Digitale Lehre an und rund um der Technischen Universität Graz
Im Rahmen des OER-Zertifizierungsspecial der SIG OER der fnma durfte ich einen kurzen Einblick in die Praxis der TU Graz geben. Meine Folien sind hier einsehbar:
Referenz: Ebner, M. (2026, Juni 10). OER-Zertifizierung an der TU Graz. Graz University of Technology. https://doi.org/10.3217/epdgp-jfr73
We are very happy that we can invite you to our closing event of the 4-year-long work on the digital infrastructure of our European alliance UNITE!. We would like to share our final results in a hybrid session – so you can join us in Stockholm as well as online:
How can EU university alliances build a truly federated digital campus? On 13 October 2026, from 14:30 to 16:00 CET, the Unite! Alliance invites stakeholders to an interactive session exploring the practical realities of developing a shared digital higher education ecosystem.
Find here the webpage for further details and for the registration: [Eventpage]
Es freut mich, dass ich nach 3 Jahren nochmals bei Andreas zu Gast sein durfte in seinem großartigen LectureCast. Dieses Mal rund um das Thema der KI-Avatare.
Nach gut 3 Jahren zum zweiten Mal zu Gast im LectureCast ist PD Dr. Martin Ebner, Leiter der Abteilung für Lerntechnologien an der TU Graz und Kopf der österreichischen MOOC-Plattform iMOOX-AT. Im Mittelpunkt unseres Gesprächs steht die KI-gestützte Produktion von Lehrvideos mit Avataren und mehrsprachigen Synchronisierungen. Ebner schildert, wie an der TU Graz zunächst aus dem Wunsch nach niedrigschwelliger Mehrsprachigkeit im europäischen Hochschulraum heraus erste Experimente mit Talking-Head-Avataren entstanden, welche Rolle das Prinzip „Human in the Loop“ dabei für die Qualitätssicherung spielt und welche rechtlichen wie ethischen Fragen insbesondere in Bezug auf das synthetisierte Double dabei noch ungeklärt sind. Darüber hinaus diskutieren wir, ob die Personalisierung von Lernmaterialien durch KI mittelfristig zu einem Paradigmenwechsel in der Hochschullehre führt: weg vom idealtypischen Lehrenden-Avatar, hin zu einem Lernangebot, das Lernende selbst nach ihren Präferenzen konfigurieren können. Ebners Fazit bleibt dabei nüchtern-optimistisch: je mehr Technologie, desto wichtiger werde die Rolle der Lehrperson für die Orchestrierung des Lernprozesses.
Unser Beitrag zu „Sieben Mythen der KI-Nutzung“ hat viele Reaktionen hervorgerufen und nun wurde er auch in die Zeitschrift „Die Österreichische Volkshochschule“ aufgenommen.
Abstract:
Wer das Internet nutzt, kommt im Frühjahr 2026 nicht um Anwendungen generativer Künstlicher Intelligenz (kurz KI) herum. Suchmaschinen bieten neben Links standardmäßig KI-generierte Antworten an, Chatbots unterstützen bei der Buchung von Websites, Schüler:innen lassen sich Tests passend zu den Arbeitsblättern der Lehrer:innen generieren usw. – Doch nicht alles, was uns die KI-Anwendungen liefern, wie wir sie nutzen und ihre Ergebnisse verstehen, ist zutreffend und unproblematisch. Das liegt auch an Missverständnissen darüber, wie KI-Anwendungen funktionieren. Aus unserer Sicht – es gibt dazu noch keine empirische Evidenz – verdienen folgende sieben Aussagen besondere Aufmerksamkeit, insbesondere auch im Kontext von Bildung, Schule und Hochschule:
In diesem Beitrag möchten wir diese als „Mythen“ bezeichneten Aussagen beschreiben und aufzeigen, dass und warum sie nicht zutreffend sind. Damit möchten wir einen zukünftig fundierten Umgang und durch die Beschreibung von KI-Mythen Forschung dazu initiieren und unterstützen.
Referenz: Schön, S., Brünner, B., Ebner, M., Diesenreither, S., Hanfstingl, B., Krammer, G (2026) Sieben Mythen der KI-Nutzung. Die Österreichische Volkshochschule. Jg. 2026 / 286. [Link]
This is an impactful contributions, methodological rigor, and exceptional novelty in the research field of AI in education.
Our contribution titled „Generative AI Chatbots in Secondary Mathematics Education: Development and Implementation of a Dynamic Large Language Model-Based Learning Assistant for Quadrilaterals“ is now published.
