Digitale Lehre an und rund um der Technischen Universität Graz
Veröffentlichungen zu jeweiligen Thema
Im Rahmen der GMW 2026 Tagung hat Sandra einen Vortrag zu „KI-generierte Lerngrafiken und -Illustrationen. Erste Einsichten zur Erstellung und Überlegungen zur systematischen Qualitätsbewertung“ gehalten. Hier einmal die Folien, Publikation folgt dann noch:
[Link zum Download der Folien]
This is an impactful contributions, methodological rigor, and exceptional novelty in the research field of AI in education.
Issue 21/04 of our journal on emerging technologies for learning got published. Enjoy the readings as usual for free :-).
Table of Contents:
Nevertheless, if you are interested to become a reviewer for the journal, please just contact me 🙂 .
Im Rahmen einer Tagung in Wien bin ich gebeten worden etwas zur Digitalen Mündigkeit zu sagen – daher gibt es hier nun meine entsprechenden Folien zum Vortrag „Digitale Mündigkeit als Basis für die digitale Gesellschaft von morgen„
Im Rahmen der heurigen DELFI Tagung haben wir einen Beitrag zu „Wenn Sehen nicht mehr Glauben heißt: Förderung von AI Literacy im Umgang mit Deepfakes im Schulunterricht“ eingereicht. Dieser ist nun im Tagungsband erschienen.
Zusammenfassung:
„Ich habe es mit meinen eigenen Augen gesehen“ galt lange als Ausdruck besonders verlässlicher Evidenz. Mit der zunehmenden Verbreitung generativer KI verliert diese Annahme jedoch an Bedeutung. Deepfakes stellen neue Herausforderungen für Medienkompetenz, Informationsbewertung und den kritischen Umgang mit digitalen Inhalten dar. Der Beitrag präsentiert ein praxisorientiertes Unterrichtskonzept, das Deepfakes als Ausgangspunkt nutzt, um grundlegende Prinzipien künstlicher Intelligenz sowie deren gesellschaftliche Auswirkungen zu vermitteln. Das Konzept wurde in der 1. und 2. Klasse einer österreichischen Handelsakademie sowie einer 3. Klasse einer Handelsschule umgesetzt. Zur Untersuchung der unmittelbaren Wahrnehmungen von KI wurden 80 schriftliche Reflexionen direkt nach der Rezeption einer Dokumentation zu Deepfakes qualitativ ausgewertet. Die Ergebnisse zeigen, dass sich die Lernenden mit der Leistungsfähigkeit generativer KI, der zunehmenden Schwierigkeit der Unterscheidung zwischen authentischen und KI-generierten Inhalten sowie gesellschaftlichen Risiken wie Fake News und Manipulation auseinandersetzen. Darüber hinaus formulieren sie konkrete Handlungsstrategien zum kritischen Umgang mit KI-generierten Inhalten. Die Ergebnisse weisen darauf hin, dass die durch Deepfakes ausgelöste Irritation einen geeigneten Ausgangspunkt für die Auseinandersetzung mit AI-Literacy und kritischer Medienreflexion darstellen kann.
[Tagungsband]
[Beitrag @ ResearchGate]
Zitation: Wolf, D., Ebner, E., Miklos, J. (2026) Wenn Sehen nicht mehr Glauben heißt: Förderung von AI Literacy im Umgang mit Deepfakes im Schulunterricht. 24. Fachtagung Bildungstechnologien (DELFI). DOI: 10.18420/delfi2026. Bonn: Gesellschaft für Informatik e.V.. PISSN: 2944-7682. pp. 297-303. Potsdam, Germany. 15.-17. September 2026. Schumacher, Clara; Röpke, René(ed.)
This is an impactful contributions, methodological rigor, and exceptional novelty in the research field of AI in education.
For all my colleagues and friends in the field of IT, for learning and teaching at universities, specifically in university alliances: We will present our lessons learned from Unite! University Network for Innovation, Technology and Engineering after 4 years, in a hybrid presentation at October 13, 14:30-16:00 CET at KTH Royal Institute of Technology / online. Please book a spot for online participation if you are interested in – or even visit us at KTH in Stockholm ❤️
Please find more information here:
https://www.unite-university.eu/unitenews/european-digital-campus-lessons-learned-from-unite
Presentations and insights from my wonderful experts and colleagues such as Joakim Petersson, Jesus Alcober, Juha Martikainen, Jarek Krysiak, Katharina Gasplmayr, Christian Hoppe, Roberto Bertonasco, Behnam Taraghi and more ….
