Socially Intelligent Agents to Improve the Effectiveness of Educational Games
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
We describe preliminary research on devising intelligent agents that can improve the educational effectiveness of collaborative, educational computer games. We illustrate how these agents can overcome some of the shortcomings of educational games by explicitly monitoring how students interact with the games, by modeling both the students' cognitive and emotional states, and by generating calibrated interventions to trigger constructive reasoning and reflection when needed. Introduction In this paper, we explore the potential of enriching educational computer games with socially intelligent agents that can help students learn effectively from the games while maintaining the high level of engagement and motivation that constitutes the strong appeal of electronic games in non-educational settings. Our research is developed in the context of EGEMS, the Electronic Games for Education in Math and Science project at the University of British Columbia (UBC). EGEMS is an interdisciplinary pro...
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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