Demystifying the mist: Why do individuals hesitate to accept <scp>AI</scp> educational services?
Bibliographic record
Abstract
Rapid advances in AI technology are fuelling the proliferation of AI applications across industries, including educational services. With the allure of intelligent tutoring, individuals now face the choice of their educational approach-either parental engagement or utilizing AI educational services. This research employs an experimental design approach to examine individuals' decision-making processes involving AI educational services. Across five studies, we observe that, relative to AI educational services, parental engagement induces less guilt, receives a higher valuation and increases individuals' willingness to recommend it to others. We attribute these preferences to a perceived parental responsibility. Intrinsic attribution and conformity promote individuals' WOM. This research is the first to uncover the impact of educational approaches on individuals' guilt and downstream behaviours in the AI-in-Education field, shedding light on attribution as its underlying mechanism and offering actionable strategies to enhance individuals' WOM. The findings offer novel insights to AI-human interaction psychological research and hold practical implications for AI-in-Education industry practitioners.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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".