Embracing AI-Driven Change in Education: A Student–Instructor Centered Institutional Framework

10 pages•Published: June 18, 2026

Abstract

The rapid evolution of generative Artificial Intelligence is reshaping the higher education sector, creating new pedagogical opportunities for teaching and learning while simultaneously raising significant governance, and regulatory challenges. While commercial AI tools are widely accessible, universities require institutionally governed solutions that ensure data sovereignty, pedagogical control, and compliance.
This paper introduces a student–instructor centered framework to AI adoption in higher education, grounded in the institutional design and deployment of a fully in-house developed AI platform. The initiative was developed through a structured co-design process involving faculty members in pilot implementations, structured feedback collection, and iterative prioritization of system enhancements.
The resulting platform integrates generative AI into teaching within a secure and controlled ecosystem: all data are internally managed, hosted within the European Union, and excluded from external training or profiling purposes. Through Retrieval-Augmented Generation (RAG), instructors can configure course-specific AI agents, define behavioral parameters, and constrain the knowledge perimeter to reduce hallucinations and ensure contextual alignment.
The model further emphasizes instructor oversight and institutional governance. Faculty retain visibility over student–AI interactions, access usage analytics and conversation exports, and collect student feedback. At the institutional level, embedded guardrails prevent high-risk practices such as automated grading, aligning the system with the European AI Act’s risk-based approach.
By combining pedagogical co-design, institutional control, technological robustness, and regulatory alignment, this work proposes a replicable framework for secure, governed, and student–instructor centered AI integration in higher education.

Keyphrases: ai in teaching and learning, ai platform development, data sovereignty, eu regulatory alignment, european ai act compliance, generative artificial intelligence, institutional ai governance, pedagogical & co design focus, responsible ai, retrieval augmented generation (rag)

In: Laurence Desnos, Carmen Diaz, Janina Mincer-Daszkiewicz, Lazaros Merakos, Raimund Vogl, Stuart McLellan and Ulrike Lucke (editors). Proceedings of EUNIS 2026 Annual Congress, vol 109, pages 231-240.

Links:	https://easychair.org/publications/paper/Z82m
	https://doi.org/10.29007/q7fr

BibTeX entry

@inproceedings{EUNIS2026:Embracing_AI_Driven_Change,
  author    = {Lisa Cosaro and Nicola Russo},
  title     = {Embracing AI-Driven Change in Education: A Student–Instructor Centered Institutional Framework},
  booktitle = {Proceedings of EUNIS 2026 Annual Congress},
  editor    = {Laurence Desnos and Carmen Diaz and Janina Mincer-Daszkiewicz and Lazaros Merakos and Raimund Vogl and Stuart McLellan and Ulrike Lucke},
  series    = {EPiC Series in Computing},
  volume    = {109},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/Z82m},
  doi       = {10.29007/q7fr},
  pages     = {231-240},
  year      = {2026}}

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