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Record W7127554581

Perspectives on EdTech Ethics: What is the Problem with Recommendations?

2025· article· en· W7127554581 on OpenAlexaboutno aff
Juliana Elisa Raffaghelli, Sigrid Hartong, Pablo Rivera, Oana Negru-Subtirica

Bibliographic record

VenuePadua Research Archive (University of Padova) · 2025
Typearticle
Languageen
FieldComputer Science
TopicDigital Education and Society
Canadian institutionsnot available
Fundersnot available
KeywordsEthics of technologyInformation ethicsApplied ethicsBrainstormingNormative ethicsConsequentialismMeta-ethicsInterpretation (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

Ethics is a word that covers a wide semantic range, being present in both common conversation, professional discourse and the discipline of Moral Philosophy(Singer, 2024). Despite a longstanding tradition in educational philosophy, Its application in the field of educational technologies can be problematic(Brown et al., 2020; Green, 2021; Kerr et al., 2020). Comprising five institutions and twelve researchers, our working group engaged in diverse research and educational practices concerning technology. We deemed it essential to find a common ground and vision of what we considered problematic since the project’s inception: the ethics of educational technology, and its “teachability”. We were also concerned about the educational futures we are building by adopting the terms ethics and its meanings' plethora. Our starting step was hence a pragmatic exploration of problems arising with the application of classical ethics: deontology, or the ethics of universal principles; consequentialism or the ethics of impact; and virtue ethics, or the ethics of individual commitment. We applied transnational and interdisciplinary lenses, by collecting the four national perspectives; brainstorming about each participants’ “wiring” to each perspective, collecting 121 ideas. Through a collective session, eight of us engaged in a common interpretation and re-categorisation of the key ideas collected, re-arranging them into three main topics: Problems (18 ideas); Methods to approach problems (15 ideas) and future desiderable scenarios (17 ideas). The dynamic culminated in a final synthesis, achieving a semantic map based on 6 connected problems: Planetary ruins, Power, Global inequality, collocating Techno-solutionism as a response with social and individual implications (Technology Addiction and Dependency, Bias and Discrimination, and Cultural Impoverishment). In the face of the technological solutions’ impact, Ethical solutionism and the Individualisation of responsibility are limited (and even deleterous) further responses. We subsequently delineated 6 terms as methods to reconsider ethics (Ethics as critical inquiry, Mediation, Technological Co-tedignDesign, Contextualisation, Ethics as practise of care, Future Imagination); and 4 Future Scenarios we’d like to build (Cultural and socio-technological diversity; Collective Technological Sovereignty and Agency; Planetary Care; “Good” Pedagogical Technologies). Our mapping of ethics aims at interrogating the limited actionability of “ethical recommendations” and the implications of ethics’ “checklisting”. References Brown, D. B., Roberts, D. V., Jacobsen, D. M., Hurrell, C., Kerr, K., Streun, H. van, Neutzling, N. J., Lowry, J., Zarkovic, S., Ansorger, J., Marles, T., Lockyer, E., & Parthenis, D. (2020). Ethical Use of Technology in Digital Learning Environments: Graduate Student Perspectives. University of Calgary. https://doi.org/10.11575/ant1-kb38 Green, B. (2021). The Contestation of Tech Ethics: A Sociotechnical Approach to Ethics and Technology in Action. http://arxiv.org/abs/2106.01784 Kerr, A., Barry, M., & Kelleher, J. D. (2020). Expectations of artificial intelligence and the performativity of ethics: Implications for communication governance. Big Data & Society, 7(1), 205395172091593. https://doi.org/10.1177/2053951720915939 Singer, P. (2024, December 18). ethics. Encyclopedia Britannica. https://www.britannica.com/topic/ethics-philosophy

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.308
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.347
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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