, inquiry, collaboration, formative assessment, and reflection throughout the PBL cycle.

Methodological Approach

The theoretical model presented in this contribution was developed through a mixed-method, design-oriented research approach. It integrates:

• a systematic review of international research on PBL, learning design, and digital platforms in primary education;
• empirical needs analysis involving multiple stakeholder groups (primary teachers, teacher educators, students, educational experts, and EdTech developers);
• a multi-stage Delphi study evaluating the pedagogical and technological affordances of digital learning platforms used in primary education;
• iterative synthesis of theoretical and empirical findings into a coherent design model.

This methodological triangulation strengthens the validity, transferability, and practical relevance of the proposed model.

Intended Purpose and European/International Dimension

The purpose of the proposed ECER contribution is to stimulate theoretically informed and practice-relevant discussion on how PBL can be sustainably designed within digital learning environments across different European and international contexts. While empirically grounded in one national context, the model addresses challenges common across education systems: curriculum integration, teacher competence development, platform selection, and pedagogical coherence.

The discussion is particularly relevant for European debates on digital education, competence-based curricula, teacher education, and the responsible use of educational technologies. By framing PBL as a learning design process supported—but not driven—by digital platforms, the contribution offers transferable insights for researchers, teacher educators, policymakers, and practitioners working across diverse educational systems.