Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how chatbots can be used to address two key struggles that students face in first year – a sense of being disconnected from the instructor, and information overload. The authors propose that chatbots can be a useful tool for helping students navigate the volumes of information that confront them as they begin attending university, while at the same time feeling somewhat personally connected with the instructor. This is achieved without increasing instructor time commitment, and perhaps reducing it in large classes. The paper reveals the results of applying this tool in a large first year class and proposes improvements for future iterations. Design/methodology/approach A tool was designed and implemented and tested against research insights. Findings Chatbots are an effective means to reduce student transition challenges. Research limitations/implications Technology which feels social and personal as well as functioning on a tool that students use will make the student feel more connected to the course and the instructor. Practical implications Tools aiding transition should be easy to use and allow customizable information access. Originality/value Chatbots are an unexplored tool. They have the benefit of addressing information overload as well as making the student feel socially connected without increasing instructor workload.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".