Social bots of conviction as dialogue facilitators for history education: Promoting historical empathy in teens through dialogue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Within the broad range of the various types of chatbots, "bots of conviction" (BoCs) shift the focus from offering information to provoking reflection. In this paper we present the design of a "social" BoC, i.e. one designed to engage not one user but a group of participants in reflective dialogue, with the bot and each other. Our social BoC is designed as a digital experience to support history education for high school students (ages 14-18) and was evaluated with a total of 15 teenagers split into 5 groups. The goal was to assess the efficacy of our approach as a tool to promote historical empathy through dialogue. Our findings highlight the BoC's role in engaging the students in constructive dialogue with each other; and the ways in which it guided perspective taking and collective reflection about the past, while at the same time foregrounding connections to the present.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it