The DOI matches its Crossref record; 45 of the 46 references it cites were found in a registry or an index.
The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives
Open accessAbstract
In recent months, Artificial Intelligence (AI) has had, and will continue to have, a dramatic impact on Higher Education (HE). A study conducted by researchers at a leading university in Australia surveyed 30 of their teaching staff, drawn predominantly from their teaching academy, and interviewed eight of them regarding the impact of AI on HE. Data were analyzed using the procedures of Inductive Thematic Analysis and revealed a lack of any homogenous sentiment around AI in HE and much ambiguity regarding best practice regarding recent technological developments. The results indicate concerns exist around concepts relating to academic integrity, however, these concerns may be exaggerated. Almost half of the participants indicated they were using AI within their teaching roles with the most common design change being modifications to assessments. Less than a quarter of staff agreed the university has adequately equipped them for AI, and more than three quarters indicated they would like support. They unanimously assumed the technology will improve. Keeping in mind universities’ obligation to serve students by preparing them for industry, it is vitally important that the HE sector stays informed of developments in AI and commit to ongoing research and discussions regarding best practice in response to AI. However, anything regarding AI and future developments will be extremely difficult to predict.
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“Among university teachers, academic integrity and cheating are major concerns (Bozkurt, 2024;Johnston et al., 2024;Lee et al., 2024;Newell et al., 2024;Overono & Ditta, 2025;Perkins et al., 2024;Xia et al., 2024;Yusuf et al., 2024).”
From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Lee et al., 2024
In the citing paper
“As in other empirical contexts (e.g., Bozkurt, 2024;Johnston et al., 2024;Lee et al., 2024;Newell et al., 2024;Yusuf et al., 2024), academic integrity was a focus, especially for teachers.”
From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Lee et al., 2024
In the citing paper
“Research participants' concerns included and extended beyond academic integrity, the topic that dominates much academic research on genAI in higher education (Bozkurt, 2024;Johnston et al., 2024;Lee et al., 2024;Newell et al., 2024;Overono & Ditta, 2025;Perkins et al., 2024;Xia et al., 2024;Yusuf et al., 2024).”
From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Lee et al., 2024
In the citing paper
“Environments with abundant resources more readily establish stable training programs, clear procedural guidelines, and sustainable teaching support, thereby enhancing students’ trust in institutional legitimacy and reducing uncertainty-related anxiety (Lee et al., 2024).”
From Multidimensional responses to generative AI in higher education: an integrative framework linking institutional governance, teaching practice, and student psychological adaptation (2026) · cites this paper as Lee et al., 2024
In the citing paper
“studies where AI-assisted journaling reduced writing anxiety”
From Enhancing Student Success through AI-Assisted Reflective Learning in Entrepreneurship Education: An Empirical Study (2026) · cites this paper as Lee et al., 2024
In the citing paper
“Additional concerns such as bias in AI outputs (Lee et al., 2024) and lack of transparency in model decisions (Wang et al., 2024) further compound this hesitancy.”
From Educators’ perceptions of generative AI: Investigating attitudes, barriers and learning needs in higher education (2025) · cites this paper as Lee et al., 2024
In the citing paper
“Lim et al. (2023) and Lee et al. (2024) note that generative AI in the education sphere provides an incredible opportunity for educators to reimagine what learning looks like, despite the notable fears of cheating, academic integrity, and plagiarism that accompany it.”
From Effects of Generative AI on Engagement and Performance Within Asynchronous Online Courses (2026) · cites this paper as Lee et al. (2024)
In the citing paper
“Many educators and academic institutions are currently facing challenges in determining how best to incorporate these tools into their teaching methods without compromising educational values [4].”
From Engineering Educators' Perspectives on the Impact of Generative AI in Higher Education (2025) · cites this paper as [4]
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Metadata
- Authors
- Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E.
- Affiliations
- The University of Adelaide (AU)
- Corresponding author
- Lee, Daniel
- ORCID
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 6
- Pages
- 100221
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4394934695
- Semantic Scholar
- 1517ab19bd54ff13f1431562c12955cc0e27ab83
- Cited by
- 402
- References
- 46
- Citations by year
- 2026: 195 · 2025: 165 · 2024: 42
- FWCI
- 151.46
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Digital Transformation of Higher Education
- Keywords
- generative AI, higher education, teacher perceptions, academic integrity, AI in teaching, assessment design, faculty readiness
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