Perspectives of academic staff on artificial intelligence in higher education: exploring areas of relevance

Despite the recent increase in research on artificial intelligence in education (AIED), studies investigating the perspectives of academic staff and the implications for future-oriented teaching at higher education institutions remain scarce. This exploratory study provides initial insight into the perspectives of 112 academic staff by focusing on three aspects considered relevant for sustainable, future-oriented teaching in higher education in the age of AI: instructional design, domain specificity, and ethics. The results indicate that participants placed the greatest importance on AIED ethics. Furthermore, participants indicated a strong interest in (mandatory) professional development on AI and more comprehensive institutional support. Faculty who perceived AIED instructional design as important were more likely to use AI-based tools in their teaching practice. However, the perceived relevance of AIED domain specificity and ethics did not predict AI tool integration, which suggests an intention–behavior gap that warrants further investigation into factors such as AI literacy and structural conditions in higher education. The findings may serve as a basis for further discussion and development of adequate support services for higher education teaching and learning in the age of AI.

Authors

Institutions

Publication Details

Journal
Frontiers in Education
Published
2025-03-26
DOI
https://doi.org/10.3389/feduc.2025.1484904
Citations
9
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
15.24
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Perspectives of academic staff on artificial intelligence in higher education: exploring areas of relevance

Dana-Kristin Mah, Marc Egloffstein, Nils Knoth
9 citations
Frontiers in Education
Online Learning and Analytics
15.24
article

Perspectives of academic staff on artificial intelligence in higher education: exploring areas of relevance

Dana-Kristin Mah, Marc Egloffstein, Nils Knoth
article en
9 citations

Abstract

Despite the recent increase in research on artificial intelligence in education (AIED), studies investigating the perspectives of academic staff and the implications for future-oriented teaching at higher education institutions remain scarce. This exploratory study provides initial insight into the perspectives of 112 academic staff by focusing on three aspects considered relevant for sustainable, future-oriented teaching in higher education in the age of AI: instructional design, domain specificity, and ethics. The results indicate that participants placed the greatest importance on AIED ethics. Furthermore, participants indicated a strong interest in (mandatory) professional development on AI and more comprehensive institutional support. Faculty who perceived AIED instructional design as important were more likely to use AI-based tools in their teaching practice. However, the perceived relevance of AIED domain specificity and ethics did not predict AI tool integration, which suggests an intention–behavior gap that warrants further investigation into factors such as AI literacy and structural conditions in higher education. The findings may serve as a basis for further discussion and development of adequate support services for higher education teaching and learning in the age of AI.

Frontiers in EducationVol. 10
University of Kassel (DE), Technische Hochschule Mannheim (DE), Leuphana University of Lüneburg (DE), University of Mannheim (DE)
Openalex Percentile: Top 1%
Online Learning and Analytics
15.24
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.