Evaluating a Large Language Model’s Ability to Synthesize a Health Science Master’s Thesis: Case Study
Notice bibliographique
Résumé
Background: Large language models (LLMs) can aid students in mastering a new topic fast, but for the educational institutions responsible for assessing and grading the academic level of students, it can be difficult to discern whether a text has originated from a student's own cognition or has been synthesized by an LLM. Universities have traditionally relied on a submitted written thesis as proof of higher-level learning, on which to grant grades and diplomas. But what happens when LLMs are able to mimic the academic writing of subject matter experts? This is now a real dilemma. The ubiquitous availability of LLMs challenges trust in the master's thesis as evidence of subject matter comprehension and academic competencies. Objective: In this study, we aimed to assess the quality of rapid machine-generated papers against the standards of the health science master's program we are currently affiliated with. Methods: In an exploratory case study, we used ChatGPT (OpenAI) to generate 2 research papers as conceivable student submissions for master's thesis graduation from a health science master's program. One paper simulated a qualitative health science research project and another simulated a quantitative health science research project. Results: Using a stepwise approach, we prompted ChatGPT to (1) synthesize 2 credible datasets, and (2) generate 2 papers, that-in our judgment-would have been able to pass as credible medium-quality graduation research papers at the health science master's program the authors are currently affiliated with. It took 2.5 hours of iterative dialogue with ChatGPT to develop the qualitative paper and 3.5 hours to develop the quantitative paper. Making the synthetic datasets that served as a starting point for our ChatGPT-driven paper development took 1.5 and 16 hours for the qualitative and quantitative datasets, respectively. This included learning and prompt optimization, and for the quantitative dataset, it included the time it took to create tables, estimate relevant bivariate correlation coefficients, and prepare these coefficients to be read by ChatGPT. Conclusions: Our demonstration highlights the ease with which an LLM can synthesize research data, conduct scientific analyses, and produce credible research papers required for graduation from a master's program. A clear and well-written master's thesis, citing subject matter authorities and true to the expectations for academic writing, can no longer be regarded as solid proof of either extensive study or subject matter mastery. To uphold the integrity of academic standards and the value of university diplomas, we recommend that master's programs prioritize oral examinations and school exams. This shift is now crucial to ensure a fair and rigorous assessment of higher-order learning and abilities at the master's level.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,092 | 0,294 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».