A novel ethical analysis of educational XR and AI in literature
Bibliographic record
Abstract
This review paper aims to search the landscape of Extended Reality or XReality technology in light of Artificial Intelligence or AI. Through an examination of the Web of Science or WoS, a total of 29 studies were selected for review. We extracted information on the XReality with AI trends in the studies and further classified the pedagogical impact of the studies using the E3XReality framework suggesting that XReality technology may fall into three levels (three Es for XR hence E3XR): 1) Ethics (making sure we do and receive no harm in learning), 2) Educational effectiveness (making sure learning is effective to our goals and outcomes), and 3) Eudaimonia (making sure learning is both effective and ethical). Using open and axial coding, we find that a survey of perceptions or review of the literature was most often made, followed by the study of technical and pedagogical interventions. VR followed by MR was noted the most in the reviewed studies and surprisingly no mention of AR in the silo was made. The use of AI with XReality was mostly done to provide actionable insight, followed by insight and control. The analysis of studies against the conceptual framework E3XReality suggested that current work is largely at the education state and more work is needed to transition to a more sophisticated state of Eudaimonia. Further, several challenges were obtained from the 29 reviewed studies. The contribution of this paper is to offer an extensive synthesis of challenges as well as future recommendations for using XReality with AI in education.
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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 | 0.087 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".