Our publication, „Whitepaper on Evaluating GenAI Innovation in Higher Education,“ is included in the proceedings of the EDMedia 2026 conference.
Abstract:
Generative artificial intelligence (genAI) is increasingly shaping higher education by enabling new forms of content creation, assessment, learner support, personalization, and synthetic media. However, its value cannot be determined solely by technical performance, novelty, or efficiency. This paper presents an evaluation framework for quality genAI applications in higher education. This paper 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, we identified recurring decision points and risk patterns to formulate a five-phase, non-linear evaluation model. The framework guides institutions through specifying context, assessing feasibility, selecting implementation strategies, conducting multi-layered pilots, and performing data-informed analysis. A defining feature is the integration of explicit „Sustain-or-Discontinue“ decision gates at each stage, ensuring resources are committed only to viable applications while providing a structured pathway to terminate initiatives that fail to meet pedagogical or ethical standards. We argue that the core institutional capability lies not in rapid adoption, but in the discipline to evaluate responsibly, balancing experimentation with the rigor to govern and discontinue when necessary.
[publication @ conference’s homepage]
[preview @ ResearchGate]
Reference: Schön, S., Brünner, B., Ebner, M. & Leitner, P. (2026). Whitepaper on Evaluating GenAI Innovation in Higher Education. In Proceedings of EdMedia 2026 Edinburgh (pp. 697-703). Waynesville, NC: Association for the Advancement of Computing in Education (AACE). Retrieved June 15, 2026 from https://www.learntechlib.org/primary/p/2129693/.
This is an impactful contributions, methodological rigor, and exceptional novelty in the research field of AI in education.