A Survey on Student Use of Generative AI Chatbots for Academic Research
Bibliographic record
Abstract
Objectives – To understand how many undergraduate and graduate students use generative AI as part of their academic work, how often they use it, and for what tasks they use it. We also sought to identify how trustworthy students find generative AI and how they would feel about a locally maintained generative AI tool. Finally, we explored student interest in trainings related to using generative AI in academic work. This survey will help librarians better understand the rate at which generative AI is being adopted by university students and the need for librarians to incorporate generative AI into their work. Methods – A team of three library staff members and one student intern created, executed, and analyzed a survey of 360 undergraduate and graduate students at Harvard University. The survey was distributed via email lists and at cafes and libraries throughout campus. Data were collected and analyzed using Qualtrics. Results – We found that nearly 65% of respondents have used or plan to use generative AI chatbots for academic work, even though most respondents (65%) do not find their outputs trustworthy enough for academic work. The findings show that students actively use these tools but desire guidance around effectively using them. Conclusion – This research shows students are engaging with generative AI for academic work but do not fully trust the information that it produces. Librarians must be at the forefront of understanding the significant impact this technology will have on information-seeking behaviors and research habits. To effectively support students, librarians must know how to use these tools to advise students on how to critically evaluate AI output and effectively incorporate it into their 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".