Teaching a Conversational Agent using Natural Language: Effect on Learning and Engagement
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
Abstract Conversational teachable agents offer a promising platform to support learning, both in the classroom and in remote settings. In this context, the agent takes the role of the novice, while the student takes on the role of teacher, eliciting the Protégé effect, a pedagogical phenomenon known to increase engagement in the teaching task, and also improve cognitive outcomes. In prior work, interactions with teachable agents show frequent use of interface elements to support learning, and few examples utilise natural language as the sole interaction modality between both user and agent. This work investigates the effect of teaching using natural language while interacting with a virtual agent via the Curiosity Notebook on learning outcomes and engagement. A method of teaching by selecting sentences from source material is compared to paraphrasing from the source material and typing. The results indicate that teaching by paraphrasing in natural language is perceived as being more helpful for learning, but that this perception of improvement is not reflected in actual learning gain over a fixed interaction. Participants also show a short-term preference for teaching via sentence-selection, which they perceive as requiring less effort. There is a positive relationship between the amount of paraphrasing users engage in and improved learning outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it