Digital Readiness in Higher Education: A Multi-Institutional Evidence Study and AI-Assisted Approach

12 pages•Published: June 18, 2026

Zisis Manouskos, Nikolaos Avouris, Sophia Daskalaki, Christos Rodosthenous, Vasos Papageorgiou, Dimitris Daskopoulos, Eftychios Protopapadakis, Nikolaos Samaras, Pantelis Balaouras and Konstantinos Tsimpanis

Abstract

Higher Education Institutions (HEIs) need to assess their digital readiness in order to embark on an informed digital transformation, following an evidence-based decision-making approach. This study investigates the feasibility of assessing digital readiness in Higher Education Institutions (HEIs) by analyzing whether institutional documents provide evidence aligned with the DigiReady (DR) framework. We examined 75 documents from four Greek and one Cypriot HEI, finding that evidence was concentrated in governance-related dimensions, Digital Leadership (48%), Strategy (46.7%), and Networks and Collaboration (38.7%), while operational and teaching-related areas (D3-D6) were less represented. Documents were classified into strategic/governance, implementation/operational, and evaluation/outcome-oriented types, informing systematic evidence collection and the creation of an intelligent knowledge base. To explore AI-assisted analysis, nine open-source Large Language Models (LLMs) were evaluated on paragraph-level classification. Mid-sized, instruction-tuned models (3B-7B parameters) achieved the best balance of accuracy and efficiency, outperforming larger models, highlighting the importance of model design and tuning. Despite limitations, including regional focus, a single benchmark document, overlapping dimensions, and dataset imbalance, AI-assisted methods show strong potential to convert fragmented institutional evidence into actionable insights for scalable, reproducible digital readiness assessment.

Keyphrases: artificial intelligence, digiready+ framework, digital readiness, digital transformation, evidence mapping, higher education institutions, large language models, text classification

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 12-23.

Links:	https://easychair.org/publications/paper/BMtG
	https://doi.org/10.29007/79cr

BibTeX entry

@inproceedings{EUNIS2026:Digital_Readiness_Higher_Education,
  author    = {Zisis Manouskos and Nikolaos Avouris and Sophia Daskalaki and Christos Rodosthenous and Vasos Papageorgiou and Dimitris Daskopoulos and Eftychios Protopapadakis and Nikolaos Samaras and Pantelis Balaouras and Konstantinos Tsimpanis},
  title     = {Digital Readiness in Higher Education: A Multi-Institutional Evidence Study and AI-Assisted Approach},
  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/BMtG},
  doi       = {10.29007/79cr},
  pages     = {12-23},
  year      = {2026}}

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