I will organize a parallel session in the context of the 14th International Conference on Learning and Collaboration Technologies (LCT 2027), an Affiliated Conference of HCI International 2027 (HCII 2027). This session is scheduled to take place between 28 – 30 July as a ‚hybrid‘ event.
My parallel session proposal is entitled:
„AI and Data-driven Technologies for Personalized, Inclusive, and Equitable Learning“
Session description:
The session “AI and Data-driven Technologies for Personalized, Inclusive, and Equitable Learning” explores emerging trends, technologies, and pedagogical innovations shaping education in a rapidly evolving digital landscape. It examines how artificial intelligence, self regulated learning environments, open educational resources (OER), and data-driven approaches are redefining teaching and learning. Experts should share research and case studies highlighting new models for personalized, inclusive, and sustainable digital education. Key topics include the integration of AI in education, the impact of learning analytics on student success, and strategies for fostering digital literacy and equity across diverse learning contexts. The session will also discuss the challenges of ethics, privacy, and accessibility, emphasizing the need for open, collaborative frameworks and global best practices. Participants will gain insights into future-ready skills and the critical role of innovation in shaping equitable, high-quality learning experiences for the next generation.
Important deadlines:
– 30 November 2026: Submit your abstract (800 words) for the review process.
– 10 December 2026: Notification of review outcome
– 29 January 2027: Submit the camera-ready version
– 12 February 2027: Register for the conference
If you are interested in it, just send me a short message, martin.ebner [at] tugraz.at. I will get back to you with a personal invitation.
Our publication (presented at least year ICL conference) about „Prompting.School: A Design-Based Research Approach to Teaching AI Literacy Through Guided Prompt Engineering Practice“ is published now.
Abstract:
We introduce prompting.school, a browser-based learning platform that teaches foundational prompt engineering skills to support AI literacy. Developed using a design-based research approach, the platform was evaluated through four iterative cycles with pre-service and in-service teachers, as well as vocational learners. Aligned with the UNESCO AI Competency Framework and DigComp 2.3 AT, it combines scaffolded lessons, interactive feedback, and real-time AI responses to promote self-regulated learning. Findings show that structured, hands-on prompting practice enhances learners’ understanding, with notable differences in engagement and performance across educational contexts.
[publication @ publisher’s homepage]
[draft @ ResearchGate]
Reference: Brünner, B., Jahic, I., Ebner, M. (2026). Prompting.School: A Design-Based Research Approach to Teaching AI Literacy Through Guided Prompt Engineering Practice. In: Auer, M.E., Toth, P. (eds) Innovation via Collaborative Learning in Engineering Education. ICL 2025. Lecture Notes in Networks and Systems, vol 1848. Springer, Cham. [https://doi.org/10.1007/978-3-032-20381-6_62]
This is an impactful contributions, methodological rigor, and exceptional novelty in the research field of AI in education.
Our publication titled „Craft and Technology Education in a Rural Area: Insights from the First Year of “Technik Für Kinder Vulkanland” has been published.
Abstract:
This study examines the first year of Technik für Kinder Vulkanland (TfKV), a rural makerspace initiative in South-Eastern Austria, through an exploratory mixed-methods evaluation focusing on parents’ and mentors’ perspectives. The initiative is designed as a weekly club format for children and adolescents aged 7 to 14, complemented by holiday workshops. The evaluation addresses five research questions covering pre-participation expectations and feasibility of the weekly club format (RQ1), pilot experiences after one month of regular attendance (RQ2), medium-term participation experiences after six months (RQ3), holiday workshop formats (RQ4), and reflective perspectives from one mentor and the lead mentor (RQ5). The results show that rural distance is not universally experienced as a barrier; structured weekly continuity is perceived as stabilizing; mentoring quality and intergenerational interaction are central to perceived success; and participation is associated with process-oriented learning, perseverance, and growing confidence in dealing with mistakes, exceeding the primarily motivational effects of episodic exposure formats. At the same time, mentor reflections reveal pedagogical tensions regarding scaffolding practices and instructional orientation, particularly between tool-centered skill development and project-centered meaning-making. Taken together, the findings suggest that the TfKV club format is perceived not merely as a recreational workshop but as a potential regional hub for craft and technology education. The study provides first insights into the viability and developmental dynamics of a community-embedded rural makerspace model that integrates continuity and mentoring structures.
