Effects of an Adaptive Robot Encouraging Teamwork on Students’ Learning
Bibliographic record
Abstract
In this work, we designed a teachable robot that encourages a pair of students to discuss their thoughts and teaching decisions during the tutoring session. The robot adapts to the students’ talking activity and adjusts the frequency and type of encouragement. We hypothesize that the robot’s encouragement of group discussion can enhance the social engagement of group members, leading to improved learning and enjoyment. We ran a user study (n = 68), where a pair of participants (dyad) worked together to teach a humanoid robot about rocks and minerals. In the adaptive condition, the robot uses reinforcement learning to maximise interaction between the dyad members. Results show that the adaptive robot was successful in creating more dialogue between dyad members and in increasing task engagement, but did not affect learning or enjoyment. Over time, the adaptive robot was also able to encourage both members to contribute more equally to the conversation.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".