More than tools? Making sense of the ongoing digitizations of higher education
Open accessAbstract
In times of hype surrounding the ‘Internet of Things’, ‘blockchain universities’, ‘learning analytics’ and the like, this special issue of the International Journal of Educational Technology in Higher Education starts from a simple proposition – how might we develop critical perspectives and alternative visions of technology in higher education? Our initial call resulted in the six articles that you find attached. These were written from a diversity of perspectives, and therefore address a range of concerns and approaches. We have articles that consider the implications of neuroscience for understanding educational technology, phenomenological reinterpretations of the ‘affordances’ of technology, and the politics of ‘big data’ in higher education reform. Elsewhere is a reassessment of mobile learning, a critical exploration of the ideological underpinnings of national digital strategies, and pedagogical analysis of personalized and adaptive learning. Contributions have been made by authors across Europe working in psychology and the behavioural sciences, social sciences, education research, communications and the arts. We feel confident that this collection meets our initial intention of problematizing the claims and assumptions surrounding higher education in the digital age. This is an interesting and insightful set of arguments, offering a useful counterpoint to articles featured elsewhere in the journal.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Disciplinary divisions widen gaps: STEM integrates AI systematically, whereas humanities and social sciences offer limited structured exposure.
“Disciplinary divisions further widen gaps: STEM integrates AI systematically (Zawacki-Richter et al., 2019), whereas humanities and social sciences offer limited structured exposure, despite considerable potential benefits (Castañeda and Selwyn, 2018).”
From From avoidance to adoption: strategies for integrating AI in higher education contexts (2026) · cites this paper as Castañeda and Selwyn, 2018
Cites
Felski, R. (2015) · doi:10.7208/chicago/9780226294179.001.0001 · Cited by 1,709 · cited 1× · see citing works
“As Rita Felski (2015) reminds us, just because other work is not critical does not mean that it is un critical.”
Background
Flagged first, then by citations
Cited by
Sorted by citations
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
“Some critical voices in educational technology remind us that we should go beyond the tools, and talk again about learning and pedagogy, as well as acknowledging the human aspects of digital technology use in education (Castañeda & Selwyn, 2018).”
BackgroundBond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0176-8 · Cited by 1,097 · see citing works
“Therefore, calls have been made for a greater understanding of the role that educational technology plays in affecting student engagement, in order to strengthen teaching practice and lead to improved outcomes for students (Castañeda & Selwyn, 2018; Krause & Coates, 2008; Nelson Laird & Kuh, 2005).”
BackgroundRevolutionizing education with AI: Exploring the transformative potential of ChatGPT
Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13152 · Cited by 975 · see citing works
“While AI has the potential to revolutionize education, achieving positive educational outcomes requires more than just the use of sophisticated AI computing technologies (Castaneda & Selwyn, 2018).”
BackgroundArtificial Intelligence in Education: The Three Paradigms
Ouyang, F., & Jiao, P. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100020 · Cited by 991 · see citing works
Ouyang, F., Zheng, L., & Jiao, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10925-9 · Cited by 831 · see citing works
Emergency remote teaching in higher education: mapping the first global online semester
Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00282-x · Cited by 527 · see citing works
The application of AI technologies in STEM education: a systematic review from 2011 to 2021
Xu, W., & Ouyang, F. (2022) · International Journal of STEM Education · doi:10.1186/s40594-022-00377-5 · Cited by 495 · see citing works
The Impact of Artificial Intelligence (AI) on Students’ Academic Development
Vieriu, A. M., & Petrea, G. (2025) · Education Sciences · doi:10.3390/educsci15030343 · Cited by 493 · see citing works
Facilitating Student Engagement Through Educational Technology: Towards a Conceptual Framework
Bond, M., & Bedenlier, S. (2019) · Journal of Interactive Media in Education · doi:10.5334/jime.528 · Cited by 374 · see citing works
Bond, M., Zawacki‐Richter, O., & Nichols, M. (2019) · British Journal of Educational Technology · doi:10.1111/bjet.12730 · Cited by 243 · see citing works
A systematic review of AI role in the educational system based on a proposed conceptual framework
Xu, W., & Ouyang, F. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10774-y · Cited by 230 · see citing works
Nelson, A. S., Santamaría, P. V., Javens, J. S., & Ricaurte, M. (2025) · Education Sciences · doi:10.3390/educsci15050611 · Cited by 79 · see citing works
Chen, A., Zhang, Y., Jia, J., Liang, M., Cha, Y., & Lim, C. P. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13064 · Cited by 67 · see citing works
Rodés, V., Porta, M., Garófalo, L., & Enríquez, C. R. (2021) · Journal of Interactive Media in Education · doi:10.5334/jime.657 · Cited by 35 · see citing works
From avoidance to adoption: strategies for integrating AI in higher education contexts
Gerard, J., Hadi, M. U., Natsheh, B. R. R., Singh, M., & Reid, A. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1869723
- Authors
- Castañeda, L., & Selwyn, N.
- Affiliations
- Universidad de Murcia (ES)
- Monash University (AU)
- Corresponding author
- Castañeda, Linda
- Venue
- International Journal of Educational Technology in Higher Education
- Published
- 2018-05-14
- Volume
- 15
- Issue
- 1
- ISSN
- 2365-9440 (ISSN-L)
- OpenAlex
- W2803746316
- Cited by
- 445
- References
- 1
- Citations by year
- 2026: 58 · 2025: 58 · 2024: 63 · 2023: 70 · 2022: 59 · 2021: 70 · 2020: 37 · 2019: 24 · 2018: 6
- Type
- conference abstract
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- International Journal of Educational Technology in Higher Education (publishedVersion, cc-by)
- Monash University Research Portal (Monash University) (submittedVersion, cc-by-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Digital Education and Society
- Physical Sciences › Computer Science › Information Systems › Educational Innovations and Challenges
- Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
- Keywords
- critique of technology, educational technology, neuroeducation, technology affordances, phenomenology, higher education reform, mobile learning, national digital strategy, personalized learning, adaptive learning, Internet of Things, blockchain, learning analytics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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