[full paper @ publisher’s homepage]
[draft @ ResearchGate]
Reference: Wolf, D., Ebner, M., Miklós, J. (2026). Craft and Technology Education in a Rural Area: Insights from the First Year of “Technik Für Kinder Vulkanland”. In: Smith, B.K., Borge, M. (eds) Learning and Collaboration Technologies. HCII 2026. Lecture Notes in Computer Science, vol 16731. Springer, Cham. https://doi.org/10.1007/978-3-032-30542-8_22
Our publication titled „Development and Evaluation of an AI-Supported Teacher Assistant Tool (AI-TAT) to Foster Self-Regulated Learning“ has been published.
Abstract:
Self-regulated learning (SRL) is increasingly recognised as a key competence for students, yet many teachers struggle to integrate SRL concepts into concrete classroom materials like worksheets. In this paper, SRLly is presented, an AI-supported teacher assistance tool (AI-TAT) that helps teachers revise existing worksheets to better scaffold SRL processes. Rather than submitting entire documents to a large language model (LLM), SRLly performs local preprocessing of uploaded PDFs to extract text with positional metadata, segments the worksheet into meaningful blocks, and sends only relevant blocks for analysis. Based on this structured representation, the system generates actionable recommendations aligned with four SRL dimensions (planning, strategy, monitoring, reflection). Recommendations are presented in an easy-to-use interface that supports teachers’ workflows by providing short rationales and concrete recommendations with ready-to-use text snippets that can be copied. We conducted an initial evaluation during a teacher professional development training with 30 teachers at an Austrian school (student age range 10–18). Overall perceptions were positive: 76% of participants rated SRLly as good or very good. Likert-scale results indicated high perceived usefulness and feasibility, while trustworthiness ratings were more mixed, highlighting reliability of AI output as a key area for improvement. Our findings suggest that structured, material-centred AI feedback can support teachers in integrating SRL into everyday worksheet design, while underscoring the need for stronger reliability and real-world validation.
[full article @ publisher’s homepage]
[draft @ ResearchGate]
Reference: Geier, G., Ebner, M., Burgsteiner, H. (2026). Development and Evaluation of an AI-Supported Teacher Assistant Tool (AI-TAT) to Foster Self-Regulated Learning. In: Smith, B.K., Borge, M. (eds) Learning and Collaboration Technologies. HCII 2026. Lecture Notes in Computer Science, vol 16733. Springer, Cham. https://doi.org/10.1007/978-3-032-30784-2_12
Our publication about „On Using an LLM-Based Approach for Automatic Evaluation of Programming Tasks in MOOC Videos“ has been published.
Abstract:
Interactive videos promote learner engagement by transforming the passive activity of watching videos into an active learning process. LIVE, a web platform developed in previous research that allows for the integration of interactive elements into videos, also supports embedding programming tasks. However, at the beginning of our research, the output of the submitted solution was automatically compared to the reference output, but the code itself still had to be checked manually, as no automatic process was available to verify the solution and provide feedback. To address this, a stand-alone application was developed that automatically evaluates students’ solutions to Python programming tasks using a self-hosted large language model. This application was then integrated into the existing platform. To test this approach, a massive open online course containing four interactive videos with several programming tasks and quizzes was created. Students’ performance was analyzed using quiz results, task submissions, self-assessments, and user statistics provided by the course platform and LIVE. The results indicate that utilizing a large language model for automated feedback can positively impact learning outcomes. However, some limitations must be taken into account, such as the need to correct some of the model’s generated outputs manually due to insufficient feedback quality for certain tasks. This suggests that the combination of automated and manual evaluation seems to be the most effective approach for now.
[full article @ publisher’s homepage]
[draft @ ResearchGate]
Reference: Kindlhofer, C., Wachtler, J., Ebner, M. (2026). On Using an LLM-Based Approach for Automatic Evaluation of Programming Tasks in MOOC Videos. In: Smith, B.K., Borge, M. (eds) Learning and Collaboration Technologies. HCII 2026. Lecture Notes in Computer Science, vol 16733. Springer, Cham. https://doi.org/10.1007/978-3-032-30784-2_13