Students as Informants: Methodological Considerations in Education Agent Research
Bibliographic record
Abstract
Education agents serve an important role in assisting and facilitating prospective international students in their information seeking and application processes (Yang et al., 2020). Across the globe, educational institutions and prospective students contract agents in order to simplify the complex process of navigating a range of study opportunities and formal processes (BUILA, 2021; Nikula et al., 2023). Many institutions and students appear satisfied with the services provided by agents (BUILA, 2021; Huang et al., 2022; QUILT, 2024; Roy, 2017). However, actors operating in this industry have also been reported to behave in an unethical manner. Examples of such behaviours range from providing students with false information, overpromising, forging documents, writing personal letters on behalf of students, and other unwanted behaviours (Fittante, 2023; Ghosh & Garrison, 2024; Nikula & Kivistö, 2020; Parliamentary Joint Committee, 2023). This type of misconduct has made the use of agents controversial and has prompted calls for stronger quality assurance mechanisms and more robust evidence to identify which agents are reliable partners (Nikula et al., 2023). To achieve this, the student perspective is essential, but collecting it presents several methodological challenges (Nikula et al., 2025). This article examines these methodological issues in light of our previous and ongoing research.
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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 | 0.471 | 0.614 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".