Running a double-blind true social experiment with a goal oriented adaptive AI-based conversational agent in educational research
Bibliographic record
Abstract
This study introduces an innovative AI-facilitated interview-like survey system generating a combination of qualitative and quantitative data insights for higher education research. We employed a goal oriented adaptive AI-based Conversational Agent (AICA) which collected data directly from 1223 participants globally and ran a double-blind true social experiment online. During interviews, the AI established strong rapport with the participants, offering them personalized guidance while fostering comfort, ownership, and commitment to the study. In this entirely automated experiment, we empirically tested 8 hypotheses related to students' university selection. The results confirmed 5 of these hypotheses while refuting 3 factors previously identified in the literature. The study showcases the potential of AICAs to efficiently collect and analyse data from substantial sample sizes in real-time, fostering a streamlined and harmonious research process producing results that are not only statistically reliable and bias-free but also broadly generalizable.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".