Perspectives on EdTech Ethics: What is the Problem with Recommendations?
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".