Veranstaltungsprogramm

<p>This contribution examines participation as co‑creation within an interdisciplinary research project focused on the participatory design of GenAI-supported Social‑Emotional Learning (SEL) micro‑learning interventions in Higher Education. The study is theoretically grounded in critical participation theory, which conceptualizes participation as a redistribution of epistemic and decision‑making power rather than as learner engagement or consultative involvement (Arnstein, 1969), as well as in sociocultural perspectives that frame learning as situated participation in socially organized practices (Hickey &amp; Zuiker, 2005; Wenger, 1998).<br />Participation as co‑creation is operationalized through a multi‑phase participatory design process. In the first phase, learners contribute as co‑constructors of SEL priorities through a student‑centered needs assessment that combines qualitative and quantitative methods. Drawing on co‑design theory, participants are treated as experts of their lived experiences, and their contributions directly inform the identification and contextualization of core SEL competencies rather than merely validating predefined curricular frameworks (Sanders &amp; Stappers, 2008). By embedding learner perspectives at the level of conceptual framework formation, this approach explicitly seeks to move beyond tokenistic forms of participation.<br />In the second phase, interdisciplinary co‑design workshops translate learner‑generated insights into a structured SEL framework aligned with curricular learning outcomes and micro‑learning pedagogies. Informed by students‑as‑partners approaches in Higher Education, these workshops function as sites of negotiated meaning‑making across disciplinary and professional boundaries, supporting shared ownership of both pedagogical goals and design decisions (Bovill et al., 2011).<br />Within this participatory architecture, Generative Artificial Intelligence (GenAI) is positioned as a teacher‑facing adaptive design infrastructure rather than as a tool for direct student interaction. Informed by human‑centered perspectives on AI in education (Luckin, 2017), GenAI supports educators in responding dynamically to learner needs by analyzing patterns of learner progression and achievement, generating adaptive content variations, and iteratively refining micro‑learning pathways. Although students are not asked to directly interact with GenAI along their learning journey, its integration enables continuous feedback loops through which learner trajectories inform ongoing content design and refinement. <br />The expected outcomes include a theoretically grounded framework for operationalizing participation as co‑creation in SEL design, empirical insights into teacher‑mediated GenAI‑supported adaptation, and a critical reflection on the opportunities and limitations of participatory educational design through AI‑enabled infrastructures.</p>

<p>ABSTRACT .The skills of the future (future skills) refer to those skills and competencies that are likely to be in high demand in the coming years, both by society and the world of work, in order to address the changes and challenges that will be determined by technological advances and the changing local and global economic landscape. Both academics and professionals agree that the level of knowledge and development of these skills will enable graduates to better face the challenges of the future. [1]. The authors of this paper have developed an open model to identify the future skills required in the technical labour market. This model is based on three key aspects that have been the subject of research carried out as part of the project <em>ZuKo4Saxony</em>: (1) the scientific arguments that support a project about future skills in companies from Saxony (Germany); (2) to present a model and framework of future skills based in the previous works of Ehlers (2020; 2023) and Kotsiou (2022) among others; and (3) the empirical results of a survey aimed at obtaining current and relevant information and data on future competences/skills and will map trends and needs of various production and service sectors in the Saxony region.</p>

<p>APPROACH. Ehlers [1] and Kotsiou et al. [7] have already analysed and compared numerous frameworks and have offered definitions and synthesised models of future skills. Ehlers [1] defines ‘future skills’ as the ‘ability to successfully solve a complex problem in an unknown future action context’. It refers to an individual's disposition to act in a self-organised manner. Kotsiou et al. [7] understands ‘future skills’ as the different knowledge, attitudes, values, skills, and competencies intended to prepare learners for the future. Ehlers' model of future skills divides the future skills into three interconnected dimensions or clusters (Subject-development-related competences, Individual-object-related competences and Organisational competences). Within each competence dimension or cluster the competences are organised into categories, and there are a number of specific competences associated with each of these categories.</p>

<p>CONCLUSIONS/RECOMMENDATIONS/SUMMARY. This model has been developed on the basis of a literature review, the findings of previous research, and the results of an empirical study conducted among small and medium-sized enterprises in Saxony, in which a number of larger companies also participated. The present model identifies not only the competencies but also classifies them into dimensions and expandable categories, thereby facilitating further research into this topic within other technical work contexts around the world.</p>

Traditional knowledge management systems (KMS) support the storage, retrieval, and recombination of explicit knowledge but remain limited in facilitating tacit, practice-embedded, and context-dependent knowledge reuse. This limitation is particularly critical in collaborative business processes (CBP), where reuse frequently fails because the original context and situated meaning of knowledge are insufficiently preserved. Two persistent challenges follow: the "Tacit Gap" – the difficulty of transferring tacit, practice-embedded knowledge through repository-based systems – and the "Context Gap" – the loss of contextual grounding during knowledge reuse. With the emergence of generative artificial intelligence (GenAI) and large language models (LLMs), KMS are shifting towards agentic, dialogue-based systems that can act as interactive, context-sensitive intermediaries in organizational knowledge processes. However, organizational applications of GenAI in knowledge management often remain tool-driven, and research has not yet consolidated how GenAI capabilities can be systematically aligned with established knowledge theories.

Addressing this gap, this research in progress asks: How can GenAI role functionalities be systematically designed to support knowledge conversion and knowledge reuse in collaborative business processes? Following a theory-driven, design-oriented research approach, we develop a taxonomy using Nickerson et al.'s iterative method. The taxonomy combines two complementary theoretical perspectives: the SECI model of knowledge conversion (Nonaka & Takeuchi, 1995) and Markus's (2001) typology of knowledge reuse situations. The resulting 4×4 taxonomy specifies 16 distinct GenAI role functionalities, each defined by its knowledge conversion output, its target reuse situation, and its SECI-specific GenAI capability grounding, and each assignable to exactly one cell through two operational coding rules.

To demonstrate technical feasibility, four roles – the Scribe Agent, Semantic Linking Agent, Mentor Agent, and Concept Mining Agent – were instantiated in a prototype, the Intelligent Knowledge Management Assistant System (IKMAS). IKMAS implements a state-machine-based processing flow in which an LLM-based router classifies incoming requests along the taxonomy, executes role-specific agent configurations, and independently verifies outputs, looping back to context enrichment when applicability conditions are missing. Preliminary testing shows that user requests were assigned to the corresponding roles and that role-specific instructions were consistently executed. The completed study is expected to contribute a theoretically grounded taxonomy as design knowledge for GenAI-enhanced KMS, a reference architecture demonstrating its technical operationalization, and a basis for evaluating GenAI as a context-sensitive knowledge intermediary in organizations.