Artificial Intelligence and Teachers’ New Ethical Obligations
Bibliographic record
Abstract
Largely thought to be immune from automation, the teaching profession is now being challenged on multiple fronts by new digital infrastructures and smart software that automate pedagogical decision-making and supporting teaching practises. To better understand this emerging and ethically fraught intensification of technologies in today’s classrooms, we asked, “what new ethical obligations are teachers facing as a result of AI technology adoption in schools?” We began by defining AI, then turned to posthumanism to grapple with how networked, AI-enhanced digital technologies extend and intermesh with human beings cognitively, affectively, morally, corporeally, spatially, temporally, socially and politically. We catalogued Artificial Intelligence (AI) technologies that have been deployed in some of today’s K-12 classrooms (AIEDK-12)s and developed a topology of AIEDK-12 technologies based on (1) teachers’ professional activities being supported by AI, (2) AI being used by and for learners to facilitate their learning and development; (3) additions to K-12 curricula about AI; and (4) AI-based technologies being used by schools, districts and ministries of education to inform decisions that affect teachers. We then consider how a posthumanist investigative approach to disclosive ethics —”interviewing objects”— can shed new light on the implications of widespread deployment of AIEdK12 on teachers’ work. We interviewed three AI-based educational applications, recasting teachers and students as involved and evolving human-AI hybrids. In the process, we uncovered some of the new complications and ethical conundrums being introduced to teachers’ professional practises.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.088 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".