Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs

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@article{220f781518174724b6884250a73f449a,
title = "Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs",
abstract = "Faculty perspectives on the use of artificial intelligence (AI) in higher education are crucial for AI{\textquoteright}s meaningful integration into teaching and learning, yet research is scarce. This paper presents a study designed to gain insight into faculty members{\textquoteright} (N = 122) AI self-efficacy and distinct latent profiles, perceived benefits, challenges, use, and professional development needs related to AI. The respondents saw greater equity in education as AI{\textquoteright}s greatest benefit, while students and faculty members{\textquoteright} lack of AI literacy was among the greatest challenges, with the majority interested in professional development. Latent class analysis revealed four distinct faculty member profiles: optimistic, critical, critically reflected, and neutral. The optimistic profile moderates the relationship between self-efficacy and usage. The development of adequate support services is suggested for successful and sustainable digital transformation.",
keywords = "Educational science, AI self-efficacy, AI literacy, Artificial intelligence in higher education, Faculty perspective, Digital transformation, Latent class analysis",
author = "Dana-Kristin Mah and Nele Gro{\ss}",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",
year = "2024",
month = dec,
doi = "10.1186/s41239-024-00490-1",
language = "English",
volume = "21",
journal = "International Journal of Educational Technology in Higher Education",
issn = "1698-580X",
publisher = "Springer Netherlands",
number = "1",

}

RIS

TY - JOUR

T1 - Artificial intelligence in higher education

T2 - exploring faculty use, self-efficacy, distinct profiles, and professional development needs

AU - Mah, Dana-Kristin

AU - Groß, Nele

N1 - Publisher Copyright: © The Author(s) 2024.

PY - 2024/12

Y1 - 2024/12

N2 - Faculty perspectives on the use of artificial intelligence (AI) in higher education are crucial for AI’s meaningful integration into teaching and learning, yet research is scarce. This paper presents a study designed to gain insight into faculty members’ (N = 122) AI self-efficacy and distinct latent profiles, perceived benefits, challenges, use, and professional development needs related to AI. The respondents saw greater equity in education as AI’s greatest benefit, while students and faculty members’ lack of AI literacy was among the greatest challenges, with the majority interested in professional development. Latent class analysis revealed four distinct faculty member profiles: optimistic, critical, critically reflected, and neutral. The optimistic profile moderates the relationship between self-efficacy and usage. The development of adequate support services is suggested for successful and sustainable digital transformation.

AB - Faculty perspectives on the use of artificial intelligence (AI) in higher education are crucial for AI’s meaningful integration into teaching and learning, yet research is scarce. This paper presents a study designed to gain insight into faculty members’ (N = 122) AI self-efficacy and distinct latent profiles, perceived benefits, challenges, use, and professional development needs related to AI. The respondents saw greater equity in education as AI’s greatest benefit, while students and faculty members’ lack of AI literacy was among the greatest challenges, with the majority interested in professional development. Latent class analysis revealed four distinct faculty member profiles: optimistic, critical, critically reflected, and neutral. The optimistic profile moderates the relationship between self-efficacy and usage. The development of adequate support services is suggested for successful and sustainable digital transformation.

KW - Educational science

KW - AI self-efficacy

KW - AI literacy

KW - Artificial intelligence in higher education

KW - Faculty perspective

KW - Digital transformation

KW - Latent class analysis

UR - http://www.scopus.com/inward/record.url?scp=85207292318&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/e8fc9451-4bf7-30ea-99f8-4e7ff30067e1/

U2 - 10.1186/s41239-024-00490-1

DO - 10.1186/s41239-024-00490-1

M3 - Journal articles

VL - 21

JO - International Journal of Educational Technology in Higher Education

JF - International Journal of Educational Technology in Higher Education

SN - 1698-580X

IS - 1

M1 - 58

ER -

DOI