Agentic education · Learning science

Actionable companion · Learning design & outcomes

From AI evidence to responsible practice: Attitudes of medical and life sciences university students and postdoctoral fellows toward AI chatbots in education: an international cross-sectional survey

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Health-professions education findings do not establish effectiveness in K–12 classrooms.

Original authors’ abstract, reproduced under the linked source license:

Abstract Artificial intelligence chatbots (AICs) are advanced systems capable of generating and processing human-like text, and are being increasingly integrated in various fields, including education. Despite their potential to significantly impact learning, little is known about university students’ and postdoctoral fellows’ (US&PD) views on AICs in educational settings. This study investigated the familiarity, perceptions, and factors influencing adoption of AICs by US&PDs in the life and medical sciences. We conducted a cross-sectional online survey. Recruitment involved two approaches: (1) using R script on PubMeD metadata to extract contact details of corresponding authors with recent MEDLINE-indexed publications, and (2) collecting publicly listed contact information of program administrators from the top 50 global, English-speaking universities, as ranked by the Quacquarelli Symonds (QS) University World Rankings. Both authors and administrators were contacted and requested to forward the survey to US&PDs. The survey was administered via SurveyMonkey from February 2 to March 18, 2024, with two reminder emails sent between February 14 and 26, 2024. A total of 1209 responses from 73 countries were analyzed. Most respondents identified as female (62.07%) and were enrolled in doctoral (40.48%) or master’s programs (17.55%). Over 63% were familiar with AICs, with ChatGPT being the most used (60.3%). While many recognized the educational value of AICs, concerns about reliability and integration into academia persisted. Calls for more training and institutional support were common. The study underscores the potential and challenges of AICs in education. While enthusiasm exists, significant concerns remain about their implementation, requiring targeted training and policy development. Keywords: AI chatbots, Artificial intelligence, Attitudes, ChatGPT, Education, Perceptions Subject terms: Molecular medicine, Epidemiology

What kind of evidence is this?

Conversational & pedagogical agents · Survey / observational study

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

Prior to participant recruitment, this study’s research protocol was registered on the Open Science Framework (OSF) (https://osf.io/aqx86). We also uploaded relevant study materials and raw and clean data on OSF (https://osf.io/9m7dz/). Our manuscript was preprinted prior to it being accepted for publication in the present peer-reviewed journal (10.1101/2025.06.25.25328523).

Limitations and uncertainty · source passage

A major strength of this study is that by including a large, random, and international sample of US&PDs in the medical and life sciences, our findings are more generalizable across this group. The inclusion of two distinct recruitment methods further enhanced the comprehensiveness of our study, allowing us to capture diverse perspectives from respondents across numerous institutions. Additionally, our use of the R extraction method, an established approach for semi-automatically collecting names and email addresses, minimized human error and streamlined the data collection process. The integration of both quantitative and qualitative data also allowed us to gain a deeper, more comprehensive understanding of the research question. Finally, the coding of open-ended responses was conducted independently and in duplicate, with subsequent review by a third reviewer, ensuring a rigorous and iterative thematic analysis process.

Design the system before the pilot

Define whether the agent asks questions, gives hints, explains or assesses. Test hallucinated feedback, excessive praise, misleading confidence and responses when the learner is confused. A conversational interface alone does not establish autonomous agency.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Jeremy Y Ng; Aimun Qadeer Shah; Emi Roni; Madiha Asna; Jasleen Brar; Sylvia Kathirkamanathan; Wendy Li; Cynthia Lokker; Alfonso Iorio; R Brian Haynes; David Moher. Attitudes of medical and life sciences university students and postdoctoral fellows toward AI chatbots in education: an international cross-sectional survey. Scientific Reports (2026-03-14). https://doi.org/10.1038/s41598-026-42085-y

Companion prepared 2026-11-01. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.