A proposed methodology for investigating student-chatbot interaction patterns in giving peer feedback
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 A chatbot is artificial intelligence software that converses with a user in natural language. It can be instrumental in mitigating teaching workloads by coaching or answering student inquiries. To understand student-chatbot interactions, this study is engineered to optimize student learning experience and instructional design. In this study, we developed a chatbot that supplemented disciplinary writing instructions to enhance peer reviewer’s feedback on draft essays. With 23 participants from a lower-division post-secondary education course, we delved into characteristics of student-chatbot interactions. Our analysis revealed students were often overconfident about their learning and comprehension. Drawing on these findings, we propose a new methodology to identify where improvements can be made in conversation patterns in educational chatbots. These guidelines include analyzing interaction pattern logs to progressively redesign chatbot scripts that improve discussions and optimize learning. We describe new methodology providing valuable insights for designing more effective instructional chatbots by enhancing and engaging student learning experiences through improved peer feedback.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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