Evaluating GenAI Innovation in Higher Education. A Whitepaper

The rapid integration of Generative Artificial Intelligence (GenAI) into higher education presents a dual challenge: leveraging its potential for personalized learning and content creation while mitigating risks related to data sovereignty, algorithmic bias, and pedagogical efficacy. Current discourse often lacks structured frameworks for systematically evaluating GenAI innovations beyond technical feasibility. This whitepaper addresses this gap by proposing a practice-derived, five-stage evaluation framework developed through a cross-case synthesis of different GenAI implementations at Graz University of Technology. Drawing on real-world use cases ranging from AI-generated multilingual educational videos and RAG-based chatbots to automated assessment tools, we outline a non-linear lifecycle approach comprising: (a) specifying context and use cases, (b) assessing feasibility (legal, ethical, and technical), (c) selecting implementation strategies, (d) piloting with multi-layered evaluation, and (e) performing data-informed analysis. The framework emphasizes that GenAI must be treated as a pedagogical intervention requiring continuous governance, human oversight, and institutional accountability rather than a static technological tool. We argue that sustainable adoption depends on the ability of institutions to iteratively refine, scale, or discontinue applications based on evidence of educational value and compliance. This work, already accepted and discussed as a poster presentation at the EdMedia Conference 2026, aims to provide higher education institutions with a robust, adaptable methodology for navigating the complexities of responsible GenAI innovation.