Abstract:
As artificial intelligence becomes more and more a part of education, the challenge is not about having access to generative tools, but about connecting them with the goals of the curriculum and the needs of the classroom. This chapter presents the design and evaluation of a large language model–based chatbot developed specifically for teaching quadrilaterals in lower secondary mathematics. The chatbot integrates fine-tuning with retrieval-augmented generation (RAG), combining accurate, curriculum-aligned content with flexible, conversational support. The chatbot allows learners to ask conceptual questions, solve problems step by step, receive guided hints, and generate flashcards or exercises of varying difficulty. A hybrid routing mechanism selects the most appropriate response strategy based on user intent. Evaluations using both isolated prompts and multi-turn dialogues demonstrate that the hybrid system significantly outperforms standard LLM baselines in terms of accuracy, consistency, and pedagogical suitability. A classroom trial with 20 students confirmed the tool’s usability and effectiveness; students reported high satisfaction and meaningful engagement. This study demonstrates that, with careful content and architectural structuring, generative AI can enhance student learning while supporting differentiated instruction. Future directions include scaling the approach to other topics and incorporating multimodal capabilities.
[full article @ publisher’s homepage]
[draft @ ResearchGate]
Reference: Mallweger, M., Brünner, B., Ebner, M. (2026). Generative AI Chatbots in Secondary Mathematics Education: Development and Implementation of a Dynamic Large Language Model-Based Learning Assistant for Quadrilaterals. In: Auer, M.E., Nikou, S.A. (eds) GenAI in Novel Educational Applications. Studies in Computational Intelligence, vol 1260. Springer, Cham. https://doi.org/10.1007/978-3-032-16153-6_7
This is an impactful contributions, methodological rigor, and exceptional novelty in the research field of AI in education.
We also did, as usual, the SIG (Special Interest Group) for Emerging Technologies at the EDMedia Conference. This year the session was titled „Guiding Innovation with Rapid EdTech User Experience Research Nodes„
This presentation is from the Special Interest Group Emerging Technologies for Learning and Teaching from EdMedia 2026 conference workshop on Guiding Innovation with Rapid EdTech User Experience Research Nodes (RETURN). It includes supporting resources for the RETURN Manifesto, selected EdTech case examples, and documentation related to rapid user experience research in educational technology contexts.
This is an impactful contributions, methodological rigor, and exceptional novelty in the research field of AI in education.
Wir suchen wieder und würden uns sehr über eure Bewerbungen freuen. Wenn jemand also in einem tollen Team, tollem Umfeld und sehr innovativen Bereich arbeiten möchte, dann denke ich, dass das hier das Richtige sein könnte. Und wir wollen damit auch die Hochschulbildung gemeinsam ein klein wenig verbessern.
Also schnell uns eine Bewerbung schicken: [Link zur Ausschreibung]
Another talk we did at this year’s EDMEDIA conference is about the research of Daniela, titled „Code Meets Career: Employer Interpretations of Coding Bootcamp Graduates in Austria and Germany„
Coding bootcamps have expanded rapidly in response to persistent skill shortages in the IT sector and as alternative pathways into the field, particularly for groups underrepresented in technology, such as refugees and women. However, little is known about how these pathways are perceived and valued within organizations, especially in Austria and Germany.
This study therefore examines how companies in Austria and Germany perceive, evaluate, and integrate coding bootcamp graduates. Drawing on a qualitative content analysis of five semi-structured interviews with decision-makers in software development, IT consulting, and human resources, the study focuses on socially situated employer perspectives on skills, potential, and legitimacy, rather than measuring effectiveness in terms of employment outcomes.
The findings reveal an ambivalent picture: while graduates are valued for their motivation, adaptability, teamwork, and practical orientation, concerns persist regarding their theoretical depth and level of project experience. In the case of refugee participants, language proficiency is additionally identified as a key challenge. Overall, the study provides exploratory insights into how coding bootcamp qualifications are interpreted and evaluated by employers, rather than aiming to produce generalizable conclusions.
Another presentation at this year’s EDMedia Conférence is about „The OER Paradox in Ukraine„
This study analyses the impact of the new Law of Ukraine „On Copyright and Related Rights“ (2811-IX) on the use of educational materials in the context of digital transformation and military crisis. Using the benchmarking methodology „15 cases in 15 countries“ and qualitative interviews with teachers, the work compares Ukrainian norms with the practice of EU countries. The results show that broad educational exceptions (in particular, Articles 22 and 24) create a situation of „legal comfort“ for the academic community, allowing the legal use of protected content in closed digital environments. However, this gives rise to the „OER paradox“: the absence of legal barriers to the use of proprietary resources reduces the motivation to create full-fledged open educational resources under free licenses. The paper highlights the need for institutional incentives to overcome dependence on closed content and integrate Ukraine into the global open education movement.
This year, we present a poster about „A Whitepaper on Evaluating GenAI Innovation in Higher Education“ at EDMedia 2026.
Generative artificial intelligence (genAI) is increasingly shaping higher education by enabling new forms of content creation, assessment, learner support, personalization, and synthetic media. A whitepaper now presents a practice-derived framework developed through a cross-case synthesis of nine diverse GenAI implementations at Graz University of Technology. Analyzing projects ranging from AI-generated content to RAG-based chatbots, recurring decision points and risk patterns were identified to formulate a five-phase, non-linear evaluation model.
[Link to the poster]
[Link to ResearchGate]
This is an impactful contributions, methodological rigor, and exceptional novelty in the research field of AI in education.